diff options
112 files changed, 3637 insertions, 5790 deletions
diff --git a/client/geocode/autocomplete.component.js b/client/geocode/autocomplete.component.js new file mode 100644 index 00000000..12419cf1 --- /dev/null +++ b/client/geocode/autocomplete.component.js @@ -0,0 +1,208 @@ +import React, { Component } from 'react' +// import PropTypes from 'prop-types' +import { bindActionCreators } from 'redux' +import { connect } from 'react-redux' + +function formatLabel(label, value) { + if (!value) { + return label + } + let len = 0 + return ( + <span> + { + label.split(new RegExp(value.replace(/[-\[\]\(\)\+\*\\\^\$\{\}\.\?\&\|\<\>]/g, ''), "i")) // eslint-disable-line + .reduce((prev, current, i) => { + if (!i) { + len += current.length + return [current] + } + const ret = prev.concat(<b key={i}>{label.substr(len, value.length)}</b>, current) + len += value.length + current.length + return ret + }, []) + } + </span> + ) +} + +function sanitizeForAutocomplete(s) { + return (s || "") + .toLowerCase() + .replace(/[^a-zA-Z0-9 ]/g, '') + .trim() + .replace(/\\/g, '') +} + +class Autocomplete extends Component { + constructor(props) { + super() + this.state = { + q: props.q || "", + selected: 0, + matches: [] + } + this.handleKeyDown = this.handleKeyDown.bind(this) + this.handleChange = this.handleChange.bind(this) + this.handleCancel = this.handleCancel.bind(this) + } + + componentWillMount() { + // build index based on what's in the hierarchy + const { nodes } = this.props.hierarchy + let index = [] + this.index = index + Object.keys(nodes).forEach(key => { + const node = nodes[key] + if (!key || !node || !node.name || !node.parent) return + let { name } = node + let prefixName = name + if (node.is_attribute) { + const parent = nodes[node.parent] + if (parent) { + prefixName = parent.name + " (" + name + ")" + } + } + index.push([sanitizeForAutocomplete(prefixName), name, node.id]) + node.synonyms + .split("\n") + .map(word => word = word.trim()) + .filter(word => !!word) + .forEach(word => index.push([prefixName, name, node.id])) + }) + } + + handleKeyDown(e) { + let id + switch (e.keyCode) { + case 27: // escape + e.preventDefault() + this.handleCancel() + break + case 37: // left + case 38: // up + e.preventDefault() + this.setState({ + selected: (this.state.matches.length + this.state.selected - 1) % this.state.matches.length + }) + return false + case 39: // right + case 40: // down + e.preventDefault() + this.setState({ + selected: (this.state.selected + 1) % this.state.matches.length + }) + return false + case 13: // enter + id = this.state.matches[this.state.selected] + e.preventDefault() + this.handleSelect(id) + return false + default: + break + } + } + + handleChange(e) { + // search for the given string in our index + const q = e.target.value + let value = sanitizeForAutocomplete(q) + if (!value.length) { + this.setState({ + q, + selected: 0, + matches: [], + }) + return + } + const re = new RegExp(value) + let matches = [] + let seen = {} + this.index.forEach(pair => { + if (seen[pair[2]]) return + if (pair[0].match(re)) { + seen[pair[2]] = true + if (pair[1].indexOf(value) === 0) { + matches.unshift(pair[2]) + } else { + matches.push(pair[2]) + } + } + }) + this.setState({ + q, + selected: 0, + matches: matches.slice(0, 10), + }) + } + + handleSelect(id) { + const { nodes } = this.props.hierarchy + const node = nodes[id] + if (this.props.onSelect) this.props.onSelect(node) + this.setState({ q: "", selected: 0, matches: [] }) + } + + handleCancel() { + if (this.props.onCancel) this.props.onCancel() + this.setState({ q: "", selected: 0, matches: [] }) + } + + render() { + // const suggestions = this.state.suggestions.map((suggestion)) + const { nodes } = this.props.hierarchy + const { q, selected } = this.state + const matches = this.state.matches.map((match, i) => { + const node = nodes[match] + const parent = nodes[node.parent] + let label + if (node.is_attribute) { + label = ( + <span> + {formatLabel(parent.name, q)} + {' '}<small>{'('}{formatLabel(node.name, q)}{')'}</small> + </span> + ) + } else { + label = formatLabel(node.name, q) + } + return ( + <div + key={i} + className={selected === i ? 'selected' : ''} + onClick={() => this.handleSelect(node.id)} + onMouseEnter={() => this.setState({ selected: i })} + > + {label} + </div> + ) + }) + return ( + <div className="autocomplete"> + <input + type="text" + name="q" + value={this.state.q} + onKeyDown={this.handleKeyDown} + onChange={this.handleChange} + autoFocus + autoCapitalize="off" + autoComplete="off" + placeholder="Start typing a name" + /> + <div className="matches"> + {matches} + </div> + </div> + ) + } +} + +const mapStateToProps = (state, ownProps) => ({ + onSelect: ownProps.onSelect, +}) + +const mapDispatchToProps = (dispatch) => ({ +}) + +export default connect(mapStateToProps, mapDispatchToProps)(Autocomplete) diff --git a/client/geocode/geocode.component.js b/client/geocode/geocode.component.js new file mode 100644 index 00000000..e57d238d --- /dev/null +++ b/client/geocode/geocode.component.js @@ -0,0 +1,25 @@ +import React, { Component } from 'react' + +import Autocomplete from './autocomplete.component' + +class GeocodeContainer extends Component { + // constructor() { + // super() + // } + render() { + // const { } = this.props + return ( + <div className=''> + <select> + {} + </select> + <div> + <Autocomplete /> + </div> + </div> + ) + } +} + + +export default GeocodeContainer diff --git a/client/geocode/index.js b/client/geocode/index.js new file mode 100644 index 00000000..4f0da3f6 --- /dev/null +++ b/client/geocode/index.js @@ -0,0 +1,10 @@ +import React from 'react' +import ReactDOM from 'react-dom' + +// import { toArray } from '../util' + +import GeocodeContainer from './autocomplete.component' + +ReactDOM.render( + <GeocodeContainer />, document.querySelector('#container') +) diff --git a/old/faiss/requirements.txt b/old/faiss/requirements.txt deleted file mode 100644 index 1d60aabc..00000000 --- a/old/faiss/requirements.txt +++ /dev/null @@ -1,11 +0,0 @@ -Pillow -h5py -tensorflow -Keras -Flask -opencv-python -imagehash -scikit-image -scikit-learn -imutils - diff --git a/old/faiss/run.sh b/old/faiss/run.sh deleted file mode 100644 index 8f9e77e2..00000000 --- a/old/faiss/run.sh +++ /dev/null @@ -1 +0,0 @@ -uwsgi --http 127.0.0.1:5000 --file wsgi.py --callable app --processes 1 diff --git a/old/faiss/static/css/app.css b/old/faiss/static/css/app.css deleted file mode 100644 index a3b24736..00000000 --- a/old/faiss/static/css/app.css +++ /dev/null @@ -1,289 +0,0 @@ -/* css boilerplate */ - -* { box-sizing: border-box; } -html,body { - margin: 0; padding: 0; - width: 100%; height: 100%; -} -body { - font-family: Helvetica, sans-serif; - font-weight: 300; - padding-top: 60px; -} - -/* header */ - -header { - position: fixed; - top: 0; - left: 0; - height: 60px; - width: 100%; - background: #11f; - color: white; - align-items: stretch; - display: flex; - flex-wrap: wrap; - justify-content: space-between; - z-index: 3; -} -header > section { - justify-content: flex-start; - align-items: center; - display: flex; - flex: 1 0; - font-weight: bold; -} -header > section:last-of-type { - justify-content: flex-end; -} -header a { - color: hsla(0,0%,100%,.89); - text-decoration: none; - line-height: 18px; - font-size: 14px; - font-weight: 700; - padding: .35rem .4rem; - white-space: nowrap; -} -header .logged-in { - font-size: 12px; - font-weight: normal; - padding: 0 0.5rem; -} -header .logout { - padding: 0 6px; - border-left: 1px solid #99f; -} -header .logout a { - font-size: 12px; -} -.menuToggle { - width: 30px; - height: 30px; - margin: 5px; - cursor: pointer; - line-height: 1; -} - -/* form at the top */ - -#form { - display: flex; - flex-direction: row; - justify-content: space-between; - align-items: center; - margin: 20px; - padding: 20px; - border: 1px solid #ddd; -} -input[type=text] { - border: 1px solid #888; - padding: 4px; - font-size: 15px; -} -input[type=file] { - max-width: 200px; - border-radius: 2px; -} -input[type=file]:invalid + button { visibility: hidden!important; } -input[type=file]:valid + button { visibility: visible!important; } -#form > div { - display: flex; - flex-direction: row; - align-items: center; -} -#form > div * { - margin: 0 3px; -} - -/* saving UI form */ - -label { - display: block; - white-space: nowrap; - padding-bottom: 10px; -} -label:last-child { - padding-bottom: 0; -} -label span { - display: inline-block; - min-width: 80px; -} -.saving_ui { - display: none; -} -.saving .saving_ui { - display: flex; - border: 1px solid #ddd; - margin: 20px; - padding: 20px; - flex-direction: row; - justify-content: space-between; -} - -/* query box, shows either searched image, directory name, etc */ - -.loading .results, -.prefetch .query, .prefetch .results, -.browsing .score, .browsing .browse, -.photo .browse, -.saving .score { - display: none; -} -.browsing .query div { display: inline; margin-left: 5px; font-weight: bold; } -.saving .query div { display: inline; margin-left: 5px; font-weight: bold; } -.load_message { - opacity: 0; -} -.loading .load_message { - display: block; - margin: 20px; - font-weight: bold; -} - -.query { - margin: 20px; -} -.query > div { - margin-top: 10px; - position: relative; - display: flex; - flex-direction: row; - align-items: flex-start; -} -.query img { - cursor: crosshair; - max-width: 400px; - display: block; -} -.query > div > .box { - position: absolute; - border: 1px solid #11f; - background: rgba(17,17,255,0.1); - pointer-events: none; -} -.query canvas { - margin-left: 20px; - max-width: 200px; -} - -/* search results */ - -.results { - display: flex; - flex-direction: row; - flex-wrap: wrap; -} -.results > div { - display: flex; - flex-direction: column; - justify-content: flex-end; - width: 210px; - margin: 15px; - padding: 5px; - border: 1px solid transparent; -} -.results > div.saved { - border-radius: 2px; - background: #fafaaa; -} -.results > div img { - cursor: pointer; - max-width: 210px; - margin-bottom: 10px; -} -.results > div > div { - display: flex; - flex-direction: row; - justify-content: space-between; - align-items: center; -} -.results a:visited .btn { - color: #99d; -} -.score { - font-size: 12px; - color: #444; -} - - -/* spinner */ - -.loader { - display: flex; - align-items: center; - justify-content: center; - position: absolute; - top: 0; left: 0; - width: 100%; height: 100%; - background: rgba(255,255,255,0.9); -} -.loader > div { - background: white; - padding: 20px; - box-shadow: 0 1px 2px #bbb; - border-radius: 2px; -} -.spinner { - position: relative; - width: 32px; - height: 32px; - color: #11f; - margin: 0 auto; -} -.spinner:after { - position: absolute; - margin: auto; - width: 100%; - height: 100%; - top: 0; - left: 0; - right: 0; - bottom: 0; - content: " "; - display: inline-block; - border-radius: 50%; - border-style: solid; - border-width: 0.15em; - -webkit-background-clip: padding-box; - border-color: currentColor currentColor currentColor transparent; - box-sizing: border-box; - -webkit-animation: ld-cycle 0.7s infinite linear; - animation: ld-cycle 0.7s infinite linear; -} -@-webkit-keyframes ld-cycle { - 0%, 50%, 100% { - animation-timing-function: cubic-bezier(0.5, 0.5, 0.5, 0.5); - } - 0% { - -webkit-transform: rotate(0); - transform: rotate(0); - } - 50% { - -webkit-transform: rotate(180deg); - transform: rotate(180deg); - } - 100% { - -webkit-transform: rotate(360deg); - transform: rotate(360deg); - } -} -@keyframes ld-cycle { - 0%, 50%, 100% { - animation-timing-function: cubic-bezier(0.5, 0.5, 0.5, 0.5); - } - 0% { - -webkit-transform: rotate(0); - transform: rotate(0); - } - 50% { - -webkit-transform: rotate(180deg); - transform: rotate(180deg); - } - 100% { - -webkit-transform: rotate(360deg); - transform: rotate(360deg); - } -} diff --git a/old/faiss/static/favicon.ico b/old/faiss/static/favicon.ico Binary files differdeleted file mode 100644 index d97f2f59..00000000 --- a/old/faiss/static/favicon.ico +++ /dev/null diff --git a/old/faiss/static/img/play.png b/old/faiss/static/img/play.png Binary files differdeleted file mode 100644 index 40f76045..00000000 --- a/old/faiss/static/img/play.png +++ /dev/null diff --git a/old/faiss/static/index.html b/old/faiss/static/index.html deleted file mode 100644 index cf59c628..00000000 --- a/old/faiss/static/index.html +++ /dev/null @@ -1,83 +0,0 @@ -<!doctype html> -<html> -<head> -<meta charset="UTF-8" /> -<base href='/search/' /> -<link rel="stylesheet" href="static/css/app.css"> -<link rel="shortcut icon" href="static/favicon.ico" /> -<title>VFrame Image Import</title> -</head> -<body class='prefetch'> - -<header> - <section class="navbar-section"> - <a href="/"><img class="menuToggle" alt='logo' src="static/css/vframe-logo.png" /></a> - <a href="/categories/">Categories</a> - <a href="/groups/user/">Assignments</a> - <a href="/images/new/">Add Image</a> - <a href="/search/">Search</a> - </section> - - <section class="navbar-section last-navbar-section"> - <span class="menu-help"><a href="/static/explore/treemap.html">Explore Data</a></span> - <span class="menu-help"><a href="/help/">Help</a></span> - <span class="login-out logged-in"><span class="capitalize"></span></span> - <span class="logout login-out"><a href="/accounts/logout/">Logout</a></span> - </section> -</header> - -<div class="container"> - -<div id="form"> - <div> - <input type="file" name="img" accept="image/*" required> - <button class='btn upload_again'>Upload Again</button> - </div> - <div class="general_ui"> - <button class='btn panic'>Panic</button> - <button class='btn random'>Random</button> - <button class='btn view_saved'>View Saved</button> - </div> -</div> - -<div class="saving_ui"> - <div> - <label><span>Title</span> <input type="text" name="title" placeholder="Enter a title"></label> - <label><span></span><input type="checkbox" name="graphic"> Graphic content</label> - </label> - <label><span></span> - <button class='btn create_new_group'>Create New Group</button> - <button class='btn check'>Check Duplicates</button> - <button class='btn reset'>Clear Selection</button> - </label> - </div> -</div> - -<div class="query"> - <span class='msg'></span> - <div></div> -</div> - -<div class="results"> -</div> - - -</div> -<script type="text/html" id="result-template"> - <div class='{className}'> - <img src="{img}" crossorigin="anonymous"> - <div> - <div class='score'>{score}</div> - <a href='{metadata}'><button class='btn metadata'>Info</button></a> - <a href='{browse}'><button class='btn browse'>Expand</button></a> - <a href='{search}'><button class='btn search'>Search</button></a> - </div> - </div> -</script> -</body> -<script src="static/js/store2.min.js"></script> -<script src="static/js/dataUriToBlob.js"></script> -<script src="static/js/app.js"></script> -</html> - - diff --git a/old/faiss/static/js/app.js b/old/faiss/static/js/app.js deleted file mode 100644 index 77164c76..00000000 --- a/old/faiss/static/js/app.js +++ /dev/null @@ -1,491 +0,0 @@ -/* eslint no-use-before-define: 0, camelcase: 0, one-var-declaration-per-line: 0, one-var: 0, quotes: 0, prefer-destructuring: 0, no-alert: 0, no-console: 0, no-multi-assign: 0 */ - -function loadApp() { - const result_template = document.querySelector('#result-template').innerHTML - const results_el = document.querySelector('.results') - const query_div = document.body.querySelector('.query > div') - let bounds - let token, username - let x, y, mouse_x, mouse_y, dx, dy, box - let dragging = false - let cropping = false - let creating = false - let did_check = false - - function init() { - login() - bind() - route() - } - function bind() { - window.onpopstate = route - document.querySelector('[name=img]').addEventListener('change', upload) - on('click', '.results a', preventDefault) - on('click', '.search', search) - on('click', '.panic', panic) - on('click', '.upload_again', upload_again) - on('click', '.browse', browse) - on('click', '.results img', save) - on('click', '.view_saved', loadSaved) - on('click', '.create_new_group', createNewGroup) - on('click', '.reset', reset) - on('click', '.random', random) - on('click', '.check', check) - on('mousedown', '.query img', down) - window.addEventListener('mousemove', move) - window.addEventListener('mouseup', up) - window.addEventListener('keydown', keydown) - } - function route() { - const path = window.location.pathname.split('/') - // remove initial slash - path.shift() - // remove dummy route - if (path[0] === 'search') path.shift() - switch (path[0]) { - case 'fetch': - search({ target: { url: window.location.search.substr(1).split('=')[1] } }) - break - case 'view': - search(path.slice(1)) - break - case 'q': - if (path.length === 3) { - search({ target: { dir: path[1], fn: path[2] } }) - } else { - browse({ target: { dir: path[1], fn: null } }) - } - break - case 'saved': - loadSaved() - break - default: - break - } - } - function keydown(e) { - switch (e.keyCode) { - case 27: // escape - panic() - break - default: - break - } - } - - // load search results - function loadResults(data) { - console.log(data) - if (!data.query.url) return - // console.log(data) - document.body.className = 'searching' - const path = getPathFromImage(data.query.url) - pushState('searching', "/search/fetch/?url=" + path.url) - if (path.dir === 'uploaded' && path.fn.match('_filename')) { - loadMessage( - "<a href='javascript:history.go(-1)'>< Back</a> | " - + "Searching subregion, " - + "found " + data.results.length + " images" - ) - } else { - loadMessage( - "Found " + data.results.length + " images" - ) - } - loadQuery(data.query.url) - if (!data.results.length) { - results_el.innerHTML = "No results" - return - } - const saved = window.store.get('saved', []) - - results_el.innerHTML = data.results.map(res => { - const { distance, file, hash, frame, url } = res - const isSaved = saved.indexOf(url) !== -1 - const { type } = getPathFromImage(url) - let className = isSaved ? 'saved' : '' - className += ' ' + type - let t = result_template - .replace('{score}', Math.floor(clamp(1 - distance, 0, 1) * 100) + "%") - .replace('{browse}', '/search/q/' + hash) - .replace('{search}', '/search/view/' + [file, hash, frame].join('/')) - .replace('{metadata}', '/metadata/' + hash) - .replace('{className}', className) - .replace('{saved_msg}', isSaved ? 'Saved' : 'Save') - .replace('{img}', url) - return t - }).join('') - } - - function loadDirectory(data) { - console.log(data) - document.body.className = 'browsing' - pushState('searching', "/search/q/" + data.path) - loadMessage("Video: <b>" + data.path + "</b>") - loadQuery("") - if (!data.results.length) { - results_el.innerHTML = "No frames found" - return - } - const saved = window.store.get('saved', []) - results_el.innerHTML = data.results - .map(result => [parseInt(result.frame, 10), result]) - .sort((a, b) => a[0] - b[0]) - .map(pair => { - let { file, hash, frame, url } = pair[1] - const isSaved = saved.indexOf(url) !== -1 - let className = isSaved ? 'saved' : '' - let t = result_template - .replace('{img}', url) - .replace('{browse}', '/search/q/' + hash) - .replace('{search}', '/search/view/' + [file, hash, frame].join('/')) - .replace('{metadata}', '/metadata/' + hash) - .replace('{className}', className) - .replace('{saved_msg}', isSaved ? 'Saved' : 'Save') - return t - }).join('') - } - function loadSaved() { - document.body.className = 'saving' - pushState('View saved', "/search/saved") - const saved = window.store.get('saved', []) - cropping = false - loadMessage(saved.length + " saved image" + (saved.length === 1 ? "" : "s")) - loadQuery('') - const box_el = document.querySelector('.box') - if (box_el) box_el.parentNode.removeChild(box_el) - results_el.innerHTML = saved.map(href => { - const { url, dir } = getPathFromImage({ src: href }) - let className = 'saved' - let t = result_template - .replace('{img}', href) - .replace('{browse}', '/search/q/' + dir) - .replace('{search}', '/search/fetch/?url=' + url) - .replace('{metadata}', '/metadata/' + dir) - .replace('{className}', className) - .replace('{saved_msg}', 'Saved') - return t - }).join('') - } - function loadQuery(path) { - if (cropping) return - const qd = document.querySelector('.query div') - qd.innerHTML = '' - if (path.match(/(gif|jpe?g|png)$/)) { - const img = new Image() - img.setAttribute('crossorigin', 'anonymous') - img.src = path.replace('sm', 'md') - qd.appendChild(img) - } else { - qd.innerHTML = path || "" - } - } - function loadMessage(msg) { - document.querySelector('.query .msg').innerHTML = msg - } - - // panic button - function panic() { - loadMessage('Query cleared') - loadQuery('') - results_el.innerHTML = '' - } - - // adding stuff to localstorage - function save(e) { - const { url } = getPathFromImage(e.target) - const saved = window.store.get('saved', []) - let newList = saved || [] - if (saved.indexOf(url) !== -1) { - newList = saved.filter(f => f !== url) - e.target.parentNode.classList.remove('saved') - } else { - newList.push(url) - e.target.parentNode.classList.add('saved') - } - window.store.set('saved', newList) - } - function reset() { - const shouldReset = window.confirm("This will reset the saved images. Are you sure?") - if (!shouldReset) return - window.store.set('saved', []) - loadSaved() - document.querySelector('[name=title]').value = '' - window.alert("Reset saved images") - } - - // submit the new group - function createNewGroup() { - const title = document.querySelector('[name=title]').value.trim().replace(/[^-_a-zA-Z0-9 ]/g, "") - const saved = window.store.get('saved', []) - const graphic = document.querySelector('[name=graphic]').checked - if (!title.length) return alert("Please enter a title for this group") - if (!saved.length) return alert("Please pick some images to save") - if (!did_check) { - alert('Automatically checking for duplicates. Please doublecheck your selection.') - return check() - } - if (creating) return null - creating = true - return http_post("/api/images/import/new/", { - title, - graphic, - saved - }).then(res => { - console.log(res) - window.store.set('saved', []) - window.location.href = '/groups/show/' + res.image_group.id - }).catch(res => { - alert('Error creating group. 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\ No newline at end of file diff --git a/old/faiss/static/js/store2.min.js b/old/faiss/static/js/store2.min.js deleted file mode 100644 index 75e3ca37..00000000 --- a/old/faiss/static/js/store2.min.js +++ /dev/null @@ -1,5 +0,0 @@ -/*! store2 - v2.7.0 - 2018-03-04 -* Copyright (c) 2018 Nathan Bubna; Licensed (MIT OR GPL-3.0) */ - -!function(a,b){var c={version:"2.7.0",areas:{},apis:{},inherit:function(a,b){for(var c in a)b.hasOwnProperty(c)||(b[c]=a[c]);return b},stringify:function(a){return void 0===a||"function"==typeof a?a+"":JSON.stringify(a)},parse:function(a){try{return JSON.parse(a)}catch(b){return a}},fn:function(a,b){c.storeAPI[a]=b;for(var d in c.apis)c.apis[d][a]=b},get:function(a,b){return a.getItem(b)},set:function(a,b,c){a.setItem(b,c)},remove:function(a,b){a.removeItem(b)},key:function(a,b){return a.key(b)},length:function(a){return a.length},clear:function(a){a.clear()},Store:function(a,b,d){var e=c.inherit(c.storeAPI,function(a,b,c){return 0===arguments.length?e.getAll():"function"==typeof b?e.transact(a,b,c):void 0!==b?e.set(a,b,c):"string"==typeof a||"number"==typeof a?e.get(a):a?e.setAll(a,b):e.clear()});e._id=a;try{b.setItem("_-bad-_","wolf"),e._area=b,b.removeItem("_-bad-_")}catch(a){}return e._area||(e._area=c.inherit(c.storageAPI,{items:{},name:"fake"})),e._ns=d||"",c.areas[a]||(c.areas[a]=e._area),c.apis[e._ns+e._id]||(c.apis[e._ns+e._id]=e),e},storeAPI:{area:function(a,b){var d=this[a];return d&&d.area||(d=c.Store(a,b,this._ns),this[a]||(this[a]=d)),d},namespace:function(a,b){if(!a)return this._ns?this._ns.substring(0,this._ns.length-1):"";var d=a,e=this[d];return e&&e.namespace||(e=c.Store(this._id,this._area,this._ns+d+"."),this[d]||(this[d]=e),b||e.area("session",c.areas.session)),e},isFake:function(){return"fake"===this._area.name},toString:function(){return"store"+(this._ns?"."+this.namespace():"")+"["+this._id+"]"},has:function(a){return this._area.has?this._area.has(this._in(a)):!!(this._in(a)in this._area)},size:function(){return this.keys().length},each:function(a,b){for(var d=0,e=c.length(this._area);d<e;d++){var f=this._out(c.key(this._area,d));if(void 0!==f&&a.call(this,f,b||this.get(f))===!1)break;e>c.length(this._area)&&(e--,d--)}return b||this},keys:function(a){return this.each(function(a,b){b.push(a)},a||[])},get:function(a,b){var d=c.get(this._area,this._in(a));return null!==d?c.parse(d):b||d},getAll:function(a){return this.each(function(a,b){b[a]=this.get(a)},a||{})},transact:function(a,b,c){var d=this.get(a,c),e=b(d);return this.set(a,void 0===e?d:e),this},set:function(a,b,d){var e=this.get(a);return null!=e&&d===!1?b:c.set(this._area,this._in(a),c.stringify(b),d)||e},setAll:function(a,b){var c,d;for(var e in a)d=a[e],this.set(e,d,b)!==d&&(c=!0);return c},add:function(a,b){var d=this.get(a);if(d instanceof Array)b=d.concat(b);else if(null!==d){var e=typeof d;if(e===typeof b&&"object"===e){for(var f in b)d[f]=b[f];b=d}else b=d+b}return c.set(this._area,this._in(a),c.stringify(b)),b},remove:function(a){var b=this.get(a);return c.remove(this._area,this._in(a)),b},clear:function(){return this._ns?this.each(function(a){c.remove(this._area,this._in(a))},1):c.clear(this._area),this},clearAll:function(){var a=this._area;for(var b in c.areas)c.areas.hasOwnProperty(b)&&(this._area=c.areas[b],this.clear());return this._area=a,this},_in:function(a){return"string"!=typeof a&&(a=c.stringify(a)),this._ns?this._ns+a:a},_out:function(a){return this._ns?a&&0===a.indexOf(this._ns)?a.substring(this._ns.length):void 0:a}},storageAPI:{length:0,has:function(a){return this.items.hasOwnProperty(a)},key:function(a){var b=0;for(var c in this.items)if(this.has(c)&&a===b++)return c},setItem:function(a,b){this.has(a)||this.length++,this.items[a]=b},removeItem:function(a){this.has(a)&&(delete this.items[a],this.length--)},getItem:function(a){return this.has(a)?this.items[a]:null},clear:function(){for(var a in this.items)this.removeItem(a)},toString:function(){return this.length+" items in "+this.name+"Storage"}}},d=c.Store("local",function(){try{return localStorage}catch(a){}}());d.local=d,d._=c,d.area("session",function(){try{return sessionStorage}catch(a){}}()),"function"==typeof b&&void 0!==b.amd?b("store2",[],function(){return d}):"undefined"!=typeof module&&module.exports?module.exports=d:(a.store&&(c.conflict=a.store),a.store=d)}(this,this.define); -//# sourceMappingURL=store2.min.js.map
\ No newline at end of file diff --git a/old/faiss/static/metadata.html b/old/faiss/static/metadata.html deleted file mode 100644 index e74e1ee1..00000000 --- a/old/faiss/static/metadata.html +++ /dev/null @@ -1,11 +0,0 @@ -<!DOCTYPE html> -<html> - <head> - <meta charset="UTF-8"> - <title>VFrame Metadata</title> - <link rel="shortcut icon" href="/search/static/favicon.ico" /> - <meta name="viewport" content="width=device-width,initial-scale=1.0"> - </head> - <body> - <script type="text/javascript" src="/search/static/js/metadata-app.js"></script></body> -</html> diff --git a/old/faiss/static/search.html b/old/faiss/static/search.html deleted file mode 100644 index 056d06c1..00000000 --- a/old/faiss/static/search.html +++ /dev/null @@ -1 +0,0 @@ -search.html
\ No newline at end of file diff --git a/old/faiss/util.py b/old/faiss/util.py deleted file mode 100644 index 97afbc22..00000000 --- a/old/faiss/util.py +++ /dev/null @@ -1,29 +0,0 @@ -import time -import simplejson as json -import pickle -from os import path -from collections import namedtuple - -# Converts JSON el['key'] to Pythonic object-style el.key -def _json_object_hook(d): - return namedtuple('X', d.keys())(*d.values()) - -# Load a JSON recipe -def load_recipe(path): - with open(path) as fh: - return json.load(fh, object_hook=_json_object_hook) - -# Load a pickle file -def load_pickle(data_dir, pkl_fn): - load_start = time.time() - with open(path.join(str(data_dir), str(pkl_fn)), 'rb') as fh: - raw = fh.read() - data = pickle.loads(raw) - load_end = time.time() - load_time = load_end - load_start - print("Pickle load time: {:.1f}s".format(load_time)) - return data - -def read_json(fn): - with open(fn, 'r') as json_file: - return json.load(json_file) diff --git a/old/faiss/wsgi.py b/old/faiss/wsgi.py deleted file mode 100644 index 371862fb..00000000 --- a/old/faiss/wsgi.py +++ /dev/null @@ -1,5 +0,0 @@ -from server import app - -if __name__ == "__main__": - app.run() - diff --git a/old/server/app/README.md b/old/server/app/README.md deleted file mode 100644 index 8bc70132..00000000 --- a/old/server/app/README.md +++ /dev/null @@ -1,17 +0,0 @@ -# Startup - -Run supervisor - -`/usr/bin/supervisord` - -Tail log file - -`tail -f /var/log/uwsgi/app/app.log` - -`/opt/redis/redis-stable/src/redis-server &` - -`celery worker -A celery_worker.celery --loglevel=info &` - -If using on Production Server - -`/usr/bin/nohup /usr/bin/supervisord &`
\ No newline at end of file diff --git a/old/server/app/__init__.py b/old/server/app/__init__.py deleted file mode 100644 index bce3f9ee..00000000 --- a/old/server/app/__init__.py +++ /dev/null @@ -1,39 +0,0 @@ -import logging -from logging.handlers import RotatingFileHandler - -from flask import Flask -from flask_bootstrap import Bootstrap - -from flask import Flask - -from config import config, Config - -bootstrap = Bootstrap() -#celery = Celery(__name__, broker=Config.CELERY_BROKER_URL) -from .basemodels import celery - -def create_app(config_name): - app = Flask(__name__) - app.config.from_object(config[config_name]) - config[config_name].init_app(app) - - bootstrap.init_app(app) - celery.conf.update(app.config) - - from .main import main as main_blueprint - app.register_blueprint(main_blueprint) - - #handler = RotatingFileHandler('debug.log', maxBytes=10000, backupCount=1) - #handler.setLevel(logging.INFO) - #app.logger.addHandler(handler) - - format = "%(asctime)s - [%(levelname)s] %(message)s" - logging.basicConfig(filename='debug.log', - filemode='a', - format=format, - level=logging.DEBUG) - console = logging.StreamHandler() - console.setLevel(logging.DEBUG) - logging.getLogger(__name__).addHandler(console) - - return app diff --git a/old/server/app/basemodels.py b/old/server/app/basemodels.py deleted file mode 100644 index 475ab0c2..00000000 --- a/old/server/app/basemodels.py +++ /dev/null @@ -1,5 +0,0 @@ -from config import config, Config -from celery import Celery - -#bootstrap = Bootstrap() -celery = Celery(__name__, broker=Config.CELERY_BROKER_URL) diff --git a/old/server/app/favicon.ico b/old/server/app/favicon.ico Binary files differdeleted file mode 100644 index 4d001b21..00000000 --- a/old/server/app/favicon.ico +++ /dev/null diff --git a/old/server/app/index.html b/old/server/app/index.html deleted file mode 100644 index 3c1b0dfd..00000000 --- a/old/server/app/index.html +++ /dev/null @@ -1,161 +0,0 @@ -<!doctype html> -<html> -<head> - <meta charset="utf-8"> - <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" /> - <link rel="shortcut icon" href="/static/img/favicon.ico" /> - <title>DullDream (v2 x ZkM)</title> - <link rel="stylesheet" type="text/css" href="static/css/dullbrown-theme.css"> -</head> -<body> - -<header> - <h1><a href="/"><img src="/static/img/dulldream_logo_200.png" alt="DullDream"></a></h1> - <h2 class="subtitle">Neural network photo effect</h2> -</header> - -<div class="upload_view container"> - <div class="row"> - <div id="photo_area" class="dash_border"> - <input class="hidden_input" id="user_file" type="file" accept="image/*"> - <canvas class="photo" id="user_photo_canvas" width="512" height="512"></canvas> - <div class="center_inner"> - <label id="take_photo_btn" for="user_file" class="upload_center_btn"> - <div class='btn-lg btn'>Take Photo</div> - </label> - <div id="details"></div> - <div id="progress"></div> - </div> - - <div id="preloader_anim"> - <img src="/static/img/loader.gif"> - </div> - </div> - </div> - - <div id="upload_controls" class="row"> - <div class="align_center"> - <div id="restart_btn"> - <a id="restart_btn" class="btn btn-md btn-default" role="button">Change Image</a> - <input type='file' accept="image/*"> - </div> - <div id="dropdown_btn"> - <select id="dropdown"></select> - </div> - <div id="upload_btn"> - <a id="take_photo_btn" class="btn btn-md btn-important" role="button">Upload</a> - </div> - </div> - <div class="align_center consent_box"> - <label> - <input type="checkbox" id="agree" value="1" checked> - I consent to have my dulled image displayed at ZkM. - </label> - </div> - </div> - - <div id="about_btn" class="row"> - <div class="align_center"> - <a class="btn btn-sm btn-default about_button" role="button">About</a> - <a class="btn btn-sm btn-default privacy_button" role="button">Privacy</a> - <p class="notice"> - All images uploaded can be used for exhibition and review purposes. - </p> - <p class="notice"> - Currently this work is on view at <a href="http://zkm.de/en/event/2017/10/open-codes">ZKM</a>. View recent DullDreams <a href="/gallery">here</a>. - </p> - </div> - </div> -</div> - -<div class="about_view modal"> - <div class="inner"> - <header> - <h1><img src="/static/img/dulldream_logo_200.png" alt="DullDream"></h1> - </header> - <div class='content'> - <p> - <b><i>DullDream™ by DullTech™</i></b> is a series of experiments appropriating neural network image recognition technology to make visual representation less interesting. - </p> - <p> - Can machine learning help us desensitize? Our impactful lives are clogging up social media feeds with unique filter settings, leaving us nostalgic for a vanilla future. Can machine learning help us achieve this? Take the excitement out of our lives, prepare us for a time where we will all have to be the same, have the same values and culture? Painting a future where the Dull is no longer a dream but a nightmare? - </p> - <p> - DullDream™ was developed for Transmediale 2017 - Ever Elusive by <a href="http://constantdullaart.com">Constant Dullaart</a> in collaboration with <a href="http://ahprojects.com">Adam Harvey</a>. It has generously been made possible by the Creative Industries Fund NL. - </p> - </div> - <center><a class="btn btn-sm btn-default" href="/" role="button">Home</a></center> - </div> -</div> - -<div class="privacy_view modal"> - <div class="inner"> - <header> - <h1><img src="/static/img/dulldream_logo_200.png" alt="DullDream"></h1> - </header> - <div class='content'> - <h3>Privacy Notice</h3> - <p> - Images uploaded to this site are being used for a public art display at <a href="http://zkm.de/en/event/2017/10/open-codes">ZKM</a> - </p> - <p> - If you would not like to be included, be sure to uncheck the permission box on the upload page. - </p> - - </div> - <center><a class="btn btn-sm btn-default" href="/" role="button">Home</a></center> - </div> -</div> - - -<div class="result_view"> - <div class="final_result"> - </div> - - <div class="row made_with"> - Made with DullDream.xyz for ZKM OpenCodes 2017 - </div> - - <div class="row"> - <button class='btn' id="show_all_results">Detailed Analysis</button> - </div> - - <div class="all_results"> - </div> - - <div id="share_btns" class="row"> - <a id="permalink" href="#">Permalink</a> - </div> - - <div id="about_btn" class="row"> - <div class="align_center"> - <a href="/" class="btn btn-sm btn-default home_button" role="button">Home</a> - <a class="btn btn-sm btn-default about_button" role="button">About</a> - <a class="btn btn-sm btn-default privacy_button" role="button">Privacy</a> - </div> - - </div> - -</div> - -<div id="footer"> - DullDream™ (beta) by <a href="http://constantdullaart.com">Constant Dullaart</a>. - Made in collaboration with <a href="http://ahprojects.com">Adam Harvey</a> -</div> - -</body> -<script type="text/html" id="result_template"> - <div class="row"> - <img src="{img}"><br> - <b>{title}</b> - </div> -</script> -<script type="text/json" id="dropdown_options">[]</script> -<script type="text/javascript" src="static/js/vendor/jquery-3.3.1.min.js"></script> -<script type="text/javascript" src="static/js/vendor/ExifReader.js"></script> -<script type="text/javascript" src="static/js/vendor/canvas-to-blob.js"></script> -<script type="text/javascript" src="static/js/vendor/prefixfree.js"></script> -<script type="text/javascript" src="static/js/util.js"></script> -<script type="text/javascript" src="static/js/upload.js"></script> -<script type="text/javascript" src="static/js/app.js"></script> -</html> diff --git a/old/server/app/main/__init__.py b/old/server/app/main/__init__.py deleted file mode 100644 index a21e2754..00000000 --- a/old/server/app/main/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from flask import Blueprint - -main = Blueprint('main', __name__) - -from . import views, errors, tasks, utils
\ No newline at end of file diff --git a/old/server/app/main/errors.py b/old/server/app/main/errors.py deleted file mode 100644 index 60b5f227..00000000 --- a/old/server/app/main/errors.py +++ /dev/null @@ -1,32 +0,0 @@ -from flask import render_template, request, jsonify -from . import main - - -@main.app_errorhandler(403) -def forbidden(e): - if request.accept_mimetypes.accept_json and \ - not request.accept_mimetypes.accept_html: - response = jsonify({'error': 'forbidden'}) - response.status_code = 403 - return response - return render_template('403.html'), 403 - - -@main.app_errorhandler(404) -def page_not_found(e): - if request.accept_mimetypes.accept_json and \ - not request.accept_mimetypes.accept_html: - response = jsonify({'error': 'not found'}) - response.status_code = 404 - return response - return render_template('404.html'), 404 - - -@main.app_errorhandler(500) -def internal_server_error(e): - if request.accept_mimetypes.accept_json and \ - not request.accept_mimetypes.accept_html: - response = jsonify({'error': 'internal server error'}) - response.status_code = 500 - return response - return render_template('500.html'), 500 diff --git a/old/server/app/main/forms.py b/old/server/app/main/forms.py deleted file mode 100644 index bc1399ad..00000000 --- a/old/server/app/main/forms.py +++ /dev/null @@ -1,60 +0,0 @@ -from flask.ext.wtf import Form -from wtforms import StringField, TextAreaField, BooleanField, SelectField,\ - SubmitField -from wtforms.validators import Required, Length, Email, Regexp -from wtforms import ValidationError -from flask.ext.pagedown.fields import PageDownField -from ..models import Role, User - - -class NameForm(Form): - name = StringField('What is your name?', validators=[Required()]) - submit = SubmitField('Submit') - - -class EditProfileForm(Form): - name = StringField('Real name', validators=[Length(0, 64)]) - location = StringField('Location', validators=[Length(0, 64)]) - about_me = TextAreaField('About me') - submit = SubmitField('Submit') - - -class EditProfileAdminForm(Form): - email = StringField('Email', validators=[Required(), Length(1, 64), - Email()]) - username = StringField('Username', validators=[ - Required(), Length(1, 64), Regexp('^[A-Za-z][A-Za-z0-9_.]*$', 0, - 'Usernames must have only letters, ' - 'numbers, dots or underscores')]) - confirmed = BooleanField('Confirmed') - role = SelectField('Role', coerce=int) - name = StringField('Real name', validators=[Length(0, 64)]) - location = StringField('Location', validators=[Length(0, 64)]) - about_me = TextAreaField('About me') - submit = SubmitField('Submit') - - def __init__(self, user, *args, **kwargs): - super(EditProfileAdminForm, self).__init__(*args, **kwargs) - self.role.choices = [(role.id, role.name) - for role in Role.query.order_by(Role.name).all()] - self.user = user - - def validate_email(self, field): - if field.data != self.user.email and \ - User.query.filter_by(email=field.data).first(): - raise ValidationError('Email already registered.') - - def validate_username(self, field): - if field.data != self.user.username and \ - User.query.filter_by(username=field.data).first(): - raise ValidationError('Username already in use.') - - -class PostForm(Form): - body = PageDownField("What's on your mind?", validators=[Required()]) - submit = SubmitField('Submit') - - -class CommentForm(Form): - body = StringField('Enter your comment', validators=[Required()]) - submit = SubmitField('Submit') diff --git a/old/server/app/main/img_proc_config.py b/old/server/app/main/img_proc_config.py deleted file mode 100644 index db124978..00000000 --- a/old/server/app/main/img_proc_config.py +++ /dev/null @@ -1,20 +0,0 @@ -# paths for image processors -import os -from os.path import join - -class ImgProcConfig: - - def __init__(self): - dir_models = '/data_store/apps/dulldream/dnn_models' - - # mask rcnn - self.mask_rcnn_class_config = '/dulldream/src/config/coco_meta.json' - self.mask_rcnn_model = join(dir_models,'tf/mask_rcnn_coco.h5') - - # p2p - self.p2p_ckpts_dir = join(dir_models,'p2p/coco2014_person') - self.p2p_epoch = 'latest' - - # p2p objects only - self.p2p_bg_ckpts_dir = join(dir_models,'p2p/coco2014_objects') - self.p2p_bg_epoch = 'latest' diff --git a/old/server/app/main/paths.py b/old/server/app/main/paths.py deleted file mode 100644 index 69c21627..00000000 --- a/old/server/app/main/paths.py +++ /dev/null @@ -1,19 +0,0 @@ -from flask import current_app as app - -def get_paths(agree): - if agree: - return ( - app.config['UPLOADS'], - app.config['RENDERS'], - app.config['JSON_DIR'], - app.config['UPLOADS_URI'], - app.config['RENDERS_URI'], - ) - else: - return ( - app.config['UPLOADS_PRIVATE'], - app.config['RENDERS_PRIVATE'], - app.config['JSON_PRIVATE_DIR'], - app.config['UPLOADS_PRIVATE_URI'], - app.config['RENDERS_PRIVATE_URI'], - ) diff --git a/old/server/app/main/tasks.py b/old/server/app/main/tasks.py deleted file mode 100644 index 970e6988..00000000 --- a/old/server/app/main/tasks.py +++ /dev/null @@ -1,374 +0,0 @@ -import os -import sys -import time -import datetime -import json -from PIL import Image, ImageFilter -import cv2 as cv -import numpy as np -from . import main, utils -from .. import basemodels -from flask import current_app as app -from .paths import get_paths -celery = basemodels.celery -from celery.utils.log import get_task_logger -celery_logger = get_task_logger(__name__) -import imutils - - -# init image processors -sys.path.append('/dulldream/src/') -from .img_proc_config import ImgProcConfig -from image_processors.mask_rcnn import MaskRCNN -from image_processors.pix2pix import Pix2Pix -from utils import imx -from utils import fiox - - -# initialize image processor -img_proc_config = ImgProcConfig() -p2p = Pix2Pix(img_proc_config.p2p_ckpts_dir,epoch=img_proc_config.p2p_epoch) -p2p_objects = Pix2Pix(img_proc_config.p2p_bg_ckpts_dir,epoch=img_proc_config.p2p_epoch) - -mask_rcnn = MaskRCNN(img_proc_config.mask_rcnn_class_config, - model_path=img_proc_config.mask_rcnn_model) - - -@celery.task(bind=True) -def task_dull(self, uuid_name, agree, mask_rcnn_result): - """Process image and update during""" - celery_logger.debug('process_image_task, uuid: {}'.format(uuid_name)) - - upload_dir, render_dir, json_dir, upload_uri, render_uri = get_paths(agree) - - files = [] - im = Image.open(os.path.join(upload_dir, uuid_name + '.jpg')).convert('RGB') - #im_np = cv.cvtColor(imx.ensure_np(im),cv.COLOR_RGB2BGR) - im_np = imx.ensure_np(im) - im_np = im_np[:,:,::-1] - im = im.resize((256,256)) - im_np_256 = imutils.resize(im_np,width=256) - - # Add original - fpath = os.path.join(render_dir, uuid_name + '_orig.jpg') - im.save(fpath, 'JPEG', quality=95) - files.append({ - 'title': 'Original', - 'fn': render_uri + uuid_name + '_orig.jpg' - }) - - if mask_rcnn_result['valid']: - # ----------------------------------------------- - # Segment image (processed in views) - # seems to be an error with async celery processor? - # ----------------------------------------------- - - # parse mrcnn data - im_mask = cv.imread(mask_rcnn_result['fp_im_mask']) - seg_mask = cv.imread(mask_rcnn_result['fp_seg_mask']) - #score = mask_rcnn_result['score'] - #name = mask_rcnn_result['name'] - #color = mask_rcnn_result['color'] - files.append({ - 'title': 'Semantic Segmentation', - 'fn': render_uri + uuid_name + '_seg_mask.jpg' - }) - files.append({ - 'title': 'Semantic Segmentation Isolate', - 'fn': render_uri + uuid_name + '_im_mask.jpg' - }) - - - # ----------------------------------------------- - # run rag generator - # ----------------------------------------------- - - self.update_state( - state = 'PROCESSING', - meta = { - 'percent': 0.50, - 'message': 'Applying Region Adjacency Graph', - 'uuid': uuid_name - }) - - # save the regions adjancency graph - im_rag = imx.create_rag_mean(im_mask,compactness=30,n_segments=128) - fpath = os.path.join(render_dir, uuid_name + '_rgraph.jpg') - imx.save_np_as_pil(fpath,im_rag,quality=95) - files.append({ - 'title': 'Region Adjacency Graph', - 'fn': render_uri + uuid_name + '_rgraph.jpg' - }) - - - # ----------------------------------------------- - # generate p2p fake - # ----------------------------------------------- - - self.update_state( - state = 'PROCESSING', - meta = { - 'percent': 0.75, - 'message': 'Running generative adversarial network...', - 'uuid': uuid_name - }) - - - # convert segmentation to mask - seg_mask_gray = cv.cvtColor(seg_mask,cv.COLOR_BGR2GRAY) - seg_mask_gray[seg_mask_gray > 1] = 255 - - # find best P2P fit - ims_p2p = [] - match_amts = [] - iters = 15 - for i in range(0,iters): - im_p2p = p2p.create_p2p(im_rag) - ims_p2p.append(im_p2p) - im_p2p_mask = cv.cvtColor(im_p2p,cv.COLOR_RGB2GRAY) - im_p2p_mask[im_p2p_mask > 1] = 255 - # find where masks intersect - matches = np.bitwise_and(im_p2p_mask,seg_mask_gray) - amt = len(np.where(matches == 255)[0]) - match_amts.append(amt) - self.update_state( - state = 'PROCESSING', - meta = { - 'percent': 0.75, - 'message': 'Generating ({}/{})'.format(i,iters), - 'uuid': uuid_name - }) - - best_idx = np.argmax(match_amts) - im_p2p = ims_p2p[best_idx] - - fpath = os.path.join(render_dir, uuid_name + '_gan.jpg') - imx.save_np_as_pil(fpath,im_p2p,quality=95) - files.append({ - 'title': 'Generative Adversarial Network', - 'fn': render_uri + uuid_name + '_gan.jpg' - }) - - - # ----------------------------------------------- - # generate p2p fake - # ----------------------------------------------- - - # announce to user - self.update_state( - state = 'PROCESSING', - meta = { - 'percent': 0.90, - 'message': 'Compositing images...', - 'uuid': uuid_name - }) - - - # apply masked cloning - im_p2p_gray = cv.cvtColor(im_p2p,cv.COLOR_BGR2GRAY) - im_clone_mask = np.zeros_like(im_p2p_gray,dtype=np.uint8) - im_clone_mask[im_p2p_gray > 1] = 255 - - - # apply smoothed copy+paste clone - im_blur_mask = np.zeros(im_np_256.shape[:2],dtype=np.float64) - im_blur_mask[im_p2p_gray > 1] = 1.0 - im_blur_mask = np.array([im_blur_mask,im_blur_mask,im_blur_mask]).transpose((1,2,0)) - - # erode mask to remove black border - kernel = np.ones((3,3),np.uint8) - im_blur_mask = cv.erode(im_blur_mask,kernel,iterations = 3) - - # feather mask - feather_amt = (3,3) - im_blur_mask = (cv.GaussianBlur(im_blur_mask,feather_amt, 0) > 0) * 1.0 #? - im_blur_mask = cv.GaussianBlur(im_blur_mask,feather_amt, 0) - im_blur_mask = np.clip(im_blur_mask,0.0,1.0) - - # mask p2p fg --> photo bg - im_dull = im_np_256.astype(np.float64) * (1.0 - im_blur_mask) + im_p2p.astype(np.float64) * im_blur_mask - im_dull = im_dull.astype(np.uint8) - - - else: - print('No person. Apply background P2P') - celery_logger.debug('No person. Apply background P2P, uuid: {}'.format(uuid_name)) - im_bg_blur = cv.GaussianBlur(im_np_256,(31,31),0) - im_bg_rag = imx.create_rag_mean(im_bg_blur,compactness=30,n_segments=64) - - # apply gan - im_dull = p2p_objects.create_p2p(im_bg_rag) - - # resize back to full 512px - im_dull_512 = imutils.resize(im_dull,width=512) - - # save dulldream image - fpath = os.path.join(render_dir, uuid_name + '_dull.jpg') - imx.save_np_as_pil(fpath,im_dull_512,quality=95) - files.append({ - 'title': 'Your DullDream', - 'fn': render_uri + uuid_name + '_dull.jpg' - }) - - - # ----------------------------------------------- - # Write data to disk - # ----------------------------------------------- - - data = { - 'uuid': uuid_name, - 'date': str(datetime.datetime.now()), - 'files': files - } - - json_path = os.path.join(json_dir, uuid_name + '.json') - with open(json_path, 'w') as json_file: - json.dump(data, json_file) - - return { - 'percent': 100, - 'state': 'complete', - 'uuid': uuid_name - } - - - - -@celery.task(bind=True) -def blur_task(self, uuid_name, agree, extra): - """Process image and update during""" - celery_logger.debug('process_image_task, uuid: {}'.format(uuid_name)) - - upload_dir, render_dir, json_dir, upload_uri, render_uri = get_paths(agree) - - files = [] - - im = Image.open(os.path.join(upload_dir, uuid_name + '.jpg')).convert('RGB') - im = im.resize((256,256)) - files.append({ - 'title': 'Original image', - 'fn': upload_uri + uuid_name + '.jpg' - }) - - self.update_state( - state = 'PROCESSING', - meta = { - 'percent': 0.25, - 'message': 'Applying blur', - 'uuid': uuid_name - }) - - im_np = utils.ensure_np(im) - im_blur = cv.blur(im_np, (5,5), 1.0) - im_blur_pil = utils.ensure_pil(im_blur) - - fn = uuid_name + '_blur.jpg' - fpath = os.path.join(render_dir, fn) - im_blur_pil.save(fpath, 'JPEG', quality=95) - - files.append({ - 'title': 'Blurred image', - 'fn': render_uri + uuid_name + '_blur.jpg' - }) - - time.sleep(3) - - self.update_state( - state = 'PROCESSING', - meta = { - 'percent': 0.50, - 'message': 'Sleeping for some reason', - 'uuid': uuid_name - }) - time.sleep(2) - - self.update_state( - state = 'PROCESSING', - meta = { - 'percent': 0.75, - 'message': 'Sleeping some more', - 'uuid': uuid_name - }) - time.sleep(2) - - data = { - 'uuid': uuid_name, - 'date': str(datetime.datetime.now()), - 'files': files - } - - json_path = os.path.join(json_dir, uuid_name + '.json') - with open(json_path, 'w') as json_file: - json.dump(data, json_file) - - celery_logger.debug('ok') - - return { - 'percent': 100, - 'state': 'complete', - 'uuid': uuid_name, - } - -@celery.task(bind=True) -def sleep_task(self, uuid_name): - celery_logger.debug('sleep_task'.format(uuid_name)) - msgs = [ - {'msg':'Uploaded OK','time':.1}, - {'msg':'Segmenting Image...','time':2}, - {'msg':'Found: Person, Horse','time':1}, - {'msg':'Creating Pix2Pix','time':2} - ] - for i,m in enumerate(msgs): - percent = int(float(i)/float(len(msgs))*100.0) - self.update_state( - state = 'PROCESSING', - meta = { - 'percent': percent, - 'message': m['msg'], - 'uuid': uuid_name - }) - celery_logger.debug(m['msg']) - time.sleep(m['time']) - - return { - 'percent': 100, - 'state': 'complete', - 'uuid': uuid_name - } - -def make_task_json(): - dropdown = {} - for k,v in task_lookup.items(): - if 'active' not in v or v['active'] is not False: - is_default = 'default' in v and v['default'] is True - task = { - 'name': k, - 'title': v['title'], - 'selected': is_default, - } - dropdown[k] = task - return json.dumps(dropdown) - -# Add all valid tasks to this lookup. -# Set 'active': False to disable a task -# Set 'default': True to define the default task - -task_lookup = { - 'sleep': { - 'title': 'Sleep Test', - 'task': sleep_task, - 'active': False - }, - 'blur': { - 'title': 'Blur', - 'task': blur_task, - 'active': False - }, - 'task_dull': { - 'title': 'DullDream V2', - 'task': task_dull, - 'active': True, - 'default': True - } -} - diff --git a/old/server/app/main/utils.py b/old/server/app/main/utils.py deleted file mode 100644 index 62d9c95f..00000000 --- a/old/server/app/main/utils.py +++ /dev/null @@ -1,37 +0,0 @@ -from flask import current_app as app -from PIL import Image -import numpy as np -import cv2 as cv -import os -from os.path import join - -def ensure_pil(im): - try: - im.verify() - return im - except: - return Image.fromarray(im.astype('uint8'), 'RGB') - -def ensure_np(im): - if type(im) == np.ndarray: - return im - return np.asarray(im, np.uint8) - -def get_recent_uploads(limit=10): - d_uploads = app.config['UPLOADS'] - d_renders = app.config['RENDERS'] - - # list all files in uploads dir - filenames = [s for s in os.listdir(d_uploads) - if os.path.isfile(os.path.join(d_uploads, s))] - # sort uploaded files by date - filenames.sort(key=lambda s: os.path.getmtime(os.path.join(d_uploads, s)),reverse=True) - basenames = [os.path.splitext(os.path.basename(f))[0] for f in filenames] - basenames = basenames[:limit] - filenames = [f for f in basenames if os.path.isfile(join(d_renders,'{}_dull.jpg'.format(f)))] - - # create list for uploads and renders - uploads = [join('/img/uploads',f) for f in filenames] - renders = [join('/img/renders','{}_dull'.format(f)) for f in filenames] - urls = [join('/d',f) for f in basenames] - return uploads, renders, urls diff --git a/old/server/app/main/views.py b/old/server/app/main/views.py deleted file mode 100644 index 11a8ca53..00000000 --- a/old/server/app/main/views.py +++ /dev/null @@ -1,300 +0,0 @@ -import os -import uuid -import json -from flask import render_template, redirect, url_for, send_from_directory -from flask import request, make_response, jsonify -from . import main, utils -from .tasks import task_lookup, make_task_json -from PIL import Image, ImageOps -import cv2 as cv - -from .paths import get_paths - -from flask import current_app as app -from werkzeug.utils import secure_filename -import imutils - -# ------------------------------------------------------------ -# Temp: run mask rcnn outside celery -# ------------------------------------------------------------ - -# init image processors -import sys -from .img_proc_config import ImgProcConfig -sys.path.append('/dulldream/src/') -from image_processors.mask_rcnn import MaskRCNN -from utils import imx -from utils import fiox - -img_proc_congif = ImgProcConfig() -mask_rcnn = MaskRCNN(img_proc_congif.mask_rcnn_class_config, - model_path=img_proc_congif.mask_rcnn_model) - -# ------------------------------------------------------------ -# Tasks -# ------------------------------------------------------------ - -@main.route('/status/<task_name>/<task_id>') -def task_status(task_name, task_id): - """Return celery image processing status""" - if task_name in task_lookup: - task = task_lookup[task_name]['task'].AsyncResult(task_id) - else: - return jsonify({ - 'state': 'error', - 'percent': 100, - 'message': 'Unknown task' - }) - - app.logger.info('task state: {}'.format(task.state)) - if task.state == 'PENDING': - response = { - 'state': task.state, - 'percent': 0, - 'message': 'Pending...' - } - elif task.state != 'FAILURE': - response = { - 'state': task.state, - 'percent': task.info.get('percent', 0), - 'uuid': task.info.get('uuid', 0), - 'message': task.info.get('message', '') - } - if 'result' in task.info: - response['result'] = task.info['result'] - else: - # something went wrong in the background job - response = { - 'state': task.state, - 'percent': 100, - 'message': str(task.info), # this is the exception raised - } - return jsonify(response) - -# ------------------------------------------------------------ -# POST Routes -# ------------------------------------------------------------ - -@main.route('/upload/sleep', methods=['GET', 'POST']) -def sleep_test(): - async_task = task_lookup['sleep']['task'].apply_async(args=['sleep_test']) - task_url = url_for('main.task_status', task_name='sleep', task_id=async_task.id) - return jsonify({ - 'result': True, - 'task_url': task_url, - }) - -@main.route('/upload', methods=['POST']) -def upload(): - - style = request.form['style'] - print('style',style) - if style in task_lookup: - task = task_lookup[style]['task'] - print('task',task) - else: - return jsonify({ - 'result': False, - 'error': 'Unknown task', - }) - - file = request.files['user_image'] - agree = bool(request.form['agree']) - ext = request.form['ext'] - if ext is None: - ext = request.files['ext'] - - uuid_name = str(uuid.uuid4()) - - app.logger.info('[+] style: {}'.format(style)) - app.logger.info('[+] ext: {}'.format(ext)) - app.logger.info('[+] uuid_name: {}'.format(uuid_name)) - app.logger.info('[+] agreed: {}'.format(agree)) - - # convert PNG to JPG - print('[+] Resizing image') - - # LOL MaskRCNN needs to be run outside of the Celery Task - im = Image.open(file.stream).convert('RGB') - im = ImageOps.fit(im,(512,512)) - if agree: - upload_folder = app.config['UPLOADS'] - else: - upload_folder = app.config['UPLOADS_PRIVATE'] - - fpath = os.path.join(upload_folder, uuid_name + '.jpg') - - # Save image to disk - print('[+] Save image to {}'.format(fpath)) - im.save(fpath, 'JPEG', quality=100) - im_pil_256 = im.resize((256,256)) - - print('[+] ensure_np...') - im_np = imx.ensure_np(im_pil_256) - #print('[+] resize np...') - #im_np = imutils.resize(im_np,width=256) - - upload_dir, render_dir, json_dir, upload_uri, render_uri = get_paths(agree) - - print('[+] Run mrcnn...') - try: - result = mask_rcnn.create_segmentations(im_np,concat=True) - except: - print('[-] Error. Could not run mask_rcnn') - result = [] - - if len(result) > 0: - result = result[0] - - # save data, then pass to celery task - print('[+] Save masks') - seg_mask = result['seg_mask'] - fpath_seg_mask = os.path.join(render_dir, uuid_name + '_seg_mask.jpg') - #cv.imwrite(fpath_seg_mask,cv.cvtColor(seg_mask,cv.COLOR_BGR2RGB)) - #seg_mask = seg_mask[:,:,::-1] - seg_mask_pil = imx.ensure_pil(seg_mask) - seg_mask_pil.save(fpath_seg_mask, 'JPEG', quality=100) - - im_mask = result['im_mask'] - fpath_im_mask = os.path.join(render_dir, uuid_name + '_im_mask.jpg') - #im_mask = im_mask[:,:,::-1] - im_mask_pil = imx.ensure_pil(im_mask) - im_mask_pil.save(fpath_im_mask, 'JPEG',quality=100) - #cv.imwrite(fpath_im_mask,cv.cvtColor(im_mask,cv.COLOR_BGR2RGB)) - - celery_result = { - 'score':str(result['score']), - 'name':str(result['name']), - 'class_index':str(result['class_index']), - 'color':str(result['color']), - 'fp_im_mask':fpath_im_mask, - 'fp_seg_mask':fpath_seg_mask, - 'valid':True - } - else: - print('[-] no reults. process background only') - celery_result = { - 'score':None, - 'name':None, - 'class_index':None, - 'color':None, - 'fp_im_mask':None, - 'fp_seg_mask':None, - 'valid':False - } - - print('[+] Start celery') - async_task = task.apply_async(args=[uuid_name, agree, celery_result]) - task_url = url_for('main.task_status', task_name=style, task_id=async_task.id) - - return jsonify({ - 'result': True, - 'task_url': task_url, - 'uuid': uuid_name - }) - - - -# ---------------------------------------------------- -# Fileserver, temp solution -# ---------------------------------------------------- - -@main.route('/img/<string:imtype>/<string:uuid_name>') -def get_image(imtype,uuid_name): - """Return image files from render or uploads""" - if imtype == 'uploads': - d = app.config['UPLOADS'] - suffix = '' - elif imtype == 'renders': - d = app.config['RENDERS'] - suffix = '' - elif imtype == 'fcn': - d = app.config['RENDERS'] - suffix = '_fcn8' - - fname = uuid_name + suffix + '.jpg' - fpath = os.path.join(d, fname) - - if os.path.isfile(fpath): - return send_from_directory(d,fname) - else: - return send_from_directory('static', 'img/404.jpg') - -# ---------------------------------------------------- -# Deleting images -# ---------------------------------------------------- - -def destroy_data(uuid_name, is_public): - uri_base = app.config['URI_BASE'] - upload_dir, render_dir, json_dir, upload_uri, render_uri = get_paths(is_public) - - json_path = os.path.join(json_dir, uuid_name + '.json') - with open(json_path) as json_file: - data = json.load(json_file) - for f in data['files']: - path = os.path.join(uri_base, f['fn'][1:]) - if os.path.exists(path): - os.remove(path) - os.remove(json_path) - -@main.route('/d/<uuid_name>/destroy', strict_slashes=False) # public -def route_public_destroy(uuid_name): - destroy_data(uuid_name, True) - return redirect("/", code=302) - -@main.route('/p/<uuid_name>/destroy', strict_slashes=False) # private -def route_private_destroy(uuid_name): - destroy_data(uuid_name, False) - return redirect("/", code=302) - -# ---------------------------------------------------- -# Static routes -# ---------------------------------------------------- - -# Most of the pages are served with the single page app in index.html: - -task_json = make_task_json() - -@main.route('/', strict_slashes=False) -def index(): - return render_template('index.html', task_json=task_json) - -@main.route('/about', strict_slashes=False) -def about(): - return render_template('index.html', task_json=task_json) - -@main.route('/d/<uuid_name>', strict_slashes=False) # public -def route_public(uuid_name): - return render_template('index.html', task_json=task_json) - -@main.route('/p/<uuid_name>', strict_slashes=False) # private -def route_private(uuid_name): - return render_template('index.html', task_json=task_json) - -@main.route('/privacy', strict_slashes=False) -def privacy(): - return render_template('index.html', task_json=task_json) - -# Some of the pages have their own static file: - -@main.route('/gallery', strict_slashes = False) -def gallery(): - app.logger.info('access gallery') - uploads, renders, urls = utils.get_recent_uploads(limit=50) - uuids = [os.path.splitext(os.path.basename(f))[0] for f in uploads] - images = [{'upload':u,'render':r, 'url':url} for u,r,url in zip(uploads,renders,urls)] - return render_template('gallery.html',images=images) - -@main.route('/zkm', strict_slashes=False) -def zkm(): - app.logger.info('access ZkM') - return render_template('zkm.html') - -@main.route('/celery', strict_slashes=False) -def celery_route(): - return render_template('celery.html') - -@main.route('/projector', strict_slashes=False) -def projector(): - uploads, renders,urls = utils.get_recent_uploads() - return render_template('projector.html', uploads=uploads, renders=renders) diff --git a/old/server/app/static/css/bootstrap.min.css b/old/server/app/static/css/bootstrap.min.css deleted file mode 100644 index ed3905e0..00000000 --- a/old/server/app/static/css/bootstrap.min.css +++ /dev/null @@ -1,6 +0,0 @@ -/*! - * Bootstrap v3.3.7 (http://getbootstrap.com) - * Copyright 2011-2016 Twitter, Inc. - * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) - *//*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */html{font-family:sans-serif;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}body{margin:0}article,aside,details,figcaption,figure,footer,header,hgroup,main,menu,nav,section,summary{display:block}audio,canvas,progress,video{display:inline-block;vertical-align:baseline}audio:not([controls]){display:none;height:0}[hidden],template{display:none}a{background-color:transparent}a:active,a:hover{outline:0}abbr[title]{border-bottom:1px dotted}b,strong{font-weight:700}dfn{font-style:italic}h1{margin:.67em 0;font-size:2em}mark{color:#000;background:#ff0}small{font-size:80%}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}img{border:0}svg:not(:root){overflow:hidden}figure{margin:1em 40px}hr{height:0;-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}pre{overflow:auto}code,kbd,pre,samp{font-family:monospace,monospace;font-size:1em}button,input,optgroup,select,textarea{margin:0;font:inherit;color:inherit}button{overflow:visible}button,select{text-transform:none}button,html input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer}button[disabled],html input[disabled]{cursor:default}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}input{line-height:normal}input[type=checkbox],input[type=radio]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;padding:0}input[type=number]::-webkit-inner-spin-button,input[type=number]::-webkit-outer-spin-button{height:auto}input[type=search]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}fieldset{padding:.35em .625em .75em;margin:0 2px;border:1px solid silver}legend{padding:0;border:0}textarea{overflow:auto}optgroup{font-weight:700}table{border-spacing:0;border-collapse:collapse}td,th{padding:0}/*! 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-} -.navbar-default .navbar-toggle:hover { - border-color: #fff; -} -.navbar-default .navbar-collapse, .navbar-default .navbar-form{ - border:0; -} - -/* ------------------------------------------------ */ -/* Jumbotron */ -.jumbotron { - padding-top: 0px; - padding-bottom: 0px; - margin-bottom: 0px; - color: inherit; -} -.jumbotron{ - background: transparent; - color:black; -} -.jumbotron h1{ - color:#ddd; - margin-bottom:0px; -} -.jumbotron a.btn-primary{ - background:#ddd; - color:#333; -} -.jumbotron a.btn-primary:hover{ - background:#eee; - color:#222; -} -.jumbotron p > a.jcallout{ - color:#eee; - padding-bottom: 3px; - border-bottom:1px dotted; - text-decoration: none; -} -.jumbotron p > a.jcallout:hover{ - color:#fff; - border-bottom:1px solid #ccc; - text-decoration: none; -} -.jumbotron a.btn-default{ - color:#eee; - border:1px solid #eee; - background: transparent; -} -.jumbotron a.btn-default:hover{ - background: #22f; - border:1px solid #ccc; -} -.jumbotron a.btn-default:active{ - color:#eee; - border:1px solid #ccc; -} - -/* Input button override --------------------------------------------------- */ -/*input[type="file"] { - display: none; -} -input[type="button"] { - display: none; -}*/ -input.hidden_input{ - position: absolute; - top: 0; - left: 0; - width: 100%; - height: 100%; - opacity: 0; -} - -/* Global styles --------------------------------------------------- */ -h1,h2,h3,h4,h5,h6 { - font-weight: bold; - font-style: italic; - text-align: center; -} -h1 { - font-size:56px; - margin-top: 20px; - margin-bottom: 0; -} -h2 { - font-size:20px; - margin-top: 0; - margin-bottom: 24px; -} -ul { - list-style: none; - margin:0; - padding:0; -} -li { - display: inline-block; -} -img.img_responsive_dull { - max-width: 100%; - height: auto; -} -#photo_area{ - width: 512px; - height: 512px; - max-width: 97vw; - max-height: 97vw; - min-width: 240px; - min-height: 240px; - margin:0 auto; - text-align: center; -} -.dash_border{ - background-color: rgba(255,255,255,.2); - border:1px dashed #000; -} - -label { - display: block; -} - -div.center_inner { - position: relative; - top: 50%; - -webkit-transform: translateY(-50%); - -ms-transform: translateY(-50%); - transform: translateY(-50%); -} -#upload_controls{ - margin-top:25px; - display: none; -} -#restart_btn, #rotate_btn, #upload_btn, #dropdown_btn { - display: inline-block; - margin-left:5px; - margin-right:5px; -} -.custom-file-upload { - display: inline-block; - padding: 6px 12px; - cursor: pointer; -} - -.align_center{ - text-align: center; -} - -ul.action-buttons{ - margin-top: 40px; - list-style: none; - margin-left: 0; - padding-left:0; -} -li { - list-style: none; - margin-left: 0; - padding-left:0; - /*margin-bottom:20px;*/ -} - -.btn { - display: inline-block; - color: #333; - background-color: #fff; - border: 1px solid #adadad; - font-family: Helvetica, sans-serif; - padding: 6px 12px; - margin: 0; - font-size: 14px; - font-weight: 400; - line-height: 1.42857143; - text-align: center; - white-space: nowrap; - vertical-align: middle; - -ms-touch-action: manipulation; - touch-action: manipulation; - cursor: pointer; - -webkit-user-select: none; - -moz-user-select: none; - -ms-user-select: none; - user-select: none; - border-radius: 4px; - text-decoration: none; - transition: all 0.15s; -} -.desktop .btn:hover { - background-color: #fff; -} -.btn.btn-lg { - padding: 10px 16px; - font-size: 18px; - line-height: 1.3333333; - border-radius: 6px; -} -.btn.btn-sm { - padding: 5px 10px; - font-size: 12px; - line-height: 1.5; - border-radius: 3px; -} -.btn.btn-important { - border-width: 2px; - border-color: #444; -} -#photo_area { - position: relative; - cursor: pointer; -} -#restart_btn { - position: relative; - cursor: pointer; -} -input[type=file] { - cursor: pointer; - opacity: 0; - position: absolute; - top: 0; left: 0; - width: 100%; height: 100%; -} -.desktop #photo_area .btn { - background: #fff; -} -.desktop #photo_area .btn:hover { - background: #eee; -} -.consent_box { - margin-top: 10px; - font-size: smaller; - color: #444; -} - -/* Intro page --------------------------------------------------- */ -canvas { - display: block; -} -.photo { - display: none; - width:100%; - height:100%; -} - -/* form visibility */ - -#preloader_anim { - display: none; -} -#about_btn { - margin: 20px 0px 80px 0; -} -#share_btns { - display: none; - margin:20px; -} -.notice { - color: #444; - font-size: small; -} -a.btn-default { - background-color: transparent; -} -a.btn-default:hover { - color: #333; - background-color: #fff; - border-color: #adadad; -} -.debug-view { - margin-bottom: 20px; - font-size:14px; - color:#333; -} -.debug-view img { - margin-bottom:4px; -} -#full_results, #hide_more { - display: none; -} - -select { - display: inline-block; - height: 34px; - padding: 6px 12px; - font-size: 14px; - line-height: 1.428571429; - color: #555; - vertical-align: middle; - background-color: #eee; - background-image: none; - border: 1px solid #bbb; - border-radius: 4px; - transition: all .15s; - cursor: pointer; -} -.desktop select:hover { - background-color: #fff; - cursor: pointer; -} - -/* About ---------------------------------------------------- */ -.modal { - pointer-events: none; - opacity: 0; - width: 100%; - height: 100%; - position: fixed; - top: 0; left: 0; - transition: all 0.2s; -} -.modal.visible { - pointer-events: auto; - opacity: 1; -} -.modal p { - font-size: 16px; - line-height: 24px; -} -.modal .inner { - margin: 10vh auto; - background: rgba(255,255,255,0.5); - padding: 20px 20px 40px 20px; - width: 600px; - max-width: 90vw; -} -.modal .content { - margin-bottom: 20px; -} - -/* Result ---------------------------------------------------- */ -.result_view { - display: none; - text-align: center; -} -.final_result img { - width: 512px; - height: 512px; - border: 1px dashed #000; - margin: 10px; -} -.all_results { - display: none; -} -.all_results div { - margin-bottom: 20px; -} -.all_results img { - width: 384px; - height: 384px; - margin: 10px; -} - -.made_with { - margin-bottom: 10px; -} -#delete_btns { - margin-top: 10px; - font-size: 10px; -} -a#destroy_data { - color: #888; -} -.desktop a#destroy_data:hover { - color: #f00; -} - -/* Footer ---------------------------------------------------- */ -#footer{ - /*background: #ddd;*/ - /*padding: 20px 0;*/ - /*position: fixed;*/ - bottom: 0; - width: 100%; - text-align: center; - padding-top: 40px; - padding-bottom: 20px; -} -#footer ul li a { - font-weight: bold; - text-decoration: none; -} -#footer a { - color:#333; - text-decoration: underline; -} -#footer a:hover { - color:#111; -} - -@media screen and (max-width: 500px) { - .modal { - width: 100vw; - height: 100vh; - display: flex; - align-items: center; - justify-content: center; - } - .modal .inner { - margin: 0; - width: 100vw; - height: 100vh; - max-width: 100vw; - } -} - - -/* Gallery */ - -.gallery-preview-row{ - margin-bottom: 40px -} -.gallery-preview-row img{ - width:100%; - padding:2px; -} - -@media (max-width: 600px) { - #preloader_anim { - position: relative; - top: -50px; - } -} - -/*hide dropdown*/ -#dropdown_btn{ - display: none; -}
\ No newline at end of file diff --git a/old/server/app/static/css/projector.css b/old/server/app/static/css/projector.css deleted file mode 100644 index 401f0dff..00000000 --- a/old/server/app/static/css/projector.css +++ /dev/null @@ -1,52 +0,0 @@ -html, body, #wrapper { - height:100%; - width: 100%; - margin: 0; - padding: 0; - border: 0; - background-color: #000; -} -table{ - padding:0; - margin:0; - border-spacing: 0; -} -table td{ - padding:0; - margin:0; -} -#wrapper td { - vertical-align: middle; - text-align: center; -} -#wrapper img{ - margin-top:-350px; - margin-left:-350px; -} -.left{ - background-color: #000; -} -.right{ - background-color: #000;; -} - -#container{ -} -#container-left{ - width:50%; - float:left; - position: relative; -} -#container-right{ - width:50%; - float:right; - position: relative; -} - -.cycle{position:relative;display: none;} -.cycle img{position:absolute;z-index:1} -.cycle img.active{z-index:100} -.cycle img{ - width:700px; - height:700px; -}
\ No newline at end of file diff --git a/old/server/app/static/js/app.js b/old/server/app/static/js/app.js deleted file mode 100644 index 454d5c37..00000000 --- a/old/server/app/static/js/app.js +++ /dev/null @@ -1,158 +0,0 @@ -var app = (function(){ - - var app = {} - - app.init = function(){ - upload.init() - app.bind() - app.build() - app.resize() - app.route() - } - app.bind = function(){ - $(window).on('resize', app.resize) - $(".about_button").on('click', app.about_show) - $(".privacy_button").on('click', app.privacy_show) - $(".modal").on('click', app.modal_hide) - $(".modal .btn").on('click', app.modal_hide) - $(".modal .inner").on('click', preventDefault) - $("#destroy_data").on('click', app.destroyData) - $("#show_all_results").on('click', app.showAllResults) - } - app.build = function(){ - var items = JSON.parse(decodeEntities($("#dropdown_options").html())) - var $dropdown = $("#dropdown") - var options = Object.keys(items).sort().map(key => { - var item = items[key] - var option = document.createElement('option') - option.value = item.name - option.innerHTML = item.title - if (item.selected) option.selected = true - $dropdown.append(option) - }) - var loader = new Image () - loader.src = '/static/img/loader.gif' - } - app.resize = function(){ - var $el = $('#photo_area') - var w = $el.width() - $el.height($el.width()) - } - app.route = function(){ - const path = window.location.pathname.split('/') - path.shift() - switch (path[0]) { - case 'd': - app.processingComplete(path[1], true) // public - break - case 'p': - app.processingComplete(path[1], false) // private - break - case 'about': - app.about_show() - break - case 'privacy': - app.privacy_show() - break - default: - // load index, default state - break - } - } - - /* upload UI changes */ - - app.didPickPhoto = function(){ - $('#upload_controls').fadeIn() - $('#user_photo_canvas').show() - $('#take_photo_btn').hide() - } - app.didClickUpload = function(){ - $('#upload_controls').slideUp('fast') - $('#user_photo_canvas').hide() - $('#preloader_anim').fadeIn('fast') - $('#progress').fadeIn() - } - app.uploadDidComplete = function(){ - $('#preloader_anim').hide() - $('#progress').hide() - } - app.uploadDidComplete = function(){ - // $('#preloader_anim').hide() - // $('#progress').hide() - } - app.updateProgress = function(message, percentage){ - message = message || "Processing..." - percentage = percentage || 0 - $("#progress").html(message) - } - app.processingComplete = function(uuid, is_public){ - $('#preloader_anim').hide() - $('#progress').hide() - // - $("header h2").html("Your dull result") - $(".upload_view").hide() - $(".results_view").show() - var endpoint = is_public ? 'json' : 'json_private' - $.getJSON('/static/media/' + endpoint + '/' + uuid + '.json', function(data){ - console.log(data) - var template = $("#result_template").html() - var final_result = new Image - final_result.src = data.files[data.files.length-1].fn - $(".final_result").empty() - $(".all_results").empty() - $(".final_result").append(final_result) - data.files.forEach(function(file){ - var t = template.replace(/{img}/, file.fn).replace(/{title}/, file.title) - $(".all_results").append(t) - }) - $(".result_view").show() - $(".permalink").attr('href', window.location.href) - }).fail(function(){ - console.log('error fetching json') - window.location.href = '/' - }) - } - var detailed = false - app.showAllResults = function(){ - if (!detailed) { - detailed = true - $(this).html('Hide') - $(".all_results").fadeIn('fast') - } else { - detailed = false - $(this).html('Detailed Analysis') - $(".all_results").slideUp('fast') - } - } - app.destroyData = function(){ - var uuid = window.location.pathname.split('/')[2] - var confirmed = confirm("Do you really want to delete your dull dream?") - if (confirmed) { - $.get( [window.location.pathname, 'destroy'].join('/').replace('//', '/') ).always(function(){ - alert('Dull dream deleted!') - window.location.href = '/' - }) - } - } - - /* modals */ - - app.about_show = function(e){ - e.preventDefault() - $(".about_view").addClass('visible') - } - app.privacy_show = function(e){ - e.preventDefault() - $(".privacy_view").addClass('visible') - } - app.modal_hide = function(e){ - e.preventDefault() - e.stopPropagation() - $(".modal").removeClass('visible') - } - - document.addEventListener('DOMContentLoaded', app.init) - - return app -})()
\ No newline at end of file diff --git a/old/server/app/static/js/upload.js b/old/server/app/static/js/upload.js deleted file mode 100644 index 27437e43..00000000 --- a/old/server/app/static/js/upload.js +++ /dev/null @@ -1,319 +0,0 @@ -var messages = { - is_processing: "Running semantic segmentation...", - upload_failed: "Error attempting to upload the file.", - upload_cancelled: "Upload cancelled or browser dropped connection.", - unable_to_compute: "We're sorry! We were unable to compute your image.", - pending: "Sending to Generative Adversarial Network...", - complete: "Processing complete!", -} - -var upload = (function(){ - var upload = {} - var uploading = false - - var MAX_SIDE = 512 - - upload.init = function(){ - upload.bind() - } - - upload.bind = function(){ - $("input[type=file]").on('change', upload.change) - $("#upload_btn").on('click', upload.go) - document.body.addEventListener("dragover", upload.dragover) - document.body.addEventListener("dragleave", upload.dragover) - document.body.addEventListener("drop", upload.change) - } - - upload.dragover = function(e){ - e.stopPropagation() - e.preventDefault() - } - - upload.change = function(e){ - e.preventDefault() - var files = e.dataTransfer ? e.dataTransfer.files : e.target.files - if (files.length) { - var file = files[files.length - 1] - if (!file.type.match('image.*')) - return - var reader = new FileReader() - reader.onload = onReaderLoad - reader.readAsDataURL(file) - } - function onReaderLoad(e) { - // Don't leak! - reader.onload = null - var img = new Image - img.onload = function(){ - img.onload = null - upload.ready(img) - } - img.src = e.target.result - } - } - - upload.ready = function(img){ - var resized = renderToCanvas(img, { correctOrientation: true }) - var canvas = document.querySelector('#user_photo_canvas') - ctx = canvas.getContext('2d') - ctx.fillStyle = 'black' - ctx.fillRect(0, 0, MAX_SIDE, MAX_SIDE) - var x_offset = (MAX_SIDE - resized.width) / 2 - var y_offset = (MAX_SIDE - resized.height) / 2 - - ctx.drawImage(resized, x_offset, y_offset) - app.didPickPhoto() - } - - upload.go = function(){ - if (uploading) return - uploading = true - app.didClickUpload() - try { - var canvas = document.querySelector('#user_photo_canvas') - var cb = canvas.toBlob(function(blob){ - upload.send(blob) - }, 'image/jpeg', 89) - } catch(e){ - app.updateProgress(messages.unable_to_compute) - } - } - - upload.send = function(blob){ - console.log("sending upload...") - var fd = new FormData() - fd.append('user_image', blob) - fd.append('ext', 'jpg') - fd.append('style', $("#dropdown").val()) - fd.append('agree', $("#agree").val() || 0) - - var xhr = new XMLHttpRequest() - xhr.upload.addEventListener("progress", upload.progress, false) - xhr.addEventListener("load", upload.complete, false) - xhr.addEventListener("error", upload.failed, false) - xhr.addEventListener("abort", upload.canceled, false) - xhr.open("POST", "/upload") - xhr.send(fd) - } - - upload.progress = function (e) { - if (e.lengthComputable) { - var percentComplete = Math.round(e.loaded * 100 / e.total) - if (percentComplete > 99) { - app.updateProgress(messages.is_processing) - } else { - app.updateProgress("Uploaded " + percentComplete.toString() + '%') - } - } - else { - app.updateProgress(messages.unable_to_compute) - } - } - - upload.complete = function (e) { - uploading = false - try { - var data = JSON.parse(e.target.responseText) - } catch (e) { - return app.updateProgress(messages.upload_failed) - } - app.uploadDidComplete() - upload.data = data - upload.task_progress(data.task_url) - } - - upload.failed = function (evt) { - uploading = false - app.updateProgress(messages.upload_failed) - } - - upload.cancelled = function (evt) { - uploading = false - app.updateProgress(messages.upload_cancelled) - } - - upload.task_progress = function (status_url) { - var is_public = $("#agree").val() || 0 - var uuid = upload.data.uuid - $.getJSON(status_url, function(data){ - console.log(data) - var alive = true - var delay = 500 - switch(data.state) { - case 'PENDING': - app.updateProgress(messages.pending) - delay = 2000 - break - case 'PROCESSING': - app.updateProgress(data.message, data.percent) - delay = 500 - break - case 'SUCCESS': - app.updateProgress(messages.complete) - if (is_public) { - history.pushState({}, 'DullDream', '/d/' + uuid) - } else { - history.pushState({}, 'DullDream', '/p/' + uuid) - } - app.processingComplete(uuid, is_public) // truthy if private - alive = false - break - default: - // NB: error state - alive = false - break - } - if (alive) { - setTimeout(function() { - upload.task_progress(status_url) - }, delay) - } - }) - } - - - function renderToCanvas(img, options) { - if (!img) return - options = options || {} - - // Canvas max size for any side - var maxSize = MAX_SIDE - var canvas = document.createElement('canvas') - var ctx = canvas.getContext('2d') - var initialScale = options.scale || 1 - // Scale to needed to constrain canvas to max size - var scale = getScale(img.width * initialScale, img.height * initialScale, maxSize, maxSize, true) - // Still need to apply the user defined scale - scale *= initialScale - var width = canvas.width = Math.round(img.width * scale) - var height = canvas.height = Math.round(img.height * scale) - var correctOrientation = options.correctOrientation - var jpeg = !!img.src.match(/data:image\/jpeg|\.jpeg$|\.jpg$/i) - var hasDataURI = !!img.src.match(/^data:/) - - ctx.save() - - // Can only correct orientation on JPEGs represented as dataURIs - // for the time being - if (correctOrientation && jpeg && hasDataURI) { - applyOrientationCorrection(canvas, ctx, img.src) - } - // Resize image if too large - if (scale !== 1) { - ctx.scale(scale, scale) - } - - ctx.drawImage(img, 0, 0) - ctx.restore() - - return canvas - } - - function getScale(width, height, viewportWidth, viewportHeight, fillViewport) { - fillViewport = !!fillViewport - var landscape = (width / height) > (viewportWidth / viewportHeight) - if (landscape) { - if (fillViewport) { - return fitVertical() - } else if (width > viewportWidth) { - return fitHorizontal() - } - } else { - if (fillViewport) { - return fitHorizontal() - } else if (height > viewportHeight) { - return fitVertical() - } - } - return 1 - - function fitHorizontal() { - return viewportWidth / width - } - - function fitVertical() { - return viewportHeight / height - } - } - - function applyOrientationCorrection(canvas, ctx, uri) { - var orientation = getOrientation(uri) - // Only apply transform if there is some non-normal orientation - if (orientation && orientation !== 1) { - var transform = orientationToTransform[orientation] - var rotation = transform.rotation - var mirror = transform.mirror - var flipAspect = rotation === 90 || rotation === 270 - if (flipAspect) { - // Fancy schmancy swap algo - canvas.width = canvas.height + canvas.width - canvas.height = canvas.width - canvas.height - canvas.width -= canvas.height - } - if (rotation > 0) { - applyRotation(canvas, ctx, rotation) - } - } - } - - function applyRotation(canvas, ctx, deg) { - var radians = deg * (Math.PI / 180) - if (deg === 90) { - ctx.translate(canvas.width, 0) - } else if (deg === 180) { - ctx.translate(canvas.width, canvas.height) - } else if (deg == 270) { - ctx.translate(0, canvas.height) - } - ctx.rotate(radians) - } - - function getOrientation (uri) { - var exif = new ExifReader - // Split off the base64 data - var base64String = uri.split(',')[1] - // Read off first 128KB, which is all we need to - // get the EXIF data - var arr = base64ToUint8Array(base64String, 0, Math.pow(2, 17)) - try { - exif.load(arr.buffer) - return exif.getTagValue('Orientation') - } catch (err) { - return 1 - } - } - - function base64ToUint8Array(string, start, finish) { - var start = start || 0 - var finish = finish || string.length - // atob that shit - var binary = atob(string) - var buffer = new Uint8Array(binary.length) - for (var i = start; i < finish; i++) { - buffer[i] = binary.charCodeAt(i) - } - return buffer - } - - /** - * Mapping from EXIF orientation values to data - * regarding the rotation and mirroring necessary to - * render the canvas correctly - * Derived from: - * http://www.daveperrett.com/articles/2012/07/28/exif-orientation-handling-is-a-ghetto/ - */ - var orientationToTransform = { - 1: { rotation: 0, mirror: false }, - 2: { rotation: 0, mirror: true }, - 3: { rotation: 180, mirror: false }, - 4: { rotation: 180, mirror: true }, - 5: { rotation: 90, mirror: true }, - 6: { rotation: 90, mirror: false }, - 7: { rotation: 270, mirror: true }, - 8: { rotation: 270, mirror: false } - } - - - return upload -})()
\ No newline at end of file diff --git a/old/server/app/static/js/util.js b/old/server/app/static/js/util.js deleted file mode 100644 index 851f634a..00000000 --- a/old/server/app/static/js/util.js +++ /dev/null @@ -1,32 +0,0 @@ -var is_iphone = (navigator.userAgent.match(/iPhone/i)) || (navigator.userAgent.match(/iPod/i)) -var is_ipad = (navigator.userAgent.match(/iPad/i)) -var is_android = (navigator.userAgent.match(/Android/i)) -var is_mobile = is_iphone || is_ipad || is_android -var is_desktop = ! is_mobile; - -document.body.parentNode.classList.add(is_desktop ? 'desktop' : 'mobile') - -function preventDefault(e){ - e.preventDefault() - e.stopPropagation() -} - -var decodeEntities = (function() { - // this prevents any overhead from creating the object each time - var element = document.createElement('div'); - - function decodeHTMLEntities (str) { - if(str && typeof str === 'string') { - // strip script/html tags - str = str.replace(/<script[^>]*>([\S\s]*?)<\/script>/gmi, ''); - str = str.replace(/<\/?\w(?:[^"'>]|"[^"]*"|'[^']*')*>/gmi, ''); - element.innerHTML = str; - str = element.textContent; - element.textContent = ''; - } - - return str; - } - - return decodeHTMLEntities; -})();
\ No newline at end of file diff --git a/old/server/app/static/js/vendor/ExifReader.js b/old/server/app/static/js/vendor/ExifReader.js deleted file mode 100644 index a8343ede..00000000 --- a/old/server/app/static/js/vendor/ExifReader.js +++ /dev/null @@ -1,1363 +0,0 @@ -// Generated by CoffeeScript 1.6.2 -/* -# ExifReader 1.1.1 -# http://github.com/mattiasw/exifreader -# Copyright (C) 2011-2014 Mattias Wallander <mattias@wallander.eu> -# Licensed under the GNU Lesser General Public License version 3 or later -# See license text at http://www.gnu.org/licenses/lgpl.txt -*/ - - -(function() { - (typeof exports !== "undefined" && exports !== null ? exports : this).ExifReader = (function() { - ExifReader.prototype._MIN_DATA_BUFFER_LENGTH = 2; - - ExifReader.prototype._JPEG_ID_SIZE = 2; - - ExifReader.prototype._JPEG_ID = 0xffd8; - - ExifReader.prototype._APP_MARKER_SIZE = 2; - - ExifReader.prototype._APP0_MARKER = 0xffe0; - - ExifReader.prototype._APP1_MARKER = 0xffe1; - - ExifReader.prototype._APP15_MARKER = 0xffef; - - ExifReader.prototype._APP_ID_OFFSET = 4; - - ExifReader.prototype._BYTES_Exif = 0x45786966; - - ExifReader.prototype._TIFF_HEADER_OFFSET = 10; - - ExifReader.prototype._BYTE_ORDER_BIG_ENDIAN = 0x4949; - - ExifReader.prototype._BYTE_ORDER_LITTLE_ENDIAN = 0x4d4d; - - function ExifReader() { - var _this = this; - - this._getTagValueAt = { - 1: function(offset) { - return _this._getByteAt(offset); - }, - 2: function(offset) { - return _this._getAsciiAt(offset); - }, - 3: function(offset) { - return _this._getShortAt(offset); - }, - 4: function(offset) { - return _this._getLongAt(offset); - }, - 5: function(offset) { - return _this._getRationalAt(offset); - }, - 7: function(offset) { - return _this._getUndefinedAt(offset); - }, - 9: function(offset) { - return _this._getSlongAt(offset); - }, - 10: function(offset) { - return _this._getSrationalAt(offset); - } - }; - this._tiffHeaderOffset = 0; - } - - /* - # Loads all the Exif tags from the specified image file buffer. - # - # data ArrayBuffer Image file data - */ - - - ExifReader.prototype.load = function(data) { - return this.loadView(new DataView(data)); - }; - - /* - # Loads all the Exif tags from the specified image file buffer view. Probably - # used when DataView isn't supported by the browser. - # - # @_dataView DataView Image file data view - */ - - - ExifReader.prototype.loadView = function(_dataView) { - this._dataView = _dataView; - this._tags = {}; - this._checkImageHeader(); - this._readTags(); - return this._dataView = null; - }; - - ExifReader.prototype._checkImageHeader = function() { - if (this._dataView.byteLength < this._MIN_DATA_BUFFER_LENGTH || this._dataView.getUint16(0, false) !== this._JPEG_ID) { - throw new Error('Invalid image format'); - } - this._parseAppMarkers(this._dataView); - if (!this._hasExifData()) { - throw new Error('No Exif data'); - } - }; - - ExifReader.prototype._parseAppMarkers = function(dataView) { - var appMarkerPosition, fieldLength, _results; - - appMarkerPosition = this._JPEG_ID_SIZE; - _results = []; - while (true) { - if (dataView.byteLength < appMarkerPosition + this._APP_ID_OFFSET + 5) { - break; - } - if (this._isApp1ExifMarker(dataView, appMarkerPosition)) { - fieldLength = dataView.getUint16(appMarkerPosition + this._APP_MARKER_SIZE, false); - this._tiffHeaderOffset = appMarkerPosition + this._TIFF_HEADER_OFFSET; - } else if (this._isAppMarker(dataView, appMarkerPosition)) { - fieldLength = dataView.getUint16(appMarkerPosition + this._APP_MARKER_SIZE, false); - } else { - break; - } - _results.push(appMarkerPosition += this._APP_MARKER_SIZE + fieldLength); - } - return _results; - }; - - ExifReader.prototype._isApp1ExifMarker = function(dataView, appMarkerPosition) { - return dataView.getUint16(appMarkerPosition, false) === this._APP1_MARKER && dataView.getUint32(appMarkerPosition + this._APP_ID_OFFSET, false) === this._BYTES_Exif && dataView.getUint8(appMarkerPosition + this._APP_ID_OFFSET + 4, false) === 0x00; - }; - - ExifReader.prototype._isAppMarker = function(dataView, appMarkerPosition) { - var appMarker; - - appMarker = dataView.getUint16(appMarkerPosition, false); - return appMarker >= this._APP0_MARKER && appMarker <= this._APP15_MARKER; - }; - - ExifReader.prototype._hasExifData = function() { - return this._tiffHeaderOffset !== 0; - }; - - ExifReader.prototype._readTags = function() { - this._setByteOrder(); - this._read0thIfd(); - this._readExifIfd(); - this._readGpsIfd(); - return this._readInteroperabilityIfd(); - }; - - ExifReader.prototype._setByteOrder = function() { - if (this._dataView.getUint16(this._tiffHeaderOffset) === this._BYTE_ORDER_BIG_ENDIAN) { - return this._littleEndian = true; - } else if (this._dataView.getUint16(this._tiffHeaderOffset) === this._BYTE_ORDER_LITTLE_ENDIAN) { - return this._littleEndian = false; - } else { - throw new Error('Illegal byte order value. Faulty image.'); - } - }; - - ExifReader.prototype._read0thIfd = function() { - var ifdOffset; - - ifdOffset = this._getIfdOffset(); - return this._readIfd('0th', ifdOffset); - }; - - ExifReader.prototype._getIfdOffset = function() { - return this._tiffHeaderOffset + this._getLongAt(this._tiffHeaderOffset + 4); - }; - - ExifReader.prototype._readExifIfd = function() { - var ifdOffset; - - if (this._tags['Exif IFD Pointer'] != null) { - ifdOffset = this._tiffHeaderOffset + this._tags['Exif IFD Pointer'].value; - return this._readIfd('exif', ifdOffset); - } - }; - - ExifReader.prototype._readGpsIfd = function() { - var ifdOffset; - - if (this._tags['GPS Info IFD Pointer'] != null) { - ifdOffset = this._tiffHeaderOffset + this._tags['GPS Info IFD Pointer'].value; - return this._readIfd('gps', ifdOffset); - } - }; - - ExifReader.prototype._readInteroperabilityIfd = function() { - var ifdOffset; - - if (this._tags['Interoperability IFD Pointer'] != null) { - ifdOffset = this._tiffHeaderOffset + this._tags['Interoperability IFD Pointer'].value; - return this._readIfd('interoperability', ifdOffset); - } - }; - - ExifReader.prototype._readIfd = function(ifdType, offset) { - var fieldIndex, numberOfFields, tag, _i, _results; - - numberOfFields = this._getShortAt(offset); - offset += 2; - _results = []; - for (fieldIndex = _i = 0; 0 <= numberOfFields ? _i < numberOfFields : _i > numberOfFields; fieldIndex = 0 <= numberOfFields ? ++_i : --_i) { - tag = this._readTag(ifdType, offset); - if (tag !== void 0) { - this._tags[tag.name] = { - 'value': tag.value, - 'description': tag.description - }; - } - _results.push(offset += 12); - } - return _results; - }; - - ExifReader.prototype._readTag = function(ifdType, offset) { - var tagCode, tagCount, tagDescription, tagName, tagType, tagValue, tagValueOffset; - - tagCode = this._getShortAt(offset); - tagType = this._getShortAt(offset + 2); - tagCount = this._getLongAt(offset + 4); - if (this._typeSizes[tagType] === void 0) { - return void 0; - } - if (this._typeSizes[tagType] * tagCount <= 4) { - tagValue = this._getTagValue(offset + 8, tagType, tagCount); - } else { - tagValueOffset = this._getLongAt(offset + 8); - tagValue = this._getTagValue(this._tiffHeaderOffset + tagValueOffset, tagType, tagCount); - } - if (tagType === this._tagTypes['ASCII']) { - tagValue = this._splitNullSeparatedAsciiString(tagValue); - } - if (this._tagNames[ifdType][tagCode] != null) { - if ((this._tagNames[ifdType][tagCode]['name'] != null) && (this._tagNames[ifdType][tagCode]['description'] != null)) { - tagName = this._tagNames[ifdType][tagCode]['name']; - tagDescription = this._tagNames[ifdType][tagCode]['description'](tagValue); - } else { - tagName = this._tagNames[ifdType][tagCode]; - if (tagValue instanceof Array) { - tagDescription = tagValue.join(', '); - } else { - tagDescription = tagValue; - } - } - return { - 'name': tagName, - 'value': tagValue, - 'description': tagDescription - }; - } else { - return { - 'name': "undefined-" + tagCode, - 'value': tagValue, - 'description': tagValue - }; - } - }; - - ExifReader.prototype._getTagValue = function(offset, type, count) { - var tagValue, value, valueIndex; - - value = (function() { - var _i, _results; - - _results = []; - for (valueIndex = _i = 0; 0 <= count ? _i < count : _i > count; valueIndex = 0 <= count ? ++_i : --_i) { - tagValue = this._getTagValueAt[type](offset); - offset += this._typeSizes[type]; - _results.push(tagValue); - } - return _results; - }).call(this); - if (value.length === 1) { - value = value[0]; - } else if (type === this._tagTypes['ASCII']) { - value = this._getAsciiValue(value); - } - return value; - }; - - ExifReader.prototype._getAsciiValue = function(charArray) { - var charCode, newCharArray; - - return newCharArray = (function() { - var _i, _len, _results; - - _results = []; - for (_i = 0, _len = charArray.length; _i < _len; _i++) { - charCode = charArray[_i]; - _results.push(String.fromCharCode(charCode)); - } - return _results; - })(); - }; - - ExifReader.prototype._getByteAt = function(offset) { - return this._dataView.getUint8(offset); - }; - - ExifReader.prototype._getAsciiAt = function(offset) { - return this._dataView.getUint8(offset); - }; - - ExifReader.prototype._getShortAt = function(offset) { - return this._dataView.getUint16(offset, this._littleEndian); - }; - - ExifReader.prototype._getLongAt = function(offset) { - return this._dataView.getUint32(offset, this._littleEndian); - }; - - ExifReader.prototype._getRationalAt = function(offset) { - return this._getLongAt(offset) / this._getLongAt(offset + 4); - }; - - ExifReader.prototype._getUndefinedAt = function(offset) { - return this._getByteAt(offset); - }; - - ExifReader.prototype._getSlongAt = function(offset) { - return this._dataView.getInt32(offset, this._littleEndian); - }; - - ExifReader.prototype._getSrationalAt = function(offset) { - return this._getSlongAt(offset) / this._getSlongAt(offset + 4); - }; - - ExifReader.prototype._splitNullSeparatedAsciiString = function(string) { - var character, i, tagValue, _i, _len; - - tagValue = []; - i = 0; - for (_i = 0, _len = string.length; _i < _len; _i++) { - character = string[_i]; - if (character === '\x00') { - i++; - continue; - } - if (tagValue[i] == null) { - tagValue[i] = ''; - } - tagValue[i] += character; - } - return tagValue; - }; - - ExifReader.prototype._typeSizes = { - 1: 1, - 2: 1, - 3: 2, - 4: 4, - 5: 8, - 7: 1, - 9: 4, - 10: 8 - }; - - ExifReader.prototype._tagTypes = { - 'BYTE': 1, - 'ASCII': 2, - 'SHORT': 3, - 'LONG': 4, - 'RATIONAL': 5, - 'UNDEFINED': 7, - 'SLONG': 9, - 'SRATIONAL': 10 - }; - - ExifReader.prototype._tagNames = { - '0th': { - 0x0100: 'ImageWidth', - 0x0101: 'ImageLength', - 0x0102: 'BitsPerSample', - 0x0103: 'Compression', - 0x0106: 'PhotometricInterpretation', - 0x010e: 'ImageDescription', - 0x010f: 'Make', - 0x0110: 'Model', - 0x0111: 'StripOffsets', - 0x0112: { - 'name': 'Orientation', - 'description': function(value) { - switch (value) { - case 1: - return 'top-left'; - case 2: - return 'top-right'; - case 3: - return 'bottom-right'; - case 4: - return 'bottom-left'; - case 5: - return 'left-top'; - case 6: - return 'right-top'; - case 7: - return 'right-bottom'; - case 8: - return 'left-bottom'; - default: - return 'Undefined'; - } - } - }, - 0x0115: 'SamplesPerPixel', - 0x0116: 'RowsPerStrip', - 0x0117: 'StripByteCounts', - 0x011a: 'XResolution', - 0x011b: 'YResolution', - 0x011c: 'PlanarConfiguration', - 0x0128: { - 'name': 'ResolutionUnit', - 'description': function(value) { - switch (value) { - case 2: - return 'inches'; - case 3: - return 'centimeters'; - default: - return 'Unknown'; - } - } - }, - 0x012d: 'TransferFunction', - 0x0131: 'Software', - 0x0132: 'DateTime', - 0x013b: 'Artist', - 0x013e: 'WhitePoint', - 0x013f: 'PrimaryChromaticities', - 0x0201: 'JPEGInterchangeFormat', - 0x0202: 'JPEGInterchangeFormatLength', - 0x0211: 'YCbCrCoefficients', - 0x0212: 'YCbCrSubSampling', - 0x0213: { - 'name': 'YCbCrPositioning', - 'description': function(value) { - switch (value) { - case 1: - return 'centered'; - case 2: - return 'co-sited'; - default: - return 'undefied ' + value; - } - } - }, - 0x0214: 'ReferenceBlackWhite', - 0x8298: { - 'name': 'Copyright', - 'description': function(value) { - return value.join('; '); - } - }, - 0x8769: 'Exif IFD Pointer', - 0x8825: 'GPS Info IFD Pointer' - }, - 'exif': { - 0x829a: 'ExposureTime', - 0x829d: 'FNumber', - 0x8822: { - 'name': 'ExposureProgram', - 'description': function(value) { - switch (value) { - case 0: - return 'Undefined'; - case 1: - return 'Manual'; - case 2: - return 'Normal program'; - case 3: - return 'Aperture priority'; - case 4: - return 'Shutter priority'; - case 5: - return 'Creative program'; - case 6: - return 'Action program'; - case 7: - return 'Portrait mode'; - case 8: - return 'Landscape mode'; - default: - return 'Unknown'; - } - } - }, - 0x8824: 'SpectralSensitivity', - 0x8827: 'ISOSpeedRatings', - 0x8828: { - 'name': 'OECF', - 'description': function(value) { - return '[Raw OECF table data]'; - } - }, - 0x9000: { - 'name': 'ExifVersion', - 'description': function(value) { - var charCode, string, _i, _len; - - string = ''; - for (_i = 0, _len = value.length; _i < _len; _i++) { - charCode = value[_i]; - string += String.fromCharCode(charCode); - } - return string; - } - }, - 0x9003: 'DateTimeOriginal', - 0x9004: 'DateTimeDigitized', - 0x9101: { - 'name': 'ComponentsConfiguration', - 'description': function(value) { - var character, string, _i, _len; - - string = ''; - for (_i = 0, _len = value.length; _i < _len; _i++) { - character = value[_i]; - switch (character) { - case 0x31: - string += 'Y'; - break; - case 0x32: - string += 'Cb'; - break; - case 0x33: - string += 'Cr'; - break; - case 0x34: - string += 'R'; - break; - case 0x35: - string += 'G'; - break; - case 0x36: - string += 'B'; - } - } - return string; - } - }, - 0x9102: 'CompressedBitsPerPixel', - 0x9201: 'ShutterSpeedValue', - 0x9202: 'ApertureValue', - 0x9203: 'BrightnessValue', - 0x9204: 'ExposureBiasValue', - 0x9205: 'MaxApertureValue', - 0x9206: 'SubjectDistance', - 0x9207: { - 'name': 'MeteringMode', - 'description': function(value) { - switch (value) { - case 1: - return 'Average'; - case 2: - return 'CenterWeightedAverage'; - case 3: - return 'Spot'; - case 4: - return 'MultiSpot'; - case 5: - return 'Pattern'; - case 6: - return 'Partial'; - case 255: - return 'Other'; - default: - return 'Unknown'; - } - } - }, - 0x9208: { - 'name': 'LightSource', - 'description': function(value) { - switch (value) { - case 1: - return 'Daylight'; - case 2: - return 'Fluorescent'; - case 3: - return 'Tungsten (incandescent light)'; - case 4: - return 'Flash'; - case 9: - return 'Fine weather'; - case 10: - return 'Cloudy weather'; - case 11: - return 'Shade'; - case 12: - return 'Daylight fluorescent (D 5700 – 7100K)'; - case 13: - return 'Day white fluorescent (N 4600 – 5400K)'; - case 14: - return 'Cool white fluorescent (W 3900 – 4500K)'; - case 15: - return 'White fluorescent (WW 3200 – 3700K)'; - case 17: - return 'Standard light A'; - case 18: - return 'Standard light B'; - case 19: - return 'Standard light C'; - case 20: - return 'D55'; - case 21: - return 'D65'; - case 22: - return 'D75'; - case 23: - return 'D50'; - case 24: - return 'ISO studio tungsten'; - case 255: - return 'Other light source'; - default: - return 'Unknown'; - } - } - }, - 0x9209: { - 'name': 'Flash', - 'description': function(value) { - switch (value) { - case 0x00: - return 'Flash did not fire'; - case 0x01: - return 'Flash fired'; - case 0x05: - return 'Strobe return light not detected'; - case 0x07: - return 'Strobe return light detected'; - case 0x09: - return 'Flash fired, compulsory flash mode'; - case 0x0d: - return 'Flash fired, compulsory flash mode, return light not detected'; - case 0x0f: - return 'Flash fired, compulsory flash mode, return light detected'; - case 0x10: - return 'Flash did not fire, compulsory flash mode'; - case 0x18: - return 'Flash did not fire, auto mode'; - case 0x19: - return 'Flash fired, auto mode'; - case 0x1d: - return 'Flash fired, auto mode, return light not detected'; - case 0x1f: - return 'Flash fired, auto mode, return light detected'; - case 0x20: - return 'No flash function'; - case 0x41: - return 'Flash fired, red-eye reduction mode'; - case 0x45: - return 'Flash fired, red-eye reduction mode, return light not detected'; - case 0x47: - return 'Flash fired, red-eye reduction mode, return light detected'; - case 0x49: - return 'Flash fired, compulsory flash mode, red-eye reduction mode'; - case 0x4d: - return 'Flash fired, compulsory flash mode, red-eye reduction mode, return light not detected'; - case 0x4f: - return 'Flash fired, compulsory flash mode, red-eye reduction mode, return light detected'; - case 0x59: - return 'Flash fired, auto mode, red-eye reduction mode'; - case 0x5d: - return 'Flash fired, auto mode, return light not detected, red-eye reduction mode'; - case 0x5f: - return 'Flash fired, auto mode, return light detected, red-eye reduction mode'; - default: - return 'Unknown'; - } - } - }, - 0x920a: 'FocalLength', - 0x9214: { - 'name': 'SubjectArea', - 'description': function(value) { - switch (value.length) { - case 2: - return "Location; X: " + value[0] + ", Y: " + value[1]; - case 3: - return "Circle; X: " + value[0] + ", Y: " + value[1] + ", diameter: " + value[2]; - case 4: - return "Rectangle; X: " + value[0] + ", Y: " + value[1] + ", width: " + value[2] + ", height: " + value[3]; - default: - return 'Unknown'; - } - } - }, - 0x927c: { - 'name': 'MakerNote', - 'description': function(value) { - return '[Raw maker note data]'; - } - }, - 0x9286: { - 'name': 'UserComment', - 'description': function(value) { - switch (value.slice(0, 8).map(function(charCode) { - return String.fromCharCode(charCode); - }).join('')) { - case 'ASCII\x00\x00\x00': - return value.slice(8, value.length).map(function(charCode) { - return String.fromCharCode(charCode); - }).join(''); - case 'JIS\x00\x00\x00\x00\x00': - return '[JIS encoded text]'; - case 'UNICODE\x00': - return '[Unicode encoded text]'; - case '\x00\x00\x00\x00\x00\x00\x00\x00': - return '[Undefined encoding]'; - } - } - }, - 0x9290: 'SubSecTime', - 0x9291: 'SubSecTimeOriginal', - 0x9292: 'SubSecTimeDigitized', - 0xa000: { - 'name': 'FlashpixVersion', - 'description': function(value) { - var charCode, string, _i, _len; - - string = ''; - for (_i = 0, _len = value.length; _i < _len; _i++) { - charCode = value[_i]; - string += String.fromCharCode(charCode); - } - return string; - } - }, - 0xa001: { - 'name': 'ColorSpace', - 'description': function(value) { - switch (value) { - case 1: - return 'sRGB'; - case 0xffff: - return 'Uncalibrated'; - default: - return 'Unknown'; - } - } - }, - 0xa002: 'PixelXDimension', - 0xa003: 'PixelYDimension', - 0xa004: 'RelatedSoundFile', - 0xa005: 'Interoperability IFD Pointer', - 0xa20b: 'FlashEnergy', - 0xa20c: { - 'name': 'SpatialFrequencyResponse', - 'description': function(value) { - return '[Raw SFR table data]'; - } - }, - 0xa20e: 'FocalPlaneXResolution', - 0xa20f: 'FocalPlaneYResolution', - 0xa210: { - 'name': 'FocalPlaneResolutionUnit', - 'description': function(value) { - switch (value) { - case 2: - return 'inches'; - case 3: - return 'centimeters'; - default: - return 'Unknown'; - } - } - }, - 0xa214: { - 'name': 'SubjectLocation', - 'description': function(value) { - return "X: " + value[0] + ", Y: " + value[1]; - } - }, - 0xa215: 'ExposureIndex', - 0xa217: { - 'name': 'SensingMethod', - 'description': function(value) { - switch (value) { - case 1: - return 'Undefined'; - case 2: - return 'One-chip color area sensor'; - case 3: - return 'Two-chip color area sensor'; - case 4: - return 'Three-chip color area sensor'; - case 5: - return 'Color sequential area sensor'; - case 7: - return 'Trilinear sensor'; - case 8: - return 'Color sequential linear sensor'; - default: - return 'Unknown'; - } - } - }, - 0xa300: { - 'name': 'FileSource', - 'description': function(value) { - switch (value) { - case 3: - return 'DSC'; - default: - return 'Unknown'; - } - } - }, - 0xa301: { - 'name': 'SceneType', - 'description': function(value) { - switch (value) { - case 1: - return 'A directly photographed image'; - default: - return 'Unknown'; - } - } - }, - 0xa302: { - 'name': 'CFAPattern', - 'description': function(value) { - return '[Raw CFA pattern table data]'; - } - }, - 0xa401: { - 'name': 'CustomRendered', - 'description': function(value) { - switch (value) { - case 0: - return 'Normal process'; - case 1: - return 'Custom process'; - default: - return 'Unknown'; - } - } - }, - 0xa402: { - 'name': 'ExposureMode', - 'description': function(value) { - switch (value) { - case 0: - return 'Auto exposure'; - case 1: - return 'Manual exposure'; - case 2: - return 'Auto bracket'; - default: - return 'Unknown'; - } - } - }, - 0xa403: { - 'name': 'WhiteBalance', - 'description': function(value) { - switch (value) { - case 0: - return 'Auto white balance'; - case 1: - return 'Manual white balance'; - default: - return 'Unknown'; - } - } - }, - 0xa404: { - 'name': 'DigitalZoomRatio', - 'description': function(value) { - switch (value) { - case 0: - return 'Digital zoom was not used'; - default: - return value; - } - } - }, - 0xa405: { - 'name': 'FocalLengthIn35mmFilm', - 'description': function(value) { - switch (value) { - case 0: - return 'Unknown'; - default: - return value; - } - } - }, - 0xa406: { - 'name': 'SceneCaptureType', - 'description': function(value) { - switch (value) { - case 0: - return 'Standard'; - case 1: - return 'Landscape'; - case 2: - return 'Portrait'; - case 3: - return 'Night scene'; - default: - return 'Unknown'; - } - } - }, - 0xa407: { - 'name': 'GainControl', - 'description': function(value) { - switch (value) { - case 0: - return 'None'; - case 1: - return 'Low gain up'; - case 2: - return 'High gain up'; - case 3: - return 'Low gain down'; - case 4: - return 'High gain down'; - default: - return 'Unknown'; - } - } - }, - 0xa408: { - 'name': 'Contrast', - 'description': function(value) { - switch (value) { - case 0: - return 'Normal'; - case 1: - return 'Soft'; - case 2: - return 'Hard'; - default: - return 'Unknown'; - } - } - }, - 0xa409: { - 'name': 'Saturation', - 'description': function(value) { - switch (value) { - case 0: - return 'Normal'; - case 1: - return 'Low saturation'; - case 2: - return 'High saturation'; - default: - return 'Unknown'; - } - } - }, - 0xa40a: { - 'name': 'Sharpness', - 'description': function(value) { - switch (value) { - case 0: - return 'Normal'; - case 1: - return 'Soft'; - case 2: - return 'Hard'; - default: - return 'Unknown'; - } - } - }, - 0xa40b: { - 'name': 'DeviceSettingDescription', - 'description': function(value) { - return '[Raw device settings table data]'; - } - }, - 0xa40c: { - 'name': 'SubjectDistanceRange', - 'description': function(value) { - switch (value) { - case 1: - return 'Macro'; - case 2: - return 'Close view'; - case 3: - return 'Distant view'; - default: - return 'Unknown'; - } - } - }, - 0xa420: 'ImageUniqueID' - }, - 'gps': { - 0x0000: { - 'name': 'GPSVersionID', - 'description': function(value) { - var _ref, _ref1; - - if ((value[0] === (_ref = value[1]) && _ref === 2) && (value[2] === (_ref1 = value[3]) && _ref1 === 0)) { - return 'Version 2.2'; - } else { - return 'Unknown'; - } - } - }, - 0x0001: { - 'name': 'GPSLatitudeRef', - 'description': function(value) { - switch (value.join('')) { - case 'N': - return 'North latitude'; - case 'S': - return 'South latitude'; - default: - return 'Unknown'; 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\ No newline at end of file diff --git a/old/server/app/static/js/vendor/prefixfree.js b/old/server/app/static/js/vendor/prefixfree.js deleted file mode 100644 index 40b6e693..00000000 --- a/old/server/app/static/js/vendor/prefixfree.js +++ /dev/null @@ -1,527 +0,0 @@ -/** - * StyleFix 1.0.3 & PrefixFree 1.0.7 - * @author Lea Verou - * MIT license - */ - -(function(){ - -if(!window.addEventListener) { - return; -} - -var self = window.StyleFix = { - link: function(link) { - var url = link.href || link.getAttribute('data-href'); - try { - // Ignore stylesheets with data-noprefix attribute as well as alternate stylesheets or without (data-)href attribute - if(!url || link.rel !== 'stylesheet' || link.hasAttribute('data-noprefix')) { - return; - } - } - catch(e) { - return; - } - - var base = url.replace(/[^\/]+$/, ''), - base_scheme = (/^[a-z]{3,10}:/.exec(base) || [''])[0], - base_domain = (/^[a-z]{3,10}:\/\/[^\/]+/.exec(base) || [''])[0], - base_query = /^([^?]*)\??/.exec(url)[1], - parent = link.parentNode, - xhr = new XMLHttpRequest(), - process; - - xhr.onreadystatechange = function() { - if(xhr.readyState === 4) { - process(); - } - }; - - process = function() { - var css = xhr.responseText; - - if(css && link.parentNode && (!xhr.status || xhr.status < 400 || xhr.status > 600)) { - css = self.fix(css, true, link); - - // Convert relative URLs to absolute, if needed - if(css && base) { - css = css.replace(/url\(\s*?((?:"|')?)(.+?)\1\s*?\)/gi, function($0, quote, url) { - if(/^([a-z]{3,10}:|#)/i.test(url)) { // Absolute & or hash-relative - return $0; - } - else if(/^\/\//.test(url)) { // Scheme-relative - // May contain sequences like /../ and /./ but those DO work - return 'url("' + base_scheme + url + '")'; - } - else if(/^\//.test(url)) { // Domain-relative - return 'url("' + base_domain + url + '")'; - } - else if(/^\?/.test(url)) { // Query-relative - return 'url("' + base_query + url + '")'; - } - else { - // Path-relative - return 'url("' + base + url + '")'; - } - }); - - // behavior URLs shoudn’t be converted (Issue #19) - // base should be escaped before added to RegExp (Issue #81) - var escaped_base = base.replace(/([\\\^\$*+[\]?{}.=!:(|)])/g,"\\$1"); - css = css.replace(RegExp('\\b(behavior:\\s*?url\\(\'?"?)' + escaped_base, 'gi'), '$1'); - } - - var style = document.createElement('style'); - style.textContent = '/*# sourceURL='+link.getAttribute('href')+' */\n/*@ sourceURL='+link.getAttribute('href')+' */\n' + css; - style.media = link.media; - style.disabled = link.disabled; - style.setAttribute('data-href', link.getAttribute('href')); - - if(link.id) style.id = link.id; - - parent.insertBefore(style, link); - parent.removeChild(link); - - style.media = link.media; // Duplicate is intentional. See issue #31 - } - }; - - try { - xhr.open('GET', url); - xhr.send(null); - } catch (e) { - // Fallback to XDomainRequest if available - if (typeof XDomainRequest != "undefined") { - xhr = new XDomainRequest(); - xhr.onerror = xhr.onprogress = function() {}; - xhr.onload = process; - xhr.open("GET", url); - xhr.send(null); - } - } - - link.setAttribute('data-inprogress', ''); - }, - - styleElement: function(style) { - if (style.hasAttribute('data-noprefix')) { - return; - } - var disabled = style.disabled; - - style.textContent = self.fix(style.textContent, true, style); - - style.disabled = disabled; - }, - - styleAttribute: function(element) { - var css = element.getAttribute('style'); - - css = self.fix(css, false, element); - - element.setAttribute('style', css); - }, - - process: function() { - // Linked stylesheets - $('link[rel="stylesheet"]:not([data-inprogress])').forEach(StyleFix.link); - - // Inline stylesheets - $('style').forEach(StyleFix.styleElement); - - // Inline styles - $('[style]').forEach(StyleFix.styleAttribute); - - var event = document.createEvent('Event'); - event.initEvent('StyleFixProcessed', true, true); - document.dispatchEvent(event); - - }, - - register: function(fixer, index) { - (self.fixers = self.fixers || []) - .splice(index === undefined? self.fixers.length : index, 0, fixer); - }, - - fix: function(css, raw, element) { - if(self.fixers) { - for(var i=0; i<self.fixers.length; i++) { - css = self.fixers[i](css, raw, element) || css; - } - } - - return css; - }, - - camelCase: function(str) { - return str.replace(/-([a-z])/g, function($0, $1) { return $1.toUpperCase(); }).replace('-',''); - }, - - deCamelCase: function(str) { - return str.replace(/[A-Z]/g, function($0) { return '-' + $0.toLowerCase() }); - } -}; - -/************************************** - * Process styles - **************************************/ -(function(){ - setTimeout(function(){ - $('link[rel="stylesheet"]').forEach(StyleFix.link); - }, 10); - - document.addEventListener('DOMContentLoaded', StyleFix.process, false); -})(); - -function $(expr, con) { - return [].slice.call((con || document).querySelectorAll(expr)); -} - -})(); - -/** - * PrefixFree - */ -(function(root){ - -if(!window.StyleFix || !window.getComputedStyle) { - return; -} - -// Private helper -function fix(what, before, after, replacement, css) { - what = self[what]; - - if(what.length) { - var regex = RegExp(before + '(' + what.join('|') + ')' + after, 'gi'); - - css = css.replace(regex, replacement); - } - - return css; -} - -var self = window.PrefixFree = { - prefixCSS: function(css, raw, element) { - var prefix = self.prefix; - - // Gradient angles hotfix - if(self.functions.indexOf('linear-gradient') > -1) { - // Gradients are supported with a prefix, convert angles to legacy - css = css.replace(/(\s|:|,)(repeating-)?linear-gradient\(\s*(-?\d*\.?\d*)deg/ig, function ($0, delim, repeating, deg) { - return delim + (repeating || '') + 'linear-gradient(' + (90-deg) + 'deg'; - }); - } - - css = fix('functions', '(\\s|:|,)', '\\s*\\(', '$1' + prefix + '$2(', css); - css = fix('keywords', '(\\s|:)', '(\\s|;|\\}|$)', '$1' + prefix + '$2$3', css); - css = fix('properties', '(^|\\{|\\s|;)', '\\s*:', '$1' + prefix + '$2:', css); - - // Prefix properties *inside* values (issue #8) - if (self.properties.length) { - var regex = RegExp('\\b(' + self.properties.join('|') + ')(?!:)', 'gi'); - - css = fix('valueProperties', '\\b', ':(.+?);', function($0) { - return $0.replace(regex, prefix + "$1") - }, css); - } - - if(raw) { - css = fix('selectors', '', '\\b', self.prefixSelector, css); - css = fix('atrules', '@', '\\b', '@' + prefix + '$1', css); - } - - // Fix double prefixing - css = css.replace(RegExp('-' + prefix, 'g'), '-'); - - // Prefix wildcard - css = css.replace(/-\*-(?=[a-z]+)/gi, self.prefix); - - return css; - }, - - property: function(property) { - return (self.properties.indexOf(property) >=0 ? self.prefix : '') + property; - }, - - value: function(value, property) { - value = fix('functions', '(^|\\s|,)', '\\s*\\(', '$1' + self.prefix + '$2(', value); - value = fix('keywords', '(^|\\s)', '(\\s|$)', '$1' + self.prefix + '$2$3', value); - - if(self.valueProperties.indexOf(property) >= 0) { - value = fix('properties', '(^|\\s|,)', '($|\\s|,)', '$1'+self.prefix+'$2$3', value); - } - - return value; - }, - - prefixSelector: function(selector) { - return self.selectorMap[selector] || selector - }, - - // Warning: Prefixes no matter what, even if the property is supported prefix-less - prefixProperty: function(property, camelCase) { - var prefixed = self.prefix + property; - - return camelCase? StyleFix.camelCase(prefixed) : prefixed; - } -}; - -/************************************** - * Properties - **************************************/ -(function() { - var prefixes = {}, - properties = [], - shorthands = {}, - style = getComputedStyle(document.documentElement, null), - dummy = document.createElement('div').style; - - // Why are we doing this instead of iterating over properties in a .style object? Because Webkit. - // 1. Older Webkit won't iterate over those. - // 2. Recent Webkit will, but the 'Webkit'-prefixed properties are not enumerable. The 'webkit' - // (lower case 'w') ones are, but they don't `deCamelCase()` into a prefix that we can detect. - - var iterate = function(property) { - if(property.charAt(0) === '-') { - properties.push(property); - - var parts = property.split('-'), - prefix = parts[1]; - - // Count prefix uses - prefixes[prefix] = ++prefixes[prefix] || 1; - - // This helps determining shorthands - while(parts.length > 3) { - parts.pop(); - - var shorthand = parts.join('-'); - - if(supported(shorthand) && properties.indexOf(shorthand) === -1) { - properties.push(shorthand); - } - } - } - }, - supported = function(property) { - return StyleFix.camelCase(property) in dummy; - } - - // Some browsers have numerical indices for the properties, some don't - if(style && style.length > 0) { - for(var i=0; i<style.length; i++) { - iterate(style[i]) - } - } - else { - for(var property in style) { - iterate(StyleFix.deCamelCase(property)); - } - } - - // Find most frequently used prefix - var highest = {uses:0}; - for(var prefix in prefixes) { - var uses = prefixes[prefix]; - - if(highest.uses < uses) { - highest = {prefix: prefix, uses: uses}; - } - } - - self.prefix = '-' + highest.prefix + '-'; - self.Prefix = StyleFix.camelCase(self.prefix); - - self.properties = []; - - // Get properties ONLY supported with a prefix - for(var i=0; i<properties.length; i++) { - var property = properties[i]; - - if(property.indexOf(self.prefix) === 0) { // we might have multiple prefixes, like Opera - var unprefixed = property.slice(self.prefix.length); - - if(!supported(unprefixed)) { - self.properties.push(unprefixed); - } - } - } - - // IE fix - if(self.Prefix == 'Ms' - && !('transform' in dummy) - && !('MsTransform' in dummy) - && ('msTransform' in dummy)) { - self.properties.push('transform', 'transform-origin'); - } - - self.properties.sort(); -})(); - -/************************************** - * Values - **************************************/ -(function() { -// Values that might need prefixing -var functions = { - 'linear-gradient': { - property: 'backgroundImage', - params: 'red, teal' - }, - 'calc': { - property: 'width', - params: '1px + 5%' - }, - 'element': { - property: 'backgroundImage', - params: '#foo' - }, - 'cross-fade': { - property: 'backgroundImage', - params: 'url(a.png), url(b.png), 50%' - }, - 'image-set': { - property: 'backgroundImage', - params: 'url(a.png) 1x, url(b.png) 2x' - } -}; - - -functions['repeating-linear-gradient'] = -functions['repeating-radial-gradient'] = -functions['radial-gradient'] = -functions['linear-gradient']; - -// Note: The properties assigned are just to *test* support. -// The keywords will be prefixed everywhere. -var keywords = { - 'initial': 'color', - 'grab': 'cursor', - 'grabbing': 'cursor', - 'zoom-in': 'cursor', - 'zoom-out': 'cursor', - 'box': 'display', - 'flexbox': 'display', - 'inline-flexbox': 'display', - 'flex': 'display', - 'inline-flex': 'display', - 'grid': 'display', - 'inline-grid': 'display', - 'max-content': 'width', - 'min-content': 'width', - 'fit-content': 'width', - 'fill-available': 'width', - 'contain-floats': 'width' -}; - -self.functions = []; -self.keywords = []; - -var style = document.createElement('div').style; - -function supported(value, property) { - style[property] = ''; - style[property] = value; - - return !!style[property]; -} - -for (var func in functions) { - var test = functions[func], - property = test.property, - value = func + '(' + test.params + ')'; - - if (!supported(value, property) - && supported(self.prefix + value, property)) { - // It's supported, but with a prefix - self.functions.push(func); - } -} - -for (var keyword in keywords) { - var property = keywords[keyword]; - - if (!supported(keyword, property) - && supported(self.prefix + keyword, property)) { - // It's supported, but with a prefix - self.keywords.push(keyword); - } -} - -})(); - -/************************************** - * Selectors and @-rules - **************************************/ -(function() { - -var -selectors = { - ':any-link': null, - '::backdrop': null, - ':fullscreen': null, - ':full-screen': ':fullscreen', - //sigh - '::placeholder': null, - ':placeholder': '::placeholder', - '::input-placeholder': '::placeholder', - ':input-placeholder': '::placeholder', - ':read-only': null, - ':read-write': null, - '::selection': null -}, - -atrules = { - 'keyframes': 'name', - 'viewport': null, - 'document': 'regexp(".")' -}; - -self.selectors = []; -self.selectorMap = {}; -self.atrules = []; - -var style = root.appendChild(document.createElement('style')); - -function supported(selector) { - style.textContent = selector + '{}'; // Safari 4 has issues with style.innerHTML - - return !!style.sheet.cssRules.length; -} - -for(var selector in selectors) { - var standard = selectors[selector] || selector - var prefixed = selector.replace(/::?/, function($0) { return $0 + self.prefix }) - if(!supported(standard) && supported(prefixed)) { - self.selectors.push(standard); - self.selectorMap[standard] = prefixed; - } -} - -for(var atrule in atrules) { - var test = atrule + ' ' + (atrules[atrule] || ''); - - if(!supported('@' + test) && supported('@' + self.prefix + test)) { - self.atrules.push(atrule); - } -} - -root.removeChild(style); - -})(); - -// Properties that accept properties as their value -self.valueProperties = [ - 'transition', - 'transition-property', - 'will-change' -] - -// Add class for current prefix -root.className += ' ' + self.prefix; - -StyleFix.register(self.prefixCSS); - - -})(document.documentElement); diff --git a/old/server/app/templates/403.html b/old/server/app/templates/403.html deleted file mode 100644 index f83c6dfc..00000000 --- a/old/server/app/templates/403.html +++ /dev/null @@ -1,35 +0,0 @@ -{%- extends "base.html" %} - -{% import "bootstrap/utils.html" as utils %} - -{% block content %} -<div class="container"> - - <div class="row"> - <h1><a href="/"><img src="/static/img/dulldream_logo_200.png"></a></h1> - <h2 class="subtitle">403</h2> - </div> - - - <div class="row"> - <div class="col-sm-12 align_center"> - <p>Your request could not be handled</p> - </div> - </div> - - <div id="about_btn" class="row"> - <div class="col-sm-12"> - <div class="align_center"> - <a class="btn btn-sm btn-default" href="/" role="button">Home</a> - </div> - </div> - </div> - -</div> - -{% block footer %} -{{super()}} -{% endblock %} - -{% endblock %} - diff --git a/old/server/app/templates/404.html b/old/server/app/templates/404.html deleted file mode 100644 index a7f7d45a..00000000 --- a/old/server/app/templates/404.html +++ /dev/null @@ -1,33 +0,0 @@ -{%- extends "base.html" %} - -{% import "bootstrap/utils.html" as utils %} - -{% block content %} -<div class="container"> - - <div class="row"> - <h1><a href="/"><img src="/static/img/dulldream_logo_200.png"></a></h1> - <h2 class="subtitle">404</h2> - </div> - - <div class="row"> - <div class="col-sm-12 align_center"> - <p>Your request could not be handled</p> - </div> - - <div id="about_btn" class="row"> - <div class="col-sm-12"> - <div class="align_center"> - <a class="btn btn-sm btn-default" href="/" role="button">Home</a> - </div> - </div> - </div> - -</div> - -{% block footer %} -{{super()}} -{% endblock %} - -{% endblock %} - diff --git a/old/server/app/templates/500.html b/old/server/app/templates/500.html deleted file mode 100644 index b323c12c..00000000 --- a/old/server/app/templates/500.html +++ /dev/null @@ -1,34 +0,0 @@ -{%- extends "base.html" %} - -{% import "bootstrap/utils.html" as utils %} - -{% block content %} -<div class="container"> - - <div class="row"> - <h1><a href="/"><img src="/static/img/dulldream_logo_200.png"></a></h1> - <h2 class="subtitle">505</h2> - </div> - - <div class="row"> - <div class="col-sm-12 align_center"> - <p>Your request could not be handled</p> - </div> - </div> - - <div id="about_btn" class="row"> - <div class="col-sm-12"> - <div class="align_center"> - <a class="btn btn-sm btn-default" href="/" role="button">Home</a> - </div> - </div> - </div> - -</div> - -{% block footer %} -{{super()}} -{% endblock %} - -{% endblock %} - diff --git a/old/server/app/templates/base.html b/old/server/app/templates/base.html deleted file mode 100644 index 16402af8..00000000 --- a/old/server/app/templates/base.html +++ /dev/null @@ -1,33 +0,0 @@ -{%- extends "bootstrap/base.html" %} - -{% block title %}DullDream (v2 x ZkM){% endblock %} - -{% block head %} - {{super()}} - <link rel="shortcut icon" href="{{url_for('static', filename='img/favicon.ico')}}"> -{% endblock %} - -{% block styles %} - {{super()}} - <link rel="stylesheet" type="text/css" href="{{url_for('static', filename='css/bootstrap.min.css')}}"> - <link rel="stylesheet" type="text/css" href="{{url_for('static', filename='css/dullbrown-theme.css')}}"> -{% endblock %} - -{%- block content %} - {{super()}} -{% endblock content %} - -{%- block footer %} - <div id="footer" class="footer"> - <div class="container"> - <p class=""> - DullDream™ (beta) by <a href="http://constantdullaart.com">Constant Dullaart</a>. - Made in collaboration with <a href="http://ahprojects.com">Adam Harvey</a> - </p> - </div> - </div> -{% endblock footer %} - -{% block scripts %} - {{super()}} -{% endblock scripts %}
\ No newline at end of file diff --git a/old/server/app/templates/celery.html b/old/server/app/templates/celery.html deleted file mode 100644 index ddcd25cb..00000000 --- a/old/server/app/templates/celery.html +++ /dev/null @@ -1,43 +0,0 @@ -{%- extends "base.html" %} - -{% import "bootstrap/utils.html" as utils %} - -{% block content %} -<style> - .progress { - width: 100%; - text-align: center; - } -</style> - -<div class="container"> - - <div class="row"> - <h1><a href="/"><img src="/static/img/dulldream_logo_200.png"></a></h1> - <h2 class="subtitle">Celery Test</h2> - </div> - - <div class="row"> - <div class="col-sm-12 align_center"> - <button id="submit-data">Submit Data</button><br><br> - <div id="progress"></div> - </div> - </div> - -</div> - -{% block footer %} -{{ super() }} -{% endblock %} - -{% endblock %} - - -{% block scripts %} - {{super()}} - -<script type="text/javascript" src="{{url_for('static', filename='js/old-js/nanobar.min.js')}}"></script> -<script type="text/javascript" src="{{url_for('static', filename='js/old-js/celery.js')}}"></script> - - -{% endblock scripts %}
\ No newline at end of file diff --git a/old/server/app/templates/display.html b/old/server/app/templates/display.html deleted file mode 100644 index f73a6ca5..00000000 --- a/old/server/app/templates/display.html +++ /dev/null @@ -1,69 +0,0 @@ -{%- extends "base.html" %} - -{% import "bootstrap/utils.html" as utils %} - -{% block content %} - -<style type="text/css"> - .display_im{ - width:100%; - max-width: 360px; - min-width: 256px; - margin-bottom: 10px; - } - .display_left{ - - } - .display_right{ - - } - .caption{ - font-size: 11px; - } -</style> -<div class="container"> - - <div class="row"> - <h1><a href="/"><img src="/static/img/dulldream_logo_200.png"></a></h1> - <h2 class="subtitle">Result</h2> - </div> - - <div class="row"> - <div style="height:50px"></div> - <div class="col-sm-12" style="text-align: center;"> - <img class="display_im" src="{{ url_for('main.get_image', imtype='renders', uuid_name='{}'.format( uuid_name )) }}" /> - <p class="caption">Rendered result</p> - </div> - </div> - - <div class="col-sm-12" style="text-align: center;"> - <img class="display_im" src="{{ url_for('main.get_image', imtype='uploads', uuid_name='{}'.format( uuid_name )) }}" /> - <p class="caption">Original image</p> - </div> - </div> - - <div class="col-sm-12" style="text-align: center;"> - <img class="display_im" src="{{ url_for('main.get_image', imtype='fcn', uuid_name='{}'.format( uuid_name )) }}" /> - <p class="caption">Semantic segmentation</p> - </div> - </div> - - </div> - - <div id="about_btn" class="row"> - <div class="col-sm-12"> - <div class="align_center"> - <a class="btn btn-sm btn-default" href="/" role="button">Home</a> - </div> - </div> - </div> - - </div> -</div> - -{% block footer %} -{{super()}} -{% endblock footer %} - -{% endblock %} - diff --git a/old/server/app/templates/index.html b/old/server/app/templates/index.html deleted file mode 100644 index f740bb5b..00000000 --- a/old/server/app/templates/index.html +++ /dev/null @@ -1,161 +0,0 @@ -<!doctype html> -<html> -<head> - <meta charset="utf-8"> - <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" /> - <link rel="shortcut icon" href="/static/img/favicon.ico" /> - <title>DullDream (v2 x ZkM)</title> - <link rel="stylesheet" type="text/css" href="{{url_for('static', filename='css/dullbrown-theme.css')}}"> -</head> -<body> - -<header> - <h1><a href="/"><img src="/static/img/dulldream_logo_200.png" alt="DullDream"></a></h1> - <h2 class="subtitle">Neural network photo effect</h2> -</header> - -<div class="upload_view container"> - <div class="row"> - <div id="photo_area" class="dash_border"> - <input class="hidden_input" id="user_file" type="file" accept="image/*"> - <canvas class="photo" id="user_photo_canvas" width="512" height="512"></canvas> - <div class="center_inner"> - <label id="take_photo_btn" for="user_file" class="upload_center_btn"> - <div class='btn-lg btn'>Take Photo</div> - </label> - <div id="details"></div> - <div id="progress"></div> - </div> - - <div id="preloader_anim"> - <img src="/static/img/loader.gif"> - </div> - </div> - </div> - - <div id="upload_controls" class="row"> - <div class="align_center"> - <div id="restart_btn"> - <a id="restart_btn" class="btn btn-md btn-default" role="button">Change Image</a> - <input type='file' accept="image/*"> - </div> - <div id="dropdown_btn"> - <select id="dropdown"></select> - </div> - <div id="upload_btn"> - <a id="take_photo_btn" class="btn btn-md btn-important" role="button">Upload</a> - </div> - </div> - <div class="align_center consent_box"> - <label> - <input type="checkbox" id="agree" value="1" checked> - I consent to have my dulled image displayed at ZkM. - </label> - </div> - </div> - - <div id="about_btn" class="row"> - <div class="align_center"> - <a class="btn btn-sm btn-default about_button" role="button">About</a> - <a class="btn btn-sm btn-default privacy_button" role="button">Privacy</a> - <p class="notice"> - All images uploaded can be used for exhibition and review purposes. - </p> - <p class="notice"> - Currently this work is on view at <a href="http://zkm.de/en/event/2017/10/open-codes">ZKM</a>. View recent DullDreams <a href="/gallery">here</a>. - </p> - </div> - </div> -</div> - -<div class="about_view modal"> - <div class="inner"> - <header> - <h1><img src="/static/img/dulldream_logo_200.png" alt="DullDream"></h1> - </header> - <div class='content'> - <p> - <b><i>DullDream™ by DullTech™</i></b> is a series of experiments appropriating neural network image recognition technology to make visual representation less interesting. - </p> - <p> - Can machine learning help us desensitize? Our impactful lives are clogging up social media feeds with unique filter settings, leaving us nostalgic for a vanilla future. Can machine learning help us achieve this? Take the excitement out of our lives, prepare us for a time where we will all have to be the same, have the same values and culture? Painting a future where the Dull is no longer a dream but a nightmare? - </p> - <p> - DullDream™ (version 2) was developed for the OpenCodes exhibition at ZKM. It based on the original DullDream™ (<a href="http://dulldream.xyz">version 1</a>), developed for Transmediale 2017 - Ever Elusive by <a href="http://constantdullaart.com">Constant Dullaart</a> in collaboration with <a href="http://ahprojects.com">Adam Harvey</a>. DullDream (V2) has been generously made possible by support from ZKM. - </p> - </div> - <center><a class="btn btn-sm btn-default" href="/" role="button">Home</a></center> - </div> -</div> - -<div class="privacy_view modal"> - <div class="inner"> - <header> - <h1><img src="/static/img/dulldream_logo_200.png" alt="DullDream"></h1> - </header> - <div class='content'> - <h3>Privacy Notice</h3> - <p> - Images uploaded to this site are being used for a public art display at <a href="http://zkm.de/en/event/2017/10/open-codes">ZKM</a> - </p> - <p> - If you would not like to be included, be sure to uncheck the permission box on the upload page. - </p> - - </div> - <center><a class="btn btn-sm btn-default" href="/" role="button">Home</a></center> - </div> -</div> - - -<div class="result_view"> - <div class="final_result"> - </div> - - <div class="row made_with"> - Made with DullDream.xyz for ZKM OpenCodes 2017 - </div> - - <div class="row"> - <button class='btn' id="show_all_results">Detailed Analysis</button> - </div> - - <div class="all_results"> - </div> - - <div id="share_btns" class="row"> - <a id="permalink" href="#">Permalink</a> - </div> - - <div id="about_btn" class="row"> - <div class="align_center"> - <a href="/" class="btn btn-sm btn-default home_button" role="button">Home</a> - <a class="btn btn-sm btn-default about_button" role="button">About</a> - <a class="btn btn-sm btn-default privacy_button" role="button">Privacy</a> - </div> - - </div> - -</div> - -<div id="footer"> - DullDream™ by <a href="http://constantdullaart.com">Constant Dullaart</a>.<br> - <small>Made in collaboration with <a href="http://ahprojects.com">Adam Harvey</a></small> -</div> - -</body> -<script type="text/html" id="result_template"> - <div class="row"> - <img src="{img}"><br> - <b>{title}</b> - </div> -</script> -<script type="text/json" id="dropdown_options">{{ task_json }}</script> -<script type="text/javascript" src="{{url_for('static', filename='js/vendor/jquery-3.3.1.min.js')}}"></script> -<script type="text/javascript" src="{{url_for('static', filename='js/vendor/ExifReader.js')}}"></script> -<script type="text/javascript" src="{{url_for('static', filename='js/vendor/canvas-to-blob.js')}}"></script> -<script type="text/javascript" src="{{url_for('static', filename='js/vendor/prefixfree.js')}}"></script> -<script type="text/javascript" src="{{url_for('static', filename='js/util.js')}}"></script> -<script type="text/javascript" src="{{url_for('static', filename='js/upload.js')}}"></script> -<script type="text/javascript" src="{{url_for('static', filename='js/app.js')}}"></script> -</html>
\ No newline at end of file diff --git a/old/server/celery_worker.py b/old/server/celery_worker.py deleted file mode 100644 index 1545a884..00000000 --- a/old/server/celery_worker.py +++ /dev/null @@ -1,7 +0,0 @@ -#!/usr/bin/env python -import os -from app.basemodels import celery -from app import create_app - -app = create_app(os.getenv('FLASK_CONFIG') or 'default') -app.app_context().push() diff --git a/old/server/config.py b/old/server/config.py deleted file mode 100644 index 5042efb6..00000000 --- a/old/server/config.py +++ /dev/null @@ -1,78 +0,0 @@ -# -*- coding: utf-8 -*- -"""Application configuration.""" -import os -from os.path import join -basedir = os.path.abspath(os.path.dirname(__file__)) - -class Config(object): - """Base configuration.""" - - #SECRET_KEY = os.environ.get('MYFLASKAPP_SECRET', 'secret-key') # TODO: Change me - APP_DIR = os.path.abspath(os.path.dirname(__file__)) # This directory - PROJECT_ROOT = os.path.abspath(os.path.join(APP_DIR, os.pardir)) - #BCRYPT_LOG_ROUNDS = 13 - DEBUG_TB_ENABLED = False # Disable Debug toolbar - #DEBUG_TB_INTERCEPT_REDIRECTS = False - CACHE_TYPE = 'simple' # Can be "memcached", "redis", etc. - HOST = '0.0.0.0' - FLASK_DEBUG_DISABLE_STRICT = True - #WTF_CSRF_SECRET_KEY = '94ksadkf49DKEDFJ.&' - BOOTSTRAP_GOOGLE_ANALYTICS_ACCOUNT = None - BOOTSTRAP_SERVE_LOCAL = True - SECRET_KEY = os.environ.get('SECRET_KEY') or '94ksadkf49DKEDFJ.&' - CELERY_BROKER_URL = 'redis://localhost:6379/0' - CELERY_RESULT_BACKEND = 'redis://localhost:6379/0' - - FLASKY_SLOW_DB_QUERY_TIME=0.5 - - @staticmethod - def init_app(app): - pass - - -class DevelopmentConfig(Config): - """Development configuration.""" - ENV = 'dev' - DEBUG = True - -class ProductionConfig(Config): - ENV = 'production' - DEBUG = False - # @classmethod - # def init_app(cls, app): - # Config.init_app(app) - # # import logging - # # app.logger.addHandler(mail_handler) - - -class DigitalOceanConfig(Config): - """Production configuration.""" - def init_app(cls, app): - ProductionConfig.init_app(app) - # log to syslog - import logging - from logging.handlers import SysLogHandler - syslog_handler = SysLogHandler() - syslog_handler.setLevel(logging.WARNING) - app.logger.addHandler(syslog_handler) - - -class UnixConfig(ProductionConfig): - @classmethod - def init_app(cls, app): - ProductionConfig.init_app(app) - - # log to syslog - import logging - from logging.handlers import SysLogHandler - syslog_handler = SysLogHandler() - syslog_handler.setLevel(logging.WARNING) - app.logger.addHandler(syslog_handler) - - -config = { - 'development': DevelopmentConfig, - 'production': ProductionConfig, - 'digitalocean': DigitalOceanConfig, - 'default': DevelopmentConfig -} diff --git a/old/server/deploy.sh b/old/server/deploy.sh deleted file mode 100755 index c2594cab..00000000 --- a/old/server/deploy.sh +++ /dev/null @@ -1,20 +0,0 @@ -#!/bin/bash - -d_src="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"/ -d_dst=/home/dull/dulldream/www/dulldream_xyz -ssh_alis=dulldream-root -#DOCKER_DIR_P1="$(dirname "$CWD")" - -echo "Syncing DullDream LOCAL to -> REMOTE" -echo $d_src -echo $d_dst - - -#rsync -a -e 'ssh' \ -rsync -r -v --progress -e 'ssh' \ - --delete \ - --exclude='.DS_Store' \ - --exclude='deploy.sh' \ - $d_src $ssh_alis:$d_dst - -echo "Synced :)"
\ No newline at end of file diff --git a/old/server/dulldream.wsgi.py b/old/server/dulldream.wsgi.py deleted file mode 100644 index ed992528..00000000 --- a/old/server/dulldream.wsgi.py +++ /dev/null @@ -1,13 +0,0 @@ -#!/usr/bin/python -import sys -sys.path.insert(0, "/home/dulldream/dulldream/www/dulldream_xyz/") - -from app import create_app - -import logging -logging.basicConfig(stream=sys.stderr) -# logging.basicConfig(filename='error.log',level=logging.DEBUG) - -application = create_app('production') -application.secret_key = 'curlier6982!1decentralizationists' - diff --git a/old/server/run-celery.sh b/old/server/run-celery.sh deleted file mode 100755 index e38174fa..00000000 --- a/old/server/run-celery.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/bin/bash -celery worker -A celery_worker.celery --loglevel=info - diff --git a/old/server/run-dev.sh b/old/server/run-dev.sh deleted file mode 100755 index b4eb2a61..00000000 --- a/old/server/run-dev.sh +++ /dev/null @@ -1 +0,0 @@ -FLASK_CONFIG=development python run.py
\ No newline at end of file diff --git a/old/server/run-gunicorn.sh b/old/server/run-gunicorn.sh deleted file mode 100755 index 64debabd..00000000 --- a/old/server/run-gunicorn.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/bash -gunicorn -w 1 -b 0.0.0.0:8000 run:app diff --git a/old/server/run-redis.sh b/old/server/run-redis.sh deleted file mode 100755 index e9ceb845..00000000 --- a/old/server/run-redis.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/bash -/usr/local/bin/redis-server /etc/redis/redis.conf diff --git a/old/server/run.py b/old/server/run.py deleted file mode 100644 index ff2d5009..00000000 --- a/old/server/run.py +++ /dev/null @@ -1,12 +0,0 @@ -#!/usr/bin/python3 -import os -from flask import Flask -from app import create_app - -app = create_app(os.getenv('FLASK_CONFIG') or 'default') -import logging -logging.basicConfig(filename='error.log',level=logging.DEBUG) - -if __name__ == '__main__': - app.run(host='0.0.0.0', debug=True, threaded=False, port=8000) - pass diff --git a/package-lock.json b/package-lock.json index 187e090a..a4d114fc 100644 --- a/package-lock.json +++ b/package-lock.json @@ -7163,6 +7163,66 @@ } } }, + "react-router": { + "version": "4.3.1", + "resolved": "https://registry.npmjs.org/react-router/-/react-router-4.3.1.tgz", + "integrity": "sha512-yrvL8AogDh2X42Dt9iknk4wF4V8bWREPirFfS9gLU1huk6qK41sg7Z/1S81jjTrGHxa3B8R3J6xIkDAA6CVarg==", + "requires": { + "history": "^4.7.2", + "hoist-non-react-statics": "^2.5.0", + "invariant": "^2.2.4", + "loose-envify": "^1.3.1", + "path-to-regexp": "^1.7.0", + "prop-types": "^15.6.1", + "warning": "^4.0.1" + }, + "dependencies": { + "isarray": { + "version": "0.0.1", + "resolved": "https://registry.npmjs.org/isarray/-/isarray-0.0.1.tgz", + "integrity": "sha1-ihis/Kmo9Bd+Cav8YDiTmwXR7t8=" + }, + "path-to-regexp": { + "version": "1.7.0", + "resolved": "https://registry.npmjs.org/path-to-regexp/-/path-to-regexp-1.7.0.tgz", + "integrity": "sha1-Wf3g9DW62suhA6hOnTvGTpa5k30=", + "requires": { + "isarray": "0.0.1" + } + }, + "warning": { + "version": "4.0.3", + "resolved": "https://registry.npmjs.org/warning/-/warning-4.0.3.tgz", + "integrity": "sha512-rpJyN222KWIvHJ/F53XSZv0Zl/accqHR8et1kpaMTD/fLCRxtV8iX8czMzY7sVZupTI3zcUTg8eycS2kNF9l6w==", + "requires": { + "loose-envify": "^1.0.0" + } + } + } + }, + "react-router-dom": { + "version": "4.3.1", + "resolved": "https://registry.npmjs.org/react-router-dom/-/react-router-dom-4.3.1.tgz", + "integrity": "sha512-c/MlywfxDdCp7EnB7YfPMOfMD3tOtIjrQlj/CKfNMBxdmpJP8xcz5P/UAFn3JbnQCNUxsHyVVqllF9LhgVyFCA==", + "requires": { + "history": "^4.7.2", + "invariant": "^2.2.4", + "loose-envify": "^1.3.1", + "prop-types": "^15.6.1", + "react-router": "^4.3.1", + "warning": "^4.0.1" + }, + "dependencies": { + "warning": { + "version": "4.0.3", + "resolved": "https://registry.npmjs.org/warning/-/warning-4.0.3.tgz", + "integrity": "sha512-rpJyN222KWIvHJ/F53XSZv0Zl/accqHR8et1kpaMTD/fLCRxtV8iX8czMzY7sVZupTI3zcUTg8eycS2kNF9l6w==", + "requires": { + "loose-envify": "^1.0.0" + } + } + } + }, "react-spin": { "version": "0.6.2", "resolved": "https://registry.npmjs.org/react-spin/-/react-spin-0.6.2.tgz", diff --git a/package.json b/package.json index 6ff9dac5..0cc38ef6 100644 --- a/package.json +++ b/package.json @@ -9,7 +9,8 @@ "build": "NODE_ENV=production webpack --config ./webpack.config.prod.js", "deploy": "NODE_ENV=production webpack --config ./webpack.config.prod.js && git commit -am 'deploy' && git push origin master && ssh vframe@vframe ./restart.sh", "watchSplash": "NODE_ENV=development webpack --config ./webpack.splash.dev.js --colors --watch", - "buildSplash": "NODE_ENV=production webpack --config ./webpack.splash.prod.js" + "buildSplash": "NODE_ENV=production webpack --config ./webpack.splash.prod.js", + "watchGeocode": "NODE_ENV=development webpack --config ./webpack.geocode.dev.js --colors --watch" }, "repository": { "type": "git", @@ -48,6 +49,7 @@ "react-dom": "^16.3.0", "react-hot-loader": "^4.3.0", "react-redux": "^5.0.7", + "react-router-dom": "^4.3.1", "react-spin": "^0.6.2", "redux": "^4.0.0", "redux-thunk": "^2.3.0", diff --git a/scraper/.gitignore b/scraper/.gitignore new file mode 100644 index 00000000..868c3dd4 --- /dev/null +++ b/scraper/.gitignore @@ -0,0 +1,4 @@ +datasets/s2 +datasets/old +datasets/scholar_entries.numbers +datasets/scholar_entries.csv diff --git a/scraper/reports/datasets b/scraper/reports/datasets new file mode 120000 index 00000000..ed9c23bf --- /dev/null +++ b/scraper/reports/datasets @@ -0,0 +1 @@ +../../site/datasets/
\ No newline at end of file diff --git a/scraper/reports/geocode_papers.html b/scraper/reports/geocode_papers.html index 529ee9c7..84ffe356 100644 --- a/scraper/reports/geocode_papers.html +++ b/scraper/reports/geocode_papers.html @@ -33,5 +33,6 @@ html,body { margin: 0; padding: 0; width: 100%; height: 100%; } <div id="container"> </div> </body> +<script src="/reports/geocode-app.js"></script> </html> diff --git a/scraper/reports/paper_title_report.html b/scraper/reports/paper_title_report.html index 51d5204e..90deaf36 100644 --- a/scraper/reports/paper_title_report.html +++ b/scraper/reports/paper_title_report.html @@ -1,9 +1,3 @@ -<!doctype html><html><head><meta charset='utf-8'><title>Paper Title Sanity Check</title><link rel='stylesheet' href='reports.css'></head><body><h2>Paper Title Sanity Check</h2><table border='1' cellpadding='3' cellspacing='3'><th>key</th><th>name</th><th>our title</th><th>found title</th><th></th><th></th><th>address</th><th>s2 id</th><tr><td>10k_US_adult_faces</td><td>10K US Adult Faces</td><td>The intrinsic memorability of face images</td><td>The intrinsic memorability of face photographs.</td><td><a href="https://pdfs.semanticscholar.org/8b2d/d5c61b23ead5ae5508bb8ce808b5ea266730.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the intrinsic memorability of face images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8b2dd5c61b23ead5ae5508bb8ce808b5ea266730</td></tr><tr><td>3d_rma</td><td>3D-RMA</td><td>Automatic 3D Face Authentication</td><td>Automatic 3D face authentication</td><td><a href="http://pdfs.semanticscholar.org/2160/788824c4c29ffe213b2cbeb3f52972d73f37.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automatic 3d face authentication&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2160788824c4c29ffe213b2cbeb3f52972d73f37</td></tr><tr><td>3dddb_unconstrained</td><td>3D Dynamic</td><td>A 3D Dynamic Database for Unconstrained Face Recognition</td><td>A 3 D Dynamic Database for Unconstrained Face Recognition</td><td><a href="https://pdfs.semanticscholar.org/4d4b/b462c9f1d4e4ab1e4aa6a75cc0bc71b38461.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3d dynamic database for unconstrained face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461</td></tr><tr><td>3dpes</td><td>3DPeS</td><td>3DPes: 3D People Dataset for Surveillance and Forensics</td><td>3DPeS: 3D people dataset for surveillance and forensics</td><td><a href="http://doi.acm.org/10.1145/2072572.2072590" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=3dpes: 3d people dataset for surveillance and forensics&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e</td></tr><tr><td>4dfab</td><td>4DFAB</td><td>4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications</td><td>4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications</td><td><a href="https://arxiv.org/pdf/1712.01443.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=4dfab: a large scale 4d facial expression database for biometric applications&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9696ad8b164f5e10fcfe23aacf74bd6168aebb15</td></tr><tr><td>50_people_one_question</td><td>50 People One Question</td><td>Merging Pose Estimates Across Space and Time</td><td>Merging Pose Estimates Across Space and Time</td><td><a href="http://pdfs.semanticscholar.org/63b2/f5348af0f969dfc2afb4977732393c6459ec.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=merging pose estimates across space and time&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725</td></tr><tr><td>a_pascal_yahoo</td><td>aPascal</td><td>Describing Objects by their Attributes</td><td>Describing objects by their attributes</td><td><a href="http://www-2.cs.cmu.edu/~dhoiem/publications/cvpr2009_attributes.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=describing objects by their attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2e384f057211426ac5922f1b33d2aa8df5d51f57</td></tr><tr><td>adience</td><td>Adience</td><td>Age and Gender Estimation of Unfiltered Faces</td><td>Age and Gender Estimation of Unfiltered Faces</td><td><a href="http://www.openu.ac.il/home/hassner/Adience/EidingerEnbarHassner_tifs.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=age and gender estimation of unfiltered faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1be498d4bbc30c3bfd0029114c784bc2114d67c0</td></tr><tr><td>afad</td><td>AFAD</td><td>Ordinal Regression with a Multiple Output CNN for Age Estimation</td><td>Ordinal Regression with Multiple Output CNN for Age Estimation</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.532" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ordinal regression with a multiple output cnn for age estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c</td></tr><tr><td>afew_va</td><td>AFEW-VA</td><td>AFEW-VA database for valence and arousal estimation in-the-wild</td><td>AFEW-VA database for valence and arousal estimation in-the-wild</td><td><a href="http://pdfs.semanticscholar.org/2624/d84503bc2f8e190e061c5480b6aa4d89277a.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=afew-va database for valence and arousal estimation in-the-wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2624d84503bc2f8e190e061c5480b6aa4d89277a</td></tr><tr><td>afew_va</td><td>AFEW-VA</td><td>Collecting Large, Richly Annotated Facial-Expression Databases from Movies</td><td>Collecting Large, Richly Annotated Facial-Expression Databases from Movies</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6200254" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=collecting large, richly annotated facial-expression databases from movies&sort=relevance" target="_blank">[s2]</a></td><td>Australian National University</td><td>b1f4423c227fa37b9680787be38857069247a307</td></tr><tr><td>affectnet</td><td>AffectNet</td><td>AffectNet: A New Database for Facial Expression, Valence, and Arousal Computation in the Wild</td><td>Skybiometry and AffectNet on Facial Emotion Recognition Using Supervised Machine Learning Algorithms</td><td><a href="http://dl.acm.org/citation.cfm?id=3232665" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=affectnet: a new database for facial expression, valence, and arousal computation in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>f152b6ee251cca940dd853c54e6a7b78fbc6b235</td></tr><tr><td>aflw</td><td>AFLW</td><td>Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization</td><td>Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=annotated facial landmarks in the wild: a large-scale, real-world database for facial landmark localization&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td></tr><tr><td>afw</td><td>AFW</td><td>Face detection, pose estimation and landmark localization in the wild</td><td>Face detection, pose estimation, and landmark localization in the wild</td><td><a href="http://vision.ics.uci.edu/papers/ZhuR_CVPR_2012/ZhuR_CVPR_2012.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face detection, pose estimation and landmark localization in the wild&sort=relevance" target="_blank">[s2]</a></td><td>University of California, Irvine</td><td>0e986f51fe45b00633de9fd0c94d082d2be51406</td></tr><tr><td>agedb</td><td>AgeDB</td><td>AgeDB: the first manually collected, in-the-wild age database</td><td>AgeDB: The First Manually Collected, In-the-Wild Age Database</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8014984" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=agedb: the first manually collected, in-the-wild age database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dcf418c778f528b5792104760f1fbfe90c6dd6a</td></tr><tr><td>alert_airport</td><td>ALERT Airport</td><td>A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets</td><td>A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.</td><td><a href="https://arxiv.org/pdf/1605.09653.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a systematic evaluation and benchmark for person re-identification: features, metrics, and datasets&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6403117f9c005ae81f1e8e6d1302f4a045e3d99d</td></tr><tr><td>am_fed</td><td>AM-FED</td><td>Affectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”</td><td>Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"</td><td><a href="http://pdfs.semanticscholar.org/5d06/437656dd94616d7d87260d5eb77513ded30f.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=affectiva mit facial expression dataset (am-fed): naturalistic and spontaneous facial expressions collected “in the wild”&sort=relevance" target="_blank">[s2]</a></td><td></td><td>47aeb3b82f54b5ae8142b4bdda7b614433e69b9a</td></tr><tr><td>apis</td><td>APiS1.0</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W10/papers/Zhu_Pedestrian_Attribute_Classification_2013_ICCV_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian attribute classification in surveillance: database and evaluation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td></tr><tr><td>ar_facedb</td><td>AR Face</td><td>The AR Face Database</td><td>The AR face database</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the ar face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6d96f946aaabc734af7fe3fc4454cf8547fcd5ed</td></tr><tr><td>awe_ears</td><td>AWE Ears</td><td>Ear Recognition: More Than a Survey</td><td>Ear Recognition: More Than a Survey</td><td><a href="http://pdfs.semanticscholar.org/84fe/5b4ac805af63206012d29523a1e033bc827e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ear recognition: more than a survey&sort=relevance" target="_blank">[s2]</a></td><td></td><td>84fe5b4ac805af63206012d29523a1e033bc827e</td></tr><tr><td>b3d_ac</td><td>B3D(AC)</td><td>A 3-D Audio-Visual Corpus of Affective Communication</td><td>A 3-D Audio-Visual Corpus of Affective Communication</td><td><a href="http://files.is.tue.mpg.de/jgall/download/jgall_avcorpus_mm10.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3-d audio-visual corpus of affective communication&sort=relevance" target="_blank">[s2]</a></td><td></td><td>d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae</td></tr><tr><td>bbc_pose</td><td>BBC Pose</td><td>Automatic and Efficient Human Pose Estimation for Sign Language Videos</td><td>Automatic and Efficient Human Pose Estimation for Sign Language Videos</td><td><a href="https://doi.org/10.1007/s11263-013-0672-6" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automatic and efficient human pose estimation for sign language videos&sort=relevance" target="_blank">[s2]</a></td><td></td><td>213a579af9e4f57f071b884aa872651372b661fd</td></tr><tr><td>berkeley_pose</td><td>BPAD</td><td>Describing People: A Poselet-Based Approach to Attribute Classification</td><td>Describing people: A poselet-based approach to attribute classification</td><td><a href="http://ttic.uchicago.edu/~smaji/papers/attributes-iccv11.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=describing people: a poselet-based approach to attribute classification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7808937b46acad36e43c30ae4e9f3fd57462853d</td></tr><tr><td>bfm</td><td>BFM</td><td>A 3D Face Model for Pose and Illumination Invariant Face Recognition</td><td>A 3D Face Model for Pose and Illumination Invariant Face Recognition</td><td><a href="https://pdfs.semanticscholar.org/6399/37b3a1b8bded3f7e9a40e85bd3770016cf3c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3d face model for pose and illumination invariant face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>639937b3a1b8bded3f7e9a40e85bd3770016cf3c</td></tr><tr><td>bio_id</td><td>BioID Face</td><td>Robust Face Detection Using the Hausdorff Distance</td><td>Robust Face Detection Using the Hausdorff Distance</td><td><a href="http://pdfs.semanticscholar.org/4053/e3423fb70ad9140ca89351df49675197196a.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=robust face detection using the hausdorff distance&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4053e3423fb70ad9140ca89351df49675197196a</td></tr><tr><td>bjut_3d</td><td>BJUT-3D</td><td>The BJUT-3D Large-Scale Chinese Face Database</td><td>A novel face recognition method based on 3D face model</td><td><a href="https://doi.org/10.1109/ROBIO.2007.4522202" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the bjut-3d large-scale chinese face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1ed1a49534ad8dd00f81939449f6389cfbc25321</td></tr><tr><td>bosphorus</td><td>The Bosphorus</td><td>Bosphorus Database for 3D Face Analysis</td><td>Bosphorus Database for 3D Face Analysis</td><td><a href="http://pdfs.semanticscholar.org/4254/fbba3846008f50671edc9cf70b99d7304543.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=bosphorus database for 3d face analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2acf7e58f0a526b957be2099c10aab693f795973</td></tr><tr><td>bp4d_plus</td><td>BP4D+</td><td>Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</td><td>Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhang_Multimodal_Spontaneous_Emotion_CVPR_2016_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multimodal spontaneous emotion corpus for human behavior analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4</td></tr><tr><td>bp4d_spontanous</td><td>BP4D-Spontanous</td><td>A high resolution spontaneous 3D dynamic facial expression database</td><td>A high-resolution spontaneous 3D dynamic facial expression database</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6553788" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a high resolution spontaneous 3d dynamic facial expression database&sort=relevance" target="_blank">[s2]</a></td><td>SUNY Binghamton</td><td>b91f54e1581fbbf60392364323d00a0cd43e493c</td></tr><tr><td>brainwash</td><td>Brainwash</td><td>Brainwash dataset</td><td>Brainwash: A Data System for Feature Engineering</td><td><a href="http://pdfs.semanticscholar.org/ae44/8015b2ff2bd3b8a5c9a3266f954f5af9ffa9.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=brainwash dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>214c966d1f9c2a4b66f4535d9a0d4078e63a5867</td></tr><tr><td>bu_3dfe</td><td>BU-3DFE</td><td>A 3D Facial Expression Database For Facial Behavior Research</td><td>A 3D facial expression database for facial behavior research</td><td><a href="http://www.cs.binghamton.edu/~lijun/Research/3DFE/Yin_FGR06_a.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3d facial expression database for facial behavior research&sort=relevance" target="_blank">[s2]</a></td><td>SUNY Binghamton</td><td>cc589c499dcf323fe4a143bbef0074c3e31f9b60</td></tr><tr><td>buhmap_db</td><td>BUHMAP-DB</td><td>Facial Feature Tracking and Expression Recognition for Sign Language</td><td>Facial feature tracking and expression recognition for sign language</td><td><a href="http://www.cmpe.boun.edu.tr/pilab/pilabfiles/databases/buhmap/files/ari2008facialfeaturetracking.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facial feature tracking and expression recognition for sign language&sort=relevance" target="_blank">[s2]</a></td><td></td><td>014b8df0180f33b9fea98f34ae611c6447d761d2</td></tr><tr><td>cafe</td><td>CAFE</td><td>The Child Affective Facial Expression (CAFE) Set: Validity and reliability from untrained adults</td><td>The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults</td><td><a href="http://pdfs.semanticscholar.org/2038/8099cc415c772926e47bcbbe554e133343d1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the child affective facial expression (cafe) set: validity and reliability from untrained adults&sort=relevance" target="_blank">[s2]</a></td><td></td><td>20388099cc415c772926e47bcbbe554e133343d1</td></tr><tr><td>caltech_10k_web_faces</td><td>Caltech 10K Web Faces</td><td>Pruning Training Sets for Learning of Object Categories</td><td>Pruning training sets for learning of object categories</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1467308" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pruning training sets for learning of object categories&sort=relevance" target="_blank">[s2]</a></td><td></td><td>636b8ffc09b1b23ff714ac8350bb35635e49fa3c</td></tr><tr><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td>Pedestrian Detection: A Benchmark</td><td>Pedestrian detection: A benchmark</td><td><a href="http://vision.ucsd.edu/~pdollar/files/papers/DollarCVPR09peds.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian detection: a benchmark&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1dc35905a1deff8bc74688f2d7e2f48fd2273275</td></tr><tr><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td>Pedestrian Detection: An Evaluation of the State of the Art</td><td>Pedestrian Detection: An Evaluation of the State of the Art</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5975165" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian detection: an evaluation of the state of the art&sort=relevance" target="_blank">[s2]</a></td><td></td><td>f72f6a45ee240cc99296a287ff725aaa7e7ebb35</td></tr><tr><td>camel</td><td>CAMEL</td><td>CAMEL Dataset for Visual and Thermal Infrared Multiple Object Detection and Tracking</td><td>Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis</td><td><a href="http://pdfs.semanticscholar.org/5801/690199c1917fa58c35c3dead177c0b8f9f2d.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=camel dataset for visual and thermal infrared multiple object detection and tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5801690199c1917fa58c35c3dead177c0b8f9f2d</td></tr><tr><td>cas_peal</td><td>CAS-PEAL</td><td>The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</td><td>The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</td><td><a href="https://doi.org/10.1109/TSMCA.2007.909557" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cas-peal large-scale chinese face database and baseline evaluations&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td></tr><tr><td>casablanca</td><td>Casablanca</td><td>Context-aware {CNNs} for person head detection</td><td>Context-Aware CNNs for Person Head Detection</td><td><a href="http://arxiv.org/pdf/1511.07917v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=context-aware {cnns} for person head detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td></tr><tr><td>casia_webface</td><td>CASIA Webface</td><td>Learning Face Representation from Scratch</td><td>Learning Face Representation from Scratch</td><td><a href="http://pdfs.semanticscholar.org/b8a2/0ed7e74325da76d7183d1ab77b082a92b447.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning face representation from scratch&sort=relevance" target="_blank">[s2]</a></td><td></td><td>853bd61bc48a431b9b1c7cab10c603830c488e39</td></tr><tr><td>celeba</td><td>CelebA</td><td>Deep Learning Face Attributes in the Wild</td><td>Deep Learning Face Attributes in the Wild</td><td><a href="http://arxiv.org/pdf/1411.7766v2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep learning face attributes in the wild&sort=relevance" target="_blank">[s2]</a></td><td>Chinese University of Hong Kong</td><td>6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4</td></tr><tr><td>celeba_plus</td><td>CelebFaces+</td><td>Deep Learning Face Representation from Predicting 10,000 Classes</td><td>Learning Deep Representation for Imbalanced Classification</td><td><a href="http://personal.ie.cuhk.edu.hk/~ccloy/files/cvpr_2016_imbalanced.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep learning face representation from predicting 10,000 classes&sort=relevance" target="_blank">[s2]</a></td><td>Shenzhen Institutes of Advanced Technology</td><td>69a68f9cf874c69e2232f47808016c2736b90c35</td></tr><tr><td>cfd</td><td>CFD</td><td>The Chicago face database: A free stimulus set of faces and norming data</td><td>The Chicago face database: A free stimulus set of faces and norming data.</td><td><a href="http://pdfs.semanticscholar.org/4df3/143922bcdf7db78eb91e6b5359d6ada004d2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the chicago face database: a free stimulus set of faces and norming data&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4df3143922bcdf7db78eb91e6b5359d6ada004d2</td></tr><tr><td>chalearn</td><td>ChaLearn</td><td>ChaLearn Looking at People: A Review of Events and Resources</td><td>ChaLearn looking at people: A review of events and resources</td><td><a href="https://arxiv.org/pdf/1701.02664.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=chalearn looking at people: a review of events and resources&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8d5998cd984e7cce307da7d46f155f9db99c6590</td></tr><tr><td>chokepoint</td><td>ChokePoint</td><td>Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition</td><td>Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition</td><td><a href="http://conradsanderson.id.au/pdfs/wong_face_selection_cvpr_biometrics_2011.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=patch-based probabilistic image quality assessment for face selection and improved video-based face recognition&sort=relevance" target="_blank">[s2]</a></td><td>University of Queensland</td><td>0486214fb58ee9a04edfe7d6a74c6d0f661a7668</td></tr><tr><td>cityscapes</td><td>Cityscapes</td><td>The Cityscapes Dataset for Semantic Urban Scene Understanding</td><td>The Cityscapes Dataset for Semantic Urban Scene Understanding</td><td><a href="https://arxiv.org/pdf/1604.01685.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cityscapes dataset for semantic urban scene understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>32cde90437ab5a70cf003ea36f66f2de0e24b3ab</td></tr><tr><td>cityscapes</td><td>Cityscapes</td><td>The Cityscapes Dataset</td><td>The Cityscapes Dataset for Semantic Urban Scene Understanding</td><td><a href="https://arxiv.org/pdf/1604.01685.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cityscapes dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>32cde90437ab5a70cf003ea36f66f2de0e24b3ab</td></tr><tr><td>clothing_co_parsing</td><td>CCP</td><td>Clothing Co-Parsing by Joint Image Segmentation and Labeling</td><td>Clothing Co-parsing by Joint Image Segmentation and Labeling</td><td><a href="https://arxiv.org/pdf/1502.00739v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clothing co-parsing by joint image segmentation and labeling&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2bf8541199728262f78d4dced6fb91479b39b738</td></tr><tr><td>cmdp</td><td>CMDP</td><td>Distance Estimation of an Unknown Person from a Portrait</td><td>Distance Estimation of an Unknown Person from a Portrait</td><td><a href="http://pdfs.semanticscholar.org/56ae/6d94fc6097ec4ca861f0daa87941d1c10b70.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=distance estimation of an unknown person from a portrait&sort=relevance" target="_blank">[s2]</a></td><td>California Institute of Technology</td><td>56ae6d94fc6097ec4ca861f0daa87941d1c10b70</td></tr><tr><td>cmu_pie</td><td>CMU PIE</td><td>The CMU Pose, Illumination, and Expression Database</td><td>The CMU Pose, Illumination, and Expression (PIE) Database</td><td><a href="http://pdfs.semanticscholar.org/4d42/3acc78273b75134e2afd1777ba6d3a398973.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cmu pose, illumination, and expression database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d423acc78273b75134e2afd1777ba6d3a398973</td></tr><tr><td>coco</td><td>COCO</td><td>Microsoft COCO: Common Objects in Context</td><td>Microsoft COCO Captions: Data Collection and Evaluation Server</td><td><a href="http://pdfs.semanticscholar.org/ba95/81c33a7eebe87c50e61763e4c8d1723538f2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=microsoft coco: common objects in context&sort=relevance" target="_blank">[s2]</a></td><td></td><td>696ca58d93f6404fea0fc75c62d1d7b378f47628</td></tr><tr><td>coco_action</td><td>COCO-a</td><td>Describing Common Human Visual Actions in Images</td><td>Describing Common Human Visual Actions in Images</td><td><a href="http://pdfs.semanticscholar.org/b38d/cf5fa5174c0d718d65cc4f3889b03c4a21df.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=describing common human visual actions in images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4946ba10a4d5a7d0a38372f23e6622bd347ae273</td></tr><tr><td>coco_qa</td><td>COCO QA</td><td>Exploring Models and Data for Image Question Answering</td><td>Exploring Models and Data for Image Question Answering</td><td><a href="http://pdfs.semanticscholar.org/aa79/9c29c0d44ece1864467af520fe70540c069b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=exploring models and data for image question answering&sort=relevance" target="_blank">[s2]</a></td><td></td><td>35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62</td></tr><tr><td>cofw</td><td>COFW</td><td>Robust face landmark estimation under occlusion</td><td>Robust Face Landmark Estimation under Occlusion</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6751298" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=robust face landmark estimation under occlusion&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2724ba85ec4a66de18da33925e537f3902f21249</td></tr><tr><td>cohn_kanade</td><td>CK</td><td>Comprehensive Database for Facial Expression Analysis</td><td>Comprehensive Database for Facial Expression Analysis</td><td><a href="http://pdfs.semanticscholar.org/23fc/83c8cfff14a16df7ca497661264fc54ed746.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=comprehensive database for facial expression analysis&sort=relevance" target="_blank">[s2]</a></td><td>Carnegie Mellon University</td><td>23fc83c8cfff14a16df7ca497661264fc54ed746</td></tr><tr><td>cohn_kanade_plus</td><td>CK+</td><td>The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression</td><td>The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression</td><td><a href="http://www.iainm.com/iainm/Publications_files/2010_The%20Extended.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the extended cohn-kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d9a02d080636e9666c4d1cc438b9893391ec6c7</td></tr><tr><td>columbia_gaze</td><td>Columbia Gaze</td><td>Gaze Locking: Passive Eye Contact Detection for Human–Object Interaction</td><td>A 3D Morphable Eye Region Model for Gaze Estimation</td><td><a href="https://pdfs.semanticscholar.org/0d43/3b9435b874a1eea6d7999e86986c910fa285.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=gaze locking: passive eye contact detection for human–object interaction&sort=relevance" target="_blank">[s2]</a></td><td>Carnegie Mellon University</td><td>c34532fe6bfbd1e6df477c9ffdbb043b77e7804d</td></tr><tr><td>complex_activities</td><td>Ongoing Complex Activities</td><td>Recognition of Ongoing Complex Activities by Sequence Prediction over a Hierarchical Label Space</td><td>Recognition of ongoing complex activities by sequence prediction over a hierarchical label space</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/WACV.2016.7477586" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=recognition of ongoing complex activities by sequence prediction over a hierarchical label space&sort=relevance" target="_blank">[s2]</a></td><td></td><td>65355cbb581a219bd7461d48b3afd115263ea760</td></tr><tr><td>cuhk01</td><td>CUHK01</td><td>Human Reidentification with Transferred Metric Learning</td><td>Human Reidentification with Transferred Metric Learning</td><td><a href="http://pdfs.semanticscholar.org/4448/4d2866f222bbb9b6b0870890f9eea1ffb2d0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=human reidentification with transferred metric learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>44484d2866f222bbb9b6b0870890f9eea1ffb2d0</td></tr><tr><td>cuhk02</td><td>CUHK02</td><td>Locally Aligned Feature Transforms across Views</td><td>Locally Aligned Feature Transforms across Views</td><td><a href="http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/4989d594.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=locally aligned feature transforms across views&sort=relevance" target="_blank">[s2]</a></td><td></td><td>38b55d95189c5e69cf4ab45098a48fba407609b4</td></tr><tr><td>cuhk03</td><td>CUHK03</td><td>DeepReID: Deep Filter Pairing Neural Network for Person Re-identification</td><td>DeepReID: Deep Filter Pairing Neural Network for Person Re-identification</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Li_DeepReID_Deep_Filter_2014_CVPR_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deepreid: deep filter pairing neural network for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3</td></tr><tr><td>cvc_01_barcelona</td><td>CVC-01</td><td>Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection</td><td>Adaptive Image Sampling and Windows Classification for On – board Pedestrian Detection</td><td><a href="http://pdfs.semanticscholar.org/57fe/081950f21ca03b5b375ae3e84b399c015861.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=adaptive image sampling and windows classification for on-board pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>57fe081950f21ca03b5b375ae3e84b399c015861</td></tr><tr><td>czech_news_agency</td><td>UFI</td><td>Unconstrained Facial Images: Database for Face Recognition under Real-world Conditions</td><td>Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions</td><td><a href="http://pdfs.semanticscholar.org/4b41/06614c1d553365bad75d7866bff0de6056ed.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=unconstrained facial images: database for face recognition under real-world conditions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b4106614c1d553365bad75d7866bff0de6056ed</td></tr><tr><td>d3dfacs</td><td>D3DFACS</td><td>A FACS Valid 3D Dynamic Action Unit database with Applications to 3D Dynamic Morphable Facial Modelling</td><td>A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling</td><td><a href="http://www.cs.bath.ac.uk/~dpc/D3DFACS/ICCV_final_2011.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a facs valid 3d dynamic action unit database with applications to 3d dynamic morphable facial modelling&sort=relevance" target="_blank">[s2]</a></td><td>Jacobs University</td><td>070de852bc6eb275d7ca3a9cdde8f6be8795d1a3</td></tr><tr><td>dartmouth_children</td><td>Dartmouth Children</td><td>The Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus set</td><td>The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set</td><td><a href="http://pdfs.semanticscholar.org/4e6e/e936eb50dd032f7138702fa39b7c18ee8907.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the dartmouth database of children's faces: acquisition and validation of a new face stimulus set&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4e6ee936eb50dd032f7138702fa39b7c18ee8907</td></tr><tr><td>data_61</td><td>Data61 Pedestrian</td><td>A Multi-Modal Graphical Model for Scene Analysis</td><td>A Multi-modal Graphical Model for Scene Analysis</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/WACV.2015.139" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a multi-modal graphical model for scene analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>563c940054e4b456661762c1ab858e6f730c3159</td></tr><tr><td>deep_fashion</td><td>DeepFashion</td><td>DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</td><td>Fashion Landmark Detection in the Wild</td><td><a href="http://pdfs.semanticscholar.org/d8ca/e259c1c5bba0c096f480dc7322bbaebfac1a.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deepfashion: powering robust clothes recognition and retrieval with rich annotations&sort=relevance" target="_blank">[s2]</a></td><td>Chinese University of Hong Kong</td><td>4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7</td></tr><tr><td>deep_fashion</td><td>DeepFashion</td><td>Fashion Landmark Detection in the Wild</td><td>Fashion Landmark Detection in the Wild</td><td><a href="http://pdfs.semanticscholar.org/d8ca/e259c1c5bba0c096f480dc7322bbaebfac1a.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=fashion landmark detection in the wild&sort=relevance" target="_blank">[s2]</a></td><td>Chinese University of Hong Kong</td><td>4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7</td></tr><tr><td>disfa</td><td>DISFA</td><td>DISFA: A Spontaneous Facial Action Intensity Database</td><td>Extended DISFA Dataset: Investigating Posed and Spontaneous Facial Expressions</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7789672" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=disfa: a spontaneous facial action intensity database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a5acda0e8c0937bfed013e6382da127103e41395</td></tr><tr><td>distance_nighttime</td><td>Long Distance Heterogeneous Face</td><td>Nighttime Face Recognition at Long Distance: Cross-distance and Cross-spectral Matching</td><td>Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching</td><td><a href="http://pdfs.semanticscholar.org/4156/b7e88f2e0ab0a7c095b9bab199ae2b23bd06.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=nighttime face recognition at long distance: cross-distance and cross-spectral matching&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06</td></tr><tr><td>duke_mtmc</td><td>Duke MTMC</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td><a href="http://pdfs.semanticscholar.org/b5f2/4f49f9a5e47d6601399dc068158ad88d7651.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=performance measures and a data set for multi-target, multi-camera tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td></tr><tr><td>emotio_net</td><td>EmotioNet Database</td><td>EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild</td><td>EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780969" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=emotionet: an accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c900e0ad4c95948baaf0acd8449fde26f9b4952a</td></tr><tr><td>eth_andreas_ess</td><td>ETHZ Pedestrian</td><td>Depth and Appearance for Mobile Scene Analysis</td><td>Depth and Appearance for Mobile Scene Analysis</td><td><a href="http://www.mmp.rwth-aachen.de/publications/pdf/ess-depthandappearance-iccv07.pdf/at_download/file" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=depth and appearance for mobile scene analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>13f06b08f371ba8b5d31c3e288b4deb61335b462</td></tr><tr><td>europersons</td><td>EuroCity Persons</td><td>The EuroCity Persons Dataset: A Novel Benchmark for Object Detection</td><td>The EuroCity Persons Dataset: A Novel Benchmark for Object Detection</td><td><a href="https://arxiv.org/pdf/1805.07193.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the eurocity persons dataset: a novel benchmark for object detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4</td></tr><tr><td>expw</td><td>ExpW</td><td>Learning Social Relation Traits from Face Images</td><td>From Facial Expression Recognition to Interpersonal Relation Prediction</td><td><a href="http://arxiv.org/abs/1609.06426" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning social relation traits from face images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td></tr><tr><td>expw</td><td>ExpW</td><td>From Facial Expression Recognition to Interpersonal Relation Prediction</td><td>From Facial Expression Recognition to Interpersonal Relation Prediction</td><td><a href="http://arxiv.org/abs/1609.06426" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=from facial expression recognition to interpersonal relation prediction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td></tr><tr><td>face_research_lab</td><td>Face Research Lab London</td><td>Face Research Lab London Set. figshare</td><td>Anxiety promotes memory for mood-congruent faces but does not alter loss aversion.</td><td><a href="http://pdfs.semanticscholar.org/c652/6dd3060d63a6c90e8b7ff340383c4e0e0dd8.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face research lab london set. figshare&sort=relevance" target="_blank">[s2]</a></td><td>University College London</td><td>c6526dd3060d63a6c90e8b7ff340383c4e0e0dd8</td></tr><tr><td>face_scrub</td><td>FaceScrub</td><td>A data-driven approach to cleaning large face datasets</td><td>A data-driven approach to cleaning large face datasets</td><td><a href="https://doi.org/10.1109/ICIP.2014.7025068" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a data-driven approach to cleaning large face datasets&sort=relevance" target="_blank">[s2]</a></td><td>University of Illinois, Urbana-Champaign</td><td>0d3bb75852098b25d90f31d2f48fd0cb4944702b</td></tr><tr><td>face_tracer</td><td>FaceTracer</td><td>FaceTracer: A Search Engine for Large Collections of Images with Faces</td><td>Face swapping: automatically replacing faces in photographs</td><td><a href="https://classes.cs.uoregon.edu/16F/cis607photo/faces.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facetracer: a search engine for large collections of images with faces&sort=relevance" target="_blank">[s2]</a></td><td>Columbia University</td><td>670637d0303a863c1548d5b19f705860a23e285c</td></tr><tr><td>face_tracer</td><td>FaceTracer</td><td>Face Swapping: Automatically Replacing Faces in Photographs</td><td>Face swapping: automatically replacing faces in photographs</td><td><a href="https://classes.cs.uoregon.edu/16F/cis607photo/faces.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face swapping: automatically replacing faces in photographs&sort=relevance" target="_blank">[s2]</a></td><td>Columbia University</td><td>670637d0303a863c1548d5b19f705860a23e285c</td></tr><tr><td>facebook_100</td><td>Facebook100</td><td>Scaling Up Biologically-Inspired Computer Vision: A Case Study in Unconstrained Face Recognition on Facebook</td><td>Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981788" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scaling up biologically-inspired computer vision: a case study in unconstrained face recognition on facebook&sort=relevance" target="_blank">[s2]</a></td><td>Harvard University</td><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td></tr><tr><td>faceplace</td><td>Face Place</td><td>Recognizing disguised faces</td><td>Recognizing disguised faces</td><td><a href="http://pdfs.semanticscholar.org/d936/7ceb0be378c3a9ddf7cb741c678c1a3c574c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=recognizing disguised faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>25474c21613607f6bb7687a281d5f9d4ffa1f9f3</td></tr><tr><td>families_in_the_wild</td><td>FIW</td><td>Visual Kinship Recognition of Families in the Wild</td><td>Visual Kinship Recognition of Families in the Wild</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8337841" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=visual kinship recognition of families in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>dd65f71dac86e36eecbd3ed225d016c3336b4a13</td></tr><tr><td>fddb</td><td>FDDB</td><td>FDDB: A Benchmark for Face Detection in Unconstrained Settings</td><td>A Benchmark for Face Detection in Unconstrained Settings</td><td><a href="http://pdfs.semanticscholar.org/75da/1df4ed319926c544eefe17ec8d720feef8c0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=fddb: a benchmark for face detection in unconstrained settings&sort=relevance" target="_blank">[s2]</a></td><td>University of Massachusetts</td><td>75da1df4ed319926c544eefe17ec8d720feef8c0</td></tr><tr><td>fei</td><td>FEI</td><td>Captura e Alinhamento de Imagens: Um Banco de Faces Brasileiro</td><td>Generalização cartográfica automatizada para um banco de dados cadastral</td><td><a href="https://pdfs.semanticscholar.org/b6b1/b0632eb9d4ab1427278f5e5c46f97753c73d.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=captura e alinhamento de imagens: um banco de faces brasileiro&sort=relevance" target="_blank">[s2]</a></td><td></td><td>b6b1b0632eb9d4ab1427278f5e5c46f97753c73d</td></tr><tr><td>feret</td><td>FERET</td><td>The FERET Verification Testing Protocol for Face Recognition Algorithms</td><td>The FERET Verification Testing Protocol for Face Recognition Algorithms</td><td><a href="http://pdfs.semanticscholar.org/0c4a/139bb87c6743c7905b29a3cfec27a5130652.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the feret verification testing protocol for face recognition algorithms&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td></tr><tr><td>feret</td><td>FERET</td><td>The FERET database and evaluation procedure for face-recognition algorithms</td><td>The FERET database and evaluation procedure for face-recognition algorithms</td><td><a href="http://pdfs.semanticscholar.org/dc8b/25e35a3acb812beb499844734081722319b4.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the feret database and evaluation procedure for face-recognition algorithms&sort=relevance" target="_blank">[s2]</a></td><td></td><td>dc8b25e35a3acb812beb499844734081722319b4</td></tr><tr><td>feret</td><td>FERET</td><td>FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results</td><td>FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results</td><td><a href="http://pdfs.semanticscholar.org/31de/9b3dd6106ce6eec9a35991b2b9083395fd0b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=feret ( face recognition technology ) recognition algorithm development and test results&sort=relevance" target="_blank">[s2]</a></td><td></td><td>31de9b3dd6106ce6eec9a35991b2b9083395fd0b</td></tr><tr><td>feret</td><td>FERET</td><td>The FERET Evaluation Methodology for Face-Recognition Algorithms</td><td>The FERET Evaluation Methodology for Face-Recognition Algorithms</td><td><a href="http://pdfs.semanticscholar.org/0f0f/cf041559703998abf310e56f8a2f90ee6f21.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the feret evaluation methodology for face-recognition algorithms&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0f0fcf041559703998abf310e56f8a2f90ee6f21</td></tr><tr><td>ferplus</td><td>FER+</td><td>Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution</td><td>Training deep networks for facial expression recognition with crowd-sourced label distribution</td><td><a href="http://arxiv.org/pdf/1608.01041v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=training deep networks for facial expression recognition with crowd-sourced label distribution&sort=relevance" target="_blank">[s2]</a></td><td></td><td>298cbc3dfbbb3a20af4eed97906650a4ea1c29e0</td></tr><tr><td>fia</td><td>CMU FiA</td><td>The CMU Face In Action (FIA) Database</td><td>The CMU Face In Action (FIA) Database</td><td><a href="http://pdfs.semanticscholar.org/bb47/a03401811f9d2ca2da12138697acbc7b97a3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cmu face in action (fia) database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>47662d1a368daf70ba70ef2d59eb6209f98b675d</td></tr><tr><td>fiw_300</td><td>300-W</td><td>300 faces In-the-wild challenge: Database and results</td><td>A Semi-automatic Methodology for Facial Landmark Annotation</td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_cvpr_2013_amfg_w.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=300 faces in-the-wild challenge: database and results&sort=relevance" target="_blank">[s2]</a></td><td>University of Twente</td><td>013909077ad843eb6df7a3e8e290cfd5575999d2</td></tr><tr><td>fiw_300</td><td>300-W</td><td>300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge</td><td>A Semi-automatic Methodology for Facial Landmark Annotation</td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_cvpr_2013_amfg_w.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=300 faces in-the-wild challenge: the first facial landmark localization challenge&sort=relevance" target="_blank">[s2]</a></td><td>University of Twente</td><td>013909077ad843eb6df7a3e8e290cfd5575999d2</td></tr><tr><td>fiw_300</td><td>300-W</td><td>A semi-automatic methodology for facial landmark annotation</td><td>A Semi-automatic Methodology for Facial Landmark Annotation</td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_cvpr_2013_amfg_w.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a semi-automatic methodology for facial landmark annotation&sort=relevance" target="_blank">[s2]</a></td><td>University of Twente</td><td>013909077ad843eb6df7a3e8e290cfd5575999d2</td></tr><tr><td>frav3d</td><td>FRAV3D</td><td>MULTIMODAL 2D, 2.5D & 3D FACE VERIFICATION</td><td>2D and 3D face recognition: A survey</td><td><a href="http://pdfs.semanticscholar.org/2f5d/44dc3e1b5955942133ff872ebd31716ec604.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multimodal 2d, 2.5d & 3d face verification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2f5d44dc3e1b5955942133ff872ebd31716ec604</td></tr><tr><td>frgc</td><td>FRGC</td><td>Overview of the Face Recognition Grand Challenge</td><td>Overview of the face recognition grand challenge</td><td><a href="http://www3.nd.edu/~kwb/PhillipsEtAlCVPR_2005.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=overview of the face recognition grand challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18ae7c9a4bbc832b8b14bc4122070d7939f5e00e</td></tr><tr><td>gallagher</td><td>Gallagher</td><td>Clothing Cosegmentation for Recognizing People</td><td>Clothing Cosegmentation for Shopping Images With Cluttered Background</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7423747" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clothing cosegmentation for recognizing people&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dbe8e5121c534339d6e41f8683e85f87e6abf81</td></tr><tr><td>gavab_db</td><td>Gavab</td><td>GavabDB: a 3D face database</td><td>Expression invariant 3D face recognition with a Morphable Model</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4813376" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=gavabdb: a 3d face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>42505464808dfb446f521fc6ff2cfeffd4d68ff1</td></tr><tr><td>geofaces</td><td>GeoFaces</td><td>GeoFaceExplorer: Exploring the Geo-Dependence of Facial Attributes</td><td>GeoFaceExplorer: exploring the geo-dependence of facial attributes</td><td><a href="http://doi.acm.org/10.1145/2676440.2676443" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=geofaceexplorer: exploring the geo-dependence of facial attributes&sort=relevance" target="_blank">[s2]</a></td><td>University of Kentucky</td><td>17b46e2dad927836c689d6787ddb3387c6159ece</td></tr><tr><td>georgia_tech_face_database</td><td>Georgia Tech Face</td><td>Maximum likelihood training of the embedded HMM for face detection and recognition</td><td>MAXIMUM LIKELIHOOD TRAINING OF THE EMBEDDED HMM FOR FACE DETECTION AND RECOGNITION Ara V. Ne an and Monson H. Hayes III Center for Signal and Image Processing School of Electrical and Computer Engineering</td><td><a href="http://pdfs.semanticscholar.org/3dc3/f0b64ef80f573e3a5f96e456e52ee980b877.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=maximum likelihood training of the embedded hmm for face detection and recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3dc3f0b64ef80f573e3a5f96e456e52ee980b877</td></tr><tr><td>graz</td><td>Graz Pedestrian</td><td>Generic Object Recognition with Boosting</td><td>Object recognition using segmentation for feature detection</td><td><a href="http://www.emt.tugraz.at/~tracking/Publications/fussenegger2004b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=generic object recognition with boosting&sort=relevance" target="_blank">[s2]</a></td><td></td><td>12ad3b5bbbf407f8e54ea692c07633d1a867c566</td></tr><tr><td>graz</td><td>Graz Pedestrian</td><td>Weak Hypotheses and Boosting for Generic Object Detection and Recognition</td><td>Object recognition using segmentation for feature detection</td><td><a href="http://www.emt.tugraz.at/~tracking/Publications/fussenegger2004b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=weak hypotheses and boosting for generic object detection and recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>12ad3b5bbbf407f8e54ea692c07633d1a867c566</td></tr><tr><td>graz</td><td>Graz Pedestrian</td><td>Object Recognition Using Segmentation for Feature Detection</td><td>Object recognition using segmentation for feature detection</td><td><a href="http://www.emt.tugraz.at/~tracking/Publications/fussenegger2004b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=object recognition using segmentation for feature detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>12ad3b5bbbf407f8e54ea692c07633d1a867c566</td></tr><tr><td>h3d</td><td>H3D</td><td>Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations</td><td>Poselets: Body part detectors trained using 3D human pose annotations</td><td><a href="http://vision.stanford.edu/teaching/cs231b_spring1213/papers/ICCV09_BourdevMalik.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=poselets: body part detectors trained using 3d human pose annotations&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2830fb5282de23d7784b4b4bc37065d27839a412</td></tr><tr><td>hda_plus</td><td>HDA+</td><td>The HDA+ data set for research on fully automated re-identification systems</td><td>HDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance</td><td><a href="http://pdfs.semanticscholar.org/bd88/bb2e4f351352d88ee7375af834360e223498.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the hda+ data set for research on fully automated re-identification systems&sort=relevance" target="_blank">[s2]</a></td><td></td><td>bd88bb2e4f351352d88ee7375af834360e223498</td></tr><tr><td>hda_plus</td><td>HDA+</td><td>A Multi-camera video data set for research on High-Definition surveillance</td><td>HDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance</td><td><a href="http://pdfs.semanticscholar.org/bd88/bb2e4f351352d88ee7375af834360e223498.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a multi-camera video data set for research on high-definition surveillance&sort=relevance" target="_blank">[s2]</a></td><td></td><td>bd88bb2e4f351352d88ee7375af834360e223498</td></tr><tr><td>helen</td><td>Helen</td><td>Interactive Facial Feature Localization</td><td>Interactive Facial Feature Localization</td><td><a href="http://pdfs.semanticscholar.org/95f1/2d27c3b4914e0668a268360948bce92f7db3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=interactive facial feature localization&sort=relevance" target="_blank">[s2]</a></td><td>University of Illinois, Urbana-Champaign</td><td>95f12d27c3b4914e0668a268360948bce92f7db3</td></tr><tr><td>hi4d_adsip</td><td>Hi4D-ADSIP</td><td>Hi4D-ADSIP 3-D dynamic facial articulation database</td><td>High-resolution comprehensive 3-D dynamic database for facial articulation analysis</td><td><a href="http://www.researchgate.net/profile/Wei_Quan3/publication/221430048_High-resolution_comprehensive_3-D_dynamic_database_for_facial_articulation_analysis/links/0deec534309495805d000000.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=hi4d-adsip 3-d dynamic facial articulation database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>24830e3979d4ed01b9fd0feebf4a8fd22e0c35fd</td></tr><tr><td>hipsterwars</td><td>Hipsterwars</td><td>Hipster Wars: Discovering Elements of Fashion Styles</td><td>Hipster Wars: Discovering Elements of Fashion Styles</td><td><a href="http://pdfs.semanticscholar.org/04c2/cda00e5536f4b1508cbd80041e9552880e67.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=hipster wars: discovering elements of fashion styles&sort=relevance" target="_blank">[s2]</a></td><td>Tohoku University</td><td>04c2cda00e5536f4b1508cbd80041e9552880e67</td></tr><tr><td>hollywood_headset</td><td>HollywoodHeads</td><td>Context-aware CNNs for person head detection</td><td>Context-Aware CNNs for Person Head Detection</td><td><a href="http://arxiv.org/pdf/1511.07917v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=context-aware cnns for person head detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td></tr><tr><td>hrt_transgender</td><td>HRT Transgender</td><td>Is the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender Dataset</td><td>Face recognition: A literature survey</td><td><a href="http://pdfs.semanticscholar.org/2831/2c3a47c1be3a67365700744d3d6665b86f22.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=is the eye region more reliable than the face? a preliminary study of face-based recognition on a transgender dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>28312c3a47c1be3a67365700744d3d6665b86f22</td></tr><tr><td>hrt_transgender</td><td>HRT Transgender</td><td>Investigating the Periocular-Based Face Recognition Across Gender Transformation</td><td>Face recognition: A literature survey</td><td><a href="http://pdfs.semanticscholar.org/2831/2c3a47c1be3a67365700744d3d6665b86f22.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=investigating the periocular-based face recognition across gender transformation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>28312c3a47c1be3a67365700744d3d6665b86f22</td></tr><tr><td>hrt_transgender</td><td>HRT Transgender</td><td>Face recognition across gender transformation using SVM Classifier</td><td>Face recognition: A literature survey</td><td><a href="http://pdfs.semanticscholar.org/2831/2c3a47c1be3a67365700744d3d6665b86f22.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face recognition across gender transformation using svm classifier&sort=relevance" target="_blank">[s2]</a></td><td></td><td>28312c3a47c1be3a67365700744d3d6665b86f22</td></tr><tr><td>ifad</td><td>IFAD</td><td>Indian Face Age Database: A Database for Face Recognition with Age Variation</td><td>Indian Face Age Database : A Database for Face Recognition with Age Variation</td><td><a href="https://pdfs.semanticscholar.org/55c4/0cbcf49a0225e72d911d762c27bb1c2d14aa.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=indian face age database: a database for face recognition with age variation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>55c40cbcf49a0225e72d911d762c27bb1c2d14aa</td></tr><tr><td>ifdb</td><td>IFDB</td><td>Iranian Face Database with age, pose and expression</td><td>Iranian Face Database and Evaluation with a New Detection Algorithm</td><td><a href="http://pdfs.semanticscholar.org/066d/71fcd997033dce4ca58df924397dfe0b5fd1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iranian face database with age, pose and expression&sort=relevance" target="_blank">[s2]</a></td><td></td><td>066d71fcd997033dce4ca58df924397dfe0b5fd1</td></tr><tr><td>ifdb</td><td>IFDB</td><td>Iranian Face Database and Evaluation with a New Detection Algorithm</td><td>Iranian Face Database and Evaluation with a New Detection Algorithm</td><td><a href="http://pdfs.semanticscholar.org/066d/71fcd997033dce4ca58df924397dfe0b5fd1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iranian face database and evaluation with a new detection algorithm&sort=relevance" target="_blank">[s2]</a></td><td></td><td>066d71fcd997033dce4ca58df924397dfe0b5fd1</td></tr><tr><td>iit_dehli_ear</td><td>IIT Dehli Ear</td><td>Automated human identification using ear imaging</td><td>Automated Human Identification Using Ear Imaging</td><td><a href="https://pdfs.semanticscholar.org/faf4/0ce28857aedf183e193486f5b4b0a8c478a2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automated human identification using ear imaging&sort=relevance" target="_blank">[s2]</a></td><td></td><td>faf40ce28857aedf183e193486f5b4b0a8c478a2</td></tr><tr><td>ijb_c</td><td>IJB-A</td><td>Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A</td><td>Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1B_089_ext.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pushing the frontiers of unconstrained face detection and recognition: iarpa janus benchmark a&sort=relevance" target="_blank">[s2]</a></td><td></td><td>140c95e53c619eac594d70f6369f518adfea12ef</td></tr><tr><td>ijb_c</td><td>IJB-B</td><td>IARPA Janus Benchmark-B Face Dataset</td><td>IARPA Janus Benchmark-B Face Dataset</td><td><a href="http://www.vislab.ucr.edu/Biometrics2017/program_slides/Noblis_CVPRW_IJBB.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iarpa janus benchmark-b face dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0cb2dd5f178e3a297a0c33068961018659d0f443</td></tr><tr><td>ijb_c</td><td>IJB-C</td><td>IARPA Janus Benchmark C</td><td>IARPA Janus Benchmark - C: Face Dataset and Protocol</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8411217" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iarpa janus benchmark c&sort=relevance" target="_blank">[s2]</a></td><td></td><td>57178b36c21fd7f4529ac6748614bb3374714e91</td></tr><tr><td>ilids_mcts</td><td></td><td>Imagery Library for Intelligent Detection Systems: -The i-LIDS User Guide</td><td>Imagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=imagery library for intelligent detection systems: -the i-lids user guide&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0297448f3ed948e136bb06ceff10eccb34e5bb77</td></tr><tr><td>ilids_vid_reid</td><td>iLIDS-VID</td><td>Person Re-Identi cation by Video Ranking</td><td>Person Re-identification by Exploiting Spatio-Temporal Cues and Multi-view Metric Learning</td><td><a href="https://doi.org/10.1109/LSP.2016.2574323" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person re-identi cation by video ranking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>99eb4cea0d9bc9fe777a5c5172f8638a37a7f262</td></tr><tr><td>images_of_groups</td><td>Images of Groups</td><td>Understanding Groups of Images of People</td><td>Understanding images of groups of people</td><td><a href="http://amp.ece.cmu.edu/people/Andy/Andy_files/cvpr09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=understanding groups of images of people&sort=relevance" target="_blank">[s2]</a></td><td></td><td>21d9d0deed16f0ad62a4865e9acf0686f4f15492</td></tr><tr><td>imdb_wiki</td><td>IMDB</td><td>Deep expectation of real and apparent age from a single image without facial landmarks</td><td>Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks</td><td><a href="https://doi.org/10.1007/s11263-016-0940-3" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep expectation of real and apparent age from a single image without facial landmarks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>10195a163ab6348eef37213a46f60a3d87f289c5</td></tr><tr><td>imdb_wiki</td><td>IMDB</td><td>DEX: Deep EXpectation of apparent age from a single image</td><td>DEX: Deep EXpectation of Apparent Age from a Single Image</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7406390" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=dex: deep expectation of apparent age from a single image&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8355d095d3534ef511a9af68a3b2893339e3f96b</td></tr><tr><td>imfdb</td><td>IMFDB</td><td>Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations</td><td>Indian Movie Face Database: A benchmark for face recognition under wide variations</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=indian movie face database: a benchmark for face recognition under wide variations&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ca3e88d87e1344d076c964ea89d91a75c417f5ee</td></tr><tr><td>imm_face</td><td>IMM Face Dataset</td><td>The IMM Face Database - An Annotated Dataset of 240 Face Images</td><td>Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the imm face database - an annotated dataset of 240 face images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td></tr><tr><td>immediacy</td><td>Immediacy</td><td>Multi-task Recurrent Neural Network for Immediacy Prediction</td><td>Multi-task Recurrent Neural Network for Immediacy Prediction</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.383" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-task recurrent neural network for immediacy prediction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1e3df3ca8feab0b36fd293fe689f93bb2aaac591</td></tr><tr><td>imsitu</td><td>imSitu</td><td>Situation Recognition: Visual Semantic Role Labeling for Image Understanding</td><td>Situation Recognition: Visual Semantic Role Labeling for Image Understanding</td><td><a href="http://grail.cs.washington.edu/wp-content/uploads/2016/09/yatskar2016srv.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=situation recognition: visual semantic role labeling for image understanding&sort=relevance" target="_blank">[s2]</a></td><td>University of Washington</td><td>51eba481dac6b229a7490f650dff7b17ce05df73</td></tr><tr><td>inria_person</td><td>INRIA Pedestrian</td><td>Histograms of Oriented Gradients for Human Detection</td><td>Histograms of oriented gradients for human detection</td><td><a href="http://nichol.as/papers/Dalai/Histograms%20of%20oriented%20gradients%20for%20human%20detection.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=histograms of oriented gradients for human detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5</td></tr><tr><td>jaffe</td><td>JAFFE</td><td>Coding Facial Expressions with Gabor Wavelets</td><td>Coding Facial Expressions with Gabor Wavelets</td><td><a href="http://pdfs.semanticscholar.org/45c3/1cde87258414f33412b3b12fc5bec7cb3ba9.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=coding facial expressions with gabor wavelets&sort=relevance" target="_blank">[s2]</a></td><td>Kyushu University</td><td>45c31cde87258414f33412b3b12fc5bec7cb3ba9</td></tr><tr><td>jiku_mobile</td><td>Jiku Mobile Video Dataset</td><td>The Jiku Mobile Video Dataset</td><td>A Synchronization Ground Truth for the Jiku Mobile Video Dataset</td><td><a href="http://pdfs.semanticscholar.org/ad62/c6e17bc39b4dec20d32f6ac667ae42d2c118.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the jiku mobile video dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ad62c6e17bc39b4dec20d32f6ac667ae42d2c118</td></tr><tr><td>jpl_pose</td><td>JPL-Interaction dataset</td><td>First-Person Activity Recognition: What Are They Doing to Me?</td><td>First-Person Activity Recognition: What Are They Doing to Me?</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Ryoo_First-Person_Activity_Recognition_2013_CVPR_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=first-person activity recognition: what are they doing to me?&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1aad2da473888cb7ebc1bfaa15bfa0f1502ce005</td></tr><tr><td>kdef</td><td>KDEF</td><td>The Karolinska Directed Emotional Faces – KDEF</td><td>Gaze fixation and the neural circuitry of face processing in autism</td><td><a href="http://doi.org/10.1038/nn1421" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the karolinska directed emotional faces – kdef&sort=relevance" target="_blank">[s2]</a></td><td></td><td>93884e46c49f7ae1c7c34046fbc28882f2bd6341</td></tr><tr><td>kin_face</td><td>UB KinFace</td><td>Genealogical Face Recognition based on UB KinFace Database</td><td>Understanding Kin Relationships in a Photo</td><td><a href="http://www1.ece.neu.edu/~yunfu/papers/Kinship-TMM.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=genealogical face recognition based on ub kinface database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7</td></tr><tr><td>kin_face</td><td>UB KinFace</td><td>Kinship Verification through Transfer Learning</td><td>Understanding Kin Relationships in a Photo</td><td><a href="http://www1.ece.neu.edu/~yunfu/papers/Kinship-TMM.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=kinship verification through transfer learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7</td></tr><tr><td>kin_face</td><td>UB KinFace</td><td>Understanding Kin Relationships in a Photo</td><td>Understanding Kin Relationships in a Photo</td><td><a href="http://www1.ece.neu.edu/~yunfu/papers/Kinship-TMM.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=understanding kin relationships in a photo&sort=relevance" target="_blank">[s2]</a></td><td></td><td>08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7</td></tr><tr><td>kinectface</td><td>KinectFaceDB</td><td>KinectFaceDB: A Kinect Database for Face Recognition</td><td>KinectFaceDB: A Kinect Database for Face Recognition</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6866883" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=kinectfacedb: a kinect database for face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0b440695c822a8e35184fb2f60dcdaa8a6de84ae</td></tr><tr><td>kitti</td><td>KITTI</td><td>Vision meets Robotics: The KITTI Dataset</td><td>The Role of Machine Vision for Intelligent Vehicles</td><td><a href="http://www.path.berkeley.edu/sites/default/files/my_folder_76/Pub_03.2016_Role.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vision meets robotics: the kitti dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>35ba4ebfd017a56b51e967105af9ae273c9b0178</td></tr><tr><td>lag</td><td>LAG</td><td>Large Age-Gap Face Verification by Feature Injection in Deep Networks</td><td>Large age-gap face verification by feature injection in deep networks</td><td><a href="http://pdfs.semanticscholar.org/0d2d/d4fc016cb6a517d8fb43a7cc3ff62964832e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=large age-gap face verification by feature injection in deep networks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e</td></tr><tr><td>large_scale_person_search</td><td>Large Scale Person Search</td><td>End-to-End Deep Learning for Person Search</td><td>End-to-End Deep Learning for Person Search</td><td><a href="https://pdfs.semanticscholar.org/2161/f6b7ee3c0acc81603b01dc0df689683577b9.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=end-to-end deep learning for person search&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2161f6b7ee3c0acc81603b01dc0df689683577b9</td></tr><tr><td>leeds_sports_pose</td><td>Leeds Sports Pose</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clustered pose and nonlinear appearance models for human pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td></tr><tr><td>leeds_sports_pose_extended</td><td>Leeds Sports Pose Extended</td><td>Learning Effective Human Pose Estimation from Inaccurate Annotation</td><td>Learning effective human pose estimation from inaccurate annotation</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995318" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning effective human pose estimation from inaccurate annotation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4e4746094bf60ee83e40d8597a6191e463b57f76</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: A Survey</td><td>Labeled Faces in the Wild : A Survey</td><td><a href="http://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: a survey&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</td><td>Labeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments</td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: a database for studying face recognition in unconstrained environments&sort=relevance" target="_blank">[s2]</a></td><td></td><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: Updates and New Reporting Procedures</td><td>Labeled Faces in the Wild : Updates and New Reporting Procedures</td><td><a href="http://pdfs.semanticscholar.org/2d34/82dcff69c7417c7b933f22de606a0e8e42d4.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: updates and new reporting procedures&sort=relevance" target="_blank">[s2]</a></td><td>University of Massachusetts</td><td>2d3482dcff69c7417c7b933f22de606a0e8e42d4</td></tr><tr><td>lfw_a</td><td>LFW-a</td><td>Effective Unconstrained Face Recognition by - Combining Multiple Descriptors and Learned - Background Statistics</td><td>Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.230" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=effective unconstrained face recognition by - combining multiple descriptors and learned - background statistics&sort=relevance" target="_blank">[s2]</a></td><td></td><td>133f01aec1534604d184d56de866a4bd531dac87</td></tr><tr><td>lfw_p</td><td>LFWP</td><td>Localizing Parts of Faces Using a Consensus of Exemplars</td><td>Localizing Parts of Faces Using a Consensus of Exemplars</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2011.5995602" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=localizing parts of faces using a consensus of exemplars&sort=relevance" target="_blank">[s2]</a></td><td>Columbia University</td><td>140438a77a771a8fb656b39a78ff488066eb6b50</td></tr><tr><td>m2vts</td><td>m2vts</td><td>The M2VTS Multimodal Face Database (Release 1.00)</td><td>The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</td><td><a href="https://doi.org/10.1109/TSMCA.2007.909557" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the m2vts multimodal face database (release 1.00)&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td></tr><tr><td>m2vtsdb_extended</td><td>xm2vtsdb</td><td>XM2VTSDB: The Extended M2VTS Database</td><td>Labeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments</td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=xm2vtsdb: the extended m2vts database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td></tr><tr><td>mafl</td><td>MAFL</td><td>Facial Landmark Detection by Deep Multi-task Learning</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td><a href="https://arxiv.org/pdf/1408.3967.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facial landmark detection by deep multi-task learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td></tr><tr><td>mafl</td><td>MAFL</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td><a href="https://arxiv.org/pdf/1408.3967.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning deep representation for face alignment with auxiliary attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td></tr><tr><td>malf</td><td>MALF</td><td>Fine-grained Evaluation on Face Detection in the Wild.</td><td>Fine-grained evaluation on face detection in the wild</td><td><a href="http://www.cs.toronto.edu/~byang/papers/malf_fg15.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=fine-grained evaluation on face detection in the wild.&sort=relevance" target="_blank">[s2]</a></td><td>Chinese Academy of Sciences</td><td>45e616093a92e5f1e61a7c6037d5f637aa8964af</td></tr><tr><td>mapillary</td><td>Mapillary</td><td>The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes</td><td>The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8237796" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mapillary vistas dataset for semantic understanding of street scenes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>79828e6e9f137a583082b8b5a9dfce0c301989b8</td></tr><tr><td>market_1501</td><td>Market 1501</td><td>Scalable Person Re-identification: A Benchmark</td><td>Scalable Person Re-identification: A Benchmark</td><td><a href="https://www.microsoft.com/en-us/research/wp-content/uploads/2017/01/ICCV15-ReIDDataset.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scalable person re-identification: a benchmark&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4308bd8c28e37e2ed9a3fcfe74d5436cce34b410</td></tr><tr><td>market1203</td><td>Market 1203</td><td>Orientation Driven Bag of Appearances for Person Re-identification</td><td>Orientation Driven Bag of Appearances for Person Re-identification</td><td><a href="http://pdfs.semanticscholar.org/a7fe/834a0af614ce6b50dc093132b031dd9a856b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=orientation driven bag of appearances for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td></tr><tr><td>mars</td><td>MARS</td><td>MARS: A Video Benchmark for Large-Scale Person Re-identification</td><td>MARS: A Video Benchmark for Large-Scale Person Re-Identification</td><td><a href="http://pdfs.semanticscholar.org/c038/7e788a52f10bf35d4d50659cfa515d89fbec.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=mars: a video benchmark for large-scale person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c0387e788a52f10bf35d4d50659cfa515d89fbec</td></tr><tr><td>mcgill</td><td>McGill Real World</td><td>Hierarchical Temporal Graphical Model for Head Pose Estimation and Subsequent Attribute Classification in Real-World Videos</td><td>Robust semi-automatic head pose labeling for real-world face video sequences</td><td><a href="https://doi.org/10.1007/s11042-012-1352-1" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=hierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos&sort=relevance" target="_blank">[s2]</a></td><td>McGill University</td><td>c570d1247e337f91e555c3be0e8c8a5aba539d9f</td></tr><tr><td>mcgill</td><td>McGill Real World</td><td>Robust Semi-automatic Head Pose Labeling for Real-World Face Video Sequences</td><td>Robust semi-automatic head pose labeling for real-world face video sequences</td><td><a href="https://doi.org/10.1007/s11042-012-1352-1" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=robust semi-automatic head pose labeling for real-world face video sequences&sort=relevance" target="_blank">[s2]</a></td><td>McGill University</td><td>c570d1247e337f91e555c3be0e8c8a5aba539d9f</td></tr><tr><td>megaage</td><td>MegaAge</td><td>Quantifying Facial Age by Posterior of Age Comparisons</td><td>Quantifying Facial Age by Posterior of Age Comparisons</td><td><a href="https://arxiv.org/pdf/1708.09687.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=quantifying facial age by posterior of age comparisons&sort=relevance" target="_blank">[s2]</a></td><td>Chinese University of Hong Kong</td><td>d80a3d1f3a438e02a6685e66ee908446766fefa9</td></tr><tr><td>megaface</td><td>MegaFace</td><td>The MegaFace Benchmark: 1 Million Faces for Recognition at Scale</td><td>The MegaFace Benchmark: 1 Million Faces for Recognition at Scale</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.527" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the megaface benchmark: 1 million faces for recognition at scale&sort=relevance" target="_blank">[s2]</a></td><td>University of Washington</td><td>96e0cfcd81cdeb8282e29ef9ec9962b125f379b0</td></tr><tr><td>megaface</td><td>MegaFace</td><td>Level Playing Field for Million Scale Face Recognition</td><td>Level Playing Field for Million Scale Face Recognition</td><td><a href="https://arxiv.org/pdf/1705.00393.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=level playing field for million scale face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>28d4e027c7e90b51b7d8908fce68128d1964668a</td></tr><tr><td>mifs</td><td>MIFS</td><td>Spoofing Faces Using Makeup: An Investigative Study</td><td>Spoofing faces using makeup: An investigative study</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7947686" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=spoofing faces using makeup: an investigative study&sort=relevance" target="_blank">[s2]</a></td><td>INRIA Méditerranée</td><td>23e824d1dfc33f3780dd18076284f07bd99f1c43</td></tr><tr><td>mit_cbcl</td><td>MIT CBCL</td><td>Component-based Face Recognition with 3D Morphable Models</td><td>Component-Based Face Recognition with 3D Morphable Models</td><td><a href="http://www.bheisele.com/avbpa2003.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=component-based face recognition with 3d morphable models&sort=relevance" target="_blank">[s2]</a></td><td></td><td>079a0a3bf5200994e1f972b1b9197bf2f90e87d4</td></tr><tr><td>miw</td><td>MIW</td><td>Automatic Facial Makeup Detection with Application in Face Recognition</td><td>Automatic facial makeup detection with application in face recognition</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6612994" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automatic facial makeup detection with application in face recognition&sort=relevance" target="_blank">[s2]</a></td><td>West Virginia University</td><td>fcc6fe6007c322641796cb8792718641856a22a7</td></tr><tr><td>mmi_facial_expression</td><td>MMI Facial Expression Dataset</td><td>WEB-BASED DATABASE FOR FACIAL EXPRESSION ANALYSIS</td><td>Web-based database for facial expression analysis</td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/PanticEtAl-ICME2005-final.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=web-based database for facial expression analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2a75f34663a60ab1b04a0049ed1d14335129e908</td></tr><tr><td>moments_in_time</td><td>Moments in Time</td><td>Moments in Time Dataset: one million videos for event understanding</td><td>Moments in Time Dataset: one million videos for event understanding</td><td><a href="https://arxiv.org/pdf/1801.03150.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=moments in time dataset: one million videos for event understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a5a44a32a91474f00a3cda671a802e87c899fbb4</td></tr><tr><td>morph</td><td>MORPH Commercial</td><td>MORPH: A Longitudinal Image Database of Normal Adult Age-Progression</td><td>MORPH: a longitudinal image database of normal adult age-progression</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/FGR.2006.78" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=morph: a longitudinal image database of normal adult age-progression&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td></tr><tr><td>morph_nc</td><td>MORPH Non-Commercial</td><td>MORPH: A Longitudinal Image Database of Normal Adult Age-Progression</td><td>MORPH: a longitudinal image database of normal adult age-progression</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/FGR.2006.78" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=morph: a longitudinal image database of normal adult age-progression&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td></tr><tr><td>mot</td><td>MOT</td><td>Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</td><td>Learning to associate: HybridBoosted multi-target tracker for crowded scene</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0633.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=evaluating multiple object tracking performance: the clear mot metrics&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5981e6479c3fd4e31644db35d236bfb84ae46514</td></tr><tr><td>mot</td><td>MOT</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td>Learning to associate: HybridBoosted multi-target tracker for crowded scene</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0633.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=performance measures and a data set for multi-target, multi-camera tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5981e6479c3fd4e31644db35d236bfb84ae46514</td></tr><tr><td>mot</td><td>MOT</td><td>Learning to associate: HybridBoosted multi-target tracker for crowded scene</td><td>Learning to associate: HybridBoosted multi-target tracker for crowded scene</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0633.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to associate: hybridboosted multi-target tracker for crowded scene&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5981e6479c3fd4e31644db35d236bfb84ae46514</td></tr><tr><td>mpi_large</td><td>Large MPI Facial Expression</td><td>The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</td><td>The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</td><td><a href="http://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mpi facial expression database — a validated database of emotional and conversational facial expressions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td></tr><tr><td>mpi_small</td><td>Small MPI Facial Expression</td><td>The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</td><td>The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</td><td><a href="http://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mpi facial expression database — a validated database of emotional and conversational facial expressions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td></tr><tr><td>mpii_gaze</td><td>MPIIGaze</td><td>Appearance-based Gaze Estimation in the Wild</td><td>Appearance-based gaze estimation in the wild</td><td><a href="https://scalable.mpi-inf.mpg.de/files/2015/09/zhang_CVPR15.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=appearance-based gaze estimation in the wild&sort=relevance" target="_blank">[s2]</a></td><td>Max Planck Institute for Informatics</td><td>0df0d1adea39a5bef318b74faa37de7f3e00b452</td></tr><tr><td>mpii_human_pose</td><td>MPII Human Pose</td><td>2D Human Pose Estimation: New Benchmark and State of the Art Analysis</td><td>2D Human Pose Estimation: New Benchmark and State of the Art Analysis</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6909866" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=2d human pose estimation: new benchmark and state of the art analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3325860c0c82a93b2eac654f5324dd6a776f609e</td></tr><tr><td>mr2</td><td>MR2</td><td>The MR2: A multi-racial mega-resolution database of facial stimuli</td><td>The MR2: A multi-racial, mega-resolution database of facial stimuli.</td><td><a href="https://pdfs.semanticscholar.org/be5b/455abd379240460d022a0e246615b0b86c14.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mr2: a multi-racial mega-resolution database of facial stimuli&sort=relevance" target="_blank">[s2]</a></td><td></td><td>578d4ad74818086bb64f182f72e2c8bd31e3d426</td></tr><tr><td>mrp_drone</td><td>MRP Drone</td><td>Investigating Open-World Person Re-identification Using a Drone</td><td>Investigating Open-World Person Re-identification Using a Drone</td><td><a href="http://pdfs.semanticscholar.org/ad01/687649d95cd5b56d7399a9603c4b8e2217d7.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=investigating open-world person re-identification using a drone&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ad01687649d95cd5b56d7399a9603c4b8e2217d7</td></tr><tr><td>msceleb</td><td>MsCeleb</td><td>MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</td><td>MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</td><td><a href="http://pdfs.semanticscholar.org/4603/cb8e05258bb0572ae912ad20903b8f99f4b1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ms-celeb-1m: a dataset and benchmark for large-scale face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>291265db88023e92bb8c8e6390438e5da148e8f5</td></tr><tr><td>msmt_17</td><td>MSMT17</td><td>Person Transfer GAN to Bridge Domain Gap for Person Re-Identification</td><td>Person Transfer GAN to Bridge Domain Gap for Person Re-Identification</td><td><a href="https://arxiv.org/pdf/1711.08565.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person transfer gan to bridge domain gap for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ec792ad2433b6579f2566c932ee414111e194537</td></tr><tr><td>mtfl</td><td>MTFL</td><td>Facial Landmark Detection by Deep Multi-task Learning</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td><a href="https://arxiv.org/pdf/1408.3967.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facial landmark detection by deep multi-task learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td></tr><tr><td>mtfl</td><td>MTFL</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td><a href="https://arxiv.org/pdf/1408.3967.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning deep representation for face alignment with auxiliary attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td></tr><tr><td>muct</td><td>MUCT</td><td>The MUCT Landmarked Face Database</td><td>Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the muct landmarked face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td></tr><tr><td>mug_faces</td><td>MUG Faces</td><td>The MUG Facial Expression Database</td><td>The MUG facial expression database</td><td><a href="http://ieeexplore.ieee.org/document/5617662/" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mug facial expression database&sort=relevance" target="_blank">[s2]</a></td><td>Aristotle University of Thessaloniki</td><td>f1af714b92372c8e606485a3982eab2f16772ad8</td></tr><tr><td>multi_pie</td><td>MULTIPIE</td><td>Multi-PIE</td><td>Scheduling heterogeneous multi-cores through performance impact estimation (PIE)</td><td><a href="http://dl.acm.org/citation.cfm?id=2337184" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-pie&sort=relevance" target="_blank">[s2]</a></td><td></td><td>109df0e8e5969ddf01e073143e83599228a1163f</td></tr><tr><td>names_and_faces_news</td><td>News Dataset</td><td>Names and Faces</td><td>Names and faces in the news</td><td><a href="http://www.cs.utexas.edu/~grauman/courses/spring2007/395T/papers/berg_names_and_faces.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=names and faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2fda164863a06a92d3a910b96eef927269aeb730</td></tr><tr><td>nd_2006</td><td>ND-2006</td><td>Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition</td><td>Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4401928" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=using a multi-instance enrollment representation to improve 3d face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>fd8168f1c50de85bac58a8d328df0a50248b16ae</td></tr><tr><td>nova_emotions</td><td>Novaemötions Dataset</td><td>Crowdsourcing facial expressions for affective-interaction</td><td>Competitive affective gaming: winning with a smile</td><td><a href="http://doi.acm.org/10.1145/2502081.2502115" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=crowdsourcing facial expressions for affective-interaction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7f4040b482d16354d5938c1d1b926b544652bf5b</td></tr><tr><td>nova_emotions</td><td>Novaemötions Dataset</td><td>Competitive affective gamming: Winning with a smile</td><td>Competitive affective gaming: winning with a smile</td><td><a href="http://doi.acm.org/10.1145/2502081.2502115" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=competitive affective gamming: winning with a smile&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7f4040b482d16354d5938c1d1b926b544652bf5b</td></tr><tr><td>nudedetection</td><td>Nude Detection</td><td>A Bag-of-Features Approach based on Hue-SIFT Descriptor for Nude Detection</td><td>A bag-of-features approach based on Hue-SIFT descriptor for nude detection</td><td><a href="http://ieeexplore.ieee.org/document/7077625/" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a bag-of-features approach based on hue-sift descriptor for nude detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7ace44190729927e5cb0dd5d363fcae966fe13f7</td></tr><tr><td>orl</td><td>ORL</td><td>Parameterisation of a Stochastic Model for Human Face Identification</td><td>Parameterisation of a stochastic model for human face identification</td><td><a href="http://pdfs.semanticscholar.org/5520/6f0b5f57ce17358999145506cd01e570358c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=parameterisation of a stochastic model for human face identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>55206f0b5f57ce17358999145506cd01e570358c</td></tr><tr><td>penn_fudan</td><td>Penn Fudan</td><td>Object Detection Combining Recognition and Segmentation</td><td>Object Detection Combining Recognition and Segmentation</td><td><a href="http://pdfs.semanticscholar.org/f531/a554cade14b9b340de6730683a28c292dd74.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=object detection combining recognition and segmentation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3394168ff0719b03ff65bcea35336a76b21fe5e4</td></tr><tr><td>peta</td><td>PETA</td><td>Pedestrian Attribute Recognition At Far Distance</td><td>Pedestrian Attribute Recognition At Far Distance</td><td><a href="http://personal.ie.cuhk.edu.hk/~pluo/pdf/mm14.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian attribute recognition at far distance&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2a4bbee0b4cf52d5aadbbc662164f7efba89566c</td></tr><tr><td>pets</td><td>PETS 2017</td><td>PETS 2017: Dataset and Challenge</td><td>PETS 2017: Dataset and Challenge</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8014998" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pets 2017: dataset and challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22909dd19a0ec3b6065334cb5be5392cb24d839d</td></tr><tr><td>pilot_parliament</td><td>PPB</td><td>Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classi cation</td><td>Summary of Research on Informant Accuracy in Network Data, 11 and on the Reverse Small World Problem</td><td><a href="http://pdfs.semanticscholar.org/fb82/681ac5d3487bd8e52dbb3d1fa220eeac855e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=gender shades: intersectional accuracy disparities in commercial gender classi cation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>fb82681ac5d3487bd8e52dbb3d1fa220eeac855e</td></tr><tr><td>pipa</td><td>PIPA</td><td>Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues</td><td>Beyond frontal faces: Improving Person Recognition using multiple cues</td><td><a href="https://doi.org/10.1109/CVPR.2015.7299113" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=beyond frontal faces: improving person recognition using multiple cues&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0a85bdff552615643dd74646ac881862a7c7072d</td></tr><tr><td>pku</td><td>PKU</td><td>Swiss-System Based Cascade Ranking for Gait-based Person Re-identification</td><td>Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification</td><td><a href="http://pdfs.semanticscholar.org/f6c8/d5e35d7e4d60a0104f233ac1a3ab757da53f.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=swiss-system based cascade ranking for gait-based person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f</td></tr><tr><td>pku_reid</td><td>PKU-Reid</td><td>Orientation driven bag of appearances for person re-identification</td><td>Orientation Driven Bag of Appearances for Person Re-identification</td><td><a href="http://pdfs.semanticscholar.org/a7fe/834a0af614ce6b50dc093132b031dd9a856b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=orientation driven bag of appearances for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td></tr><tr><td>pornodb</td><td>Pornography DB</td><td>Pooling in Image Representation: the Visual Codeword Point of View</td><td>Pooling in image representation: The visual codeword point of view</td><td><a href="http://pdfs.semanticscholar.org/b92a/1ed9622b8268ae3ac9090e25789fc41cc9b8.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pooling in image representation: the visual codeword point of view&sort=relevance" target="_blank">[s2]</a></td><td></td><td>b92a1ed9622b8268ae3ac9090e25789fc41cc9b8</td></tr><tr><td>precarious</td><td>Precarious</td><td>Expecting the Unexpected: Training Detectors for Unusual Pedestrians With Adversarial Imposters</td><td>Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters</td><td><a href="https://arxiv.org/pdf/1703.06283.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=expecting the unexpected: training detectors for unusual pedestrians with adversarial imposters&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9e5378e7b336c89735d3bb15cf67eff96f86d39a</td></tr><tr><td>prid</td><td>PRID</td><td>Person Re-Identification by Descriptive and Discriminative Classification</td><td>Person Re-identification by Descriptive and Discriminative Classification</td><td><a href="http://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person re-identification by descriptive and discriminative classification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>16c7c31a7553d99f1837fc6e88e77b5ccbb346b8</td></tr><tr><td>prw</td><td>PRW</td><td>Person Re-identification in the Wild</td><td>Person Re-identification in the Wild</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.357" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person re-identification in the wild&sort=relevance" target="_blank">[s2]</a></td><td>University of Technology Sydney</td><td>0b84f07af44f964817675ad961def8a51406dd2e</td></tr><tr><td>psu</td><td>PSU</td><td>Vision-based Analysis of Small Groups in Pedestrian Crowds</td><td>Vision-Based Analysis of Small Groups in Pedestrian Crowds</td><td><a href="http://vision.cse.psu.edu/publications/pdfs/GeCollinsRubackPAMI2011.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vision-based analysis of small groups in pedestrian crowds&sort=relevance" target="_blank">[s2]</a></td><td></td><td>066000d44d6691d27202896691f08b27117918b9</td></tr><tr><td>pubfig</td><td>PubFig</td><td>Attribute and Simile Classifiers for Face Verification</td><td>Attribute and simile classifiers for face verification</td><td><a href="http://homes.cs.washington.edu/~neeraj/projects/faceverification/base/papers/nk_iccv2009_attrs.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=attribute and simile classifiers for face verification&sort=relevance" target="_blank">[s2]</a></td><td>Columbia University</td><td>759a3b3821d9f0e08e0b0a62c8b693230afc3f8d</td></tr><tr><td>pubfig_83</td><td>pubfig83</td><td>Scaling Up Biologically-Inspired Computer Vision: A Case Study in Unconstrained Face Recognition on Facebook</td><td>Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981788" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scaling up biologically-inspired computer vision: a case study in unconstrained face recognition on facebook&sort=relevance" target="_blank">[s2]</a></td><td>Harvard University</td><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td></tr><tr><td>put_face</td><td>Put Face</td><td>The PUT face database</td><td>Labeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments</td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the put face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td></tr><tr><td>qmul_grid</td><td>GRID</td><td>Multi-Camera Activity Correlation Analysis</td><td>Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding</td><td><a href="https://doi.org/10.1007/s11263-010-0347-5" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-camera activity correlation analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2edb87494278ad11641b6cf7a3f8996de12b8e14</td></tr><tr><td>qmul_grid</td><td>GRID</td><td>Time-delayed correlation analysis for multi-camera activity understanding</td><td>Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding</td><td><a href="https://doi.org/10.1007/s11263-010-0347-5" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=time-delayed correlation analysis for multi-camera activity understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2edb87494278ad11641b6cf7a3f8996de12b8e14</td></tr><tr><td>qmul_surv_face</td><td>QMUL-SurvFace</td><td>Surveillance Face Recognition Challenge</td><td>Surveillance Face Recognition Challenge</td><td><a href="https://arxiv.org/pdf/1804.09691.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=surveillance face recognition challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c866a2afc871910e3282fd9498dce4ab20f6a332</td></tr><tr><td>rafd</td><td>RaFD</td><td>Presentation and validation of the Radboud Faces Database</td><td>Presentation and validation of the Radboud Faces Database</td><td><a href="https://pdfs.semanticscholar.org/3765/df816dc5a061bc261e190acc8bdd9d47bec0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=presentation and validation of the radboud faces database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3765df816dc5a061bc261e190acc8bdd9d47bec0</td></tr><tr><td>raid</td><td>RAiD</td><td>Consistent Re-identification in a Camera Network</td><td>Consistent Re-identification in a Camera Network</td><td><a href="http://pdfs.semanticscholar.org/c27f/099e6e7e3f7f9979cbe9e0a5175fc5848ea0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=consistent re-identification in a camera network&sort=relevance" target="_blank">[s2]</a></td><td></td><td>09d78009687bec46e70efcf39d4612822e61cb8c</td></tr><tr><td>rap_pedestrian</td><td>RAP</td><td>A Richly Annotated Dataset for Pedestrian Attribute Recognition</td><td>A Richly Annotated Dataset for Pedestrian Attribute Recognition</td><td><a href="http://pdfs.semanticscholar.org/221c/18238b829c12b911706947ab38fd017acef7.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a richly annotated dataset for pedestrian attribute recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>221c18238b829c12b911706947ab38fd017acef7</td></tr><tr><td>reseed</td><td>ReSEED</td><td>ReSEED: Social Event dEtection Dataset</td><td>ReSEED: social event dEtection dataset</td><td><a href="https://pub.uni-bielefeld.de/download/2663466/2686734" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=reseed: social event detection dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>54983972aafc8e149259d913524581357b0f91c3</td></tr><tr><td>saivt</td><td>SAIVT SoftBio</td><td>A Database for Person Re-Identification in Multi-Camera Surveillance Networks</td><td>A Database for Person Re-Identification in Multi-Camera Surveillance Networks</td><td><a href="http://eprints.qut.edu.au/53437/3/Bialkowski_Database4PersonReID_DICTA.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a database for person re-identification in multi-camera surveillance networks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b</td></tr><tr><td>sarc3d</td><td>Sarc3D</td><td>SARC3D: a new 3D body model for People Tracking and Re-identification</td><td>SARC3D: A New 3D Body Model for People Tracking and Re-identification</td><td><a href="https://pdfs.semanticscholar.org/e27e/f52c641c2b5100a1b34fd0b819e84a31b4df.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=sarc3d: a new 3d body model for people tracking and re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>e27ef52c641c2b5100a1b34fd0b819e84a31b4df</td></tr><tr><td>scface</td><td>SCface</td><td>SCface – surveillance cameras face database</td><td>Large Variability Surveillance Camera Face Database</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scface – surveillance cameras face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>f3b84a03985de3890b400b68e2a92c0a00afd9d0</td></tr><tr><td>scut_fbp</td><td>SCUT-FBP</td><td>SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception</td><td>SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception</td><td><a href="https://arxiv.org/pdf/1511.02459.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scut-fbp: a benchmark dataset for facial beauty perception&sort=relevance" target="_blank">[s2]</a></td><td>South China University of Technology</td><td>bd26dabab576adb6af30484183c9c9c8379bf2e0</td></tr><tr><td>scut_head</td><td>SCUT HEAD</td><td>Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture</td><td>Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture</td><td><a href="https://arxiv.org/pdf/1803.09256.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=detecting heads using feature refine net and cascaded multi-scale architecture&sort=relevance" target="_blank">[s2]</a></td><td></td><td>d3200d49a19a4a4e4e9745ee39649b65d80c834b</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</td><td>Person Re-identification by Video Ranking</td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a spatio-temporal appearance representation for video-based pedestrian re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>98bb029afe2a1239c3fdab517323066f0957b81b</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>Local descriptors encoded by Fisher vectors for person re-identification</td><td>Person Re-identification by Video Ranking</td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=local descriptors encoded by fisher vectors for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>98bb029afe2a1239c3fdab517323066f0957b81b</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>Person reidentification by video ranking</td><td>Person Re-identification by Video Ranking</td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person reidentification by video ranking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>98bb029afe2a1239c3fdab517323066f0957b81b</td></tr><tr><td>sheffield</td><td>Sheffield Face</td><td>Face Recognition: From Theory to Applications</td><td>Face Description with Local Binary Patterns: Application to Face Recognition</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.244" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face recognition: from theory to applications&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3607afdb204de9a5a9300ae98aa4635d9effcda2</td></tr><tr><td>social_relation</td><td>Social Relation</td><td>From Facial Expression Recognition to Interpersonal Relation Prediction</td><td>Learning Social Relation Traits from Face Images</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.414" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=from facial expression recognition to interpersonal relation prediction&sort=relevance" target="_blank">[s2]</a></td><td>Chinese University of Hong Kong</td><td>2a171f8d14b6b8735001a11c217af9587d095848</td></tr><tr><td>social_relation</td><td>Social Relation</td><td>Learning Social Relation Traits from Face Images</td><td>Learning Social Relation Traits from Face Images</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.414" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning social relation traits from face images&sort=relevance" target="_blank">[s2]</a></td><td>Chinese University of Hong Kong</td><td>2a171f8d14b6b8735001a11c217af9587d095848</td></tr><tr><td>soton</td><td>SOTON HiD</td><td>On a Large Sequence-Based Human Gait Database</td><td>On a large sequence-based human gait database</td><td><a href="http://pdfs.semanticscholar.org/4f93/cd09785c6e77bf4bc5a788e079df524c8d21.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=on a large sequence-based human gait database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4f93cd09785c6e77bf4bc5a788e079df524c8d21</td></tr><tr><td>sports_videos_in_the_wild</td><td>SVW</td><td>Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis</td><td>Sports Videos in the Wild (SVW): A video dataset for sports analysis</td><td><a href="http://web.cse.msu.edu/~liuxm/publication/Safdarnejad_Liu_Udpa_Andrus_Wood_Craven_FG2015.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=sports videos in the wild (svw): a video dataset for sports analysis&sort=relevance" target="_blank">[s2]</a></td><td>Michigan State University</td><td>1a40092b493c6b8840257ab7f96051d1a4dbfeb2</td></tr><tr><td>stair_actions</td><td>STAIR Action</td><td>STAIR Actions: A Video Dataset of Everyday Home Actions</td><td>STAIR Actions: A Video Dataset of Everyday Home Actions</td><td><a href="https://arxiv.org/pdf/1804.04326.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=stair actions: a video dataset of everyday home actions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9</td></tr><tr><td>stanford_drone</td><td>Stanford Drone</td><td>Learning Social Etiquette: Human Trajectory Prediction In Crowded Scenes</td><td>Learning to Predict Human Behavior in Crowded Scenes</td><td><a href="http://pdfs.semanticscholar.org/c9bd/a86e23cab9e4f15ea0c4cbb6cc02b9dfb709.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning social etiquette: human trajectory prediction in crowded scenes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c9bda86e23cab9e4f15ea0c4cbb6cc02b9dfb709</td></tr><tr><td>stickmen_buffy</td><td>Buffy Stickmen</td><td>Learning to Parse Images of Articulated Objects</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to parse images of articulated objects&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td></tr><tr><td>stickmen_buffy</td><td>Buffy Stickmen</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clustered pose and nonlinear appearance models for human pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td></tr><tr><td>stickmen_family</td><td>We Are Family Stickmen</td><td>We Are Family: Joint Pose Estimation of Multiple Persons</td><td>We Are Family: Joint Pose Estimation of Multiple Persons</td><td><a href="http://pdfs.semanticscholar.org/0dc1/1a37cadda92886c56a6fb5191ded62099c28.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=we are family: joint pose estimation of multiple persons&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0dc11a37cadda92886c56a6fb5191ded62099c28</td></tr><tr><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td>Learning to parse images of articulated bodies</td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clustered pose and nonlinear appearance models for human pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td>Learning to Parse Images of Articulated Objects</td><td>Learning to parse images of articulated bodies</td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to parse images of articulated objects&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>sun_attributes</td><td>SUN</td><td>The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</td><td>SUN attribute database: Discovering, annotating, and recognizing scene attributes</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6247998" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the sun attribute database: beyond categories for deeper scene understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>833fa04463d90aab4a9fe2870d480f0b40df446e</td></tr><tr><td>sun_attributes</td><td>SUN</td><td>SUN Attribute Database: +<!doctype html><html><head><meta charset='utf-8'><title>Paper Title Sanity Check</title><link rel='stylesheet' href='reports.css'></head><body><h2>Paper Title Sanity Check</h2><table border='1' cellpadding='3' cellspacing='3'><th>key</th><th>name</th><th>our title</th><th>found title</th><th></th><th></th><th>address</th><th>s2 id</th><tr><td>yfcc_100m</td><td>YFCC100M</td><td>YFCC100M: The New Data in Multimedia Research</td><td>YFCC100M: the new data in multimedia research</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=yfcc100m: the new data in multimedia research&sort=relevance" target="_blank">[s2]</a></td><td></td><td>010f0f4929e6a6644fb01f0e43820f91d0fad292</td></tr><tr><td>fiw_300</td><td>300-W</td><td>A semi-automatic methodology for facial landmark annotation</td><td>A Semi-automatic Methodology for Facial Landmark Annotation</td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_cvpr_2013_amfg_w.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a semi-automatic methodology for facial landmark annotation&sort=relevance" target="_blank">[s2]</a></td><td>University of Twente</td><td>013909077ad843eb6df7a3e8e290cfd5575999d2</td></tr><tr><td>buhmap_db</td><td>BUHMAP-DB</td><td>Facial Feature Tracking and Expression Recognition for Sign Language</td><td>Facial feature tracking and expression recognition for sign language</td><td><a href="http://www.cmpe.boun.edu.tr/pilab/pilabfiles/databases/buhmap/files/ari2008facialfeaturetracking.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facial feature tracking and expression recognition for sign language&sort=relevance" target="_blank">[s2]</a></td><td></td><td>014b8df0180f33b9fea98f34ae611c6447d761d2</td></tr><tr><td>vqa</td><td>VQA</td><td>VQA: Visual Question Answering</td><td>VQA: Visual Question Answering</td><td><a href="http://arxiv.org/pdf/1505.00468v3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vqa: visual question answering&sort=relevance" target="_blank">[s2]</a></td><td></td><td>01959ef569f74c286956024866c1d107099199f7</td></tr><tr><td>kitti</td><td>KITTI</td><td>Vision meets Robotics: The KITTI Dataset</td><td>Vision meets robotics: The KITTI dataset</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vision meets robotics: the kitti dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>026e3363b7f76b51cc711886597a44d5f1fd1de2</td></tr><tr><td>ilids_mcts</td><td>i-LIDS</td><td>Imagery Library for Intelligent Detection Systems: The i-LIDS User Guide</td><td>Imagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=imagery library for intelligent detection systems: the i-lids user guide&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0297448f3ed948e136bb06ceff10eccb34e5bb77</td></tr><tr><td>ucf_selfie</td><td>UCF Selfie</td><td>How to Take a Good Selfie?</td><td>How to Take a Good Selfie?</td><td><a href="http://doi.acm.org/10.1145/2733373.2806365" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=how to take a good selfie?&sort=relevance" target="_blank">[s2]</a></td><td></td><td>041d3eedf5e45ce5c5229f0181c5c576ed1fafd6</td></tr><tr><td>fiw_300</td><td>300-W</td><td>300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge</td><td>300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge</td><td><a href="https://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_iccv_2013_300_w.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=300 faces in-the-wild challenge: the first facial landmark localization challenge&sort=relevance" target="_blank">[s2]</a></td><td>University of Twente</td><td>044d9a8c61383312cdafbcc44b9d00d650b21c70</td></tr><tr><td>chokepoint</td><td>ChokePoint</td><td>Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition</td><td>Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition</td><td><a href="http://conradsanderson.id.au/pdfs/wong_face_selection_cvpr_biometrics_2011.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=patch-based probabilistic image quality assessment for face selection and improved video-based face recognition&sort=relevance" target="_blank">[s2]</a></td><td>University of Queensland</td><td>0486214fb58ee9a04edfe7d6a74c6d0f661a7668</td></tr><tr><td>hipsterwars</td><td>Hipsterwars</td><td>Hipster Wars: Discovering Elements of Fashion Styles</td><td>Hipster Wars: Discovering Elements of Fashion Styles</td><td><a href="http://pdfs.semanticscholar.org/04c2/cda00e5536f4b1508cbd80041e9552880e67.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=hipster wars: discovering elements of fashion styles&sort=relevance" target="_blank">[s2]</a></td><td>Tohoku University</td><td>04c2cda00e5536f4b1508cbd80041e9552880e67</td></tr><tr><td>psu</td><td>PSU</td><td>Vision-based Analysis of Small Groups in Pedestrian Crowds</td><td>Vision-Based Analysis of Small Groups in Pedestrian Crowds</td><td><a href="http://vision.cse.psu.edu/publications/pdfs/GeCollinsRubackPAMI2011.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vision-based analysis of small groups in pedestrian crowds&sort=relevance" target="_blank">[s2]</a></td><td></td><td>066000d44d6691d27202896691f08b27117918b9</td></tr><tr><td>ifdb</td><td>IFDB</td><td>Iranian Face Database and Evaluation with a New Detection Algorithm</td><td>Iranian Face Database and Evaluation with a New Detection Algorithm</td><td><a href="http://pdfs.semanticscholar.org/066d/71fcd997033dce4ca58df924397dfe0b5fd1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iranian face database and evaluation with a new detection algorithm&sort=relevance" target="_blank">[s2]</a></td><td></td><td>066d71fcd997033dce4ca58df924397dfe0b5fd1</td></tr><tr><td>columbia_gaze</td><td>Columbia Gaze</td><td>Gaze Locking: Passive Eye Contact Detection for Human–Object Interaction</td><td>Gaze locking: passive eye contact detection for human-object interaction</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=gaze locking: passive eye contact detection for human–object interaction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>06f02199690961ba52997cde1527e714d2b3bf8f</td></tr><tr><td>d3dfacs</td><td>D3DFACS</td><td>A FACS Valid 3D Dynamic Action Unit database with Applications to 3D Dynamic Morphable Facial Modelling</td><td>A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling</td><td><a href="http://www.cs.bath.ac.uk/~dpc/D3DFACS/ICCV_final_2011.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a facs valid 3d dynamic action unit database with applications to 3d dynamic morphable facial modelling&sort=relevance" target="_blank">[s2]</a></td><td>Jacobs University</td><td>070de852bc6eb275d7ca3a9cdde8f6be8795d1a3</td></tr><tr><td>mit_cbcl</td><td>MIT CBCL</td><td>Component-based Face Recognition with 3D Morphable Models</td><td>Component-Based Face Recognition with 3D Morphable Models</td><td><a href="http://www.bheisele.com/avbpa2003.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=component-based face recognition with 3d morphable models&sort=relevance" target="_blank">[s2]</a></td><td></td><td>079a0a3bf5200994e1f972b1b9197bf2f90e87d4</td></tr><tr><td>uccs</td><td>UCCS</td><td>Large scale unconstrained open set face database</td><td>Large scale unconstrained open set face database</td><td><a href="http://www.vast.uccs.edu/~tboult/PAPERS/BTAS13-Sapkota-Boult-UCCSFaceDB.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=large scale unconstrained open set face database&sort=relevance" target="_blank">[s2]</a></td><td>University of Colorado at Colorado Springs</td><td>07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1</td></tr><tr><td>kin_face</td><td>UB KinFace</td><td>Understanding Kin Relationships in a Photo</td><td>Understanding Kin Relationships in a Photo</td><td><a href="http://www1.ece.neu.edu/~yunfu/papers/Kinship-TMM.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=understanding kin relationships in a photo&sort=relevance" target="_blank">[s2]</a></td><td></td><td>08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7</td></tr><tr><td>raid</td><td>RAiD</td><td>Consistent Re-identification in a Camera Network</td><td>Consistent Re-identification in a Camera Network</td><td><a href="http://pdfs.semanticscholar.org/c27f/099e6e7e3f7f9979cbe9e0a5175fc5848ea0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=consistent re-identification in a camera network&sort=relevance" target="_blank">[s2]</a></td><td></td><td>09d78009687bec46e70efcf39d4612822e61cb8c</td></tr><tr><td>pipa</td><td>PIPA</td><td>Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues</td><td>Beyond frontal faces: Improving Person Recognition using multiple cues</td><td><a href="https://doi.org/10.1109/CVPR.2015.7299113" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=beyond frontal faces: improving person recognition using multiple cues&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0a85bdff552615643dd74646ac881862a7c7072d</td></tr><tr><td>kinectface</td><td>KinectFaceDB</td><td>KinectFaceDB: A Kinect Database for Face Recognition</td><td>KinectFaceDB: A Kinect Database for Face Recognition</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6866883" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=kinectfacedb: a kinect database for face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0b440695c822a8e35184fb2f60dcdaa8a6de84ae</td></tr><tr><td>prw</td><td>PRW</td><td>Person Re-identification in the Wild</td><td>Person Re-identification in the Wild</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.357" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person re-identification in the wild&sort=relevance" target="_blank">[s2]</a></td><td>University of Technology Sydney</td><td>0b84f07af44f964817675ad961def8a51406dd2e</td></tr><tr><td>feret</td><td>FERET</td><td>The FERET Verification Testing Protocol for Face Recognition Algorithms</td><td>The FERET Verification Testing Protocol for Face Recognition Algorithms</td><td><a href="http://pdfs.semanticscholar.org/0c4a/139bb87c6743c7905b29a3cfec27a5130652.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the feret verification testing protocol for face recognition algorithms&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td></tr><tr><td>graz</td><td>Graz Pedestrian</td><td>Weak Hypotheses and Boosting for Generic Object Detection and Recognition</td><td>Weak Hypotheses and Boosting for Generic Object Detection and Recognition</td><td><a href="http://pdfs.semanticscholar.org/0c91/808994a250d7be332400a534a9291ca3b60e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=weak hypotheses and boosting for generic object detection and recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0c91808994a250d7be332400a534a9291ca3b60e</td></tr><tr><td>ijb_c</td><td>IJB-B</td><td>IARPA Janus Benchmark-B Face Dataset</td><td>IARPA Janus Benchmark-B Face Dataset</td><td><a href="http://www.vislab.ucr.edu/Biometrics2017/program_slides/Noblis_CVPRW_IJBB.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iarpa janus benchmark-b face dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0cb2dd5f178e3a297a0c33068961018659d0f443</td></tr><tr><td>casablanca</td><td>Casablanca</td><td>Context-aware {CNNs} for person head detection</td><td>Context-Aware CNNs for Person Head Detection</td><td><a href="http://arxiv.org/pdf/1511.07917v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=context-aware {cnns} for person head detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td></tr><tr><td>hollywood_headset</td><td>HollywoodHeads</td><td>Context-aware CNNs for person head detection</td><td>Context-Aware CNNs for Person Head Detection</td><td><a href="http://arxiv.org/pdf/1511.07917v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=context-aware cnns for person head detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td></tr><tr><td>lag</td><td>LAG</td><td>Large Age-Gap Face Verification by Feature Injection in Deep Networks</td><td>Large age-gap face verification by feature injection in deep networks</td><td><a href="http://pdfs.semanticscholar.org/0d2d/d4fc016cb6a517d8fb43a7cc3ff62964832e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=large age-gap face verification by feature injection in deep networks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e</td></tr><tr><td>face_scrub</td><td>FaceScrub</td><td>A data-driven approach to cleaning large face datasets</td><td>A data-driven approach to cleaning large face datasets</td><td><a href="https://doi.org/10.1109/ICIP.2014.7025068" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a data-driven approach to cleaning large face datasets&sort=relevance" target="_blank">[s2]</a></td><td>University of Illinois, Urbana-Champaign</td><td>0d3bb75852098b25d90f31d2f48fd0cb4944702b</td></tr><tr><td>stickmen_family</td><td>We Are Family Stickmen</td><td>We Are Family: Joint Pose Estimation of Multiple Persons</td><td>We Are Family: Joint Pose Estimation of Multiple Persons</td><td><a href="http://pdfs.semanticscholar.org/0dc1/1a37cadda92886c56a6fb5191ded62099c28.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=we are family: joint pose estimation of multiple persons&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0dc11a37cadda92886c56a6fb5191ded62099c28</td></tr><tr><td>mpii_gaze</td><td>MPIIGaze</td><td>Appearance-based Gaze Estimation in the Wild</td><td>Appearance-based gaze estimation in the wild</td><td><a href="https://scalable.mpi-inf.mpg.de/files/2015/09/zhang_CVPR15.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=appearance-based gaze estimation in the wild&sort=relevance" target="_blank">[s2]</a></td><td>Max Planck Institute for Informatics</td><td>0df0d1adea39a5bef318b74faa37de7f3e00b452</td></tr><tr><td>afw</td><td>AFW</td><td>Face detection, pose estimation and landmark localization in the wild</td><td>Face detection, pose estimation, and landmark localization in the wild</td><td><a href="http://vision.ics.uci.edu/papers/ZhuR_CVPR_2012/ZhuR_CVPR_2012.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face detection, pose estimation and landmark localization in the wild&sort=relevance" target="_blank">[s2]</a></td><td>University of California, Irvine</td><td>0e986f51fe45b00633de9fd0c94d082d2be51406</td></tr><tr><td>voc</td><td>VOC</td><td>The PASCAL Visual Object Classes (VOC) Challenge</td><td>The Pascal Visual Object Classes (VOC) Challenge</td><td><a href="https://doi.org/10.1007/s11263-009-0275-4" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the pascal visual object classes (voc) challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a</td></tr><tr><td>feret</td><td>FERET</td><td>The FERET Evaluation Methodology for Face-Recognition Algorithms</td><td>The FERET Evaluation Methodology for Face-Recognition Algorithms</td><td><a href="http://pdfs.semanticscholar.org/0f0f/cf041559703998abf310e56f8a2f90ee6f21.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the feret evaluation methodology for face-recognition algorithms&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0f0fcf041559703998abf310e56f8a2f90ee6f21</td></tr><tr><td>imdb_wiki</td><td>IMDB</td><td>Deep expectation of real and apparent age from a single image without facial landmarks</td><td>Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks</td><td><a href="https://doi.org/10.1007/s11263-016-0940-3" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep expectation of real and apparent age from a single image without facial landmarks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>10195a163ab6348eef37213a46f60a3d87f289c5</td></tr><tr><td>inria_person</td><td>INRIA Pedestrian</td><td>Histograms of Oriented Gradients for Human Detection</td><td>Histograms of oriented gradients for human detection</td><td><a href="http://nichol.as/papers/Dalai/Histograms%20of%20oriented%20gradients%20for%20human%20detection.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=histograms of oriented gradients for human detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5</td></tr><tr><td>graz</td><td>Graz Pedestrian</td><td>Object Recognition Using Segmentation for Feature Detection</td><td>Object recognition using segmentation for feature detection</td><td><a href="http://www.emt.tugraz.at/~tracking/Publications/fussenegger2004b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=object recognition using segmentation for feature detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>12ad3b5bbbf407f8e54ea692c07633d1a867c566</td></tr><tr><td>lfw_a</td><td>LFW-a</td><td>Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics</td><td>Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.230" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=effective unconstrained face recognition by combining multiple descriptors and learned background statistics&sort=relevance" target="_blank">[s2]</a></td><td></td><td>133f01aec1534604d184d56de866a4bd531dac87</td></tr><tr><td>hrt_transgender</td><td>HRT Transgender</td><td>Is the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender Dataset</td><td>Is the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset</td><td><a href="https://doi.org/10.1109/BTAS.2013.6712710" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=is the eye region more reliable than the face? a preliminary study of face-based recognition on a transgender dataset&sort=relevance" target="_blank">[s2]</a></td><td>University of North Carolina Wilmington</td><td>137aa2f891d474fce1e7a1d1e9b3aefe21e22b34</td></tr><tr><td>eth_andreas_ess</td><td>ETHZ Pedestrian</td><td>Depth and Appearance for Mobile Scene Analysis</td><td>Depth and Appearance for Mobile Scene Analysis</td><td><a href="http://www.mmp.rwth-aachen.de/publications/pdf/ess-depthandappearance-iccv07.pdf/at_download/file" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=depth and appearance for mobile scene analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>13f06b08f371ba8b5d31c3e288b4deb61335b462</td></tr><tr><td>lfw_p</td><td>LFWP</td><td>Localizing Parts of Faces Using a Consensus of Exemplars</td><td>Localizing Parts of Faces Using a Consensus of Exemplars</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2011.5995602" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=localizing parts of faces using a consensus of exemplars&sort=relevance" target="_blank">[s2]</a></td><td>Columbia University</td><td>140438a77a771a8fb656b39a78ff488066eb6b50</td></tr><tr><td>ijb_c</td><td>IJB-A</td><td>Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A</td><td>Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1B_089_ext.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pushing the frontiers of unconstrained face detection and recognition: iarpa janus benchmark a&sort=relevance" target="_blank">[s2]</a></td><td></td><td>140c95e53c619eac594d70f6369f518adfea12ef</td></tr><tr><td>vgg_faces</td><td>VGG Face</td><td>Deep Face Recognition</td><td>Deep Face Recognition</td><td><a href="https://pdfs.semanticscholar.org/f372/ab9b3270d4e4f6a0258c83c2736c3a5c0454.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>162ea969d1929ed180cc6de9f0bf116993ff6e06</td></tr><tr><td>prid</td><td>PRID</td><td>Person Re-Identification by Descriptive and Discriminative Classification</td><td>Person Re-identification by Descriptive and Discriminative Classification</td><td><a href="http://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person re-identification by descriptive and discriminative classification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>16c7c31a7553d99f1837fc6e88e77b5ccbb346b8</td></tr><tr><td>umb</td><td>UMB</td><td>UMB-DB: A Database of Partially Occluded 3D Faces</td><td>UMB-DB: A database of partially occluded 3D faces</td><td><a href="https://doi.org/10.1109/ICCVW.2011.6130509" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=umb-db: a database of partially occluded 3d faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>16e8b0a1e8451d5f697b94c0c2b32a00abee1d52</td></tr><tr><td>celeba_plus</td><td>CelebFaces+</td><td>Deep Learning Face Representation from Predicting 10,000 Classes</td><td>Deep Learning Face Representation from Predicting 10,000 Classes</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Sun_Deep_Learning_Face_2014_CVPR_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep learning face representation from predicting 10,000 classes&sort=relevance" target="_blank">[s2]</a></td><td>Shenzhen Institutes of Advanced Technology</td><td>177bc509dd0c7b8d388bb47403f28d6228c14b5c</td></tr><tr><td>geofaces</td><td>GeoFaces</td><td>GeoFaceExplorer: Exploring the Geo-Dependence of Facial Attributes</td><td>GeoFaceExplorer: exploring the geo-dependence of facial attributes</td><td><a href="http://doi.acm.org/10.1145/2676440.2676443" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=geofaceexplorer: exploring the geo-dependence of facial attributes&sort=relevance" target="_blank">[s2]</a></td><td>University of Kentucky</td><td>17b46e2dad927836c689d6787ddb3387c6159ece</td></tr><tr><td>deep_fashion</td><td>DeepFashion</td><td>DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</td><td>DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780493" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deepfashion: powering robust clothes recognition and retrieval with rich annotations&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18010284894ed0edcca74e5bf768ee2e15ef7841</td></tr><tr><td>pilot_parliament</td><td>PPB</td><td>Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification</td><td>Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification</td><td><a href="https://pdfs.semanticscholar.org/03c1/fc9c3339813ed81ad0de540132f9f695a0f8.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=gender shades: intersectional accuracy disparities in commercial gender classification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18858cc936947fc96b5c06bbe3c6c2faa5614540</td></tr><tr><td>frgc</td><td>FRGC</td><td>Overview of the Face Recognition Grand Challenge</td><td>Overview of the face recognition grand challenge</td><td><a href="http://www3.nd.edu/~kwb/PhillipsEtAlCVPR_2005.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=overview of the face recognition grand challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18ae7c9a4bbc832b8b14bc4122070d7939f5e00e</td></tr><tr><td>yale_faces</td><td>YaleFaces</td><td>From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</td><td>From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</td><td><a href="http://pdfs.semanticscholar.org/97bb/c2b439a79d4dc0dc7199d71ed96ad5e3fd0e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=from few to many: illumination cone models for face recognition under variable lighting and pose&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18c72175ddbb7d5956d180b65a96005c100f6014</td></tr><tr><td>york_3d</td><td>UOY 3D Face Database</td><td>Three-Dimensional Face Recognition: An Eigensurface Approach</td><td>Three-dimensional face recognition: an eigensurface approach</td><td><a href="http://www-users.cs.york.ac.uk/~nep/research/3Dface/tomh/3DFaceRecognition-Eigensurface-ICIP(web)2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=three-dimensional face recognition: an eigensurface approach&sort=relevance" target="_blank">[s2]</a></td><td></td><td>19d1b811df60f86cbd5e04a094b07f32fff7a32a</td></tr><tr><td>sports_videos_in_the_wild</td><td>SVW</td><td>Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis</td><td>Sports Videos in the Wild (SVW): A video dataset for sports analysis</td><td><a href="http://web.cse.msu.edu/~liuxm/publication/Safdarnejad_Liu_Udpa_Andrus_Wood_Craven_FG2015.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=sports videos in the wild (svw): a video dataset for sports analysis&sort=relevance" target="_blank">[s2]</a></td><td>Michigan State University</td><td>1a40092b493c6b8840257ab7f96051d1a4dbfeb2</td></tr><tr><td>jpl_pose</td><td>JPL-Interaction dataset</td><td>First-Person Activity Recognition: What Are They Doing to Me?</td><td>First-Person Activity Recognition: What Are They Doing to Me?</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Ryoo_First-Person_Activity_Recognition_2013_CVPR_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=first-person activity recognition: what are they doing to me?&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1aad2da473888cb7ebc1bfaa15bfa0f1502ce005</td></tr><tr><td>adience</td><td>Adience</td><td>Age and Gender Estimation of Unfiltered Faces</td><td>Age and Gender Estimation of Unfiltered Faces</td><td><a href="http://www.openu.ac.il/home/hassner/Adience/EidingerEnbarHassner_tifs.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=age and gender estimation of unfiltered faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1be498d4bbc30c3bfd0029114c784bc2114d67c0</td></tr><tr><td>youtube_poses</td><td>YouTube Pose</td><td>Personalizing Human Video Pose Estimation</td><td>Personalizing Human Video Pose Estimation</td><td><a href="http://arxiv.org/pdf/1511.06676v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=personalizing human video pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1c2802c2199b6d15ecefe7ba0c39bfe44363de38</td></tr><tr><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td>Pedestrian Detection: A Benchmark</td><td>Pedestrian detection: A benchmark</td><td><a href="http://vision.ucsd.edu/~pdollar/files/papers/DollarCVPR09peds.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian detection: a benchmark&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1dc35905a1deff8bc74688f2d7e2f48fd2273275</td></tr><tr><td>immediacy</td><td>Immediacy</td><td>Multi-task Recurrent Neural Network for Immediacy Prediction</td><td>Multi-task Recurrent Neural Network for Immediacy Prediction</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.383" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-task recurrent neural network for immediacy prediction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1e3df3ca8feab0b36fd293fe689f93bb2aaac591</td></tr><tr><td>cafe</td><td>CAFE</td><td>The Child Affective Facial Expression (CAFE) Set: Validity and reliability from untrained adults</td><td>The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults</td><td><a href="http://pdfs.semanticscholar.org/2038/8099cc415c772926e47bcbbe554e133343d1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the child affective facial expression (cafe) set: validity and reliability from untrained adults&sort=relevance" target="_blank">[s2]</a></td><td></td><td>20388099cc415c772926e47bcbbe554e133343d1</td></tr><tr><td>bbc_pose</td><td>BBC Pose</td><td>Automatic and Efficient Human Pose Estimation for Sign Language Videos</td><td>Automatic and Efficient Human Pose Estimation for Sign Language Videos</td><td><a href="https://doi.org/10.1007/s11263-013-0672-6" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automatic and efficient human pose estimation for sign language videos&sort=relevance" target="_blank">[s2]</a></td><td></td><td>213a579af9e4f57f071b884aa872651372b661fd</td></tr><tr><td>3d_rma</td><td>3D-RMA</td><td>Automatic 3D Face Authentication</td><td>Automatic 3D face authentication</td><td><a href="http://pdfs.semanticscholar.org/2160/788824c4c29ffe213b2cbeb3f52972d73f37.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automatic 3d face authentication&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2160788824c4c29ffe213b2cbeb3f52972d73f37</td></tr><tr><td>large_scale_person_search</td><td>Large Scale Person Search</td><td>End-to-End Deep Learning for Person Search</td><td>End-to-End Deep Learning for Person Search</td><td><a href="https://pdfs.semanticscholar.org/2161/f6b7ee3c0acc81603b01dc0df689683577b9.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=end-to-end deep learning for person search&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2161f6b7ee3c0acc81603b01dc0df689683577b9</td></tr><tr><td>images_of_groups</td><td>Images of Groups</td><td>Understanding Groups of Images of People</td><td>Understanding images of groups of people</td><td><a href="http://amp.ece.cmu.edu/people/Andy/Andy_files/cvpr09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=understanding groups of images of people&sort=relevance" target="_blank">[s2]</a></td><td></td><td>21d9d0deed16f0ad62a4865e9acf0686f4f15492</td></tr><tr><td>rap_pedestrian</td><td>RAP</td><td>A Richly Annotated Dataset for Pedestrian Attribute Recognition</td><td>A Richly Annotated Dataset for Pedestrian Attribute Recognition</td><td><a href="http://pdfs.semanticscholar.org/221c/18238b829c12b911706947ab38fd017acef7.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a richly annotated dataset for pedestrian attribute recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>221c18238b829c12b911706947ab38fd017acef7</td></tr><tr><td>mot</td><td>MOT</td><td>Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</td><td>Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</td><td><a href="http://pdfs.semanticscholar.org/2e0b/00f4043e2d4b04c59c88bb54bcd907d0dcd4.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=evaluating multiple object tracking performance: the clear mot metrics&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2258e01865367018ed6f4262c880df85b94959f8</td></tr><tr><td>saivt</td><td>SAIVT SoftBio</td><td>A Database for Person Re-Identification in Multi-Camera Surveillance Networks</td><td>A Database for Person Re-Identification in Multi-Camera Surveillance Networks</td><td><a href="http://eprints.qut.edu.au/53437/3/Bialkowski_Database4PersonReID_DICTA.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a database for person re-identification in multi-camera surveillance networks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b</td></tr><tr><td>pets</td><td>PETS 2017</td><td>PETS 2017: Dataset and Challenge</td><td>PETS 2017: Dataset and Challenge</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8014998" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pets 2017: dataset and challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22909dd19a0ec3b6065334cb5be5392cb24d839d</td></tr><tr><td>gallagher</td><td>Gallagher</td><td>Clothing Cosegmentation for Recognizing People</td><td>Clothing cosegmentation for recognizing people</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2008.4587481" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clothing cosegmentation for recognizing people&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22ad2c8c0f4d6aa4328b38d894b814ec22579761</td></tr><tr><td>expw</td><td>ExpW</td><td>From Facial Expression Recognition to Interpersonal Relation Prediction</td><td>From Facial Expression Recognition to Interpersonal Relation Prediction</td><td><a href="http://arxiv.org/abs/1609.06426" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=from facial expression recognition to interpersonal relation prediction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td></tr><tr><td>mifs</td><td>MIFS</td><td>Spoofing Faces Using Makeup: An Investigative Study</td><td>Spoofing faces using makeup: An investigative study</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7947686" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=spoofing faces using makeup: an investigative study&sort=relevance" target="_blank">[s2]</a></td><td>INRIA Méditerranée</td><td>23e824d1dfc33f3780dd18076284f07bd99f1c43</td></tr><tr><td>cohn_kanade</td><td>CK</td><td>Comprehensive Database for Facial Expression Analysis</td><td>Comprehensive Database for Facial Expression Analysis</td><td><a href="http://pdfs.semanticscholar.org/23fc/83c8cfff14a16df7ca497661264fc54ed746.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=comprehensive database for facial expression analysis&sort=relevance" target="_blank">[s2]</a></td><td>Carnegie Mellon University</td><td>23fc83c8cfff14a16df7ca497661264fc54ed746</td></tr><tr><td>cas_peal</td><td>CAS-PEAL</td><td>The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</td><td>The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</td><td><a href="https://doi.org/10.1109/TSMCA.2007.909557" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cas-peal large-scale chinese face database and baseline evaluations&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td></tr><tr><td>faceplace</td><td>Face Place</td><td>Recognizing disguised faces</td><td>Recognizing disguised faces</td><td><a href="http://pdfs.semanticscholar.org/d936/7ceb0be378c3a9ddf7cb741c678c1a3c574c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=recognizing disguised faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>25474c21613607f6bb7687a281d5f9d4ffa1f9f3</td></tr><tr><td>afew_va</td><td>AFEW-VA</td><td>AFEW-VA database for valence and arousal estimation in-the-wild</td><td>AFEW-VA database for valence and arousal estimation in-the-wild</td><td><a href="http://pdfs.semanticscholar.org/2624/d84503bc2f8e190e061c5480b6aa4d89277a.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=afew-va database for valence and arousal estimation in-the-wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2624d84503bc2f8e190e061c5480b6aa4d89277a</td></tr><tr><td>cofw</td><td>COFW</td><td>Robust face landmark estimation under occlusion</td><td>Robust Face Landmark Estimation under Occlusion</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6751298" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=robust face landmark estimation under occlusion&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2724ba85ec4a66de18da33925e537f3902f21249</td></tr><tr><td>duke_mtmc</td><td>Duke MTMC</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td><a href="http://pdfs.semanticscholar.org/b5f2/4f49f9a5e47d6601399dc068158ad88d7651.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=performance measures and a data set for multi-target, multi-camera tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td></tr><tr><td>mot</td><td>MOT</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td><a href="http://pdfs.semanticscholar.org/b5f2/4f49f9a5e47d6601399dc068158ad88d7651.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=performance measures and a data set for multi-target, multi-camera tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td></tr><tr><td>h3d</td><td>H3D</td><td>Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations</td><td>Poselets: Body part detectors trained using 3D human pose annotations</td><td><a href="http://vision.stanford.edu/teaching/cs231b_spring1213/papers/ICCV09_BourdevMalik.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=poselets: body part detectors trained using 3d human pose annotations&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2830fb5282de23d7784b4b4bc37065d27839a412</td></tr><tr><td>megaface</td><td>MegaFace</td><td>Level Playing Field for Million Scale Face Recognition</td><td>Level Playing Field for Million Scale Face Recognition</td><td><a href="https://arxiv.org/pdf/1705.00393.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=level playing field for million scale face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>28d4e027c7e90b51b7d8908fce68128d1964668a</td></tr><tr><td>msceleb</td><td>MsCeleb</td><td>MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</td><td>MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</td><td><a href="http://pdfs.semanticscholar.org/4603/cb8e05258bb0572ae912ad20903b8f99f4b1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ms-celeb-1m: a dataset and benchmark for large-scale face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>291265db88023e92bb8c8e6390438e5da148e8f5</td></tr><tr><td>ferplus</td><td>FER+</td><td>Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution</td><td>Training deep networks for facial expression recognition with crowd-sourced label distribution</td><td><a href="http://arxiv.org/pdf/1608.01041v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=training deep networks for facial expression recognition with crowd-sourced label distribution&sort=relevance" target="_blank">[s2]</a></td><td></td><td>298cbc3dfbbb3a20af4eed97906650a4ea1c29e0</td></tr><tr><td>scface</td><td>SCface</td><td>SCface – surveillance cameras face database</td><td>SCface – surveillance cameras face database</td><td><a href="http://scface.org/SCface%20-%20Surveillance%20Cameras%20Face%20Database.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scface – surveillance cameras face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d</td></tr><tr><td>social_relation</td><td>Social Relation</td><td>Learning Social Relation Traits from Face Images</td><td>Learning Social Relation Traits from Face Images</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.414" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning social relation traits from face images&sort=relevance" target="_blank">[s2]</a></td><td>Chinese University of Hong Kong</td><td>2a171f8d14b6b8735001a11c217af9587d095848</td></tr><tr><td>peta</td><td>PETA</td><td>Pedestrian Attribute Recognition At Far Distance</td><td>Pedestrian Attribute Recognition At Far Distance</td><td><a href="http://personal.ie.cuhk.edu.hk/~pluo/pdf/mm14.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian attribute recognition at far distance&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2a4bbee0b4cf52d5aadbbc662164f7efba89566c</td></tr><tr><td>mmi_facial_expression</td><td>MMI Facial Expression Dataset</td><td>WEB-BASED DATABASE FOR FACIAL EXPRESSION ANALYSIS</td><td>Web-based database for facial expression analysis</td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/PanticEtAl-ICME2005-final.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=web-based database for facial expression analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2a75f34663a60ab1b04a0049ed1d14335129e908</td></tr><tr><td>bosphorus</td><td>The Bosphorus</td><td>Bosphorus Database for 3D Face Analysis</td><td>Bosphorus Database for 3D Face Analysis</td><td><a href="http://pdfs.semanticscholar.org/4254/fbba3846008f50671edc9cf70b99d7304543.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=bosphorus database for 3d face analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2acf7e58f0a526b957be2099c10aab693f795973</td></tr><tr><td>yale_faces</td><td>YaleFaces</td><td>Acquiring Linear Subspaces for Face Recognition under Variable Lighting</td><td>Acquiring linear subspaces for face recognition under variable lighting</td><td><a href="http://vision.cornell.edu/se3/wp-content/uploads/2014/09/pami05.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=acquiring linear subspaces for face recognition under variable lighting&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2ad0ee93d029e790ebb50574f403a09854b65b7e</td></tr><tr><td>frav3d</td><td>FRAV3D</td><td>MULTIMODAL 2D, 2.5D & 3D FACE VERIFICATION</td><td>Multimodal 2D, 2.5D & 3D Face Verification</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multimodal 2d, 2.5d & 3d face verification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2b926b3586399d028b46315d7d9fb9d879e4f79c</td></tr><tr><td>clothing_co_parsing</td><td>CCP</td><td>Clothing Co-Parsing by Joint Image Segmentation and Labeling</td><td>Clothing Co-parsing by Joint Image Segmentation and Labeling</td><td><a href="https://arxiv.org/pdf/1502.00739v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clothing co-parsing by joint image segmentation and labeling&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2bf8541199728262f78d4dced6fb91479b39b738</td></tr><tr><td>texas_3dfrd</td><td>Texas 3DFRD</td><td>Anthropometric 3D Face Recognition</td><td>Anthropometric 3D Face Recognition</td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ijcv_june10.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=anthropometric 3d face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2ce2560cf59db59ce313bbeb004e8ce55c5ce928</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: Updates and New Reporting Procedures</td><td>Labeled Faces in the Wild : Updates and New Reporting Procedures</td><td><a href="http://pdfs.semanticscholar.org/2d34/82dcff69c7417c7b933f22de606a0e8e42d4.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: updates and new reporting procedures&sort=relevance" target="_blank">[s2]</a></td><td>University of Massachusetts</td><td>2d3482dcff69c7417c7b933f22de606a0e8e42d4</td></tr><tr><td>a_pascal_yahoo</td><td>aPascal</td><td>Describing Objects by their Attributes</td><td>Describing objects by their attributes</td><td><a href="http://www-2.cs.cmu.edu/~dhoiem/publications/cvpr2009_attributes.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=describing objects by their attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2e384f057211426ac5922f1b33d2aa8df5d51f57</td></tr><tr><td>3dpes</td><td>3DPeS</td><td>3DPes: 3D People Dataset for Surveillance and Forensics</td><td>3DPeS: 3D people dataset for surveillance and forensics</td><td><a href="http://doi.acm.org/10.1145/2072572.2072590" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=3dpes: 3d people dataset for surveillance and forensics&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e</td></tr><tr><td>kin_face</td><td>UB KinFace</td><td>Genealogical Face Recognition based on UB KinFace Database</td><td>Genealogical face recognition based on UB KinFace database</td><td><a href="https://doi.org/10.1109/CVPRW.2011.5981801" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=genealogical face recognition based on ub kinface database&sort=relevance" target="_blank">[s2]</a></td><td>SUNY Buffalo</td><td>2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d</td></tr><tr><td>qmul_grid</td><td>GRID</td><td>Time-delayed correlation analysis for multi-camera activity understanding</td><td>Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding</td><td><a href="https://doi.org/10.1007/s11263-010-0347-5" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=time-delayed correlation analysis for multi-camera activity understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2edb87494278ad11641b6cf7a3f8996de12b8e14</td></tr><tr><td>graz</td><td>Graz Pedestrian</td><td>Generic Object Recognition with Boosting</td><td>Generic object recognition with boosting</td><td><a href="http://www.emt.tu-graz.ac.at/~pinz/onlinepapers/Reprint_Vol_28_No_3_2006.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=generic object recognition with boosting&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9</td></tr><tr><td>hrt_transgender</td><td>HRT Transgender</td><td>Investigating the Periocular-Based Face Recognition Across Gender Transformation</td><td>Investigating the Periocular-Based Face Recognition Across Gender Transformation</td><td><a href="https://doi.org/10.1109/TIFS.2014.2361479" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=investigating the periocular-based face recognition across gender transformation&sort=relevance" target="_blank">[s2]</a></td><td>University of North Carolina at Wilmington</td><td>2f43b614607163abf41dfe5d17ef6749a1b61304</td></tr><tr><td>names_and_faces_news</td><td>News Dataset</td><td>Names and Faces</td><td>Names and faces in the news</td><td><a href="http://www.cs.utexas.edu/~grauman/courses/spring2007/395T/papers/berg_names_and_faces.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=names and faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2fda164863a06a92d3a910b96eef927269aeb730</td></tr><tr><td>umd_faces</td><td>UMD</td><td>UMDFaces: An Annotated Face Dataset for Training Deep Networks</td><td>UMDFaces: An annotated face dataset for training deep networks</td><td><a href="http://arxiv.org/abs/1611.01484" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=umdfaces: an annotated face dataset for training deep networks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>31b05f65405534a696a847dd19c621b7b8588263</td></tr><tr><td>tiny_images</td><td>Tiny Images</td><td>80 million tiny images: a large dataset for non-parametric object and scene recognition</td><td>80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition</td><td><a href="http://pdfs.semanticscholar.org/31b5/8ced31f22eab10bd3ee2d9174e7c14c27c01.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=80 million tiny images: a large dataset for non-parametric object and scene recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>31b58ced31f22eab10bd3ee2d9174e7c14c27c01</td></tr><tr><td>feret</td><td>FERET</td><td>FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results</td><td>FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results</td><td><a href="http://pdfs.semanticscholar.org/31de/9b3dd6106ce6eec9a35991b2b9083395fd0b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=feret ( face recognition technology ) recognition algorithm development and test results&sort=relevance" target="_blank">[s2]</a></td><td></td><td>31de9b3dd6106ce6eec9a35991b2b9083395fd0b</td></tr><tr><td>ucf_crowd</td><td>UCF-CC-50</td><td>Multi-Source Multi-Scale Counting in Extremely Dense Crowd Images</td><td>Multi-source Multi-scale Counting in Extremely Dense Crowd Images</td><td><a href="http://www.cs.ucf.edu/~haroon/datafiles/Idrees_Counting_CVPR_2013.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-source multi-scale counting in extremely dense crowd images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>32c801cb7fbeb742edfd94cccfca4934baec71da</td></tr><tr><td>cityscapes</td><td>Cityscapes</td><td>The Cityscapes Dataset for Semantic Urban Scene Understanding</td><td>The Cityscapes Dataset for Semantic Urban Scene Understanding</td><td><a href="https://arxiv.org/pdf/1604.01685.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cityscapes dataset for semantic urban scene understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>32cde90437ab5a70cf003ea36f66f2de0e24b3ab</td></tr><tr><td>tud_campus</td><td>TUD-Campus</td><td>People-Tracking-by-Detection and People-Detection-by-Tracking</td><td>People-tracking-by-detection and people-detection-by-tracking</td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=people-tracking-by-detection and people-detection-by-tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td></tr><tr><td>tud_crossing</td><td>TUD-Crossing</td><td>People-Tracking-by-Detection and People-Detection-by-Tracking</td><td>People-tracking-by-detection and people-detection-by-tracking</td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=people-tracking-by-detection and people-detection-by-tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td></tr><tr><td>tud_pedestrian</td><td>TUD-Pedestrian</td><td>People-Tracking-by-Detection and People-Detection-by-Tracking</td><td>People-tracking-by-detection and people-detection-by-tracking</td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=people-tracking-by-detection and people-detection-by-tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td></tr><tr><td>mpii_human_pose</td><td>MPII Human Pose</td><td>2D Human Pose Estimation: New Benchmark and State of the Art Analysis</td><td>2D Human Pose Estimation: New Benchmark and State of the Art Analysis</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6909866" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=2d human pose estimation: new benchmark and state of the art analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3325860c0c82a93b2eac654f5324dd6a776f609e</td></tr><tr><td>penn_fudan</td><td>Penn Fudan</td><td>Object Detection Combining Recognition and Segmentation</td><td>Object Detection Combining Recognition and Segmentation</td><td><a href="http://pdfs.semanticscholar.org/f531/a554cade14b9b340de6730683a28c292dd74.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=object detection combining recognition and segmentation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3394168ff0719b03ff65bcea35336a76b21fe5e4</td></tr><tr><td>wider</td><td>WIDER</td><td>Recognize Complex Events from Static Images by Fusing Deep Channels</td><td>Recognize complex events from static images by fusing deep channels</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Xiong_Recognize_Complex_Events_2015_CVPR_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=recognize complex events from static images by fusing deep channels&sort=relevance" target="_blank">[s2]</a></td><td>Shenzhen Institutes of Advanced Technology</td><td>356b431d4f7a2a0a38cf971c84568207dcdbf189</td></tr><tr><td>coco_qa</td><td>COCO QA</td><td>Exploring Models and Data for Image Question Answering</td><td>Exploring Models and Data for Image Question Answering</td><td><a href="http://pdfs.semanticscholar.org/aa79/9c29c0d44ece1864467af520fe70540c069b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=exploring models and data for image question answering&sort=relevance" target="_blank">[s2]</a></td><td></td><td>35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</td><td>Labeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments</td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: a database for studying face recognition in unconstrained environments&sort=relevance" target="_blank">[s2]</a></td><td></td><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td></tr><tr><td>rafd</td><td>RaFD</td><td>Presentation and validation of the Radboud Faces Database</td><td>Presentation and validation of the Radboud Faces Database</td><td><a href="https://pdfs.semanticscholar.org/3765/df816dc5a061bc261e190acc8bdd9d47bec0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=presentation and validation of the radboud faces database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3765df816dc5a061bc261e190acc8bdd9d47bec0</td></tr><tr><td>ufdd</td><td>UFDD</td><td>Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results</td><td>Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results</td><td><a href="https://arxiv.org/pdf/1804.10275.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pushing the limits of unconstrained face detection: a challenge dataset and baseline results&sort=relevance" target="_blank">[s2]</a></td><td>Johns Hopkins University</td><td>377f2b65e6a9300448bdccf678cde59449ecd337</td></tr><tr><td>vmu</td><td>VMU</td><td>Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?</td><td>Can facial cosmetics affect the matching accuracy of face recognition systems?</td><td><a href="http://www.cse.msu.edu/~climer/DantchevaChenRossFaceCosmetics_BTAS2012.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=can facial cosmetics affect the matching accuracy of face recognition systems?&sort=relevance" target="_blank">[s2]</a></td><td></td><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td></tr><tr><td>youtube_makeup</td><td>YMU</td><td>Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?</td><td>Can facial cosmetics affect the matching accuracy of face recognition systems?</td><td><a href="http://www.cse.msu.edu/~climer/DantchevaChenRossFaceCosmetics_BTAS2012.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=can facial cosmetics affect the matching accuracy of face recognition systems?&sort=relevance" target="_blank">[s2]</a></td><td></td><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td></tr><tr><td>cuhk02</td><td>CUHK02</td><td>Locally Aligned Feature Transforms across Views</td><td>Locally Aligned Feature Transforms across Views</td><td><a href="http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/4989d594.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=locally aligned feature transforms across views&sort=relevance" target="_blank">[s2]</a></td><td></td><td>38b55d95189c5e69cf4ab45098a48fba407609b4</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</td><td>A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.434" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a spatio-temporal appearance representation for video-based pedestrian re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3b4ec8af470948a72a6ed37a9fd226719a874ebc</td></tr><tr><td>qmul_grid</td><td>GRID</td><td>Multi-Camera Activity Correlation Analysis</td><td>Multi-camera activity correlation analysis</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0163.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-camera activity correlation analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3b5b6d19d4733ab606c39c69a889f9e67967f151</td></tr><tr><td>tvhi</td><td>TVHI</td><td>High Five: Recognising human interactions in TV shows</td><td>High Five: Recognising human interactions in TV shows</td><td><a href="http://pdfs.semanticscholar.org/3cd4/0bfa1ff193a96bde0207e5140a399476466c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=high five: recognising human interactions in tv shows&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3cd40bfa1ff193a96bde0207e5140a399476466c</td></tr><tr><td>georgia_tech_face_database</td><td>Georgia Tech Face</td><td>Maximum likelihood training of the embedded HMM for face detection and recognition</td><td>MAXIMUM LIKELIHOOD TRAINING OF THE EMBEDDED HMM FOR FACE DETECTION AND RECOGNITION Ara V. Ne an and Monson H. Hayes III Center for Signal and Image Processing School of Electrical and Computer Engineering</td><td><a href="http://pdfs.semanticscholar.org/3dc3/f0b64ef80f573e3a5f96e456e52ee980b877.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=maximum likelihood training of the embedded hmm for face detection and recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3dc3f0b64ef80f573e3a5f96e456e52ee980b877</td></tr><tr><td>bio_id</td><td>BioID Face</td><td>Robust Face Detection Using the Hausdorff Distance</td><td>Robust Face Detection Using the Hausdorff Distance</td><td><a href="http://pdfs.semanticscholar.org/4053/e3423fb70ad9140ca89351df49675197196a.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=robust face detection using the hausdorff distance&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4053e3423fb70ad9140ca89351df49675197196a</td></tr><tr><td>distance_nighttime</td><td>Long Distance Heterogeneous Face</td><td>Nighttime Face Recognition at Long Distance: Cross-distance and Cross-spectral Matching</td><td>Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching</td><td><a href="http://pdfs.semanticscholar.org/4156/b7e88f2e0ab0a7c095b9bab199ae2b23bd06.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=nighttime face recognition at long distance: cross-distance and cross-spectral matching&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06</td></tr><tr><td>market_1501</td><td>Market 1501</td><td>Scalable Person Re-identification: A Benchmark</td><td>Scalable Person Re-identification: A Benchmark</td><td><a href="https://www.microsoft.com/en-us/research/wp-content/uploads/2017/01/ICCV15-ReIDDataset.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scalable person re-identification: a benchmark&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4308bd8c28e37e2ed9a3fcfe74d5436cce34b410</td></tr><tr><td>tud_multiview</td><td>TUD-Multiview</td><td>Monocular 3D Pose Estimation and Tracking by Detection</td><td>Monocular 3D pose estimation and tracking by detection</td><td><a href="http://lmb.informatik.uni-freiburg.de/lectures/seminar_brox/seminar_ws1011/cvpr10_andriluka.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=monocular 3d pose estimation and tracking by detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td></tr><tr><td>tud_stadtmitte</td><td>TUD-Stadtmitte</td><td>Monocular 3D Pose Estimation and Tracking by Detection</td><td>Monocular 3D pose estimation and tracking by detection</td><td><a href="http://lmb.informatik.uni-freiburg.de/lectures/seminar_brox/seminar_ws1011/cvpr10_andriluka.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=monocular 3d pose estimation and tracking by detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td></tr><tr><td>cuhk01</td><td>CUHK01</td><td>Human Reidentification with Transferred Metric Learning</td><td>Human Reidentification with Transferred Metric Learning</td><td><a href="http://pdfs.semanticscholar.org/4448/4d2866f222bbb9b6b0870890f9eea1ffb2d0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=human reidentification with transferred metric learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>44484d2866f222bbb9b6b0870890f9eea1ffb2d0</td></tr><tr><td>wider_attribute</td><td>WIDER Attribute</td><td>Human Attribute Recognition by Deep Hierarchical Contexts</td><td>Human Attribute Recognition by Deep Hierarchical Contexts</td><td><a href="https://pdfs.semanticscholar.org/8e28/07f2dd53b03a759e372e07f7191cae65c9fd.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=human attribute recognition by deep hierarchical contexts&sort=relevance" target="_blank">[s2]</a></td><td>Chinese University of Hong Kong</td><td>44d23df380af207f5ac5b41459c722c87283e1eb</td></tr><tr><td>vadana</td><td>VADANA</td><td>VADANA: A dense dataset for facial image analysis</td><td>VADANA: A dense dataset for facial image analysis</td><td><a href="http://vims.cis.udel.edu/publications/VADANA_BeFIT2011.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vadana: a dense dataset for facial image analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4563b46d42079242f06567b3f2e2f7a80cb3befe</td></tr><tr><td>jaffe</td><td>JAFFE</td><td>Coding Facial Expressions with Gabor Wavelets</td><td>Coding Facial Expressions with Gabor Wavelets</td><td><a href="http://pdfs.semanticscholar.org/45c3/1cde87258414f33412b3b12fc5bec7cb3ba9.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=coding facial expressions with gabor wavelets&sort=relevance" target="_blank">[s2]</a></td><td>Kyushu University</td><td>45c31cde87258414f33412b3b12fc5bec7cb3ba9</td></tr><tr><td>malf</td><td>MALF</td><td>Fine-grained Evaluation on Face Detection in the Wild.</td><td>Fine-grained evaluation on face detection in the wild</td><td><a href="http://www.cs.toronto.edu/~byang/papers/malf_fg15.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=fine-grained evaluation on face detection in the wild.&sort=relevance" target="_blank">[s2]</a></td><td>Chinese Academy of Sciences</td><td>45e616093a92e5f1e61a7c6037d5f637aa8964af</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>Local descriptors encoded by Fisher vectors for person re-identification</td><td>Local Descriptors Encoded by Fisher Vectors for Person Re-identification</td><td><a href="http://pdfs.semanticscholar.org/dd1d/51c3a59cb71cbfe1433ebeb4d973f7f9ddc1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=local descriptors encoded by fisher vectors for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>46a01565e6afe7c074affb752e7069ee3bf2e4ef</td></tr><tr><td>fia</td><td>CMU FiA</td><td>The CMU Face In Action (FIA) Database</td><td>The CMU Face In Action (FIA) Database</td><td><a href="http://pdfs.semanticscholar.org/bb47/a03401811f9d2ca2da12138697acbc7b97a3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cmu face in action (fia) database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>47662d1a368daf70ba70ef2d59eb6209f98b675d</td></tr><tr><td>kin_face</td><td>UB KinFace</td><td>Kinship Verification through Transfer Learning</td><td>Kinship Verification through Transfer Learning</td><td><a href="http://pdfs.semanticscholar.org/4793/f11fbca4a7dba898b9fff68f70d868e2497c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=kinship verification through transfer learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4793f11fbca4a7dba898b9fff68f70d868e2497c</td></tr><tr><td>am_fed</td><td>AM-FED</td><td>Affectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”</td><td>Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"</td><td><a href="http://pdfs.semanticscholar.org/5d06/437656dd94616d7d87260d5eb77513ded30f.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=affectiva mit facial expression dataset (am-fed): naturalistic and spontaneous facial expressions collected “in the wild”&sort=relevance" target="_blank">[s2]</a></td><td></td><td>47aeb3b82f54b5ae8142b4bdda7b614433e69b9a</td></tr><tr><td>apis</td><td>APiS1.0</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W10/papers/Zhu_Pedestrian_Attribute_Classification_2013_ICCV_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian attribute classification in surveillance: database and evaluation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td></tr><tr><td>svs</td><td>SVS</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W10/papers/Zhu_Pedestrian_Attribute_Classification_2013_ICCV_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian attribute classification in surveillance: database and evaluation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td></tr><tr><td>coco_action</td><td>COCO-a</td><td>Describing Common Human Visual Actions in Images</td><td>Describing Common Human Visual Actions in Images</td><td><a href="http://pdfs.semanticscholar.org/b38d/cf5fa5174c0d718d65cc4f3889b03c4a21df.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=describing common human visual actions in images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4946ba10a4d5a7d0a38372f23e6622bd347ae273</td></tr><tr><td>leeds_sports_pose</td><td>Leeds Sports Pose</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clustered pose and nonlinear appearance models for human pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td></tr><tr><td>stickmen_buffy</td><td>Buffy Stickmen</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clustered pose and nonlinear appearance models for human pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td></tr><tr><td>czech_news_agency</td><td>UFI</td><td>Unconstrained Facial Images: Database for Face Recognition under Real-world Conditions</td><td>Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions</td><td><a href="http://pdfs.semanticscholar.org/4b41/06614c1d553365bad75d7866bff0de6056ed.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=unconstrained facial images: database for face recognition under real-world conditions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b4106614c1d553365bad75d7866bff0de6056ed</td></tr><tr><td>face_tracer</td><td>FaceTracer</td><td>FaceTracer: A Search Engine for Large Collections of Images with Faces</td><td>FaceTracer: A Search Engine for Large Collections of Images with Faces</td><td><a href="http://pdfs.semanticscholar.org/73a8/1d311eedac8dea3ca24dc15b6990fa4a725e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facetracer: a search engine for large collections of images with faces&sort=relevance" target="_blank">[s2]</a></td><td>Columbia University</td><td>4c170a0dcc8de75587dae21ca508dab2f9343974</td></tr><tr><td>cmu_pie</td><td>CMU PIE</td><td>The CMU Pose, Illumination, and Expression Database</td><td>The CMU Pose, Illumination, and Expression (PIE) Database</td><td><a href="http://pdfs.semanticscholar.org/4d42/3acc78273b75134e2afd1777ba6d3a398973.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cmu pose, illumination, and expression database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d423acc78273b75134e2afd1777ba6d3a398973</td></tr><tr><td>multi_pie</td><td>MULTIPIE</td><td>Multi-PIE</td><td>The CMU Pose, Illumination, and Expression (PIE) Database</td><td><a href="http://pdfs.semanticscholar.org/4d42/3acc78273b75134e2afd1777ba6d3a398973.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-pie&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d423acc78273b75134e2afd1777ba6d3a398973</td></tr><tr><td>3dddb_unconstrained</td><td>3D Dynamic</td><td>A 3D Dynamic Database for Unconstrained Face Recognition</td><td>A 3 D Dynamic Database for Unconstrained Face Recognition</td><td><a href="https://pdfs.semanticscholar.org/4d4b/b462c9f1d4e4ab1e4aa6a75cc0bc71b38461.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3d dynamic database for unconstrained face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461</td></tr><tr><td>texas_3dfrd</td><td>Texas 3DFRD</td><td>Texas 3D Face Recognition Database</td><td>Texas 3D Face Recognition Database</td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ssiai_may10.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=texas 3d face recognition database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d58f886f5150b2d5e48fd1b5a49e09799bf895d</td></tr><tr><td>cohn_kanade_plus</td><td>CK+</td><td>The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression</td><td>The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression</td><td><a href="http://www.iainm.com/iainm/Publications_files/2010_The%20Extended.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the extended cohn-kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d9a02d080636e9666c4d1cc438b9893391ec6c7</td></tr><tr><td>cfd</td><td>CFD</td><td>The Chicago face database: A free stimulus set of faces and norming data</td><td>The Chicago face database: A free stimulus set of faces and norming data.</td><td><a href="http://pdfs.semanticscholar.org/4df3/143922bcdf7db78eb91e6b5359d6ada004d2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the chicago face database: a free stimulus set of faces and norming data&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4df3143922bcdf7db78eb91e6b5359d6ada004d2</td></tr><tr><td>leeds_sports_pose_extended</td><td>Leeds Sports Pose Extended</td><td>Learning Effective Human Pose Estimation from Inaccurate Annotation</td><td>Learning effective human pose estimation from inaccurate annotation</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995318" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning effective human pose estimation from inaccurate annotation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4e4746094bf60ee83e40d8597a6191e463b57f76</td></tr><tr><td>dartmouth_children</td><td>Dartmouth Children</td><td>The Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus set</td><td>The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set</td><td><a href="http://pdfs.semanticscholar.org/4e6e/e936eb50dd032f7138702fa39b7c18ee8907.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the dartmouth database of children's faces: acquisition and validation of a new face stimulus set&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4e6ee936eb50dd032f7138702fa39b7c18ee8907</td></tr><tr><td>soton</td><td>SOTON HiD</td><td>On a Large Sequence-Based Human Gait Database</td><td>On a large sequence-based human gait database</td><td><a href="http://pdfs.semanticscholar.org/4f93/cd09785c6e77bf4bc5a788e079df524c8d21.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=on a large sequence-based human gait database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4f93cd09785c6e77bf4bc5a788e079df524c8d21</td></tr><tr><td>deep_fashion</td><td>DeepFashion</td><td>Fashion Landmark Detection in the Wild</td><td>Fashion Landmark Detection in the Wild</td><td><a href="http://pdfs.semanticscholar.org/d8ca/e259c1c5bba0c096f480dc7322bbaebfac1a.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=fashion landmark detection in the wild&sort=relevance" target="_blank">[s2]</a></td><td>Chinese University of Hong Kong</td><td>4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7</td></tr><tr><td>violent_flows</td><td>Violent Flows</td><td>Violent Flows: Real-Time Detection of Violent Crowd Behavior</td><td>Violent flows: Real-time detection of violent crowd behavior</td><td><a href="http://www.wisdom.weizmann.ac.il/mathusers/kliper/Papers/violent_flows.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=violent flows: real-time detection of violent crowd behavior&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5194cbd51f9769ab25260446b4fa17204752e799</td></tr><tr><td>imsitu</td><td>imSitu</td><td>Situation Recognition: Visual Semantic Role Labeling for Image Understanding</td><td>Situation Recognition: Visual Semantic Role Labeling for Image Understanding</td><td><a href="http://grail.cs.washington.edu/wp-content/uploads/2016/09/yatskar2016srv.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=situation recognition: visual semantic role labeling for image understanding&sort=relevance" target="_blank">[s2]</a></td><td>University of Washington</td><td>51eba481dac6b229a7490f650dff7b17ce05df73</td></tr><tr><td>wider_face</td><td>WIDER FACE</td><td>WIDER FACE: A Face Detection Benchmark</td><td>WIDER FACE: A Face Detection Benchmark</td><td><a href="https://arxiv.org/pdf/1511.06523.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=wider face: a face detection benchmark&sort=relevance" target="_blank">[s2]</a></td><td>Chinese University of Hong Kong</td><td>52d7eb0fbc3522434c13cc247549f74bb9609c5d</td></tr><tr><td>bp4d_plus</td><td>BP4D+</td><td>Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</td><td>Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhang_Multimodal_Spontaneous_Emotion_CVPR_2016_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multimodal spontaneous emotion corpus for human behavior analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4</td></tr><tr><td>reseed</td><td>ReSEED</td><td>ReSEED: Social Event dEtection Dataset</td><td>ReSEED: social event dEtection dataset</td><td><a href="https://pub.uni-bielefeld.de/download/2663466/2686734" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=reseed: social event detection dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>54983972aafc8e149259d913524581357b0f91c3</td></tr><tr><td>orl</td><td>ORL</td><td>Parameterisation of a Stochastic Model for Human Face Identification</td><td>Parameterisation of a stochastic model for human face identification</td><td><a href="http://pdfs.semanticscholar.org/5520/6f0b5f57ce17358999145506cd01e570358c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=parameterisation of a stochastic model for human face identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>55206f0b5f57ce17358999145506cd01e570358c</td></tr><tr><td>ifad</td><td>IFAD</td><td>Indian Face Age Database: A Database for Face Recognition with Age Variation</td><td>Indian Face Age Database : A Database for Face Recognition with Age Variation</td><td><a href="https://pdfs.semanticscholar.org/55c4/0cbcf49a0225e72d911d762c27bb1c2d14aa.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=indian face age database: a database for face recognition with age variation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>55c40cbcf49a0225e72d911d762c27bb1c2d14aa</td></tr><tr><td>youtube_faces</td><td>YouTubeFaces</td><td>Face Recognition in Unconstrained Videos with Matched Background Similarity</td><td>Face recognition in unconstrained videos with matched background similarity</td><td><a href="http://www.cs.tau.ac.il/~wolf/papers/lvfw.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face recognition in unconstrained videos with matched background similarity&sort=relevance" target="_blank">[s2]</a></td><td>Open University of Israel</td><td>560e0e58d0059259ddf86fcec1fa7975dee6a868</td></tr><tr><td>data_61</td><td>Data61 Pedestrian</td><td>A Multi-Modal Graphical Model for Scene Analysis</td><td>A Multi-modal Graphical Model for Scene Analysis</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/WACV.2015.139" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a multi-modal graphical model for scene analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>563c940054e4b456661762c1ab858e6f730c3159</td></tr><tr><td>cmdp</td><td>CMDP</td><td>Distance Estimation of an Unknown Person from a Portrait</td><td>Distance Estimation of an Unknown Person from a Portrait</td><td><a href="http://pdfs.semanticscholar.org/56ae/6d94fc6097ec4ca861f0daa87941d1c10b70.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=distance estimation of an unknown person from a portrait&sort=relevance" target="_blank">[s2]</a></td><td>California Institute of Technology</td><td>56ae6d94fc6097ec4ca861f0daa87941d1c10b70</td></tr><tr><td>unbc_shoulder_pain</td><td>UNBC-McMaster Pain</td><td>PAINFUL DATA: The UNBC-McMaster Shoulder Pain Expression Archive Database</td><td>Painful data: The UNBC-McMaster shoulder pain expression archive database</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5771462" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=painful data: the unbc-mcmaster shoulder pain expression archive database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>56ffa7d906b08d02d6d5a12c7377a57e24ef3391</td></tr><tr><td>stanford_drone</td><td>Stanford Drone</td><td>Learning Social Etiquette: Human Trajectory Prediction In Crowded Scenes</td><td>Social LSTM: Human Trajectory Prediction in Crowded Spaces</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning social etiquette: human trajectory prediction in crowded scenes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>570f37ed63142312e6ccdf00ecc376341ec72b9f</td></tr><tr><td>ijb_c</td><td>IJB-C</td><td>IARPA Janus Benchmark C</td><td>IARPA Janus Benchmark - C: Face Dataset and Protocol</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8411217" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iarpa janus benchmark c&sort=relevance" target="_blank">[s2]</a></td><td></td><td>57178b36c21fd7f4529ac6748614bb3374714e91</td></tr><tr><td>50_people_one_question</td><td>50 People One Question</td><td>Merging Pose Estimates Across Space and Time</td><td>Merging Pose Estimates Across Space and Time</td><td><a href="http://pdfs.semanticscholar.org/63b2/f5348af0f969dfc2afb4977732393c6459ec.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=merging pose estimates across space and time&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725</td></tr><tr><td>mr2</td><td>MR2</td><td>The MR2: A multi-racial mega-resolution database of facial stimuli</td><td>The MR2: A multi-racial, mega-resolution database of facial stimuli.</td><td><a href="https://pdfs.semanticscholar.org/be5b/455abd379240460d022a0e246615b0b86c14.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mr2: a multi-racial mega-resolution database of facial stimuli&sort=relevance" target="_blank">[s2]</a></td><td></td><td>578d4ad74818086bb64f182f72e2c8bd31e3d426</td></tr><tr><td>cvc_01_barcelona</td><td>CVC-01</td><td>Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection</td><td>Adaptive Image Sampling and Windows Classification for On – board Pedestrian Detection</td><td><a href="http://pdfs.semanticscholar.org/57fe/081950f21ca03b5b375ae3e84b399c015861.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=adaptive image sampling and windows classification for on-board pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>57fe081950f21ca03b5b375ae3e84b399c015861</td></tr><tr><td>mot</td><td>MOT</td><td>Learning to associate: HybridBoosted multi-target tracker for crowded scene</td><td>Learning to associate: HybridBoosted multi-target tracker for crowded scene</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0633.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to associate: hybridboosted multi-target tracker for crowded scene&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5981e6479c3fd4e31644db35d236bfb84ae46514</td></tr><tr><td>disfa</td><td>DISFA</td><td>DISFA: A Spontaneous Facial Action Intensity Database</td><td>DISFA: A Spontaneous Facial Action Intensity Database</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/T-AFFC.2013.4" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=disfa: a spontaneous facial action intensity database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5a5f0287484f0d480fed1ce585dbf729586f0edc</td></tr><tr><td>wlfdb</td><td>WLFDB</td><td>WLFDB: Weakly Labeled Face Databases</td><td>WLFDB: Weakly Labeled Face Databases</td><td><a href="https://pdfs.semanticscholar.org/5ad4/e9f947c1653c247d418f05dad758a3f9277b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=wlfdb: weakly labeled face databases&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5ad4e9f947c1653c247d418f05dad758a3f9277b</td></tr><tr><td>coco</td><td>COCO</td><td>Microsoft COCO: Common Objects in Context</td><td>Microsoft COCO: Common Objects in Context</td><td><a href="http://pdfs.semanticscholar.org/71b7/178df5d2b112d07e45038cb5637208659ff7.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=microsoft coco: common objects in context&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5e0f8c355a37a5a89351c02f174e7a5ddcb98683</td></tr><tr><td>cityscapes</td><td>Cityscapes</td><td>The Cityscapes Dataset</td><td>The Cityscapes Dataset</td><td><a href="http://pdfs.semanticscholar.org/5ffd/74d2873b7cba2cbc5fd295cc7fbdedca22a2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cityscapes dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5ffd74d2873b7cba2cbc5fd295cc7fbdedca22a2</td></tr><tr><td>viper</td><td>VIPeR</td><td>Evaluating Appearance Models for Recognition, Reacquisition, and Tracking</td><td>Evaluating Appearance Models for Recognition , Reacquisition , and Tracking</td><td><a href="http://pdfs.semanticscholar.org/6273/b3491e94ea4dd1ce42b791d77bdc96ee73a8.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=evaluating appearance models for recognition, reacquisition, and tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6273b3491e94ea4dd1ce42b791d77bdc96ee73a8</td></tr><tr><td>caltech_10k_web_faces</td><td>Caltech 10K Web Faces</td><td>Pruning Training Sets for Learning of Object Categories</td><td>Pruning training sets for learning of object categories</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1467308" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pruning training sets for learning of object categories&sort=relevance" target="_blank">[s2]</a></td><td></td><td>636b8ffc09b1b23ff714ac8350bb35635e49fa3c</td></tr><tr><td>bfm</td><td>BFM</td><td>A 3D Face Model for Pose and Illumination Invariant Face Recognition</td><td>A 3D Face Model for Pose and Illumination Invariant Face Recognition</td><td><a href="https://pdfs.semanticscholar.org/6399/37b3a1b8bded3f7e9a40e85bd3770016cf3c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3d face model for pose and illumination invariant face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>639937b3a1b8bded3f7e9a40e85bd3770016cf3c</td></tr><tr><td>alert_airport</td><td>ALERT Airport</td><td>A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets</td><td>A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.</td><td><a href="https://arxiv.org/pdf/1605.09653.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a systematic evaluation and benchmark for person re-identification: features, metrics, and datasets&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6403117f9c005ae81f1e8e6d1302f4a045e3d99d</td></tr><tr><td>celeba</td><td>CelebA</td><td>Deep Learning Face Attributes in the Wild</td><td>Deep Learning Face Attributes in the Wild</td><td><a href="http://arxiv.org/pdf/1411.7766v2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep learning face attributes in the wild&sort=relevance" target="_blank">[s2]</a></td><td>Chinese University of Hong Kong</td><td>6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4</td></tr><tr><td>complex_activities</td><td>Ongoing Complex Activities</td><td>Recognition of Ongoing Complex Activities by Sequence Prediction over a Hierarchical Label Space</td><td>Recognition of ongoing complex activities by sequence prediction over a hierarchical label space</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/WACV.2016.7477586" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=recognition of ongoing complex activities by sequence prediction over a hierarchical label space&sort=relevance" target="_blank">[s2]</a></td><td></td><td>65355cbb581a219bd7461d48b3afd115263ea760</td></tr><tr><td>afad</td><td>AFAD</td><td>Ordinal Regression with a Multiple Output CNN for Age Estimation</td><td>Ordinal Regression with Multiple Output CNN for Age Estimation</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.532" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ordinal regression with a multiple output cnn for age estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c</td></tr><tr><td>sun_attributes</td><td>SUN</td><td>The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</td><td>The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</td><td><a href="http://www.cc.gatech.edu/~hays/papers/attribute_ijcv.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the sun attribute database: beyond categories for deeper scene understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>66e6f08873325d37e0ec20a4769ce881e04e964e</td></tr><tr><td>face_tracer</td><td>FaceTracer</td><td>Face Swapping: Automatically Replacing Faces in Photographs</td><td>Face swapping: automatically replacing faces in photographs</td><td><a href="https://classes.cs.uoregon.edu/16F/cis607photo/faces.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face swapping: automatically replacing faces in photographs&sort=relevance" target="_blank">[s2]</a></td><td>Columbia University</td><td>670637d0303a863c1548d5b19f705860a23e285c</td></tr><tr><td>tud_brussels</td><td>TUD-Brussels</td><td>Multi-Cue Onboard Pedestrian Detection</td><td>Multi-cue onboard pedestrian detection</td><td><a href="https://www.mpi-inf.mpg.de/fileadmin/inf/d2/wojek/poster_cwojek_cvpr09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-cue onboard pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td></tr><tr><td>tud_motionpairs</td><td>TUD-Motionparis</td><td>Multi-Cue Onboard Pedestrian Detection</td><td>Multi-cue onboard pedestrian detection</td><td><a href="https://www.mpi-inf.mpg.de/fileadmin/inf/d2/wojek/poster_cwojek_cvpr09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-cue onboard pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td></tr><tr><td>cuhk03</td><td>CUHK03</td><td>DeepReID: Deep Filter Pairing Neural Network for Person Re-identification</td><td>DeepReID: Deep Filter Pairing Neural Network for Person Re-identification</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Li_DeepReID_Deep_Filter_2014_CVPR_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deepreid: deep filter pairing neural network for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3</td></tr><tr><td>ar_facedb</td><td>AR Face</td><td>The AR Face Database</td><td>The AR face database</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the ar face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6d96f946aaabc734af7fe3fc4454cf8547fcd5ed</td></tr><tr><td>agedb</td><td>AgeDB</td><td>AgeDB: the first manually collected, in-the-wild age database</td><td>AgeDB: The First Manually Collected, In-the-Wild Age Database</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8014984" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=agedb: the first manually collected, in-the-wild age database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dcf418c778f528b5792104760f1fbfe90c6dd6a</td></tr><tr><td>stickmen_buffy</td><td>Buffy Stickmen</td><td>Learning to Parse Images of Articulated Objects</td><td>Learning to parse images of articulated bodies</td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to parse images of articulated objects&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td>Learning to parse images of articulated bodies</td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clustered pose and nonlinear appearance models for human pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td>Learning to Parse Images of Articulated Objects</td><td>Learning to parse images of articulated bodies</td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to parse images of articulated objects&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>ward</td><td>WARD</td><td>Re-identify people in wide area camera network</td><td>Re-identify people in wide area camera network</td><td><a href="https://doi.org/10.1109/CVPRW.2012.6239203" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=re-identify people in wide area camera network&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6f3c76b7c0bd8e1d122c6ea808a271fd4749c951</td></tr><tr><td>umd_faces</td><td>UMD</td><td>The Do's and Don'ts for CNN-based Face Verification</td><td>The Do’s and Don’ts for CNN-Based Face Verification</td><td><a href="https://arxiv.org/pdf/1705.07426.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the do's and don'ts for cnn-based face verification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6</td></tr><tr><td>affectnet</td><td>AffectNet</td><td>AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild</td><td>AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild</td><td><a href="https://arxiv.org/pdf/1708.03985.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=affectnet: a database for facial expression, valence, and arousal computing in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>758d7e1be64cc668c59ef33ba8882c8597406e53</td></tr><tr><td>pubfig</td><td>PubFig</td><td>Attribute and Simile Classifiers for Face Verification</td><td>Attribute and simile classifiers for face verification</td><td><a href="http://homes.cs.washington.edu/~neeraj/projects/faceverification/base/papers/nk_iccv2009_attrs.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=attribute and simile classifiers for face verification&sort=relevance" target="_blank">[s2]</a></td><td>Columbia University</td><td>759a3b3821d9f0e08e0b0a62c8b693230afc3f8d</td></tr><tr><td>fddb</td><td>FDDB</td><td>FDDB: A Benchmark for Face Detection in Unconstrained Settings</td><td>A Benchmark for Face Detection in Unconstrained Settings</td><td><a href="http://pdfs.semanticscholar.org/75da/1df4ed319926c544eefe17ec8d720feef8c0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=fddb: a benchmark for face detection in unconstrained settings&sort=relevance" target="_blank">[s2]</a></td><td>University of Massachusetts</td><td>75da1df4ed319926c544eefe17ec8d720feef8c0</td></tr><tr><td>urban_tribes</td><td>Urban Tribes</td><td>From Bikers to Surfers: Visual Recognition of Urban Tribes</td><td>From Bikers to Surfers: Visual Recognition of Urban Tribes</td><td><a href="http://pdfs.semanticscholar.org/774c/bb45968607a027ae4729077734db000a1ec5.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=from bikers to surfers: visual recognition of urban tribes&sort=relevance" target="_blank">[s2]</a></td><td>Columbia University</td><td>774cbb45968607a027ae4729077734db000a1ec5</td></tr><tr><td>wildtrack</td><td>WildTrack</td><td>WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</td><td>WILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</td><td><a href="https://pdfs.semanticscholar.org/fe1c/ec4e4995b8615855572374ae3efc94949105.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=wildtrack: a multi-camera hd dataset for dense unscripted pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>77c81c13a110a341c140995bedb98101b9e84f7f</td></tr><tr><td>berkeley_pose</td><td>BPAD</td><td>Describing People: A Poselet-Based Approach to Attribute Classification</td><td>Describing people: A poselet-based approach to attribute classification</td><td><a href="http://ttic.uchicago.edu/~smaji/papers/attributes-iccv11.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=describing people: a poselet-based approach to attribute classification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7808937b46acad36e43c30ae4e9f3fd57462853d</td></tr><tr><td>mapillary</td><td>Mapillary</td><td>The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes</td><td>The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8237796" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mapillary vistas dataset for semantic understanding of street scenes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>79828e6e9f137a583082b8b5a9dfce0c301989b8</td></tr><tr><td>nudedetection</td><td>Nude Detection</td><td>A Bag-of-Features Approach based on Hue-SIFT Descriptor for Nude Detection</td><td>A bag-of-features approach based on Hue-SIFT descriptor for nude detection</td><td><a href="http://ieeexplore.ieee.org/document/7077625/" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a bag-of-features approach based on hue-sift descriptor for nude detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7ace44190729927e5cb0dd5d363fcae966fe13f7</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: A Survey</td><td>Labeled Faces in the Wild : A Survey</td><td><a href="http://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: a survey&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td></tr><tr><td>vgg_celebs_in_places</td><td>CIP</td><td>Faces in Places: Compound Query Retrieval</td><td>Faces in Places: compound query retrieval</td><td><a href="https://pdfs.semanticscholar.org/7ebb/153704706e457ab57b432793d2b6e5d12592.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=faces in places: compound query retrieval&sort=relevance" target="_blank">[s2]</a></td><td>University of Oxford</td><td>7ebb153704706e457ab57b432793d2b6e5d12592</td></tr><tr><td>nova_emotions</td><td>Novaemötions Dataset</td><td>Competitive affective gamming: Winning with a smile</td><td>Competitive affective gaming: winning with a smile</td><td><a href="http://doi.acm.org/10.1145/2502081.2502115" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=competitive affective gamming: winning with a smile&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7f4040b482d16354d5938c1d1b926b544652bf5b</td></tr><tr><td>sun_attributes</td><td>SUN</td><td>SUN Attribute Database: Discovering, Annotating, and Recognizing Scene Attributes</td><td>SUN attribute database: Discovering, annotating, and recognizing scene attributes</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6247998" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=sun attribute database: -discovering, annotating, and recognizing scene attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>833fa04463d90aab4a9fe2870d480f0b40df446e</td></tr><tr><td>svs</td><td>SVS</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W10/papers/Zhu_Pedestrian_Attribute_Classification_2013_ICCV_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian attribute classification in surveillance: database and evaluation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td></tr><tr><td>texas_3dfrd</td><td>Texas 3DFRD</td><td>Texas 3D Face Recognition Database</td><td>Anthropometric 3D Face Recognition</td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ijcv_june10.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=texas 3d face recognition database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2ce2560cf59db59ce313bbeb004e8ce55c5ce928</td></tr><tr><td>texas_3dfrd</td><td>Texas 3DFRD</td><td>Anthropometric 3D Face Recognition</td><td>Anthropometric 3D Face Recognition</td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ijcv_june10.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=anthropometric 3d face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2ce2560cf59db59ce313bbeb004e8ce55c5ce928</td></tr><tr><td>tiny_faces</td><td>TinyFace</td><td>Low-Resolution Face Recognition</td><td>Low-Resolution Face Recognition</td><td><a href="https://arxiv.org/pdf/1811.08965.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=low-resolution face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8990cdce3f917dad622e43e033db686b354d057c</td></tr><tr><td>tiny_images</td><td>Tiny Images</td><td>80 million tiny images: a large dataset for non-parametric object and scene recognition</td><td>80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition</td><td><a href="http://pdfs.semanticscholar.org/31b5/8ced31f22eab10bd3ee2d9174e7c14c27c01.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=80 million tiny images: a large dataset for non-parametric object and scene recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>31b58ced31f22eab10bd3ee2d9174e7c14c27c01</td></tr><tr><td>towncenter</td><td>TownCenter</td><td>Stable Multi-Target Tracking in Real-Time Surveillance Video</td><td>Stable multi-target tracking in real-time surveillance video</td><td><a href="http://pdfs.semanticscholar.org/9361/b784e73e9238d5cefbea5ac40d35d1e3103f.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=stable multi-target tracking in real-time surveillance video&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9361b784e73e9238d5cefbea5ac40d35d1e3103f</td></tr><tr><td>tud_brussels</td><td>TUD-Brussels</td><td>Multi-Cue Onboard Pedestrian Detection</td><td>Multi-cue onboard pedestrian detection</td><td><a href="https://www.mpi-inf.mpg.de/fileadmin/inf/d2/wojek/poster_cwojek_cvpr09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-cue onboard pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td></tr><tr><td>tud_campus</td><td>TUD-Campus</td><td>People-Tracking-by-Detection and People-Detection-by-Tracking</td><td>People-tracking-by-detection and people-detection-by-tracking</td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=people-tracking-by-detection and people-detection-by-tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td></tr><tr><td>tud_crossing</td><td>TUD-Crossing</td><td>People-Tracking-by-Detection and People-Detection-by-Tracking</td><td>People-tracking-by-detection and people-detection-by-tracking</td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=people-tracking-by-detection and people-detection-by-tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td></tr><tr><td>tud_motionpairs</td><td>TUD-Motionparis</td><td>Multi-Cue Onboard Pedestrian Detection</td><td>Multi-cue onboard pedestrian detection</td><td><a href="https://www.mpi-inf.mpg.de/fileadmin/inf/d2/wojek/poster_cwojek_cvpr09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-cue onboard pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td></tr><tr><td>tud_multiview</td><td>TUD-Multiview</td><td>Monocular 3D Pose Estimation and Tracking by Detection</td><td>Monocular 3D pose estimation and tracking by detection</td><td><a href="http://lmb.informatik.uni-freiburg.de/lectures/seminar_brox/seminar_ws1011/cvpr10_andriluka.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=monocular 3d pose estimation and tracking by detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td></tr><tr><td>tud_pedestrian</td><td>TUD-Pedestrian</td><td>People-Tracking-by-Detection and People-Detection-by-Tracking</td><td>People-tracking-by-detection and people-detection-by-tracking</td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=people-tracking-by-detection and people-detection-by-tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td></tr><tr><td>tud_stadtmitte</td><td>TUD-Stadtmitte</td><td>Monocular 3D Pose Estimation and Tracking by Detection</td><td>Monocular 3D pose estimation and tracking by detection</td><td><a href="http://lmb.informatik.uni-freiburg.de/lectures/seminar_brox/seminar_ws1011/cvpr10_andriluka.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=monocular 3d pose estimation and tracking by detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td></tr><tr><td>tvhi</td><td>TVHI</td><td>High Five: Recognising human interactions in TV shows</td><td>High Five: Recognising human interactions in TV shows</td><td><a href="http://pdfs.semanticscholar.org/3cd4/0bfa1ff193a96bde0207e5140a399476466c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=high five: recognising human interactions in tv shows&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3cd40bfa1ff193a96bde0207e5140a399476466c</td></tr><tr><td>uccs</td><td>UCCS</td><td>Large scale unconstrained open set face database</td><td>Large scale unconstrained open set face database</td><td><a href="http://www.vast.uccs.edu/~tboult/PAPERS/BTAS13-Sapkota-Boult-UCCSFaceDB.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=large scale unconstrained open set face database&sort=relevance" target="_blank">[s2]</a></td><td>University of Colorado at Colorado Springs</td><td>07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1</td></tr><tr><td>ucf_101</td><td>UCF101</td><td>UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild</td><td>UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild</td><td><a href="https://arxiv.org/pdf/1212.0402.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ucf101: a dataset of 101 human actions classes from videos in the wild&sort=relevance" target="_blank">[s2]</a></td><td>University of Central Florida</td><td>b5f2846a506fc417e7da43f6a7679146d99c5e96</td></tr><tr><td>ucf_crowd</td><td>UCF-CC-50</td><td>Multi-Source Multi-Scale Counting in Extremely Dense Crowd Images</td><td>Multi-source Multi-scale Counting in Extremely Dense Crowd Images</td><td><a href="http://www.cs.ucf.edu/~haroon/datafiles/Idrees_Counting_CVPR_2013.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-source multi-scale counting in extremely dense crowd images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>32c801cb7fbeb742edfd94cccfca4934baec71da</td></tr><tr><td>ucf_selfie</td><td>UCF Selfie</td><td>How to Take a Good Selfie?</td><td>How to Take a Good Selfie?</td><td><a href="http://doi.acm.org/10.1145/2733373.2806365" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=how to take a good selfie?&sort=relevance" target="_blank">[s2]</a></td><td></td><td>041d3eedf5e45ce5c5229f0181c5c576ed1fafd6</td></tr><tr><td>ufdd</td><td>UFDD</td><td>Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results</td><td>Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results</td><td><a href="https://arxiv.org/pdf/1804.10275.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pushing the limits of unconstrained face detection: a challenge dataset and baseline results&sort=relevance" target="_blank">[s2]</a></td><td>Johns Hopkins University</td><td>377f2b65e6a9300448bdccf678cde59449ecd337</td></tr><tr><td>umb</td><td>UMB</td><td>UMB-DB: A Database of Partially Occluded 3D Faces</td><td>UMB-DB: A database of partially occluded 3D faces</td><td><a href="https://doi.org/10.1109/ICCVW.2011.6130509" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=umb-db: a database of partially occluded 3d faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>16e8b0a1e8451d5f697b94c0c2b32a00abee1d52</td></tr><tr><td>umd_faces</td><td>UMD</td><td>UMDFaces: An Annotated Face Dataset for Training Deep Networks</td><td>UMDFaces: An annotated face dataset for training deep networks</td><td><a href="http://arxiv.org/abs/1611.01484" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=umdfaces: an annotated face dataset for training deep networks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>31b05f65405534a696a847dd19c621b7b8588263</td></tr><tr><td>umd_faces</td><td>UMD</td><td>The Do's and Don'ts for CNN-based Face Verification</td><td>The Do’s and Don’ts for CNN-Based Face Verification</td><td><a href="https://arxiv.org/pdf/1705.07426.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the do's and don'ts for cnn-based face verification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6</td></tr><tr><td>unbc_shoulder_pain</td><td>UNBC-McMaster Pain</td><td>PAINFUL DATA: The UNBC-McMaster Shoulder Pain Expression Archive Database</td><td>Painful data: The UNBC-McMaster shoulder pain expression archive database</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5771462" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=painful data: the unbc-mcmaster shoulder pain expression archive database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>56ffa7d906b08d02d6d5a12c7377a57e24ef3391</td></tr><tr><td>urban_tribes</td><td>Urban Tribes</td><td>From Bikers to Surfers: Visual Recognition of Urban Tribes</td><td>From Bikers to Surfers: Visual Recognition of Urban Tribes</td><td><a href="http://pdfs.semanticscholar.org/774c/bb45968607a027ae4729077734db000a1ec5.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=from bikers to surfers: visual recognition of urban tribes&sort=relevance" target="_blank">[s2]</a></td><td>Columbia University</td><td>774cbb45968607a027ae4729077734db000a1ec5</td></tr><tr><td>used</td><td>USED Social Event Dataset</td><td>USED: A Large-scale Social Event Detection Dataset</td><td>USED: a large-scale social event detection dataset</td><td><a href="http://doi.acm.org/10.1145/2910017.2910624" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=used: a large-scale social event detection dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8627f019882b024aef92e4eb9355c499c733e5b7</td></tr><tr><td>v47</td><td>V47</td><td>Re-identification of Pedestrians with Variable Occlusion and Scale</td><td>Re-identification of pedestrians with variable occlusion and scale</td><td><a href="https://doi.org/10.1109/ICCVW.2011.6130477" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=re-identification of pedestrians with variable occlusion and scale&sort=relevance" target="_blank">[s2]</a></td><td></td><td>922e0a51a3b8c67c4c6ac09a577ff674cbd28b34</td></tr><tr><td>vadana</td><td>VADANA</td><td>VADANA: A dense dataset for facial image analysis</td><td>VADANA: A dense dataset for facial image analysis</td><td><a href="http://vims.cis.udel.edu/publications/VADANA_BeFIT2011.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vadana: a dense dataset for facial image analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4563b46d42079242f06567b3f2e2f7a80cb3befe</td></tr><tr><td>vgg_celebs_in_places</td><td>CIP</td><td>Faces in Places: Compound Query Retrieval</td><td>Faces in Places: compound query retrieval</td><td><a href="https://pdfs.semanticscholar.org/7ebb/153704706e457ab57b432793d2b6e5d12592.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=faces in places: compound query retrieval&sort=relevance" target="_blank">[s2]</a></td><td>University of Oxford</td><td>7ebb153704706e457ab57b432793d2b6e5d12592</td></tr><tr><td>vgg_faces</td><td>VGG Face</td><td>Deep Face Recognition</td><td>Deep Face Recognition</td><td><a href="https://pdfs.semanticscholar.org/f372/ab9b3270d4e4f6a0258c83c2736c3a5c0454.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>162ea969d1929ed180cc6de9f0bf116993ff6e06</td></tr><tr><td>vgg_faces2</td><td>VGG Face2</td><td>VGGFace2: A dataset for recognising faces across pose and age</td><td>VGGFace2: A Dataset for Recognising Faces across Pose and Age</td><td><a href="https://arxiv.org/pdf/1710.08092.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vggface2: a dataset for recognising faces across pose and age&sort=relevance" target="_blank">[s2]</a></td><td>University of Oxford</td><td>eb027969f9310e0ae941e2adee2d42cdf07d938c</td></tr><tr><td>violent_flows</td><td>Violent Flows</td><td>Violent Flows: Real-Time Detection of Violent Crowd Behavior</td><td>Violent flows: Real-time detection of violent crowd behavior</td><td><a href="http://www.wisdom.weizmann.ac.il/mathusers/kliper/Papers/violent_flows.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=violent flows: real-time detection of violent crowd behavior&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5194cbd51f9769ab25260446b4fa17204752e799</td></tr><tr><td>viper</td><td>VIPeR</td><td>Evaluating Appearance Models for Recognition, Reacquisition, and Tracking</td><td>Evaluating Appearance Models for Recognition , Reacquisition , and Tracking</td><td><a href="http://pdfs.semanticscholar.org/6273/b3491e94ea4dd1ce42b791d77bdc96ee73a8.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=evaluating appearance models for recognition, reacquisition, and tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6273b3491e94ea4dd1ce42b791d77bdc96ee73a8</td></tr><tr><td>visual_phrases</td><td>Phrasal Recognition</td><td>Recognition using Visual Phrases</td><td>Recognition using visual phrases</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995711" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=recognition using visual phrases&sort=relevance" target="_blank">[s2]</a></td><td></td><td>e8de844fefd54541b71c9823416daa238be65546</td></tr><tr><td>vmu</td><td>VMU</td><td>Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?</td><td>Can facial cosmetics affect the matching accuracy of face recognition systems?</td><td><a href="http://www.cse.msu.edu/~climer/DantchevaChenRossFaceCosmetics_BTAS2012.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=can facial cosmetics affect the matching accuracy of face recognition systems?&sort=relevance" target="_blank">[s2]</a></td><td></td><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td></tr><tr><td>voc</td><td>VOC</td><td>The PASCAL Visual Object Classes (VOC) Challenge</td><td>The Pascal Visual Object Classes Challenge: A Retrospective</td><td><a href="http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc14.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the pascal visual object classes (voc) challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>abe9f3b91fd26fa1b50cd685c0d20debfb372f73</td></tr><tr><td>vqa</td><td>VQA</td><td>VQA: Visual Question Answering</td><td>VQA: Visual Question Answering</td><td><a href="http://arxiv.org/pdf/1505.00468v3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vqa: visual question answering&sort=relevance" target="_blank">[s2]</a></td><td></td><td>01959ef569f74c286956024866c1d107099199f7</td></tr><tr><td>ward</td><td>WARD</td><td>Re-identify people in wide area camera network</td><td>Re-identify people in wide area camera network</td><td><a href="https://doi.org/10.1109/CVPRW.2012.6239203" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=re-identify people in wide area camera network&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6f3c76b7c0bd8e1d122c6ea808a271fd4749c951</td></tr><tr><td>who_goes_there</td><td>WGT</td><td>Who Goes There? Approaches to Mapping Facial Appearance Diversity</td><td>Who goes there?: approaches to mapping facial appearance diversity</td><td><a href="http://doi.acm.org/10.1145/2996913.2996997" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=who goes there? approaches to mapping facial appearance diversity&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9b9bf5e623cb8af7407d2d2d857bc3f1b531c182</td></tr><tr><td>wider</td><td>WIDER</td><td>Recognize Complex Events from Static Images by Fusing Deep Channels</td><td>Recognize complex events from static images by fusing deep channels</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Xiong_Recognize_Complex_Events_2015_CVPR_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=recognize complex events from static images by fusing deep channels&sort=relevance" target="_blank">[s2]</a></td><td>Shenzhen Institutes of Advanced Technology</td><td>356b431d4f7a2a0a38cf971c84568207dcdbf189</td></tr><tr><td>wider_attribute</td><td>WIDER Attribute</td><td>Human Attribute Recognition by Deep Hierarchical Contexts</td><td>Human Attribute Recognition by Deep Hierarchical Contexts</td><td><a href="https://pdfs.semanticscholar.org/8e28/07f2dd53b03a759e372e07f7191cae65c9fd.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=human attribute recognition by deep hierarchical contexts&sort=relevance" target="_blank">[s2]</a></td><td>Chinese University of Hong Kong</td><td>44d23df380af207f5ac5b41459c722c87283e1eb</td></tr><tr><td>wider_face</td><td>WIDER FACE</td><td>WIDER FACE: A Face Detection Benchmark</td><td>WIDER FACE: A Face Detection Benchmark</td><td><a href="https://arxiv.org/pdf/1511.06523.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=wider face: a face detection benchmark&sort=relevance" target="_blank">[s2]</a></td><td>Chinese University of Hong Kong</td><td>52d7eb0fbc3522434c13cc247549f74bb9609c5d</td></tr><tr><td>wildtrack</td><td>WildTrack</td><td>WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</td><td>WILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</td><td><a href="https://pdfs.semanticscholar.org/fe1c/ec4e4995b8615855572374ae3efc94949105.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=wildtrack: a multi-camera hd dataset for dense unscripted pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>77c81c13a110a341c140995bedb98101b9e84f7f</td></tr><tr><td>wlfdb</td><td></td><td>WLFDB: Weakly Labeled Face Databases</td><td>WLFDB: Weakly Labeled Face Databases</td><td><a href="https://pdfs.semanticscholar.org/5ad4/e9f947c1653c247d418f05dad758a3f9277b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=wlfdb: weakly labeled face databases&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5ad4e9f947c1653c247d418f05dad758a3f9277b</td></tr><tr><td>yale_faces</td><td>YaleFaces</td><td>Acquiring Linear Subspaces for Face Recognition under Variable Lighting</td><td>From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</td><td><a href="http://pdfs.semanticscholar.org/97bb/c2b439a79d4dc0dc7199d71ed96ad5e3fd0e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=acquiring linear subspaces for face recognition under variable lighting&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18c72175ddbb7d5956d180b65a96005c100f6014</td></tr><tr><td>yale_faces</td><td>YaleFaces</td><td>From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</td><td>From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</td><td><a href="http://pdfs.semanticscholar.org/97bb/c2b439a79d4dc0dc7199d71ed96ad5e3fd0e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=from few to many: illumination cone models for face recognition under variable lighting and pose&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18c72175ddbb7d5956d180b65a96005c100f6014</td></tr><tr><td>yawdd</td><td>YawDD</td><td>YawDD: A Yawning Detection Dataset</td><td>YawDD: a yawning detection dataset</td><td><a href="http://www.site.uottawa.ca/~shervin/pubs/CogniVue-Dataset-ACM-MMSys2014.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=yawdd: a yawning detection dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a94cae786d515d3450d48267e12ca954aab791c4</td></tr><tr><td>yfcc_100m</td><td>YFCC100M</td><td>YFCC100M: The New Data in Multimedia Research</td><td>The New Data and New Challenges in Multimedia Research</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=yfcc100m: the new data in multimedia research&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a6e695ddd07aad719001c0fc1129328452385949</td></tr><tr><td>york_3d</td><td>UOY 3D Face Database</td><td>Three-Dimensional Face Recognition: An Eigensurface Approach</td><td>Three-dimensional face recognition: an eigensurface approach</td><td><a href="http://www-users.cs.york.ac.uk/~nep/research/3Dface/tomh/3DFaceRecognition-Eigensurface-ICIP(web)2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=three-dimensional face recognition: an eigensurface approach&sort=relevance" target="_blank">[s2]</a></td><td></td><td>19d1b811df60f86cbd5e04a094b07f32fff7a32a</td></tr><tr><td>youtube_faces</td><td>YouTubeFaces</td><td>Face Recognition in Unconstrained Videos with Matched Background Similarity</td><td>Face recognition in unconstrained videos with matched background similarity</td><td><a href="http://www.cs.tau.ac.il/~wolf/papers/lvfw.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face recognition in unconstrained videos with matched background similarity&sort=relevance" target="_blank">[s2]</a></td><td>Open University of Israel</td><td>560e0e58d0059259ddf86fcec1fa7975dee6a868</td></tr><tr><td>youtube_makeup</td><td>YMU</td><td>Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?</td><td>Automatic facial makeup detection with application in face recognition</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6612994" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=can facial cosmetics affect the matching accuracy of face recognition systems?&sort=relevance" target="_blank">[s2]</a></td><td>West Virginia University</td><td>fcc6fe6007c322641796cb8792718641856a22a7</td></tr><tr><td>youtube_makeup</td><td>YMU</td><td>Automatic Facial Makeup Detection with Application in Face Recognition</td><td>Automatic facial makeup detection with application in face recognition</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6612994" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automatic facial makeup detection with application in face recognition&sort=relevance" target="_blank">[s2]</a></td><td>West Virginia University</td><td>fcc6fe6007c322641796cb8792718641856a22a7</td></tr><tr><td>youtube_poses</td><td>YouTube Pose</td><td>Personalizing Human Video Pose Estimation</td><td>Personalizing Human Video Pose Estimation</td><td><a href="http://arxiv.org/pdf/1511.06676v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=personalizing human video pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1c2802c2199b6d15ecefe7ba0c39bfe44363de38</td></tr></table></body></html>
\ No newline at end of file +discovering, annotating, and recognizing scene attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>833fa04463d90aab4a9fe2870d480f0b40df446e</td></tr><tr><td>imdb_wiki</td><td>IMDB</td><td>DEX: Deep EXpectation of apparent age from a single image</td><td>DEX: Deep EXpectation of Apparent Age from a Single Image</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7406390" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=dex: deep expectation of apparent age from a single image&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8355d095d3534ef511a9af68a3b2893339e3f96b</td></tr><tr><td>awe_ears</td><td>AWE Ears</td><td>Ear Recognition: More Than a Survey</td><td>Ear Recognition: More Than a Survey</td><td><a href="http://pdfs.semanticscholar.org/84fe/5b4ac805af63206012d29523a1e033bc827e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ear recognition: more than a survey&sort=relevance" target="_blank">[s2]</a></td><td></td><td>84fe5b4ac805af63206012d29523a1e033bc827e</td></tr><tr><td>casia_webface</td><td>CASIA Webface</td><td>Learning Face Representation from Scratch</td><td>Learning Face Representation from Scratch</td><td><a href="http://pdfs.semanticscholar.org/b8a2/0ed7e74325da76d7183d1ab77b082a92b447.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning face representation from scratch&sort=relevance" target="_blank">[s2]</a></td><td></td><td>853bd61bc48a431b9b1c7cab10c603830c488e39</td></tr><tr><td>used</td><td>USED Social Event Dataset</td><td>USED: A Large-scale Social Event Detection Dataset</td><td>USED: a large-scale social event detection dataset</td><td><a href="http://doi.acm.org/10.1145/2910017.2910624" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=used: a large-scale social event detection dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8627f019882b024aef92e4eb9355c499c733e5b7</td></tr><tr><td>tiny_faces</td><td>TinyFace</td><td>Low-Resolution Face Recognition</td><td>Low-Resolution Face Recognition</td><td><a href="https://arxiv.org/pdf/1811.08965.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=low-resolution face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8990cdce3f917dad622e43e033db686b354d057c</td></tr><tr><td>mafl</td><td>MAFL</td><td>Facial Landmark Detection by Deep Multi-task Learning</td><td>Facial Landmark Detection by Deep Multi-task Learning</td><td><a href="http://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facial landmark detection by deep multi-task learning&sort=relevance" target="_blank">[s2]</a></td><td>Chinese University of Hong Kong</td><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td></tr><tr><td>mtfl</td><td>MTFL</td><td>Facial Landmark Detection by Deep Multi-task Learning</td><td>Facial Landmark Detection by Deep Multi-task Learning</td><td><a href="http://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facial landmark detection by deep multi-task learning&sort=relevance" target="_blank">[s2]</a></td><td>Chinese University of Hong Kong</td><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td></tr><tr><td>10k_US_adult_faces</td><td>10K US Adult Faces</td><td>The intrinsic memorability of face images</td><td>The intrinsic memorability of face photographs.</td><td><a href="https://pdfs.semanticscholar.org/8b2d/d5c61b23ead5ae5508bb8ce808b5ea266730.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the intrinsic memorability of face images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8b2dd5c61b23ead5ae5508bb8ce808b5ea266730</td></tr><tr><td>fei</td><td>FEI</td><td>Captura e Alinhamento de Imagens: Um Banco de Faces Brasileiro</td><td>A new ranking method for principal components analysis and its application to face image analysis</td><td><a href="https://doi.org/10.1016/j.imavis.2009.11.005" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=captura e alinhamento de imagens: um banco de faces brasileiro&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8b56e33f33e582f3e473dba573a16b598ed9bcdc</td></tr><tr><td>chalearn</td><td>ChaLearn</td><td>ChaLearn Looking at People: A Review of Events and Resources</td><td>ChaLearn looking at people: A review of events and resources</td><td><a href="https://arxiv.org/pdf/1701.02664.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=chalearn looking at people: a review of events and resources&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8d5998cd984e7cce307da7d46f155f9db99c6590</td></tr><tr><td>hda_plus</td><td>HDA+</td><td>The HDA+ data set for research on fully automated re-identification systems</td><td>The HDA+ Data Set for Research on Fully Automated Re-identification Systems</td><td><a href="http://pdfs.semanticscholar.org/8f02/ec0be21461fbcedf51d864f944cfc42c875f.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the hda+ data set for research on fully automated re-identification systems&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8f02ec0be21461fbcedf51d864f944cfc42c875f</td></tr><tr><td>morph</td><td>MORPH Commercial</td><td>MORPH: A Longitudinal Image Database of Normal Adult Age-Progression</td><td>MORPH: a longitudinal image database of normal adult age-progression</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/FGR.2006.78" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=morph: a longitudinal image database of normal adult age-progression&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td></tr><tr><td>morph_nc</td><td>MORPH Non-Commercial</td><td>MORPH: A Longitudinal Image Database of Normal Adult Age-Progression</td><td>MORPH: a longitudinal image database of normal adult age-progression</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/FGR.2006.78" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=morph: a longitudinal image database of normal adult age-progression&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td></tr><tr><td>v47</td><td>V47</td><td>Re-identification of Pedestrians with Variable Occlusion and Scale</td><td>Re-identification of pedestrians with variable occlusion and scale</td><td><a href="https://doi.org/10.1109/ICCVW.2011.6130477" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=re-identification of pedestrians with variable occlusion and scale&sort=relevance" target="_blank">[s2]</a></td><td></td><td>922e0a51a3b8c67c4c6ac09a577ff674cbd28b34</td></tr><tr><td>towncenter</td><td>TownCenter</td><td>Stable Multi-Target Tracking in Real-Time Surveillance Video</td><td>Stable multi-target tracking in real-time surveillance video</td><td><a href="http://pdfs.semanticscholar.org/9361/b784e73e9238d5cefbea5ac40d35d1e3103f.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=stable multi-target tracking in real-time surveillance video&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9361b784e73e9238d5cefbea5ac40d35d1e3103f</td></tr><tr><td>helen</td><td>Helen</td><td>Interactive Facial Feature Localization</td><td>Interactive Facial Feature Localization</td><td><a href="http://pdfs.semanticscholar.org/95f1/2d27c3b4914e0668a268360948bce92f7db3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=interactive facial feature localization&sort=relevance" target="_blank">[s2]</a></td><td>University of Illinois, Urbana-Champaign</td><td>95f12d27c3b4914e0668a268360948bce92f7db3</td></tr><tr><td>4dfab</td><td>4DFAB</td><td>4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications</td><td>4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications</td><td><a href="https://arxiv.org/pdf/1712.01443.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=4dfab: a large scale 4d facial expression database for biometric applications&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9696ad8b164f5e10fcfe23aacf74bd6168aebb15</td></tr><tr><td>megaface</td><td>MegaFace</td><td>The MegaFace Benchmark: 1 Million Faces for Recognition at Scale</td><td>The MegaFace Benchmark: 1 Million Faces for Recognition at Scale</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.527" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the megaface benchmark: 1 million faces for recognition at scale&sort=relevance" target="_blank">[s2]</a></td><td>University of Washington</td><td>96e0cfcd81cdeb8282e29ef9ec9962b125f379b0</td></tr><tr><td>ilids_vid_reid</td><td>iLIDS-VID</td><td>Person Re-Identi cation by Video Ranking</td><td>Person Re-identification by Video Ranking</td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person re-identi cation by video ranking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>98bb029afe2a1239c3fdab517323066f0957b81b</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>Person reidentification by video ranking</td><td>Person Re-identification by Video Ranking</td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person reidentification by video ranking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>98bb029afe2a1239c3fdab517323066f0957b81b</td></tr><tr><td>m2vts</td><td>m2vts</td><td>The M2VTS Multimodal Face Database (Release 1.00)</td><td>The M2VTS Multimodal Face Database (Release 1.00)</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the m2vts multimodal face database (release 1.00)&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9a9877791945c6fa4c1743ec6d3fb32570ef8481</td></tr><tr><td>who_goes_there</td><td>WGT</td><td>Who Goes There? Approaches to Mapping Facial Appearance Diversity</td><td>Who goes there?: approaches to mapping facial appearance diversity</td><td><a href="http://doi.acm.org/10.1145/2996913.2996997" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=who goes there? approaches to mapping facial appearance diversity&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9b9bf5e623cb8af7407d2d2d857bc3f1b531c182</td></tr><tr><td>facebook_100</td><td>Facebook100</td><td>Scaling Up Biologically-Inspired Computer Vision: A Case Study in Unconstrained Face Recognition on Facebook</td><td>Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981788" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scaling up biologically-inspired computer vision: a case study in unconstrained face recognition on facebook&sort=relevance" target="_blank">[s2]</a></td><td>Harvard University</td><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td></tr><tr><td>pubfig_83</td><td>pubfig83</td><td>Scaling Up Biologically-Inspired Computer Vision: A Case Study in Unconstrained Face Recognition on Facebook</td><td>Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981788" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scaling up biologically-inspired computer vision: a case study in unconstrained face recognition on facebook&sort=relevance" target="_blank">[s2]</a></td><td>Harvard University</td><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td></tr><tr><td>precarious</td><td>Precarious</td><td>Expecting the Unexpected: Training Detectors for Unusual Pedestrians With Adversarial Imposters</td><td>Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters</td><td><a href="https://arxiv.org/pdf/1703.06283.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=expecting the unexpected: training detectors for unusual pedestrians with adversarial imposters&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9e5378e7b336c89735d3bb15cf67eff96f86d39a</td></tr><tr><td>mafl</td><td>MAFL</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td><a href="https://arxiv.org/pdf/1408.3967.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning deep representation for face alignment with auxiliary attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td></tr><tr><td>mtfl</td><td>MTFL</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td><a href="https://arxiv.org/pdf/1408.3967.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning deep representation for face alignment with auxiliary attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td></tr><tr><td>moments_in_time</td><td>Moments in Time</td><td>Moments in Time Dataset: one million videos for event understanding</td><td>Moments in Time Dataset: one million videos for event understanding</td><td><a href="https://arxiv.org/pdf/1801.03150.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=moments in time dataset: one million videos for event understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a5a44a32a91474f00a3cda671a802e87c899fbb4</td></tr><tr><td>aflw</td><td>AFLW</td><td>Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization</td><td>Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=annotated facial landmarks in the wild: a large-scale, real-world database for facial landmark localization&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td></tr><tr><td>market1203</td><td>Market 1203</td><td>Orientation Driven Bag of Appearances for Person Re-identification</td><td>Orientation Driven Bag of Appearances for Person Re-identification</td><td><a href="http://pdfs.semanticscholar.org/a7fe/834a0af614ce6b50dc093132b031dd9a856b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=orientation driven bag of appearances for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td></tr><tr><td>pku_reid</td><td>PKU-Reid</td><td>Orientation driven bag of appearances for person re-identification</td><td>Orientation Driven Bag of Appearances for Person Re-identification</td><td><a href="http://pdfs.semanticscholar.org/a7fe/834a0af614ce6b50dc093132b031dd9a856b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=orientation driven bag of appearances for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td></tr><tr><td>hi4d_adsip</td><td>Hi4D-ADSIP</td><td>Hi4D-ADSIP 3-D dynamic facial articulation database</td><td>Hi4D-ADSIP 3-D dynamic facial articulation database</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=hi4d-adsip 3-d dynamic facial articulation database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a8d0b149c2eadaa02204d3e4356fbc8eccf3b315</td></tr><tr><td>yawdd</td><td>YawDD</td><td>YawDD: A Yawning Detection Dataset</td><td>YawDD: a yawning detection dataset</td><td><a href="http://www.site.uottawa.ca/~shervin/pubs/CogniVue-Dataset-ACM-MMSys2014.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=yawdd: a yawning detection dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a94cae786d515d3450d48267e12ca954aab791c4</td></tr><tr><td>mrp_drone</td><td>MRP Drone</td><td>Investigating Open-World Person Re-identification Using a Drone</td><td>Investigating Open-World Person Re-identification Using a Drone</td><td><a href="http://pdfs.semanticscholar.org/ad01/687649d95cd5b56d7399a9603c4b8e2217d7.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=investigating open-world person re-identification using a drone&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ad01687649d95cd5b56d7399a9603c4b8e2217d7</td></tr><tr><td>put_face</td><td>Put Face</td><td>The PUT face database</td><td>The put face database</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the put face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ae0aee03d946efffdc7af2362a42d3750e7dd48a</td></tr><tr><td>afew_va</td><td>AFEW-VA</td><td>Collecting Large, Richly Annotated Facial-Expression Databases from Movies</td><td>Collecting Large, Richly Annotated Facial-Expression Databases from Movies</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6200254" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=collecting large, richly annotated facial-expression databases from movies&sort=relevance" target="_blank">[s2]</a></td><td>Australian National University</td><td>b1f4423c227fa37b9680787be38857069247a307</td></tr><tr><td>ucf_101</td><td>UCF101</td><td>UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild</td><td>UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild</td><td><a href="https://arxiv.org/pdf/1212.0402.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ucf101: a dataset of 101 human actions classes from videos in the wild&sort=relevance" target="_blank">[s2]</a></td><td>University of Central Florida</td><td>b5f2846a506fc417e7da43f6a7679146d99c5e96</td></tr><tr><td>m2vtsdb_extended</td><td>xm2vtsdb</td><td>XM2VTSDB: The Extended M2VTS Database</td><td>Xm2vtsdb: the Extended M2vts Database</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=xm2vtsdb: the extended m2vts database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>b62628ac06bbac998a3ab825324a41a11bc3a988</td></tr><tr><td>ifdb</td><td>IFDB</td><td>Iranian Face Database with age, pose and expression</td><td>Iranian Face Database with age, pose and expression</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iranian face database with age, pose and expression&sort=relevance" target="_blank">[s2]</a></td><td></td><td>b71d1aa90dcbe3638888725314c0d56640c1fef1</td></tr><tr><td>bp4d_spontanous</td><td>BP4D-Spontanous</td><td>A high resolution spontaneous 3D dynamic facial expression database</td><td>A high-resolution spontaneous 3D dynamic facial expression database</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6553788" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a high resolution spontaneous 3d dynamic facial expression database&sort=relevance" target="_blank">[s2]</a></td><td>SUNY Binghamton</td><td>b91f54e1581fbbf60392364323d00a0cd43e493c</td></tr><tr><td>pornodb</td><td>Pornography DB</td><td>Pooling in Image Representation: the Visual Codeword Point of View</td><td>Pooling in image representation: The visual codeword point of view</td><td><a href="http://pdfs.semanticscholar.org/b92a/1ed9622b8268ae3ac9090e25789fc41cc9b8.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pooling in image representation: the visual codeword point of view&sort=relevance" target="_blank">[s2]</a></td><td></td><td>b92a1ed9622b8268ae3ac9090e25789fc41cc9b8</td></tr><tr><td>scut_fbp</td><td>SCUT-FBP</td><td>SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception</td><td>SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception</td><td><a href="https://arxiv.org/pdf/1511.02459.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scut-fbp: a benchmark dataset for facial beauty perception&sort=relevance" target="_blank">[s2]</a></td><td>South China University of Technology</td><td>bd26dabab576adb6af30484183c9c9c8379bf2e0</td></tr><tr><td>hda_plus</td><td>HDA+</td><td>A Multi-camera video data set for research on High-Definition surveillance</td><td>HDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance</td><td><a href="http://pdfs.semanticscholar.org/bd88/bb2e4f351352d88ee7375af834360e223498.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a multi-camera video data set for research on high-definition surveillance&sort=relevance" target="_blank">[s2]</a></td><td></td><td>bd88bb2e4f351352d88ee7375af834360e223498</td></tr><tr><td>mars</td><td>MARS</td><td>MARS: A Video Benchmark for Large-Scale Person Re-identification</td><td>MARS: A Video Benchmark for Large-Scale Person Re-Identification</td><td><a href="http://pdfs.semanticscholar.org/c038/7e788a52f10bf35d4d50659cfa515d89fbec.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=mars: a video benchmark for large-scale person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c0387e788a52f10bf35d4d50659cfa515d89fbec</td></tr><tr><td>nova_emotions</td><td>Novaemötions Dataset</td><td>Crowdsourcing facial expressions for affective-interaction</td><td>Crowdsourcing facial expressions for affective-interaction</td><td><a href="https://doi.org/10.1016/j.cviu.2016.02.001" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=crowdsourcing facial expressions for affective-interaction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c06b13d0ec3f5c43e2782cd22542588e233733c3</td></tr><tr><td>mcgill</td><td>McGill Real World</td><td>Hierarchical Temporal Graphical Model for Head Pose Estimation and Subsequent Attribute Classification in Real-World Videos</td><td>Hierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=hierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a5a3bc3e5e9753769163cb30b16dbd12e266b93e</td></tr><tr><td>mcgill</td><td>McGill Real World</td><td>Robust Semi-automatic Head Pose Labeling for Real-World Face Video Sequences</td><td>Robust semi-automatic head pose labeling for real-world face video sequences</td><td><a href="https://doi.org/10.1007/s11042-012-1352-1" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=robust semi-automatic head pose labeling for real-world face video sequences&sort=relevance" target="_blank">[s2]</a></td><td>McGill University</td><td>c570d1247e337f91e555c3be0e8c8a5aba539d9f</td></tr><tr><td>qmul_surv_face</td><td>QMUL-SurvFace</td><td>Surveillance Face Recognition Challenge</td><td>Surveillance Face Recognition Challenge</td><td><a href="https://arxiv.org/pdf/1804.09691.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=surveillance face recognition challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c866a2afc871910e3282fd9498dce4ab20f6a332</td></tr><tr><td>emotio_net</td><td>EmotioNet Database</td><td>EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild</td><td>EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780969" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=emotionet: an accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c900e0ad4c95948baaf0acd8449fde26f9b4952a</td></tr><tr><td>imfdb</td><td>IMFDB</td><td>Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations</td><td>Indian Movie Face Database: A benchmark for face recognition under wide variations</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=indian movie face database: a benchmark for face recognition under wide variations&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ca3e88d87e1344d076c964ea89d91a75c417f5ee</td></tr><tr><td>bu_3dfe</td><td>BU-3DFE</td><td>A 3D Facial Expression Database For Facial Behavior Research</td><td>A 3D facial expression database for facial behavior research</td><td><a href="http://www.cs.binghamton.edu/~lijun/Research/3DFE/Yin_FGR06_a.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3d facial expression database for facial behavior research&sort=relevance" target="_blank">[s2]</a></td><td>SUNY Binghamton</td><td>cc589c499dcf323fe4a143bbef0074c3e31f9b60</td></tr><tr><td>b3d_ac</td><td>B3D(AC)</td><td>A 3-D Audio-Visual Corpus of Affective Communication</td><td>A 3-D Audio-Visual Corpus of Affective Communication</td><td><a href="http://files.is.tue.mpg.de/jgall/download/jgall_avcorpus_mm10.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3-d audio-visual corpus of affective communication&sort=relevance" target="_blank">[s2]</a></td><td></td><td>d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae</td></tr><tr><td>jiku_mobile</td><td>Jiku Mobile Video Dataset</td><td>The Jiku Mobile Video Dataset</td><td>The jiku mobile video dataset</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the jiku mobile video dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>d178cde92ab3dc0dd2ebee5a76a33d556c39448b</td></tr><tr><td>stair_actions</td><td>STAIR Action</td><td>STAIR Actions: A Video Dataset of Everyday Home Actions</td><td>STAIR Actions: A Video Dataset of Everyday Home Actions</td><td><a href="https://arxiv.org/pdf/1804.04326.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=stair actions: a video dataset of everyday home actions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9</td></tr><tr><td>megaage</td><td>MegaAge</td><td>Quantifying Facial Age by Posterior of Age Comparisons</td><td>Quantifying Facial Age by Posterior of Age Comparisons</td><td><a href="https://arxiv.org/pdf/1708.09687.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=quantifying facial age by posterior of age comparisons&sort=relevance" target="_blank">[s2]</a></td><td>Chinese University of Hong Kong</td><td>d80a3d1f3a438e02a6685e66ee908446766fefa9</td></tr><tr><td>feret</td><td>FERET</td><td>The FERET database and evaluation procedure for face-recognition algorithms</td><td>The FERET database and evaluation procedure for face-recognition algorithms</td><td><a href="http://pdfs.semanticscholar.org/dc8b/25e35a3acb812beb499844734081722319b4.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the feret database and evaluation procedure for face-recognition algorithms&sort=relevance" target="_blank">[s2]</a></td><td></td><td>dc8b25e35a3acb812beb499844734081722319b4</td></tr><tr><td>families_in_the_wild</td><td>FIW</td><td>Visual Kinship Recognition of Families in the Wild</td><td>Visual Kinship Recognition of Families in the Wild</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8337841" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=visual kinship recognition of families in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>dd65f71dac86e36eecbd3ed225d016c3336b4a13</td></tr><tr><td>scut_head</td><td>SCUT HEAD</td><td>Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture</td><td>Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture</td><td><a href="https://arxiv.org/pdf/1803.09256.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=detecting heads using feature refine net and cascaded multi-scale architecture&sort=relevance" target="_blank">[s2]</a></td><td></td><td>d3200d49a19a4a4e4e9745ee39649b65d80c834b</td></tr><tr><td>sarc3d</td><td>Sarc3D</td><td>SARC3D: a new 3D body model for People Tracking and Re-identification</td><td>SARC3D: A New 3D Body Model for People Tracking and Re-identification</td><td><a href="https://pdfs.semanticscholar.org/e27e/f52c641c2b5100a1b34fd0b819e84a31b4df.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=sarc3d: a new 3d body model for people tracking and re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>e27ef52c641c2b5100a1b34fd0b819e84a31b4df</td></tr><tr><td>fiw_300</td><td>300-W</td><td>300 faces In-the-wild challenge: Database and results</td><td>300 Faces In-The-Wild Challenge: database and results</td><td><a href="http://doi.org/10.1016/j.imavis.2016.01.002" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=300 faces in-the-wild challenge: database and results&sort=relevance" target="_blank">[s2]</a></td><td></td><td>e4754afaa15b1b53e70743880484b8d0736990ff</td></tr><tr><td>gfw</td><td>YouTube Pose</td><td>Merge or Not? Learning to Group Faces via Imitation Learning</td><td>Merge or Not? Learning to Group Faces via Imitation Learning</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=merge or not? learning to group faces via imitation learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>e58dd160a76349d46f881bd6ddbc2921f08d1050</td></tr><tr><td>visual_phrases</td><td>Phrasal Recognition</td><td>Recognition using Visual Phrases</td><td>Recognition using visual phrases</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995711" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=recognition using visual phrases&sort=relevance" target="_blank">[s2]</a></td><td></td><td>e8de844fefd54541b71c9823416daa238be65546</td></tr><tr><td>mpi_large</td><td>Large MPI Facial Expression</td><td>The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</td><td>The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</td><td><a href="http://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mpi facial expression database — a validated database of emotional and conversational facial expressions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td></tr><tr><td>mpi_small</td><td>Small MPI Facial Expression</td><td>The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</td><td>The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</td><td><a href="http://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mpi facial expression database — a validated database of emotional and conversational facial expressions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td></tr><tr><td>vgg_faces2</td><td>VGG Face2</td><td>VGGFace2: A dataset for recognising faces across pose and age</td><td>VGGFace2: A Dataset for Recognising Faces across Pose and Age</td><td><a href="https://arxiv.org/pdf/1710.08092.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vggface2: a dataset for recognising faces across pose and age&sort=relevance" target="_blank">[s2]</a></td><td>University of Oxford</td><td>eb027969f9310e0ae941e2adee2d42cdf07d938c</td></tr><tr><td>msmt_17</td><td>MSMT17</td><td>Person Transfer GAN to Bridge Domain Gap for Person Re-Identification</td><td>Person Transfer GAN to Bridge Domain Gap for Person Re-Identification</td><td><a href="https://arxiv.org/pdf/1711.08565.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person transfer gan to bridge domain gap for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ec792ad2433b6579f2566c932ee414111e194537</td></tr><tr><td>europersons</td><td>EuroCity Persons</td><td>The EuroCity Persons Dataset: A Novel Benchmark for Object Detection</td><td>The EuroCity Persons Dataset: A Novel Benchmark for Object Detection</td><td><a href="https://arxiv.org/pdf/1805.07193.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the eurocity persons dataset: a novel benchmark for object detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4</td></tr><tr><td>mug_faces</td><td>MUG Faces</td><td>The MUG Facial Expression Database</td><td>The MUG facial expression database</td><td><a href="http://ieeexplore.ieee.org/document/5617662/" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mug facial expression database&sort=relevance" target="_blank">[s2]</a></td><td>Aristotle University of Thessaloniki</td><td>f1af714b92372c8e606485a3982eab2f16772ad8</td></tr><tr><td>pku</td><td>PKU</td><td>Swiss-System Based Cascade Ranking for Gait-based Person Re-identification</td><td>Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification</td><td><a href="http://pdfs.semanticscholar.org/f6c8/d5e35d7e4d60a0104f233ac1a3ab757da53f.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=swiss-system based cascade ranking for gait-based person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f</td></tr><tr><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td>Pedestrian Detection: An Evaluation of the State of the Art</td><td>Pedestrian Detection: An Evaluation of the State of the Art</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5975165" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian detection: an evaluation of the state of the art&sort=relevance" target="_blank">[s2]</a></td><td></td><td>f72f6a45ee240cc99296a287ff725aaa7e7ebb35</td></tr><tr><td>iit_dehli_ear</td><td>IIT Dehli Ear</td><td>Automated human identification using ear imaging</td><td>Automated Human Identification Using Ear Imaging</td><td><a href="https://pdfs.semanticscholar.org/faf4/0ce28857aedf183e193486f5b4b0a8c478a2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automated human identification using ear imaging&sort=relevance" target="_blank">[s2]</a></td><td></td><td>faf40ce28857aedf183e193486f5b4b0a8c478a2</td></tr><tr><td>miw</td><td>MIW</td><td>Automatic Facial Makeup Detection with Application in Face Recognition</td><td>Automatic facial makeup detection with application in face recognition</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6612994" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automatic facial makeup detection with application in face recognition&sort=relevance" target="_blank">[s2]</a></td><td>West Virginia University</td><td>fcc6fe6007c322641796cb8792718641856a22a7</td></tr><tr><td>youtube_makeup</td><td>YMU</td><td>Automatic Facial Makeup Detection with Application in Face Recognition</td><td>Automatic facial makeup detection with application in face recognition</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6612994" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automatic facial makeup detection with application in face recognition&sort=relevance" target="_blank">[s2]</a></td><td>West Virginia University</td><td>fcc6fe6007c322641796cb8792718641856a22a7</td></tr><tr><td>nd_2006</td><td>ND-2006</td><td>Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition</td><td>Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4401928" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=using a multi-instance enrollment representation to improve 3d face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>fd8168f1c50de85bac58a8d328df0a50248b16ae</td></tr></table></body></html>
\ No newline at end of file diff --git a/scraper/reports/paper_title_report_no_location.html b/scraper/reports/paper_title_report_no_location.html index 33de04e4..66e93e87 100644 --- a/scraper/reports/paper_title_report_no_location.html +++ b/scraper/reports/paper_title_report_no_location.html @@ -1,9 +1,3 @@ -<!doctype html><html><head><meta charset='utf-8'><title>Papers with no location</title><link rel='stylesheet' href='reports.css'></head><body><h2>Papers with no location</h2><table border='1' cellpadding='3' cellspacing='3'><th>key</th><th>name</th><th>our title</th><th>found title</th><th></th><th></th><th>address</th><th>s2 id</th><tr><td>10k_US_adult_faces</td><td>10K US Adult Faces</td><td>The intrinsic memorability of face images</td><td>The intrinsic memorability of face photographs.</td><td><a href="https://pdfs.semanticscholar.org/8b2d/d5c61b23ead5ae5508bb8ce808b5ea266730.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the intrinsic memorability of face images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8b2dd5c61b23ead5ae5508bb8ce808b5ea266730</td></tr><tr><td>3d_rma</td><td>3D-RMA</td><td>Automatic 3D Face Authentication</td><td>Automatic 3D face authentication</td><td><a href="http://pdfs.semanticscholar.org/2160/788824c4c29ffe213b2cbeb3f52972d73f37.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automatic 3d face authentication&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2160788824c4c29ffe213b2cbeb3f52972d73f37</td></tr><tr><td>3dddb_unconstrained</td><td>3D Dynamic</td><td>A 3D Dynamic Database for Unconstrained Face Recognition</td><td>A 3 D Dynamic Database for Unconstrained Face Recognition</td><td><a href="https://pdfs.semanticscholar.org/4d4b/b462c9f1d4e4ab1e4aa6a75cc0bc71b38461.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3d dynamic database for unconstrained face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461</td></tr><tr><td>3dpes</td><td>3DPeS</td><td>3DPes: 3D People Dataset for Surveillance and Forensics</td><td>3DPeS: 3D people dataset for surveillance and forensics</td><td><a href="http://doi.acm.org/10.1145/2072572.2072590" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=3dpes: 3d people dataset for surveillance and forensics&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e</td></tr><tr><td>4dfab</td><td>4DFAB</td><td>4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications</td><td>4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications</td><td><a href="https://arxiv.org/pdf/1712.01443.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=4dfab: a large scale 4d facial expression database for biometric applications&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9696ad8b164f5e10fcfe23aacf74bd6168aebb15</td></tr><tr><td>50_people_one_question</td><td>50 People One Question</td><td>Merging Pose Estimates Across Space and Time</td><td>Merging Pose Estimates Across Space and Time</td><td><a href="http://pdfs.semanticscholar.org/63b2/f5348af0f969dfc2afb4977732393c6459ec.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=merging pose estimates across space and time&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725</td></tr><tr><td>a_pascal_yahoo</td><td>aPascal</td><td>Describing Objects by their Attributes</td><td>Describing objects by their attributes</td><td><a href="http://www-2.cs.cmu.edu/~dhoiem/publications/cvpr2009_attributes.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=describing objects by their attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2e384f057211426ac5922f1b33d2aa8df5d51f57</td></tr><tr><td>adience</td><td>Adience</td><td>Age and Gender Estimation of Unfiltered Faces</td><td>Age and Gender Estimation of Unfiltered Faces</td><td><a href="http://www.openu.ac.il/home/hassner/Adience/EidingerEnbarHassner_tifs.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=age and gender estimation of unfiltered faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1be498d4bbc30c3bfd0029114c784bc2114d67c0</td></tr><tr><td>afad</td><td>AFAD</td><td>Ordinal Regression with a Multiple Output CNN for Age Estimation</td><td>Ordinal Regression with Multiple Output CNN for Age Estimation</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.532" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ordinal regression with a multiple output cnn for age estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c</td></tr><tr><td>afew_va</td><td>AFEW-VA</td><td>AFEW-VA database for valence and arousal estimation in-the-wild</td><td>AFEW-VA database for valence and arousal estimation in-the-wild</td><td><a href="http://pdfs.semanticscholar.org/2624/d84503bc2f8e190e061c5480b6aa4d89277a.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=afew-va database for valence and arousal estimation in-the-wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2624d84503bc2f8e190e061c5480b6aa4d89277a</td></tr><tr><td>affectnet</td><td>AffectNet</td><td>AffectNet: A New Database for Facial Expression, Valence, and Arousal Computation in the Wild</td><td>Skybiometry and AffectNet on Facial Emotion Recognition Using Supervised Machine Learning Algorithms</td><td><a href="http://dl.acm.org/citation.cfm?id=3232665" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=affectnet: a new database for facial expression, valence, and arousal computation in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>f152b6ee251cca940dd853c54e6a7b78fbc6b235</td></tr><tr><td>aflw</td><td>AFLW</td><td>Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization</td><td>Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=annotated facial landmarks in the wild: a large-scale, real-world database for facial landmark localization&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td></tr><tr><td>agedb</td><td>AgeDB</td><td>AgeDB: the first manually collected, in-the-wild age database</td><td>AgeDB: The First Manually Collected, In-the-Wild Age Database</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8014984" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=agedb: the first manually collected, in-the-wild age database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dcf418c778f528b5792104760f1fbfe90c6dd6a</td></tr><tr><td>alert_airport</td><td>ALERT Airport</td><td>A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets</td><td>A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.</td><td><a href="https://arxiv.org/pdf/1605.09653.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a systematic evaluation and benchmark for person re-identification: features, metrics, and datasets&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6403117f9c005ae81f1e8e6d1302f4a045e3d99d</td></tr><tr><td>am_fed</td><td>AM-FED</td><td>Affectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”</td><td>Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"</td><td><a href="http://pdfs.semanticscholar.org/5d06/437656dd94616d7d87260d5eb77513ded30f.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=affectiva mit facial expression dataset (am-fed): naturalistic and spontaneous facial expressions collected “in the wild”&sort=relevance" target="_blank">[s2]</a></td><td></td><td>47aeb3b82f54b5ae8142b4bdda7b614433e69b9a</td></tr><tr><td>apis</td><td>APiS1.0</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W10/papers/Zhu_Pedestrian_Attribute_Classification_2013_ICCV_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian attribute classification in surveillance: database and evaluation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td></tr><tr><td>ar_facedb</td><td>AR Face</td><td>The AR Face Database</td><td>The AR face database</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the ar face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6d96f946aaabc734af7fe3fc4454cf8547fcd5ed</td></tr><tr><td>awe_ears</td><td>AWE Ears</td><td>Ear Recognition: More Than a Survey</td><td>Ear Recognition: More Than a Survey</td><td><a href="http://pdfs.semanticscholar.org/84fe/5b4ac805af63206012d29523a1e033bc827e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ear recognition: more than a survey&sort=relevance" target="_blank">[s2]</a></td><td></td><td>84fe5b4ac805af63206012d29523a1e033bc827e</td></tr><tr><td>b3d_ac</td><td>B3D(AC)</td><td>A 3-D Audio-Visual Corpus of Affective Communication</td><td>A 3-D Audio-Visual Corpus of Affective Communication</td><td><a href="http://files.is.tue.mpg.de/jgall/download/jgall_avcorpus_mm10.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3-d audio-visual corpus of affective communication&sort=relevance" target="_blank">[s2]</a></td><td></td><td>d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae</td></tr><tr><td>bbc_pose</td><td>BBC Pose</td><td>Automatic and Efficient Human Pose Estimation for Sign Language Videos</td><td>Automatic and Efficient Human Pose Estimation for Sign Language Videos</td><td><a href="https://doi.org/10.1007/s11263-013-0672-6" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automatic and efficient human pose estimation for sign language videos&sort=relevance" target="_blank">[s2]</a></td><td></td><td>213a579af9e4f57f071b884aa872651372b661fd</td></tr><tr><td>berkeley_pose</td><td>BPAD</td><td>Describing People: A Poselet-Based Approach to Attribute Classification</td><td>Describing people: A poselet-based approach to attribute classification</td><td><a href="http://ttic.uchicago.edu/~smaji/papers/attributes-iccv11.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=describing people: a poselet-based approach to attribute classification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7808937b46acad36e43c30ae4e9f3fd57462853d</td></tr><tr><td>bfm</td><td>BFM</td><td>A 3D Face Model for Pose and Illumination Invariant Face Recognition</td><td>A 3D Face Model for Pose and Illumination Invariant Face Recognition</td><td><a href="https://pdfs.semanticscholar.org/6399/37b3a1b8bded3f7e9a40e85bd3770016cf3c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3d face model for pose and illumination invariant face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>639937b3a1b8bded3f7e9a40e85bd3770016cf3c</td></tr><tr><td>bio_id</td><td>BioID Face</td><td>Robust Face Detection Using the Hausdorff Distance</td><td>Robust Face Detection Using the Hausdorff Distance</td><td><a href="http://pdfs.semanticscholar.org/4053/e3423fb70ad9140ca89351df49675197196a.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=robust face detection using the hausdorff distance&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4053e3423fb70ad9140ca89351df49675197196a</td></tr><tr><td>bjut_3d</td><td>BJUT-3D</td><td>The BJUT-3D Large-Scale Chinese Face Database</td><td>A novel face recognition method based on 3D face model</td><td><a href="https://doi.org/10.1109/ROBIO.2007.4522202" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the bjut-3d large-scale chinese face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1ed1a49534ad8dd00f81939449f6389cfbc25321</td></tr><tr><td>bosphorus</td><td>The Bosphorus</td><td>Bosphorus Database for 3D Face Analysis</td><td>Bosphorus Database for 3D Face Analysis</td><td><a href="http://pdfs.semanticscholar.org/4254/fbba3846008f50671edc9cf70b99d7304543.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=bosphorus database for 3d face analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2acf7e58f0a526b957be2099c10aab693f795973</td></tr><tr><td>bp4d_plus</td><td>BP4D+</td><td>Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</td><td>Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhang_Multimodal_Spontaneous_Emotion_CVPR_2016_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multimodal spontaneous emotion corpus for human behavior analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4</td></tr><tr><td>brainwash</td><td>Brainwash</td><td>Brainwash dataset</td><td>Brainwash: A Data System for Feature Engineering</td><td><a href="http://pdfs.semanticscholar.org/ae44/8015b2ff2bd3b8a5c9a3266f954f5af9ffa9.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=brainwash dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>214c966d1f9c2a4b66f4535d9a0d4078e63a5867</td></tr><tr><td>buhmap_db</td><td>BUHMAP-DB</td><td>Facial Feature Tracking and Expression Recognition for Sign Language</td><td>Facial feature tracking and expression recognition for sign language</td><td><a href="http://www.cmpe.boun.edu.tr/pilab/pilabfiles/databases/buhmap/files/ari2008facialfeaturetracking.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facial feature tracking and expression recognition for sign language&sort=relevance" target="_blank">[s2]</a></td><td></td><td>014b8df0180f33b9fea98f34ae611c6447d761d2</td></tr><tr><td>cafe</td><td>CAFE</td><td>The Child Affective Facial Expression (CAFE) Set: Validity and reliability from untrained adults</td><td>The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults</td><td><a href="http://pdfs.semanticscholar.org/2038/8099cc415c772926e47bcbbe554e133343d1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the child affective facial expression (cafe) set: validity and reliability from untrained adults&sort=relevance" target="_blank">[s2]</a></td><td></td><td>20388099cc415c772926e47bcbbe554e133343d1</td></tr><tr><td>caltech_10k_web_faces</td><td>Caltech 10K Web Faces</td><td>Pruning Training Sets for Learning of Object Categories</td><td>Pruning training sets for learning of object categories</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1467308" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pruning training sets for learning of object categories&sort=relevance" target="_blank">[s2]</a></td><td></td><td>636b8ffc09b1b23ff714ac8350bb35635e49fa3c</td></tr><tr><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td>Pedestrian Detection: A Benchmark</td><td>Pedestrian detection: A benchmark</td><td><a href="http://vision.ucsd.edu/~pdollar/files/papers/DollarCVPR09peds.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian detection: a benchmark&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1dc35905a1deff8bc74688f2d7e2f48fd2273275</td></tr><tr><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td>Pedestrian Detection: An Evaluation of the State of the Art</td><td>Pedestrian Detection: An Evaluation of the State of the Art</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5975165" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian detection: an evaluation of the state of the art&sort=relevance" target="_blank">[s2]</a></td><td></td><td>f72f6a45ee240cc99296a287ff725aaa7e7ebb35</td></tr><tr><td>camel</td><td>CAMEL</td><td>CAMEL Dataset for Visual and Thermal Infrared Multiple Object Detection and Tracking</td><td>Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis</td><td><a href="http://pdfs.semanticscholar.org/5801/690199c1917fa58c35c3dead177c0b8f9f2d.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=camel dataset for visual and thermal infrared multiple object detection and tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5801690199c1917fa58c35c3dead177c0b8f9f2d</td></tr><tr><td>cas_peal</td><td>CAS-PEAL</td><td>The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</td><td>The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</td><td><a href="https://doi.org/10.1109/TSMCA.2007.909557" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cas-peal large-scale chinese face database and baseline evaluations&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td></tr><tr><td>casablanca</td><td>Casablanca</td><td>Context-aware {CNNs} for person head detection</td><td>Context-Aware CNNs for Person Head Detection</td><td><a href="http://arxiv.org/pdf/1511.07917v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=context-aware {cnns} for person head detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td></tr><tr><td>casia_webface</td><td>CASIA Webface</td><td>Learning Face Representation from Scratch</td><td>Learning Face Representation from Scratch</td><td><a href="http://pdfs.semanticscholar.org/b8a2/0ed7e74325da76d7183d1ab77b082a92b447.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning face representation from scratch&sort=relevance" target="_blank">[s2]</a></td><td></td><td>853bd61bc48a431b9b1c7cab10c603830c488e39</td></tr><tr><td>cfd</td><td>CFD</td><td>The Chicago face database: A free stimulus set of faces and norming data</td><td>The Chicago face database: A free stimulus set of faces and norming data.</td><td><a href="http://pdfs.semanticscholar.org/4df3/143922bcdf7db78eb91e6b5359d6ada004d2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the chicago face database: a free stimulus set of faces and norming data&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4df3143922bcdf7db78eb91e6b5359d6ada004d2</td></tr><tr><td>chalearn</td><td>ChaLearn</td><td>ChaLearn Looking at People: A Review of Events and Resources</td><td>ChaLearn looking at people: A review of events and resources</td><td><a href="https://arxiv.org/pdf/1701.02664.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=chalearn looking at people: a review of events and resources&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8d5998cd984e7cce307da7d46f155f9db99c6590</td></tr><tr><td>cityscapes</td><td>Cityscapes</td><td>The Cityscapes Dataset for Semantic Urban Scene Understanding</td><td>The Cityscapes Dataset for Semantic Urban Scene Understanding</td><td><a href="https://arxiv.org/pdf/1604.01685.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cityscapes dataset for semantic urban scene understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>32cde90437ab5a70cf003ea36f66f2de0e24b3ab</td></tr><tr><td>cityscapes</td><td>Cityscapes</td><td>The Cityscapes Dataset</td><td>The Cityscapes Dataset for Semantic Urban Scene Understanding</td><td><a href="https://arxiv.org/pdf/1604.01685.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cityscapes dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>32cde90437ab5a70cf003ea36f66f2de0e24b3ab</td></tr><tr><td>clothing_co_parsing</td><td>CCP</td><td>Clothing Co-Parsing by Joint Image Segmentation and Labeling</td><td>Clothing Co-parsing by Joint Image Segmentation and Labeling</td><td><a href="https://arxiv.org/pdf/1502.00739v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clothing co-parsing by joint image segmentation and labeling&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2bf8541199728262f78d4dced6fb91479b39b738</td></tr><tr><td>cmu_pie</td><td>CMU PIE</td><td>The CMU Pose, Illumination, and Expression Database</td><td>The CMU Pose, Illumination, and Expression (PIE) Database</td><td><a href="http://pdfs.semanticscholar.org/4d42/3acc78273b75134e2afd1777ba6d3a398973.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cmu pose, illumination, and expression database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d423acc78273b75134e2afd1777ba6d3a398973</td></tr><tr><td>coco</td><td>COCO</td><td>Microsoft COCO: Common Objects in Context</td><td>Microsoft COCO Captions: Data Collection and Evaluation Server</td><td><a href="http://pdfs.semanticscholar.org/ba95/81c33a7eebe87c50e61763e4c8d1723538f2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=microsoft coco: common objects in context&sort=relevance" target="_blank">[s2]</a></td><td></td><td>696ca58d93f6404fea0fc75c62d1d7b378f47628</td></tr><tr><td>coco_action</td><td>COCO-a</td><td>Describing Common Human Visual Actions in Images</td><td>Describing Common Human Visual Actions in Images</td><td><a href="http://pdfs.semanticscholar.org/b38d/cf5fa5174c0d718d65cc4f3889b03c4a21df.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=describing common human visual actions in images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4946ba10a4d5a7d0a38372f23e6622bd347ae273</td></tr><tr><td>coco_qa</td><td>COCO QA</td><td>Exploring Models and Data for Image Question Answering</td><td>Exploring Models and Data for Image Question Answering</td><td><a href="http://pdfs.semanticscholar.org/aa79/9c29c0d44ece1864467af520fe70540c069b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=exploring models and data for image question answering&sort=relevance" target="_blank">[s2]</a></td><td></td><td>35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62</td></tr><tr><td>cofw</td><td>COFW</td><td>Robust face landmark estimation under occlusion</td><td>Robust Face Landmark Estimation under Occlusion</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6751298" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=robust face landmark estimation under occlusion&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2724ba85ec4a66de18da33925e537f3902f21249</td></tr><tr><td>cohn_kanade_plus</td><td>CK+</td><td>The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression</td><td>The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression</td><td><a href="http://www.iainm.com/iainm/Publications_files/2010_The%20Extended.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the 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Learning</td><td><a href="http://pdfs.semanticscholar.org/4448/4d2866f222bbb9b6b0870890f9eea1ffb2d0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=human reidentification with transferred metric learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>44484d2866f222bbb9b6b0870890f9eea1ffb2d0</td></tr><tr><td>cuhk02</td><td>CUHK02</td><td>Locally Aligned Feature Transforms across Views</td><td>Locally Aligned Feature Transforms across Views</td><td><a href="http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/4989d594.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=locally aligned feature transforms across views&sort=relevance" target="_blank">[s2]</a></td><td></td><td>38b55d95189c5e69cf4ab45098a48fba407609b4</td></tr><tr><td>cuhk03</td><td>CUHK03</td><td>DeepReID: Deep Filter Pairing Neural Network for Person Re-identification</td><td>DeepReID: Deep Filter Pairing Neural Network for Person Re-identification</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Li_DeepReID_Deep_Filter_2014_CVPR_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deepreid: deep filter pairing neural network for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3</td></tr><tr><td>cvc_01_barcelona</td><td>CVC-01</td><td>Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection</td><td>Adaptive Image Sampling and Windows Classification for On – board Pedestrian Detection</td><td><a href="http://pdfs.semanticscholar.org/57fe/081950f21ca03b5b375ae3e84b399c015861.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=adaptive image sampling and windows classification for on-board pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>57fe081950f21ca03b5b375ae3e84b399c015861</td></tr><tr><td>czech_news_agency</td><td>UFI</td><td>Unconstrained Facial Images: Database for Face Recognition under Real-world Conditions</td><td>Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions</td><td><a href="http://pdfs.semanticscholar.org/4b41/06614c1d553365bad75d7866bff0de6056ed.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=unconstrained facial images: database for face recognition under real-world conditions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b4106614c1d553365bad75d7866bff0de6056ed</td></tr><tr><td>dartmouth_children</td><td>Dartmouth Children</td><td>The Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus set</td><td>The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set</td><td><a href="http://pdfs.semanticscholar.org/4e6e/e936eb50dd032f7138702fa39b7c18ee8907.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the dartmouth database of children's faces: acquisition and validation of a new face stimulus set&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4e6ee936eb50dd032f7138702fa39b7c18ee8907</td></tr><tr><td>data_61</td><td>Data61 Pedestrian</td><td>A Multi-Modal Graphical Model for Scene Analysis</td><td>A Multi-modal Graphical Model for Scene Analysis</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/WACV.2015.139" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a multi-modal graphical model for scene analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>563c940054e4b456661762c1ab858e6f730c3159</td></tr><tr><td>disfa</td><td>DISFA</td><td>DISFA: A Spontaneous Facial Action Intensity Database</td><td>Extended DISFA Dataset: Investigating Posed and Spontaneous Facial Expressions</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7789672" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=disfa: a spontaneous facial action intensity database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a5acda0e8c0937bfed013e6382da127103e41395</td></tr><tr><td>distance_nighttime</td><td>Long Distance Heterogeneous Face</td><td>Nighttime Face Recognition at Long Distance: Cross-distance and Cross-spectral Matching</td><td>Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching</td><td><a href="http://pdfs.semanticscholar.org/4156/b7e88f2e0ab0a7c095b9bab199ae2b23bd06.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=nighttime face recognition at long distance: cross-distance and cross-spectral matching&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06</td></tr><tr><td>duke_mtmc</td><td>Duke MTMC</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td><a href="http://pdfs.semanticscholar.org/b5f2/4f49f9a5e47d6601399dc068158ad88d7651.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=performance measures and a data set for multi-target, multi-camera tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td></tr><tr><td>emotio_net</td><td>EmotioNet Database</td><td>EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild</td><td>EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780969" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=emotionet: an accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c900e0ad4c95948baaf0acd8449fde26f9b4952a</td></tr><tr><td>eth_andreas_ess</td><td>ETHZ Pedestrian</td><td>Depth and Appearance for Mobile Scene Analysis</td><td>Depth and Appearance for Mobile Scene Analysis</td><td><a href="http://www.mmp.rwth-aachen.de/publications/pdf/ess-depthandappearance-iccv07.pdf/at_download/file" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=depth and appearance for mobile scene analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>13f06b08f371ba8b5d31c3e288b4deb61335b462</td></tr><tr><td>europersons</td><td>EuroCity Persons</td><td>The EuroCity Persons Dataset: A Novel Benchmark for Object Detection</td><td>The EuroCity Persons Dataset: A Novel Benchmark for Object Detection</td><td><a href="https://arxiv.org/pdf/1805.07193.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the eurocity persons dataset: a novel benchmark for object detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4</td></tr><tr><td>expw</td><td>ExpW</td><td>Learning Social Relation Traits from Face Images</td><td>From Facial Expression Recognition to Interpersonal Relation Prediction</td><td><a href="http://arxiv.org/abs/1609.06426" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning social relation traits from face images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td></tr><tr><td>expw</td><td>ExpW</td><td>From Facial Expression Recognition to Interpersonal Relation Prediction</td><td>From Facial Expression Recognition to Interpersonal Relation Prediction</td><td><a href="http://arxiv.org/abs/1609.06426" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=from facial expression recognition to interpersonal relation prediction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td></tr><tr><td>faceplace</td><td>Face Place</td><td>Recognizing disguised faces</td><td>Recognizing disguised faces</td><td><a href="http://pdfs.semanticscholar.org/d936/7ceb0be378c3a9ddf7cb741c678c1a3c574c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=recognizing disguised faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>25474c21613607f6bb7687a281d5f9d4ffa1f9f3</td></tr><tr><td>families_in_the_wild</td><td>FIW</td><td>Visual Kinship Recognition of Families in the Wild</td><td>Visual Kinship Recognition of Families in the Wild</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8337841" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=visual kinship recognition of families in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>dd65f71dac86e36eecbd3ed225d016c3336b4a13</td></tr><tr><td>fei</td><td>FEI</td><td>Captura e Alinhamento de Imagens: Um Banco de Faces Brasileiro</td><td>Generalização cartográfica automatizada para um banco de dados cadastral</td><td><a href="https://pdfs.semanticscholar.org/b6b1/b0632eb9d4ab1427278f5e5c46f97753c73d.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=captura e alinhamento de imagens: um banco de faces brasileiro&sort=relevance" target="_blank">[s2]</a></td><td></td><td>b6b1b0632eb9d4ab1427278f5e5c46f97753c73d</td></tr><tr><td>feret</td><td>FERET</td><td>The FERET Verification Testing Protocol for Face Recognition Algorithms</td><td>The FERET Verification Testing Protocol for Face Recognition Algorithms</td><td><a href="http://pdfs.semanticscholar.org/0c4a/139bb87c6743c7905b29a3cfec27a5130652.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the feret verification testing protocol for face recognition algorithms&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td></tr><tr><td>feret</td><td>FERET</td><td>The FERET database and evaluation procedure for face-recognition algorithms</td><td>The FERET database and evaluation procedure for face-recognition algorithms</td><td><a href="http://pdfs.semanticscholar.org/dc8b/25e35a3acb812beb499844734081722319b4.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the feret database and evaluation procedure for face-recognition algorithms&sort=relevance" target="_blank">[s2]</a></td><td></td><td>dc8b25e35a3acb812beb499844734081722319b4</td></tr><tr><td>feret</td><td>FERET</td><td>FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results</td><td>FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results</td><td><a href="http://pdfs.semanticscholar.org/31de/9b3dd6106ce6eec9a35991b2b9083395fd0b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=feret ( face recognition technology ) recognition algorithm development and test results&sort=relevance" target="_blank">[s2]</a></td><td></td><td>31de9b3dd6106ce6eec9a35991b2b9083395fd0b</td></tr><tr><td>feret</td><td>FERET</td><td>The FERET Evaluation Methodology for Face-Recognition Algorithms</td><td>The FERET Evaluation Methodology for Face-Recognition Algorithms</td><td><a href="http://pdfs.semanticscholar.org/0f0f/cf041559703998abf310e56f8a2f90ee6f21.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the feret evaluation methodology for face-recognition algorithms&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0f0fcf041559703998abf310e56f8a2f90ee6f21</td></tr><tr><td>ferplus</td><td>FER+</td><td>Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution</td><td>Training deep networks for facial expression recognition with crowd-sourced label distribution</td><td><a href="http://arxiv.org/pdf/1608.01041v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=training deep networks for facial expression recognition with crowd-sourced label distribution&sort=relevance" target="_blank">[s2]</a></td><td></td><td>298cbc3dfbbb3a20af4eed97906650a4ea1c29e0</td></tr><tr><td>fia</td><td>CMU FiA</td><td>The CMU Face In Action (FIA) Database</td><td>The CMU Face In Action (FIA) Database</td><td><a href="http://pdfs.semanticscholar.org/bb47/a03401811f9d2ca2da12138697acbc7b97a3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cmu face in action (fia) database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>47662d1a368daf70ba70ef2d59eb6209f98b675d</td></tr><tr><td>frav3d</td><td>FRAV3D</td><td>MULTIMODAL 2D, 2.5D & 3D FACE VERIFICATION</td><td>2D and 3D face recognition: A survey</td><td><a href="http://pdfs.semanticscholar.org/2f5d/44dc3e1b5955942133ff872ebd31716ec604.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multimodal 2d, 2.5d & 3d face verification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2f5d44dc3e1b5955942133ff872ebd31716ec604</td></tr><tr><td>frgc</td><td>FRGC</td><td>Overview of the Face Recognition Grand Challenge</td><td>Overview of the face recognition grand challenge</td><td><a href="http://www3.nd.edu/~kwb/PhillipsEtAlCVPR_2005.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=overview of the face recognition grand challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18ae7c9a4bbc832b8b14bc4122070d7939f5e00e</td></tr><tr><td>gallagher</td><td>Gallagher</td><td>Clothing Cosegmentation for Recognizing People</td><td>Clothing Cosegmentation for Shopping Images With Cluttered Background</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7423747" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clothing cosegmentation for recognizing people&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dbe8e5121c534339d6e41f8683e85f87e6abf81</td></tr><tr><td>gavab_db</td><td>Gavab</td><td>GavabDB: a 3D face database</td><td>Expression invariant 3D face recognition with a Morphable Model</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4813376" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=gavabdb: a 3d face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>42505464808dfb446f521fc6ff2cfeffd4d68ff1</td></tr><tr><td>georgia_tech_face_database</td><td>Georgia Tech Face</td><td>Maximum likelihood training of the embedded HMM for face detection and recognition</td><td>MAXIMUM LIKELIHOOD TRAINING OF THE EMBEDDED HMM FOR FACE DETECTION AND RECOGNITION Ara V. Ne an and Monson H. Hayes III Center for Signal and Image Processing School of Electrical and Computer Engineering</td><td><a href="http://pdfs.semanticscholar.org/3dc3/f0b64ef80f573e3a5f96e456e52ee980b877.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=maximum likelihood training of the embedded hmm for face detection and recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3dc3f0b64ef80f573e3a5f96e456e52ee980b877</td></tr><tr><td>graz</td><td>Graz Pedestrian</td><td>Generic Object Recognition with Boosting</td><td>Object recognition using segmentation for feature detection</td><td><a href="http://www.emt.tugraz.at/~tracking/Publications/fussenegger2004b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=generic object recognition with boosting&sort=relevance" target="_blank">[s2]</a></td><td></td><td>12ad3b5bbbf407f8e54ea692c07633d1a867c566</td></tr><tr><td>graz</td><td>Graz Pedestrian</td><td>Weak Hypotheses and Boosting for Generic Object Detection and Recognition</td><td>Object recognition using segmentation for feature detection</td><td><a href="http://www.emt.tugraz.at/~tracking/Publications/fussenegger2004b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=weak hypotheses and boosting for generic object detection and recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>12ad3b5bbbf407f8e54ea692c07633d1a867c566</td></tr><tr><td>graz</td><td>Graz Pedestrian</td><td>Object Recognition Using Segmentation for Feature Detection</td><td>Object recognition using segmentation for feature detection</td><td><a href="http://www.emt.tugraz.at/~tracking/Publications/fussenegger2004b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=object recognition using segmentation for feature detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>12ad3b5bbbf407f8e54ea692c07633d1a867c566</td></tr><tr><td>h3d</td><td>H3D</td><td>Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations</td><td>Poselets: Body part detectors trained using 3D human pose annotations</td><td><a href="http://vision.stanford.edu/teaching/cs231b_spring1213/papers/ICCV09_BourdevMalik.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=poselets: body part detectors trained using 3d human pose annotations&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2830fb5282de23d7784b4b4bc37065d27839a412</td></tr><tr><td>hda_plus</td><td>HDA+</td><td>The HDA+ data set for research on fully automated re-identification systems</td><td>HDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance</td><td><a href="http://pdfs.semanticscholar.org/bd88/bb2e4f351352d88ee7375af834360e223498.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the hda+ data set for research on fully automated re-identification systems&sort=relevance" target="_blank">[s2]</a></td><td></td><td>bd88bb2e4f351352d88ee7375af834360e223498</td></tr><tr><td>hda_plus</td><td>HDA+</td><td>A Multi-camera video data set for research on High-Definition surveillance</td><td>HDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance</td><td><a href="http://pdfs.semanticscholar.org/bd88/bb2e4f351352d88ee7375af834360e223498.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a multi-camera video data set for research on high-definition surveillance&sort=relevance" target="_blank">[s2]</a></td><td></td><td>bd88bb2e4f351352d88ee7375af834360e223498</td></tr><tr><td>hi4d_adsip</td><td>Hi4D-ADSIP</td><td>Hi4D-ADSIP 3-D dynamic facial articulation database</td><td>High-resolution comprehensive 3-D dynamic database for facial articulation analysis</td><td><a href="http://www.researchgate.net/profile/Wei_Quan3/publication/221430048_High-resolution_comprehensive_3-D_dynamic_database_for_facial_articulation_analysis/links/0deec534309495805d000000.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=hi4d-adsip 3-d dynamic facial articulation database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>24830e3979d4ed01b9fd0feebf4a8fd22e0c35fd</td></tr><tr><td>hollywood_headset</td><td>HollywoodHeads</td><td>Context-aware CNNs for person head detection</td><td>Context-Aware CNNs for Person Head Detection</td><td><a href="http://arxiv.org/pdf/1511.07917v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=context-aware cnns for person head detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td></tr><tr><td>hrt_transgender</td><td>HRT Transgender</td><td>Is the Eye Region More Reliable Than the Face? 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face age database: a database for face recognition with age variation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>55c40cbcf49a0225e72d911d762c27bb1c2d14aa</td></tr><tr><td>ifdb</td><td>IFDB</td><td>Iranian Face Database with age, pose and expression</td><td>Iranian Face Database and Evaluation with a New Detection Algorithm</td><td><a href="http://pdfs.semanticscholar.org/066d/71fcd997033dce4ca58df924397dfe0b5fd1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iranian face database with age, pose and expression&sort=relevance" target="_blank">[s2]</a></td><td></td><td>066d71fcd997033dce4ca58df924397dfe0b5fd1</td></tr><tr><td>ifdb</td><td>IFDB</td><td>Iranian Face Database and Evaluation with a New Detection Algorithm</td><td>Iranian Face Database and Evaluation with a New Detection Algorithm</td><td><a href="http://pdfs.semanticscholar.org/066d/71fcd997033dce4ca58df924397dfe0b5fd1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iranian face database and evaluation with a new detection algorithm&sort=relevance" target="_blank">[s2]</a></td><td></td><td>066d71fcd997033dce4ca58df924397dfe0b5fd1</td></tr><tr><td>iit_dehli_ear</td><td>IIT Dehli Ear</td><td>Automated human identification using ear imaging</td><td>Automated Human Identification Using Ear Imaging</td><td><a href="https://pdfs.semanticscholar.org/faf4/0ce28857aedf183e193486f5b4b0a8c478a2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automated human identification using ear imaging&sort=relevance" target="_blank">[s2]</a></td><td></td><td>faf40ce28857aedf183e193486f5b4b0a8c478a2</td></tr><tr><td>ijb_c</td><td>IJB-A</td><td>Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A</td><td>Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1B_089_ext.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pushing the frontiers of unconstrained face detection and recognition: iarpa janus benchmark a&sort=relevance" target="_blank">[s2]</a></td><td></td><td>140c95e53c619eac594d70f6369f518adfea12ef</td></tr><tr><td>ijb_c</td><td>IJB-B</td><td>IARPA Janus Benchmark-B Face Dataset</td><td>IARPA Janus Benchmark-B Face Dataset</td><td><a href="http://www.vislab.ucr.edu/Biometrics2017/program_slides/Noblis_CVPRW_IJBB.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iarpa janus benchmark-b face dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0cb2dd5f178e3a297a0c33068961018659d0f443</td></tr><tr><td>ijb_c</td><td>IJB-C</td><td>IARPA Janus Benchmark C</td><td>IARPA Janus Benchmark - C: Face Dataset and Protocol</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8411217" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iarpa janus benchmark c&sort=relevance" target="_blank">[s2]</a></td><td></td><td>57178b36c21fd7f4529ac6748614bb3374714e91</td></tr><tr><td>ilids_mcts</td><td></td><td>Imagery Library for Intelligent Detection Systems: -The i-LIDS User Guide</td><td>Imagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=imagery library for intelligent detection systems: -the i-lids user guide&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0297448f3ed948e136bb06ceff10eccb34e5bb77</td></tr><tr><td>ilids_vid_reid</td><td>iLIDS-VID</td><td>Person Re-Identi cation by Video Ranking</td><td>Person Re-identification by Exploiting Spatio-Temporal Cues and Multi-view Metric Learning</td><td><a href="https://doi.org/10.1109/LSP.2016.2574323" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person re-identi cation by video ranking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>99eb4cea0d9bc9fe777a5c5172f8638a37a7f262</td></tr><tr><td>images_of_groups</td><td>Images of Groups</td><td>Understanding Groups of Images of People</td><td>Understanding images of groups of people</td><td><a href="http://amp.ece.cmu.edu/people/Andy/Andy_files/cvpr09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=understanding groups of images of people&sort=relevance" target="_blank">[s2]</a></td><td></td><td>21d9d0deed16f0ad62a4865e9acf0686f4f15492</td></tr><tr><td>imdb_wiki</td><td>IMDB</td><td>Deep expectation of real and apparent age from a single image without facial landmarks</td><td>Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks</td><td><a href="https://doi.org/10.1007/s11263-016-0940-3" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep expectation of real and apparent age from a single image without facial landmarks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>10195a163ab6348eef37213a46f60a3d87f289c5</td></tr><tr><td>imdb_wiki</td><td>IMDB</td><td>DEX: Deep EXpectation of apparent age from a single image</td><td>DEX: Deep EXpectation of Apparent Age from a Single Image</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7406390" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=dex: deep expectation of apparent age from a single image&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8355d095d3534ef511a9af68a3b2893339e3f96b</td></tr><tr><td>imfdb</td><td>IMFDB</td><td>Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations</td><td>Indian Movie Face Database: A benchmark for face recognition under wide variations</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=indian movie face database: a benchmark for face recognition under wide variations&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ca3e88d87e1344d076c964ea89d91a75c417f5ee</td></tr><tr><td>imm_face</td><td>IMM Face Dataset</td><td>The IMM Face Database - An Annotated Dataset of 240 Face Images</td><td>Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the imm face database - an annotated dataset of 240 face images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td></tr><tr><td>immediacy</td><td>Immediacy</td><td>Multi-task Recurrent Neural Network for Immediacy Prediction</td><td>Multi-task Recurrent Neural Network for Immediacy Prediction</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.383" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-task recurrent neural network for immediacy prediction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1e3df3ca8feab0b36fd293fe689f93bb2aaac591</td></tr><tr><td>inria_person</td><td>INRIA Pedestrian</td><td>Histograms of Oriented Gradients for Human Detection</td><td>Histograms of oriented gradients for human detection</td><td><a href="http://nichol.as/papers/Dalai/Histograms%20of%20oriented%20gradients%20for%20human%20detection.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=histograms of oriented gradients for human detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5</td></tr><tr><td>jiku_mobile</td><td>Jiku Mobile Video Dataset</td><td>The Jiku Mobile Video Dataset</td><td>A Synchronization Ground Truth for the Jiku Mobile Video Dataset</td><td><a href="http://pdfs.semanticscholar.org/ad62/c6e17bc39b4dec20d32f6ac667ae42d2c118.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the jiku mobile video dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ad62c6e17bc39b4dec20d32f6ac667ae42d2c118</td></tr><tr><td>jpl_pose</td><td>JPL-Interaction dataset</td><td>First-Person Activity Recognition: What Are They Doing to Me?</td><td>First-Person Activity Recognition: What Are They Doing to Me?</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Ryoo_First-Person_Activity_Recognition_2013_CVPR_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=first-person activity recognition: what are they doing to me?&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1aad2da473888cb7ebc1bfaa15bfa0f1502ce005</td></tr><tr><td>kdef</td><td>KDEF</td><td>The Karolinska Directed Emotional Faces – KDEF</td><td>Gaze fixation and the neural circuitry of face processing in autism</td><td><a href="http://doi.org/10.1038/nn1421" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the karolinska directed emotional faces – kdef&sort=relevance" target="_blank">[s2]</a></td><td></td><td>93884e46c49f7ae1c7c34046fbc28882f2bd6341</td></tr><tr><td>kin_face</td><td>UB KinFace</td><td>Genealogical Face Recognition based on UB KinFace Database</td><td>Understanding Kin Relationships in a Photo</td><td><a href="http://www1.ece.neu.edu/~yunfu/papers/Kinship-TMM.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=genealogical face recognition based on ub kinface database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7</td></tr><tr><td>kin_face</td><td>UB KinFace</td><td>Kinship Verification through Transfer Learning</td><td>Understanding Kin Relationships in a Photo</td><td><a href="http://www1.ece.neu.edu/~yunfu/papers/Kinship-TMM.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=kinship verification through transfer learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7</td></tr><tr><td>kin_face</td><td>UB KinFace</td><td>Understanding Kin Relationships in a Photo</td><td>Understanding Kin Relationships in a Photo</td><td><a href="http://www1.ece.neu.edu/~yunfu/papers/Kinship-TMM.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=understanding kin relationships in a photo&sort=relevance" target="_blank">[s2]</a></td><td></td><td>08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7</td></tr><tr><td>kinectface</td><td>KinectFaceDB</td><td>KinectFaceDB: A Kinect Database for Face Recognition</td><td>KinectFaceDB: A Kinect Database for Face Recognition</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6866883" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=kinectfacedb: a kinect database for face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0b440695c822a8e35184fb2f60dcdaa8a6de84ae</td></tr><tr><td>kitti</td><td>KITTI</td><td>Vision meets Robotics: The KITTI Dataset</td><td>The Role of Machine Vision for Intelligent Vehicles</td><td><a href="http://www.path.berkeley.edu/sites/default/files/my_folder_76/Pub_03.2016_Role.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vision meets robotics: the kitti dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>35ba4ebfd017a56b51e967105af9ae273c9b0178</td></tr><tr><td>lag</td><td>LAG</td><td>Large Age-Gap Face Verification by Feature Injection in Deep Networks</td><td>Large age-gap face verification by feature injection in deep networks</td><td><a href="http://pdfs.semanticscholar.org/0d2d/d4fc016cb6a517d8fb43a7cc3ff62964832e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=large age-gap face verification by feature injection in deep networks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e</td></tr><tr><td>large_scale_person_search</td><td>Large Scale Person Search</td><td>End-to-End Deep Learning for Person Search</td><td>End-to-End Deep Learning for Person Search</td><td><a href="https://pdfs.semanticscholar.org/2161/f6b7ee3c0acc81603b01dc0df689683577b9.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=end-to-end deep learning for person search&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2161f6b7ee3c0acc81603b01dc0df689683577b9</td></tr><tr><td>leeds_sports_pose</td><td>Leeds Sports Pose</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clustered pose and nonlinear appearance models for human pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td></tr><tr><td>leeds_sports_pose_extended</td><td>Leeds Sports Pose Extended</td><td>Learning Effective Human Pose Estimation from Inaccurate Annotation</td><td>Learning effective human pose estimation from inaccurate annotation</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995318" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning effective human pose estimation from inaccurate annotation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4e4746094bf60ee83e40d8597a6191e463b57f76</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: A Survey</td><td>Labeled Faces in the Wild : A Survey</td><td><a href="http://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: a survey&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</td><td>Labeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments</td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: a database for studying face recognition in unconstrained environments&sort=relevance" target="_blank">[s2]</a></td><td></td><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td></tr><tr><td>lfw_a</td><td>LFW-a</td><td>Effective Unconstrained Face Recognition by - Combining Multiple Descriptors and Learned - Background Statistics</td><td>Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.230" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=effective unconstrained face recognition by - combining multiple descriptors and learned - background statistics&sort=relevance" target="_blank">[s2]</a></td><td></td><td>133f01aec1534604d184d56de866a4bd531dac87</td></tr><tr><td>m2vts</td><td>m2vts</td><td>The M2VTS Multimodal Face Database (Release 1.00)</td><td>The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</td><td><a href="https://doi.org/10.1109/TSMCA.2007.909557" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the m2vts multimodal face database (release 1.00)&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td></tr><tr><td>m2vtsdb_extended</td><td>xm2vtsdb</td><td>XM2VTSDB: The Extended M2VTS Database</td><td>Labeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments</td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=xm2vtsdb: the extended m2vts database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td></tr><tr><td>mafl</td><td>MAFL</td><td>Facial Landmark Detection by Deep Multi-task Learning</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td><a href="https://arxiv.org/pdf/1408.3967.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facial landmark detection by deep multi-task learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td></tr><tr><td>mafl</td><td>MAFL</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td><a href="https://arxiv.org/pdf/1408.3967.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning deep representation for face alignment with auxiliary attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td></tr><tr><td>mapillary</td><td>Mapillary</td><td>The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes</td><td>The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8237796" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mapillary vistas dataset for semantic understanding of street scenes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>79828e6e9f137a583082b8b5a9dfce0c301989b8</td></tr><tr><td>market_1501</td><td>Market 1501</td><td>Scalable Person Re-identification: A Benchmark</td><td>Scalable Person Re-identification: A Benchmark</td><td><a href="https://www.microsoft.com/en-us/research/wp-content/uploads/2017/01/ICCV15-ReIDDataset.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scalable person re-identification: a benchmark&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4308bd8c28e37e2ed9a3fcfe74d5436cce34b410</td></tr><tr><td>market1203</td><td>Market 1203</td><td>Orientation Driven Bag of Appearances for Person Re-identification</td><td>Orientation Driven Bag of Appearances for Person Re-identification</td><td><a href="http://pdfs.semanticscholar.org/a7fe/834a0af614ce6b50dc093132b031dd9a856b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=orientation driven bag of appearances for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td></tr><tr><td>mars</td><td>MARS</td><td>MARS: A Video Benchmark for Large-Scale Person Re-identification</td><td>MARS: A Video Benchmark for Large-Scale Person Re-Identification</td><td><a href="http://pdfs.semanticscholar.org/c038/7e788a52f10bf35d4d50659cfa515d89fbec.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=mars: a video benchmark for large-scale person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c0387e788a52f10bf35d4d50659cfa515d89fbec</td></tr><tr><td>megaface</td><td>MegaFace</td><td>Level Playing Field for Million Scale Face Recognition</td><td>Level Playing Field for Million Scale Face Recognition</td><td><a href="https://arxiv.org/pdf/1705.00393.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=level playing field for million scale face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>28d4e027c7e90b51b7d8908fce68128d1964668a</td></tr><tr><td>mit_cbcl</td><td>MIT CBCL</td><td>Component-based Face Recognition with 3D Morphable Models</td><td>Component-Based Face Recognition with 3D Morphable Models</td><td><a href="http://www.bheisele.com/avbpa2003.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=component-based face recognition with 3d morphable models&sort=relevance" target="_blank">[s2]</a></td><td></td><td>079a0a3bf5200994e1f972b1b9197bf2f90e87d4</td></tr><tr><td>mmi_facial_expression</td><td>MMI Facial Expression Dataset</td><td>WEB-BASED DATABASE FOR FACIAL EXPRESSION ANALYSIS</td><td>Web-based database for facial expression analysis</td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/PanticEtAl-ICME2005-final.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=web-based database for facial expression analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2a75f34663a60ab1b04a0049ed1d14335129e908</td></tr><tr><td>moments_in_time</td><td>Moments in Time</td><td>Moments in Time Dataset: one million videos for event understanding</td><td>Moments in Time Dataset: one million videos for event understanding</td><td><a href="https://arxiv.org/pdf/1801.03150.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=moments in time dataset: one million videos for event understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a5a44a32a91474f00a3cda671a802e87c899fbb4</td></tr><tr><td>morph</td><td>MORPH Commercial</td><td>MORPH: A Longitudinal Image Database of Normal Adult Age-Progression</td><td>MORPH: a longitudinal image database of normal adult age-progression</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/FGR.2006.78" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=morph: a longitudinal image database of normal adult age-progression&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td></tr><tr><td>morph_nc</td><td>MORPH Non-Commercial</td><td>MORPH: A Longitudinal Image Database of Normal Adult Age-Progression</td><td>MORPH: a longitudinal image database of normal adult age-progression</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/FGR.2006.78" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=morph: a longitudinal image database of normal adult age-progression&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td></tr><tr><td>mot</td><td>MOT</td><td>Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</td><td>Learning to associate: HybridBoosted multi-target tracker for crowded scene</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0633.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=evaluating multiple object tracking performance: the clear mot metrics&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5981e6479c3fd4e31644db35d236bfb84ae46514</td></tr><tr><td>mot</td><td>MOT</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td>Learning to associate: HybridBoosted multi-target tracker for crowded scene</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0633.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=performance measures and a data set for multi-target, multi-camera tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5981e6479c3fd4e31644db35d236bfb84ae46514</td></tr><tr><td>mot</td><td>MOT</td><td>Learning to associate: HybridBoosted multi-target tracker for crowded scene</td><td>Learning to associate: HybridBoosted multi-target tracker for crowded scene</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0633.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to associate: hybridboosted multi-target tracker for crowded scene&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5981e6479c3fd4e31644db35d236bfb84ae46514</td></tr><tr><td>mpi_large</td><td>Large MPI Facial Expression</td><td>The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</td><td>The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</td><td><a href="http://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mpi facial expression database — a validated database of emotional and conversational facial expressions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td></tr><tr><td>mpi_small</td><td>Small MPI Facial Expression</td><td>The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</td><td>The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</td><td><a href="http://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mpi facial expression database — a validated database of emotional and conversational facial expressions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td></tr><tr><td>mpii_human_pose</td><td>MPII Human Pose</td><td>2D Human Pose Estimation: New Benchmark and State of the Art Analysis</td><td>2D Human Pose Estimation: New Benchmark and State of the Art Analysis</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6909866" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=2d human pose estimation: new benchmark and state of the art analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3325860c0c82a93b2eac654f5324dd6a776f609e</td></tr><tr><td>mr2</td><td>MR2</td><td>The MR2: A multi-racial mega-resolution database of facial stimuli</td><td>The MR2: A multi-racial, mega-resolution database of facial stimuli.</td><td><a href="https://pdfs.semanticscholar.org/be5b/455abd379240460d022a0e246615b0b86c14.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mr2: a multi-racial mega-resolution database of facial stimuli&sort=relevance" target="_blank">[s2]</a></td><td></td><td>578d4ad74818086bb64f182f72e2c8bd31e3d426</td></tr><tr><td>mrp_drone</td><td>MRP Drone</td><td>Investigating Open-World Person Re-identification Using a Drone</td><td>Investigating Open-World Person Re-identification Using a Drone</td><td><a href="http://pdfs.semanticscholar.org/ad01/687649d95cd5b56d7399a9603c4b8e2217d7.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=investigating open-world person re-identification using a drone&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ad01687649d95cd5b56d7399a9603c4b8e2217d7</td></tr><tr><td>msceleb</td><td>MsCeleb</td><td>MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</td><td>MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</td><td><a href="http://pdfs.semanticscholar.org/4603/cb8e05258bb0572ae912ad20903b8f99f4b1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ms-celeb-1m: a dataset and benchmark for large-scale face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>291265db88023e92bb8c8e6390438e5da148e8f5</td></tr><tr><td>msmt_17</td><td>MSMT17</td><td>Person Transfer GAN to Bridge Domain Gap for Person Re-Identification</td><td>Person Transfer GAN to Bridge Domain Gap for Person Re-Identification</td><td><a href="https://arxiv.org/pdf/1711.08565.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person transfer gan to bridge domain gap for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ec792ad2433b6579f2566c932ee414111e194537</td></tr><tr><td>mtfl</td><td>MTFL</td><td>Facial Landmark Detection by Deep Multi-task Learning</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td><a href="https://arxiv.org/pdf/1408.3967.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facial landmark detection by deep multi-task learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td></tr><tr><td>mtfl</td><td>MTFL</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td><a href="https://arxiv.org/pdf/1408.3967.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning deep representation for face alignment with auxiliary attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td></tr><tr><td>muct</td><td>MUCT</td><td>The MUCT Landmarked Face Database</td><td>Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the muct landmarked face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td></tr><tr><td>multi_pie</td><td>MULTIPIE</td><td>Multi-PIE</td><td>Scheduling heterogeneous multi-cores through performance impact estimation (PIE)</td><td><a href="http://dl.acm.org/citation.cfm?id=2337184" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-pie&sort=relevance" target="_blank">[s2]</a></td><td></td><td>109df0e8e5969ddf01e073143e83599228a1163f</td></tr><tr><td>names_and_faces_news</td><td>News Dataset</td><td>Names and Faces</td><td>Names and faces in the news</td><td><a href="http://www.cs.utexas.edu/~grauman/courses/spring2007/395T/papers/berg_names_and_faces.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=names and faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2fda164863a06a92d3a910b96eef927269aeb730</td></tr><tr><td>nd_2006</td><td>ND-2006</td><td>Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition</td><td>Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4401928" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=using a multi-instance enrollment representation to improve 3d face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>fd8168f1c50de85bac58a8d328df0a50248b16ae</td></tr><tr><td>nova_emotions</td><td>Novaemötions Dataset</td><td>Crowdsourcing facial expressions for affective-interaction</td><td>Competitive affective gaming: winning with a smile</td><td><a href="http://doi.acm.org/10.1145/2502081.2502115" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=crowdsourcing facial expressions for affective-interaction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7f4040b482d16354d5938c1d1b926b544652bf5b</td></tr><tr><td>nova_emotions</td><td>Novaemötions Dataset</td><td>Competitive affective gamming: Winning with a smile</td><td>Competitive affective gaming: winning with a smile</td><td><a href="http://doi.acm.org/10.1145/2502081.2502115" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=competitive affective gamming: winning with a smile&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7f4040b482d16354d5938c1d1b926b544652bf5b</td></tr><tr><td>nudedetection</td><td>Nude Detection</td><td>A Bag-of-Features Approach based on Hue-SIFT Descriptor for Nude Detection</td><td>A bag-of-features approach based on Hue-SIFT descriptor for nude detection</td><td><a href="http://ieeexplore.ieee.org/document/7077625/" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a bag-of-features approach based on hue-sift descriptor for nude detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7ace44190729927e5cb0dd5d363fcae966fe13f7</td></tr><tr><td>orl</td><td>ORL</td><td>Parameterisation of a Stochastic Model for Human Face Identification</td><td>Parameterisation of a stochastic model for human face identification</td><td><a href="http://pdfs.semanticscholar.org/5520/6f0b5f57ce17358999145506cd01e570358c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=parameterisation of a stochastic model for human face identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>55206f0b5f57ce17358999145506cd01e570358c</td></tr><tr><td>penn_fudan</td><td>Penn Fudan</td><td>Object Detection Combining Recognition and Segmentation</td><td>Object Detection Combining Recognition and Segmentation</td><td><a href="http://pdfs.semanticscholar.org/f531/a554cade14b9b340de6730683a28c292dd74.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=object detection combining recognition and segmentation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3394168ff0719b03ff65bcea35336a76b21fe5e4</td></tr><tr><td>peta</td><td>PETA</td><td>Pedestrian Attribute Recognition At Far Distance</td><td>Pedestrian Attribute Recognition At Far Distance</td><td><a href="http://personal.ie.cuhk.edu.hk/~pluo/pdf/mm14.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian attribute recognition at far distance&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2a4bbee0b4cf52d5aadbbc662164f7efba89566c</td></tr><tr><td>pets</td><td>PETS 2017</td><td>PETS 2017: Dataset and Challenge</td><td>PETS 2017: Dataset and Challenge</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8014998" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pets 2017: dataset and challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22909dd19a0ec3b6065334cb5be5392cb24d839d</td></tr><tr><td>pilot_parliament</td><td>PPB</td><td>Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classi cation</td><td>Summary of Research on Informant Accuracy in Network Data, 11 and on the Reverse Small World Problem</td><td><a href="http://pdfs.semanticscholar.org/fb82/681ac5d3487bd8e52dbb3d1fa220eeac855e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=gender shades: intersectional accuracy disparities in commercial gender classi cation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>fb82681ac5d3487bd8e52dbb3d1fa220eeac855e</td></tr><tr><td>pipa</td><td>PIPA</td><td>Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues</td><td>Beyond frontal faces: Improving Person Recognition using multiple cues</td><td><a href="https://doi.org/10.1109/CVPR.2015.7299113" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=beyond frontal faces: improving person recognition using multiple cues&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0a85bdff552615643dd74646ac881862a7c7072d</td></tr><tr><td>pku</td><td>PKU</td><td>Swiss-System Based Cascade Ranking for Gait-based Person Re-identification</td><td>Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification</td><td><a href="http://pdfs.semanticscholar.org/f6c8/d5e35d7e4d60a0104f233ac1a3ab757da53f.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=swiss-system based cascade ranking for gait-based person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f</td></tr><tr><td>pku_reid</td><td>PKU-Reid</td><td>Orientation driven bag of appearances for person re-identification</td><td>Orientation Driven Bag of Appearances for Person Re-identification</td><td><a href="http://pdfs.semanticscholar.org/a7fe/834a0af614ce6b50dc093132b031dd9a856b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=orientation driven bag of appearances for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td></tr><tr><td>pornodb</td><td>Pornography DB</td><td>Pooling in Image Representation: the Visual Codeword Point of View</td><td>Pooling in image representation: The visual codeword point of view</td><td><a href="http://pdfs.semanticscholar.org/b92a/1ed9622b8268ae3ac9090e25789fc41cc9b8.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pooling in image representation: the visual codeword point of view&sort=relevance" target="_blank">[s2]</a></td><td></td><td>b92a1ed9622b8268ae3ac9090e25789fc41cc9b8</td></tr><tr><td>precarious</td><td>Precarious</td><td>Expecting the Unexpected: Training Detectors for Unusual Pedestrians With Adversarial Imposters</td><td>Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters</td><td><a href="https://arxiv.org/pdf/1703.06283.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=expecting the unexpected: training detectors for unusual pedestrians with adversarial imposters&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9e5378e7b336c89735d3bb15cf67eff96f86d39a</td></tr><tr><td>prid</td><td>PRID</td><td>Person Re-Identification by Descriptive and Discriminative Classification</td><td>Person Re-identification by Descriptive and Discriminative Classification</td><td><a href="http://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person re-identification by descriptive and discriminative classification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>16c7c31a7553d99f1837fc6e88e77b5ccbb346b8</td></tr><tr><td>psu</td><td>PSU</td><td>Vision-based Analysis of Small Groups in Pedestrian Crowds</td><td>Vision-Based Analysis of Small Groups in Pedestrian Crowds</td><td><a href="http://vision.cse.psu.edu/publications/pdfs/GeCollinsRubackPAMI2011.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vision-based analysis of small groups in pedestrian crowds&sort=relevance" target="_blank">[s2]</a></td><td></td><td>066000d44d6691d27202896691f08b27117918b9</td></tr><tr><td>put_face</td><td>Put Face</td><td>The PUT face database</td><td>Labeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments</td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the put face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td></tr><tr><td>qmul_grid</td><td>GRID</td><td>Multi-Camera Activity Correlation Analysis</td><td>Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding</td><td><a href="https://doi.org/10.1007/s11263-010-0347-5" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-camera activity correlation analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2edb87494278ad11641b6cf7a3f8996de12b8e14</td></tr><tr><td>qmul_grid</td><td>GRID</td><td>Time-delayed correlation analysis for multi-camera activity understanding</td><td>Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding</td><td><a href="https://doi.org/10.1007/s11263-010-0347-5" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=time-delayed correlation analysis for multi-camera activity understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2edb87494278ad11641b6cf7a3f8996de12b8e14</td></tr><tr><td>qmul_surv_face</td><td>QMUL-SurvFace</td><td>Surveillance Face Recognition Challenge</td><td>Surveillance Face Recognition Challenge</td><td><a href="https://arxiv.org/pdf/1804.09691.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=surveillance face recognition challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c866a2afc871910e3282fd9498dce4ab20f6a332</td></tr><tr><td>rafd</td><td>RaFD</td><td>Presentation and validation of the Radboud Faces Database</td><td>Presentation and validation of the Radboud Faces Database</td><td><a href="https://pdfs.semanticscholar.org/3765/df816dc5a061bc261e190acc8bdd9d47bec0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=presentation and validation of the radboud faces database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3765df816dc5a061bc261e190acc8bdd9d47bec0</td></tr><tr><td>raid</td><td>RAiD</td><td>Consistent Re-identification in a Camera Network</td><td>Consistent Re-identification in a Camera Network</td><td><a href="http://pdfs.semanticscholar.org/c27f/099e6e7e3f7f9979cbe9e0a5175fc5848ea0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=consistent re-identification in a camera network&sort=relevance" target="_blank">[s2]</a></td><td></td><td>09d78009687bec46e70efcf39d4612822e61cb8c</td></tr><tr><td>rap_pedestrian</td><td>RAP</td><td>A Richly Annotated Dataset for Pedestrian Attribute Recognition</td><td>A Richly Annotated Dataset for Pedestrian Attribute Recognition</td><td><a href="http://pdfs.semanticscholar.org/221c/18238b829c12b911706947ab38fd017acef7.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a richly annotated dataset for pedestrian attribute recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>221c18238b829c12b911706947ab38fd017acef7</td></tr><tr><td>reseed</td><td>ReSEED</td><td>ReSEED: Social Event dEtection Dataset</td><td>ReSEED: social event dEtection dataset</td><td><a href="https://pub.uni-bielefeld.de/download/2663466/2686734" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=reseed: social event detection dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>54983972aafc8e149259d913524581357b0f91c3</td></tr><tr><td>saivt</td><td>SAIVT SoftBio</td><td>A Database for Person Re-Identification in Multi-Camera Surveillance Networks</td><td>A Database for Person Re-Identification in Multi-Camera Surveillance Networks</td><td><a href="http://eprints.qut.edu.au/53437/3/Bialkowski_Database4PersonReID_DICTA.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a database for person re-identification in multi-camera surveillance networks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b</td></tr><tr><td>sarc3d</td><td>Sarc3D</td><td>SARC3D: a new 3D body model for People Tracking and Re-identification</td><td>SARC3D: A New 3D Body Model for People Tracking and Re-identification</td><td><a href="https://pdfs.semanticscholar.org/e27e/f52c641c2b5100a1b34fd0b819e84a31b4df.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=sarc3d: a new 3d body model for people tracking and re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>e27ef52c641c2b5100a1b34fd0b819e84a31b4df</td></tr><tr><td>scface</td><td>SCface</td><td>SCface – surveillance cameras face database</td><td>Large Variability Surveillance Camera Face Database</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scface – surveillance cameras face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>f3b84a03985de3890b400b68e2a92c0a00afd9d0</td></tr><tr><td>scut_head</td><td>SCUT HEAD</td><td>Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture</td><td>Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture</td><td><a href="https://arxiv.org/pdf/1803.09256.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=detecting heads using feature refine net and cascaded multi-scale architecture&sort=relevance" target="_blank">[s2]</a></td><td></td><td>d3200d49a19a4a4e4e9745ee39649b65d80c834b</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</td><td>Person Re-identification by Video Ranking</td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a spatio-temporal appearance representation for video-based pedestrian re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>98bb029afe2a1239c3fdab517323066f0957b81b</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>Local descriptors encoded by Fisher vectors for person re-identification</td><td>Person Re-identification by Video Ranking</td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=local descriptors encoded by fisher vectors for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>98bb029afe2a1239c3fdab517323066f0957b81b</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>Person reidentification by video ranking</td><td>Person Re-identification by Video Ranking</td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person reidentification by video ranking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>98bb029afe2a1239c3fdab517323066f0957b81b</td></tr><tr><td>sheffield</td><td>Sheffield Face</td><td>Face Recognition: From Theory to Applications</td><td>Face Description with Local Binary Patterns: Application to Face Recognition</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.244" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face recognition: from theory to applications&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3607afdb204de9a5a9300ae98aa4635d9effcda2</td></tr><tr><td>soton</td><td>SOTON HiD</td><td>On a Large Sequence-Based Human Gait Database</td><td>On a large sequence-based human gait database</td><td><a href="http://pdfs.semanticscholar.org/4f93/cd09785c6e77bf4bc5a788e079df524c8d21.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=on a large sequence-based human gait database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4f93cd09785c6e77bf4bc5a788e079df524c8d21</td></tr><tr><td>stair_actions</td><td>STAIR Action</td><td>STAIR Actions: A Video Dataset of Everyday Home Actions</td><td>STAIR Actions: A Video Dataset of Everyday Home Actions</td><td><a href="https://arxiv.org/pdf/1804.04326.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=stair actions: a video dataset of everyday home actions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9</td></tr><tr><td>stanford_drone</td><td>Stanford Drone</td><td>Learning Social Etiquette: Human Trajectory Prediction In Crowded Scenes</td><td>Learning to Predict Human Behavior in Crowded Scenes</td><td><a href="http://pdfs.semanticscholar.org/c9bd/a86e23cab9e4f15ea0c4cbb6cc02b9dfb709.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning social etiquette: human trajectory prediction in crowded scenes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c9bda86e23cab9e4f15ea0c4cbb6cc02b9dfb709</td></tr><tr><td>stickmen_buffy</td><td>Buffy Stickmen</td><td>Learning to Parse Images of Articulated Objects</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to parse images of articulated objects&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td></tr><tr><td>stickmen_buffy</td><td>Buffy Stickmen</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clustered pose and nonlinear appearance models for human pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td></tr><tr><td>stickmen_family</td><td>We Are Family Stickmen</td><td>We Are Family: Joint Pose Estimation of Multiple Persons</td><td>We Are Family: Joint Pose Estimation of Multiple Persons</td><td><a href="http://pdfs.semanticscholar.org/0dc1/1a37cadda92886c56a6fb5191ded62099c28.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=we are family: joint pose estimation of multiple persons&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0dc11a37cadda92886c56a6fb5191ded62099c28</td></tr><tr><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td>Learning to parse images of articulated bodies</td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clustered pose and nonlinear appearance models for human pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td>Learning to Parse Images of Articulated Objects</td><td>Learning to parse images of articulated bodies</td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to parse images of articulated objects&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>sun_attributes</td><td>SUN</td><td>The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</td><td>SUN attribute database: Discovering, annotating, and recognizing scene attributes</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6247998" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the sun attribute database: beyond categories for deeper scene understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>833fa04463d90aab4a9fe2870d480f0b40df446e</td></tr><tr><td>sun_attributes</td><td>SUN</td><td>SUN Attribute Database: +<!doctype html><html><head><meta charset='utf-8'><title>Papers with no location</title><link rel='stylesheet' href='reports.css'></head><body><h2>Papers with no location</h2><table border='1' cellpadding='3' cellspacing='3'><th>key</th><th>name</th><th>our title</th><th>found title</th><th></th><th></th><th>address</th><th>s2 id</th><tr><td>yfcc_100m</td><td>YFCC100M</td><td>YFCC100M: The New Data in Multimedia Research</td><td>YFCC100M: the new data in multimedia research</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=yfcc100m: the new data in multimedia research&sort=relevance" target="_blank">[s2]</a></td><td></td><td>010f0f4929e6a6644fb01f0e43820f91d0fad292</td></tr><tr><td>buhmap_db</td><td>BUHMAP-DB</td><td>Facial Feature Tracking and Expression Recognition for Sign Language</td><td>Facial feature tracking and expression recognition for sign language</td><td><a href="http://www.cmpe.boun.edu.tr/pilab/pilabfiles/databases/buhmap/files/ari2008facialfeaturetracking.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facial feature tracking and expression recognition for sign language&sort=relevance" target="_blank">[s2]</a></td><td></td><td>014b8df0180f33b9fea98f34ae611c6447d761d2</td></tr><tr><td>vqa</td><td>VQA</td><td>VQA: Visual Question Answering</td><td>VQA: Visual Question Answering</td><td><a href="http://arxiv.org/pdf/1505.00468v3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vqa: visual question answering&sort=relevance" target="_blank">[s2]</a></td><td></td><td>01959ef569f74c286956024866c1d107099199f7</td></tr><tr><td>kitti</td><td>KITTI</td><td>Vision meets Robotics: The KITTI Dataset</td><td>Vision meets robotics: The KITTI dataset</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vision meets robotics: the kitti dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>026e3363b7f76b51cc711886597a44d5f1fd1de2</td></tr><tr><td>ilids_mcts</td><td>i-LIDS</td><td>Imagery Library for Intelligent Detection Systems: The i-LIDS User Guide</td><td>Imagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=imagery library for intelligent detection systems: the i-lids user guide&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0297448f3ed948e136bb06ceff10eccb34e5bb77</td></tr><tr><td>ucf_selfie</td><td>UCF Selfie</td><td>How to Take a Good Selfie?</td><td>How to Take a Good Selfie?</td><td><a href="http://doi.acm.org/10.1145/2733373.2806365" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=how to take a good selfie?&sort=relevance" target="_blank">[s2]</a></td><td></td><td>041d3eedf5e45ce5c5229f0181c5c576ed1fafd6</td></tr><tr><td>psu</td><td>PSU</td><td>Vision-based Analysis of Small Groups in Pedestrian Crowds</td><td>Vision-Based Analysis of Small Groups in Pedestrian Crowds</td><td><a href="http://vision.cse.psu.edu/publications/pdfs/GeCollinsRubackPAMI2011.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vision-based analysis of small groups in pedestrian crowds&sort=relevance" target="_blank">[s2]</a></td><td></td><td>066000d44d6691d27202896691f08b27117918b9</td></tr><tr><td>ifdb</td><td>IFDB</td><td>Iranian Face Database and Evaluation with a New Detection Algorithm</td><td>Iranian Face Database and Evaluation with a New Detection Algorithm</td><td><a href="http://pdfs.semanticscholar.org/066d/71fcd997033dce4ca58df924397dfe0b5fd1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iranian face database and evaluation with a new detection algorithm&sort=relevance" target="_blank">[s2]</a></td><td></td><td>066d71fcd997033dce4ca58df924397dfe0b5fd1</td></tr><tr><td>columbia_gaze</td><td>Columbia Gaze</td><td>Gaze Locking: Passive Eye Contact Detection for Human–Object Interaction</td><td>Gaze locking: passive eye contact detection for human-object interaction</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=gaze locking: passive eye contact detection for human–object interaction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>06f02199690961ba52997cde1527e714d2b3bf8f</td></tr><tr><td>mit_cbcl</td><td>MIT CBCL</td><td>Component-based Face Recognition with 3D Morphable Models</td><td>Component-Based Face Recognition with 3D Morphable Models</td><td><a href="http://www.bheisele.com/avbpa2003.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=component-based face recognition with 3d morphable models&sort=relevance" target="_blank">[s2]</a></td><td></td><td>079a0a3bf5200994e1f972b1b9197bf2f90e87d4</td></tr><tr><td>kin_face</td><td>UB KinFace</td><td>Understanding Kin Relationships in a Photo</td><td>Understanding Kin Relationships in a Photo</td><td><a href="http://www1.ece.neu.edu/~yunfu/papers/Kinship-TMM.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=understanding kin relationships in a photo&sort=relevance" target="_blank">[s2]</a></td><td></td><td>08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7</td></tr><tr><td>raid</td><td>RAiD</td><td>Consistent Re-identification in a Camera Network</td><td>Consistent Re-identification in a Camera Network</td><td><a href="http://pdfs.semanticscholar.org/c27f/099e6e7e3f7f9979cbe9e0a5175fc5848ea0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=consistent re-identification in a camera network&sort=relevance" target="_blank">[s2]</a></td><td></td><td>09d78009687bec46e70efcf39d4612822e61cb8c</td></tr><tr><td>pipa</td><td>PIPA</td><td>Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues</td><td>Beyond frontal faces: Improving Person Recognition using multiple cues</td><td><a href="https://doi.org/10.1109/CVPR.2015.7299113" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=beyond frontal faces: improving person recognition using multiple cues&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0a85bdff552615643dd74646ac881862a7c7072d</td></tr><tr><td>kinectface</td><td>KinectFaceDB</td><td>KinectFaceDB: A Kinect Database for Face Recognition</td><td>KinectFaceDB: A Kinect Database for Face Recognition</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6866883" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=kinectfacedb: a kinect database for face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0b440695c822a8e35184fb2f60dcdaa8a6de84ae</td></tr><tr><td>feret</td><td>FERET</td><td>The FERET Verification Testing Protocol for Face Recognition Algorithms</td><td>The FERET Verification Testing Protocol for Face Recognition Algorithms</td><td><a href="http://pdfs.semanticscholar.org/0c4a/139bb87c6743c7905b29a3cfec27a5130652.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the feret verification testing protocol for face recognition algorithms&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td></tr><tr><td>graz</td><td>Graz Pedestrian</td><td>Weak Hypotheses and Boosting for Generic Object Detection and Recognition</td><td>Weak Hypotheses and Boosting for Generic Object Detection and Recognition</td><td><a href="http://pdfs.semanticscholar.org/0c91/808994a250d7be332400a534a9291ca3b60e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=weak hypotheses and boosting for generic object detection and recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0c91808994a250d7be332400a534a9291ca3b60e</td></tr><tr><td>ijb_c</td><td>IJB-B</td><td>IARPA Janus Benchmark-B Face Dataset</td><td>IARPA Janus Benchmark-B Face Dataset</td><td><a href="http://www.vislab.ucr.edu/Biometrics2017/program_slides/Noblis_CVPRW_IJBB.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iarpa janus benchmark-b face dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0cb2dd5f178e3a297a0c33068961018659d0f443</td></tr><tr><td>casablanca</td><td>Casablanca</td><td>Context-aware {CNNs} for person head detection</td><td>Context-Aware CNNs for Person Head Detection</td><td><a href="http://arxiv.org/pdf/1511.07917v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=context-aware {cnns} for person head detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td></tr><tr><td>hollywood_headset</td><td>HollywoodHeads</td><td>Context-aware CNNs for person head detection</td><td>Context-Aware CNNs for Person Head Detection</td><td><a href="http://arxiv.org/pdf/1511.07917v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=context-aware cnns for person head detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td></tr><tr><td>lag</td><td>LAG</td><td>Large Age-Gap Face Verification by Feature Injection in Deep Networks</td><td>Large age-gap face verification by feature injection in deep networks</td><td><a href="http://pdfs.semanticscholar.org/0d2d/d4fc016cb6a517d8fb43a7cc3ff62964832e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=large age-gap face verification by feature injection in deep networks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e</td></tr><tr><td>stickmen_family</td><td>We Are Family Stickmen</td><td>We Are Family: Joint Pose Estimation of Multiple Persons</td><td>We Are Family: Joint Pose Estimation of Multiple Persons</td><td><a href="http://pdfs.semanticscholar.org/0dc1/1a37cadda92886c56a6fb5191ded62099c28.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=we are family: joint pose estimation of multiple persons&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0dc11a37cadda92886c56a6fb5191ded62099c28</td></tr><tr><td>voc</td><td>VOC</td><td>The PASCAL Visual Object Classes (VOC) Challenge</td><td>The Pascal Visual Object Classes (VOC) Challenge</td><td><a href="https://doi.org/10.1007/s11263-009-0275-4" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the pascal visual object classes (voc) challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a</td></tr><tr><td>feret</td><td>FERET</td><td>The FERET Evaluation Methodology for Face-Recognition Algorithms</td><td>The FERET Evaluation Methodology for Face-Recognition Algorithms</td><td><a href="http://pdfs.semanticscholar.org/0f0f/cf041559703998abf310e56f8a2f90ee6f21.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the feret evaluation methodology for face-recognition algorithms&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0f0fcf041559703998abf310e56f8a2f90ee6f21</td></tr><tr><td>imdb_wiki</td><td>IMDB</td><td>Deep expectation of real and apparent age from a single image without facial landmarks</td><td>Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks</td><td><a href="https://doi.org/10.1007/s11263-016-0940-3" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep expectation of real and apparent age from a single image without facial landmarks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>10195a163ab6348eef37213a46f60a3d87f289c5</td></tr><tr><td>inria_person</td><td>INRIA Pedestrian</td><td>Histograms of Oriented Gradients for Human Detection</td><td>Histograms of oriented gradients for human detection</td><td><a href="http://nichol.as/papers/Dalai/Histograms%20of%20oriented%20gradients%20for%20human%20detection.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=histograms of oriented gradients for human detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5</td></tr><tr><td>graz</td><td>Graz Pedestrian</td><td>Object Recognition Using Segmentation for Feature Detection</td><td>Object recognition using segmentation for feature detection</td><td><a href="http://www.emt.tugraz.at/~tracking/Publications/fussenegger2004b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=object recognition using segmentation for feature detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>12ad3b5bbbf407f8e54ea692c07633d1a867c566</td></tr><tr><td>lfw_a</td><td>LFW-a</td><td>Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics</td><td>Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.230" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=effective unconstrained face recognition by combining multiple descriptors and learned background statistics&sort=relevance" target="_blank">[s2]</a></td><td></td><td>133f01aec1534604d184d56de866a4bd531dac87</td></tr><tr><td>eth_andreas_ess</td><td>ETHZ Pedestrian</td><td>Depth and Appearance for Mobile Scene Analysis</td><td>Depth and Appearance for Mobile Scene Analysis</td><td><a href="http://www.mmp.rwth-aachen.de/publications/pdf/ess-depthandappearance-iccv07.pdf/at_download/file" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=depth and appearance for mobile scene analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>13f06b08f371ba8b5d31c3e288b4deb61335b462</td></tr><tr><td>ijb_c</td><td>IJB-A</td><td>Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A</td><td>Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1B_089_ext.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pushing the frontiers of unconstrained face detection and recognition: iarpa janus benchmark a&sort=relevance" target="_blank">[s2]</a></td><td></td><td>140c95e53c619eac594d70f6369f518adfea12ef</td></tr><tr><td>vgg_faces</td><td>VGG Face</td><td>Deep Face Recognition</td><td>Deep Face Recognition</td><td><a href="https://pdfs.semanticscholar.org/f372/ab9b3270d4e4f6a0258c83c2736c3a5c0454.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>162ea969d1929ed180cc6de9f0bf116993ff6e06</td></tr><tr><td>prid</td><td>PRID</td><td>Person Re-Identification by Descriptive and Discriminative Classification</td><td>Person Re-identification by Descriptive and Discriminative Classification</td><td><a href="http://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person re-identification by descriptive and discriminative classification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>16c7c31a7553d99f1837fc6e88e77b5ccbb346b8</td></tr><tr><td>umb</td><td>UMB</td><td>UMB-DB: A Database of Partially Occluded 3D Faces</td><td>UMB-DB: A database of partially occluded 3D faces</td><td><a href="https://doi.org/10.1109/ICCVW.2011.6130509" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=umb-db: a database of partially occluded 3d faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>16e8b0a1e8451d5f697b94c0c2b32a00abee1d52</td></tr><tr><td>deep_fashion</td><td>DeepFashion</td><td>DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</td><td>DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780493" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deepfashion: powering robust clothes recognition and retrieval with rich annotations&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18010284894ed0edcca74e5bf768ee2e15ef7841</td></tr><tr><td>pilot_parliament</td><td>PPB</td><td>Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification</td><td>Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification</td><td><a href="https://pdfs.semanticscholar.org/03c1/fc9c3339813ed81ad0de540132f9f695a0f8.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=gender shades: intersectional accuracy disparities in commercial gender classification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18858cc936947fc96b5c06bbe3c6c2faa5614540</td></tr><tr><td>frgc</td><td>FRGC</td><td>Overview of the Face Recognition Grand Challenge</td><td>Overview of the face recognition grand challenge</td><td><a href="http://www3.nd.edu/~kwb/PhillipsEtAlCVPR_2005.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=overview of the face recognition grand challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18ae7c9a4bbc832b8b14bc4122070d7939f5e00e</td></tr><tr><td>yale_faces</td><td>YaleFaces</td><td>From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</td><td>From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</td><td><a href="http://pdfs.semanticscholar.org/97bb/c2b439a79d4dc0dc7199d71ed96ad5e3fd0e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=from few to many: illumination cone models for face recognition under variable lighting and pose&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18c72175ddbb7d5956d180b65a96005c100f6014</td></tr><tr><td>york_3d</td><td>UOY 3D Face Database</td><td>Three-Dimensional Face Recognition: An Eigensurface Approach</td><td>Three-dimensional face recognition: an eigensurface approach</td><td><a href="http://www-users.cs.york.ac.uk/~nep/research/3Dface/tomh/3DFaceRecognition-Eigensurface-ICIP(web)2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=three-dimensional face recognition: an eigensurface approach&sort=relevance" target="_blank">[s2]</a></td><td></td><td>19d1b811df60f86cbd5e04a094b07f32fff7a32a</td></tr><tr><td>jpl_pose</td><td>JPL-Interaction dataset</td><td>First-Person Activity Recognition: What Are They Doing to Me?</td><td>First-Person Activity Recognition: What Are They Doing to Me?</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Ryoo_First-Person_Activity_Recognition_2013_CVPR_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=first-person activity recognition: what are they doing to me?&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1aad2da473888cb7ebc1bfaa15bfa0f1502ce005</td></tr><tr><td>adience</td><td>Adience</td><td>Age and Gender Estimation of Unfiltered Faces</td><td>Age and Gender Estimation of Unfiltered Faces</td><td><a href="http://www.openu.ac.il/home/hassner/Adience/EidingerEnbarHassner_tifs.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=age and gender estimation of unfiltered faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1be498d4bbc30c3bfd0029114c784bc2114d67c0</td></tr><tr><td>youtube_poses</td><td>YouTube Pose</td><td>Personalizing Human Video Pose Estimation</td><td>Personalizing Human Video Pose Estimation</td><td><a href="http://arxiv.org/pdf/1511.06676v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=personalizing human video pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1c2802c2199b6d15ecefe7ba0c39bfe44363de38</td></tr><tr><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td>Pedestrian Detection: A Benchmark</td><td>Pedestrian detection: A benchmark</td><td><a href="http://vision.ucsd.edu/~pdollar/files/papers/DollarCVPR09peds.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian detection: a benchmark&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1dc35905a1deff8bc74688f2d7e2f48fd2273275</td></tr><tr><td>immediacy</td><td>Immediacy</td><td>Multi-task Recurrent Neural Network for Immediacy Prediction</td><td>Multi-task Recurrent Neural Network for Immediacy Prediction</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.383" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-task recurrent neural network for immediacy prediction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1e3df3ca8feab0b36fd293fe689f93bb2aaac591</td></tr><tr><td>cafe</td><td>CAFE</td><td>The Child Affective Facial Expression (CAFE) Set: Validity and reliability from untrained adults</td><td>The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults</td><td><a href="http://pdfs.semanticscholar.org/2038/8099cc415c772926e47bcbbe554e133343d1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the child affective facial expression (cafe) set: validity and reliability from untrained adults&sort=relevance" target="_blank">[s2]</a></td><td></td><td>20388099cc415c772926e47bcbbe554e133343d1</td></tr><tr><td>bbc_pose</td><td>BBC Pose</td><td>Automatic and Efficient Human Pose Estimation for Sign Language Videos</td><td>Automatic and Efficient Human Pose Estimation for Sign Language Videos</td><td><a href="https://doi.org/10.1007/s11263-013-0672-6" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automatic and efficient human pose estimation for sign language videos&sort=relevance" target="_blank">[s2]</a></td><td></td><td>213a579af9e4f57f071b884aa872651372b661fd</td></tr><tr><td>3d_rma</td><td>3D-RMA</td><td>Automatic 3D Face Authentication</td><td>Automatic 3D face authentication</td><td><a href="http://pdfs.semanticscholar.org/2160/788824c4c29ffe213b2cbeb3f52972d73f37.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automatic 3d face authentication&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2160788824c4c29ffe213b2cbeb3f52972d73f37</td></tr><tr><td>large_scale_person_search</td><td>Large Scale Person Search</td><td>End-to-End Deep Learning for Person Search</td><td>End-to-End Deep Learning for Person Search</td><td><a href="https://pdfs.semanticscholar.org/2161/f6b7ee3c0acc81603b01dc0df689683577b9.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=end-to-end deep learning for person search&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2161f6b7ee3c0acc81603b01dc0df689683577b9</td></tr><tr><td>images_of_groups</td><td>Images of Groups</td><td>Understanding Groups of Images of People</td><td>Understanding images of groups of people</td><td><a href="http://amp.ece.cmu.edu/people/Andy/Andy_files/cvpr09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=understanding groups of images of people&sort=relevance" target="_blank">[s2]</a></td><td></td><td>21d9d0deed16f0ad62a4865e9acf0686f4f15492</td></tr><tr><td>rap_pedestrian</td><td>RAP</td><td>A Richly Annotated Dataset for Pedestrian Attribute Recognition</td><td>A Richly Annotated Dataset for Pedestrian Attribute Recognition</td><td><a href="http://pdfs.semanticscholar.org/221c/18238b829c12b911706947ab38fd017acef7.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a richly annotated dataset for pedestrian attribute recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>221c18238b829c12b911706947ab38fd017acef7</td></tr><tr><td>mot</td><td>MOT</td><td>Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</td><td>Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</td><td><a href="http://pdfs.semanticscholar.org/2e0b/00f4043e2d4b04c59c88bb54bcd907d0dcd4.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=evaluating multiple object tracking performance: the clear mot metrics&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2258e01865367018ed6f4262c880df85b94959f8</td></tr><tr><td>saivt</td><td>SAIVT SoftBio</td><td>A Database for Person Re-Identification in Multi-Camera Surveillance Networks</td><td>A Database for Person Re-Identification in Multi-Camera Surveillance Networks</td><td><a href="http://eprints.qut.edu.au/53437/3/Bialkowski_Database4PersonReID_DICTA.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a database for person re-identification in multi-camera surveillance networks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b</td></tr><tr><td>pets</td><td>PETS 2017</td><td>PETS 2017: Dataset and Challenge</td><td>PETS 2017: Dataset and Challenge</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8014998" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pets 2017: dataset and challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22909dd19a0ec3b6065334cb5be5392cb24d839d</td></tr><tr><td>gallagher</td><td>Gallagher</td><td>Clothing Cosegmentation for Recognizing People</td><td>Clothing cosegmentation for recognizing people</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2008.4587481" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clothing cosegmentation for recognizing people&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22ad2c8c0f4d6aa4328b38d894b814ec22579761</td></tr><tr><td>expw</td><td>ExpW</td><td>From Facial Expression Recognition to Interpersonal Relation Prediction</td><td>From Facial Expression Recognition to Interpersonal Relation Prediction</td><td><a href="http://arxiv.org/abs/1609.06426" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=from facial expression recognition to interpersonal relation prediction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td></tr><tr><td>cas_peal</td><td>CAS-PEAL</td><td>The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</td><td>The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</td><td><a href="https://doi.org/10.1109/TSMCA.2007.909557" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cas-peal large-scale chinese face database and baseline evaluations&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td></tr><tr><td>faceplace</td><td>Face Place</td><td>Recognizing disguised faces</td><td>Recognizing disguised faces</td><td><a href="http://pdfs.semanticscholar.org/d936/7ceb0be378c3a9ddf7cb741c678c1a3c574c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=recognizing disguised faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>25474c21613607f6bb7687a281d5f9d4ffa1f9f3</td></tr><tr><td>afew_va</td><td>AFEW-VA</td><td>AFEW-VA database for valence and arousal estimation in-the-wild</td><td>AFEW-VA database for valence and arousal estimation in-the-wild</td><td><a href="http://pdfs.semanticscholar.org/2624/d84503bc2f8e190e061c5480b6aa4d89277a.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=afew-va database for valence and arousal estimation in-the-wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2624d84503bc2f8e190e061c5480b6aa4d89277a</td></tr><tr><td>cofw</td><td>COFW</td><td>Robust face landmark estimation under occlusion</td><td>Robust Face Landmark Estimation under Occlusion</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6751298" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=robust face landmark estimation under occlusion&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2724ba85ec4a66de18da33925e537f3902f21249</td></tr><tr><td>duke_mtmc</td><td>Duke MTMC</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td><a href="http://pdfs.semanticscholar.org/b5f2/4f49f9a5e47d6601399dc068158ad88d7651.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=performance measures and a data set for multi-target, multi-camera tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td></tr><tr><td>mot</td><td>MOT</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td><a href="http://pdfs.semanticscholar.org/b5f2/4f49f9a5e47d6601399dc068158ad88d7651.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=performance measures and a data set for multi-target, multi-camera tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td></tr><tr><td>h3d</td><td>H3D</td><td>Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations</td><td>Poselets: Body part detectors trained using 3D human pose annotations</td><td><a href="http://vision.stanford.edu/teaching/cs231b_spring1213/papers/ICCV09_BourdevMalik.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=poselets: body part detectors trained using 3d human pose annotations&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2830fb5282de23d7784b4b4bc37065d27839a412</td></tr><tr><td>megaface</td><td>MegaFace</td><td>Level Playing Field for Million Scale Face Recognition</td><td>Level Playing Field for Million Scale Face Recognition</td><td><a href="https://arxiv.org/pdf/1705.00393.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=level playing field for million scale face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>28d4e027c7e90b51b7d8908fce68128d1964668a</td></tr><tr><td>msceleb</td><td>MsCeleb</td><td>MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</td><td>MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</td><td><a href="http://pdfs.semanticscholar.org/4603/cb8e05258bb0572ae912ad20903b8f99f4b1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ms-celeb-1m: a dataset and benchmark for large-scale face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>291265db88023e92bb8c8e6390438e5da148e8f5</td></tr><tr><td>ferplus</td><td>FER+</td><td>Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution</td><td>Training deep networks for facial expression recognition with crowd-sourced label distribution</td><td><a href="http://arxiv.org/pdf/1608.01041v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=training deep networks for facial expression recognition with crowd-sourced label distribution&sort=relevance" target="_blank">[s2]</a></td><td></td><td>298cbc3dfbbb3a20af4eed97906650a4ea1c29e0</td></tr><tr><td>scface</td><td>SCface</td><td>SCface – surveillance cameras face database</td><td>SCface – surveillance cameras face database</td><td><a href="http://scface.org/SCface%20-%20Surveillance%20Cameras%20Face%20Database.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scface – surveillance cameras face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d</td></tr><tr><td>peta</td><td>PETA</td><td>Pedestrian Attribute Recognition At Far Distance</td><td>Pedestrian Attribute Recognition At Far Distance</td><td><a href="http://personal.ie.cuhk.edu.hk/~pluo/pdf/mm14.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian attribute recognition at far distance&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2a4bbee0b4cf52d5aadbbc662164f7efba89566c</td></tr><tr><td>mmi_facial_expression</td><td>MMI Facial Expression Dataset</td><td>WEB-BASED DATABASE FOR FACIAL EXPRESSION ANALYSIS</td><td>Web-based database for facial expression analysis</td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/PanticEtAl-ICME2005-final.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=web-based database for facial expression analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2a75f34663a60ab1b04a0049ed1d14335129e908</td></tr><tr><td>bosphorus</td><td>The Bosphorus</td><td>Bosphorus Database for 3D Face Analysis</td><td>Bosphorus Database for 3D Face Analysis</td><td><a href="http://pdfs.semanticscholar.org/4254/fbba3846008f50671edc9cf70b99d7304543.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=bosphorus database for 3d face analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2acf7e58f0a526b957be2099c10aab693f795973</td></tr><tr><td>yale_faces</td><td>YaleFaces</td><td>Acquiring Linear Subspaces for Face Recognition under Variable Lighting</td><td>Acquiring linear subspaces for face recognition under variable lighting</td><td><a href="http://vision.cornell.edu/se3/wp-content/uploads/2014/09/pami05.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=acquiring linear subspaces for face recognition under variable lighting&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2ad0ee93d029e790ebb50574f403a09854b65b7e</td></tr><tr><td>frav3d</td><td>FRAV3D</td><td>MULTIMODAL 2D, 2.5D & 3D FACE VERIFICATION</td><td>Multimodal 2D, 2.5D & 3D Face Verification</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multimodal 2d, 2.5d & 3d face verification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2b926b3586399d028b46315d7d9fb9d879e4f79c</td></tr><tr><td>clothing_co_parsing</td><td>CCP</td><td>Clothing Co-Parsing by Joint Image Segmentation and Labeling</td><td>Clothing Co-parsing by Joint Image Segmentation and Labeling</td><td><a href="https://arxiv.org/pdf/1502.00739v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clothing co-parsing by joint image segmentation and labeling&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2bf8541199728262f78d4dced6fb91479b39b738</td></tr><tr><td>texas_3dfrd</td><td>Texas 3DFRD</td><td>Anthropometric 3D Face Recognition</td><td>Anthropometric 3D Face Recognition</td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ijcv_june10.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=anthropometric 3d face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2ce2560cf59db59ce313bbeb004e8ce55c5ce928</td></tr><tr><td>a_pascal_yahoo</td><td>aPascal</td><td>Describing Objects by their Attributes</td><td>Describing objects by their attributes</td><td><a href="http://www-2.cs.cmu.edu/~dhoiem/publications/cvpr2009_attributes.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=describing objects by their attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2e384f057211426ac5922f1b33d2aa8df5d51f57</td></tr><tr><td>3dpes</td><td>3DPeS</td><td>3DPes: 3D People Dataset for Surveillance and Forensics</td><td>3DPeS: 3D people dataset for surveillance and forensics</td><td><a href="http://doi.acm.org/10.1145/2072572.2072590" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=3dpes: 3d people dataset for surveillance and forensics&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e</td></tr><tr><td>qmul_grid</td><td>GRID</td><td>Time-delayed correlation analysis for multi-camera activity understanding</td><td>Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding</td><td><a href="https://doi.org/10.1007/s11263-010-0347-5" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=time-delayed correlation analysis for multi-camera activity understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2edb87494278ad11641b6cf7a3f8996de12b8e14</td></tr><tr><td>graz</td><td>Graz Pedestrian</td><td>Generic Object Recognition with Boosting</td><td>Generic object recognition with boosting</td><td><a href="http://www.emt.tu-graz.ac.at/~pinz/onlinepapers/Reprint_Vol_28_No_3_2006.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=generic object recognition with boosting&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9</td></tr><tr><td>names_and_faces_news</td><td>News Dataset</td><td>Names and Faces</td><td>Names and faces in the news</td><td><a href="http://www.cs.utexas.edu/~grauman/courses/spring2007/395T/papers/berg_names_and_faces.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=names and faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2fda164863a06a92d3a910b96eef927269aeb730</td></tr><tr><td>umd_faces</td><td>UMD</td><td>UMDFaces: An Annotated Face Dataset for Training Deep Networks</td><td>UMDFaces: An annotated face dataset for training deep networks</td><td><a href="http://arxiv.org/abs/1611.01484" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=umdfaces: an annotated face dataset for training deep networks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>31b05f65405534a696a847dd19c621b7b8588263</td></tr><tr><td>tiny_images</td><td>Tiny Images</td><td>80 million tiny images: a large dataset for non-parametric object and scene recognition</td><td>80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition</td><td><a href="http://pdfs.semanticscholar.org/31b5/8ced31f22eab10bd3ee2d9174e7c14c27c01.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=80 million tiny images: a large dataset for non-parametric object and scene recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>31b58ced31f22eab10bd3ee2d9174e7c14c27c01</td></tr><tr><td>feret</td><td>FERET</td><td>FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results</td><td>FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results</td><td><a href="http://pdfs.semanticscholar.org/31de/9b3dd6106ce6eec9a35991b2b9083395fd0b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=feret ( face recognition technology ) recognition algorithm development and test results&sort=relevance" target="_blank">[s2]</a></td><td></td><td>31de9b3dd6106ce6eec9a35991b2b9083395fd0b</td></tr><tr><td>ucf_crowd</td><td>UCF-CC-50</td><td>Multi-Source Multi-Scale Counting in Extremely Dense Crowd Images</td><td>Multi-source Multi-scale Counting in Extremely Dense Crowd Images</td><td><a href="http://www.cs.ucf.edu/~haroon/datafiles/Idrees_Counting_CVPR_2013.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-source multi-scale counting in extremely dense crowd images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>32c801cb7fbeb742edfd94cccfca4934baec71da</td></tr><tr><td>cityscapes</td><td>Cityscapes</td><td>The Cityscapes Dataset for Semantic Urban Scene Understanding</td><td>The Cityscapes Dataset for Semantic Urban Scene Understanding</td><td><a href="https://arxiv.org/pdf/1604.01685.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cityscapes dataset for semantic urban scene understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>32cde90437ab5a70cf003ea36f66f2de0e24b3ab</td></tr><tr><td>tud_campus</td><td>TUD-Campus</td><td>People-Tracking-by-Detection and People-Detection-by-Tracking</td><td>People-tracking-by-detection and people-detection-by-tracking</td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=people-tracking-by-detection and people-detection-by-tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td></tr><tr><td>tud_crossing</td><td>TUD-Crossing</td><td>People-Tracking-by-Detection and People-Detection-by-Tracking</td><td>People-tracking-by-detection and people-detection-by-tracking</td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=people-tracking-by-detection and people-detection-by-tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td></tr><tr><td>tud_pedestrian</td><td>TUD-Pedestrian</td><td>People-Tracking-by-Detection and People-Detection-by-Tracking</td><td>People-tracking-by-detection and people-detection-by-tracking</td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=people-tracking-by-detection and people-detection-by-tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td></tr><tr><td>mpii_human_pose</td><td>MPII Human Pose</td><td>2D Human Pose Estimation: New Benchmark and State of the Art Analysis</td><td>2D Human Pose Estimation: New Benchmark and State of the Art Analysis</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6909866" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=2d human pose estimation: new benchmark and state of the art analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3325860c0c82a93b2eac654f5324dd6a776f609e</td></tr><tr><td>penn_fudan</td><td>Penn Fudan</td><td>Object Detection Combining Recognition and Segmentation</td><td>Object Detection Combining Recognition and Segmentation</td><td><a href="http://pdfs.semanticscholar.org/f531/a554cade14b9b340de6730683a28c292dd74.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=object detection combining recognition and segmentation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3394168ff0719b03ff65bcea35336a76b21fe5e4</td></tr><tr><td>coco_qa</td><td>COCO QA</td><td>Exploring Models and Data for Image Question Answering</td><td>Exploring Models and Data for Image Question Answering</td><td><a href="http://pdfs.semanticscholar.org/aa79/9c29c0d44ece1864467af520fe70540c069b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=exploring models and data for image question answering&sort=relevance" target="_blank">[s2]</a></td><td></td><td>35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</td><td>Labeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments</td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: a database for studying face recognition in unconstrained environments&sort=relevance" target="_blank">[s2]</a></td><td></td><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td></tr><tr><td>rafd</td><td>RaFD</td><td>Presentation and validation of the Radboud Faces Database</td><td>Presentation and validation of the Radboud Faces Database</td><td><a href="https://pdfs.semanticscholar.org/3765/df816dc5a061bc261e190acc8bdd9d47bec0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=presentation and validation of the radboud faces database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3765df816dc5a061bc261e190acc8bdd9d47bec0</td></tr><tr><td>vmu</td><td>VMU</td><td>Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?</td><td>Can facial cosmetics affect the matching accuracy of face recognition systems?</td><td><a href="http://www.cse.msu.edu/~climer/DantchevaChenRossFaceCosmetics_BTAS2012.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=can facial cosmetics affect the matching accuracy of face recognition systems?&sort=relevance" target="_blank">[s2]</a></td><td></td><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td></tr><tr><td>youtube_makeup</td><td>YMU</td><td>Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?</td><td>Can facial cosmetics affect the matching accuracy of face recognition systems?</td><td><a href="http://www.cse.msu.edu/~climer/DantchevaChenRossFaceCosmetics_BTAS2012.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=can facial cosmetics affect the matching accuracy of face recognition systems?&sort=relevance" target="_blank">[s2]</a></td><td></td><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td></tr><tr><td>cuhk02</td><td>CUHK02</td><td>Locally Aligned Feature Transforms across Views</td><td>Locally Aligned Feature Transforms across Views</td><td><a href="http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/4989d594.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=locally aligned feature transforms across views&sort=relevance" target="_blank">[s2]</a></td><td></td><td>38b55d95189c5e69cf4ab45098a48fba407609b4</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</td><td>A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.434" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a spatio-temporal appearance representation for video-based pedestrian re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3b4ec8af470948a72a6ed37a9fd226719a874ebc</td></tr><tr><td>qmul_grid</td><td>GRID</td><td>Multi-Camera Activity Correlation Analysis</td><td>Multi-camera activity correlation analysis</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0163.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-camera activity correlation analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3b5b6d19d4733ab606c39c69a889f9e67967f151</td></tr><tr><td>tvhi</td><td>TVHI</td><td>High Five: Recognising human interactions in TV shows</td><td>High Five: Recognising human interactions in TV shows</td><td><a href="http://pdfs.semanticscholar.org/3cd4/0bfa1ff193a96bde0207e5140a399476466c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=high five: recognising human interactions in tv shows&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3cd40bfa1ff193a96bde0207e5140a399476466c</td></tr><tr><td>georgia_tech_face_database</td><td>Georgia Tech Face</td><td>Maximum likelihood training of the embedded HMM for face detection and recognition</td><td>MAXIMUM LIKELIHOOD TRAINING OF THE EMBEDDED HMM FOR FACE DETECTION AND RECOGNITION Ara V. Ne an and Monson H. Hayes III Center for Signal and Image Processing School of Electrical and Computer Engineering</td><td><a href="http://pdfs.semanticscholar.org/3dc3/f0b64ef80f573e3a5f96e456e52ee980b877.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=maximum likelihood training of the embedded hmm for face detection and recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3dc3f0b64ef80f573e3a5f96e456e52ee980b877</td></tr><tr><td>bio_id</td><td>BioID Face</td><td>Robust Face Detection Using the Hausdorff Distance</td><td>Robust Face Detection Using the Hausdorff Distance</td><td><a href="http://pdfs.semanticscholar.org/4053/e3423fb70ad9140ca89351df49675197196a.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=robust face detection using the hausdorff distance&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4053e3423fb70ad9140ca89351df49675197196a</td></tr><tr><td>distance_nighttime</td><td>Long Distance Heterogeneous Face</td><td>Nighttime Face Recognition at Long Distance: Cross-distance and Cross-spectral Matching</td><td>Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching</td><td><a href="http://pdfs.semanticscholar.org/4156/b7e88f2e0ab0a7c095b9bab199ae2b23bd06.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=nighttime face recognition at long distance: cross-distance and cross-spectral matching&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06</td></tr><tr><td>market_1501</td><td>Market 1501</td><td>Scalable Person Re-identification: A Benchmark</td><td>Scalable Person Re-identification: A Benchmark</td><td><a href="https://www.microsoft.com/en-us/research/wp-content/uploads/2017/01/ICCV15-ReIDDataset.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scalable person re-identification: a benchmark&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4308bd8c28e37e2ed9a3fcfe74d5436cce34b410</td></tr><tr><td>tud_multiview</td><td>TUD-Multiview</td><td>Monocular 3D Pose Estimation and Tracking by Detection</td><td>Monocular 3D pose estimation and tracking by detection</td><td><a href="http://lmb.informatik.uni-freiburg.de/lectures/seminar_brox/seminar_ws1011/cvpr10_andriluka.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=monocular 3d pose estimation and tracking by detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td></tr><tr><td>tud_stadtmitte</td><td>TUD-Stadtmitte</td><td>Monocular 3D Pose Estimation and Tracking by Detection</td><td>Monocular 3D pose estimation and tracking by detection</td><td><a href="http://lmb.informatik.uni-freiburg.de/lectures/seminar_brox/seminar_ws1011/cvpr10_andriluka.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=monocular 3d pose estimation and tracking by detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td></tr><tr><td>cuhk01</td><td>CUHK01</td><td>Human Reidentification with Transferred Metric Learning</td><td>Human Reidentification with Transferred Metric Learning</td><td><a href="http://pdfs.semanticscholar.org/4448/4d2866f222bbb9b6b0870890f9eea1ffb2d0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=human reidentification with transferred metric learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>44484d2866f222bbb9b6b0870890f9eea1ffb2d0</td></tr><tr><td>vadana</td><td>VADANA</td><td>VADANA: A dense dataset for facial image analysis</td><td>VADANA: A dense dataset for facial image analysis</td><td><a href="http://vims.cis.udel.edu/publications/VADANA_BeFIT2011.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vadana: a dense dataset for facial image analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4563b46d42079242f06567b3f2e2f7a80cb3befe</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>Local descriptors encoded by Fisher vectors for person re-identification</td><td>Local Descriptors Encoded by Fisher Vectors for Person Re-identification</td><td><a href="http://pdfs.semanticscholar.org/dd1d/51c3a59cb71cbfe1433ebeb4d973f7f9ddc1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=local descriptors encoded by fisher vectors for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>46a01565e6afe7c074affb752e7069ee3bf2e4ef</td></tr><tr><td>fia</td><td>CMU FiA</td><td>The CMU Face In Action (FIA) Database</td><td>The CMU Face In Action (FIA) Database</td><td><a href="http://pdfs.semanticscholar.org/bb47/a03401811f9d2ca2da12138697acbc7b97a3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cmu face in action (fia) database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>47662d1a368daf70ba70ef2d59eb6209f98b675d</td></tr><tr><td>kin_face</td><td>UB KinFace</td><td>Kinship Verification through Transfer Learning</td><td>Kinship Verification through Transfer Learning</td><td><a href="http://pdfs.semanticscholar.org/4793/f11fbca4a7dba898b9fff68f70d868e2497c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=kinship verification through transfer learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4793f11fbca4a7dba898b9fff68f70d868e2497c</td></tr><tr><td>am_fed</td><td>AM-FED</td><td>Affectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”</td><td>Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"</td><td><a href="http://pdfs.semanticscholar.org/5d06/437656dd94616d7d87260d5eb77513ded30f.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=affectiva mit facial expression dataset (am-fed): naturalistic and spontaneous facial expressions collected “in the wild”&sort=relevance" target="_blank">[s2]</a></td><td></td><td>47aeb3b82f54b5ae8142b4bdda7b614433e69b9a</td></tr><tr><td>apis</td><td>APiS1.0</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W10/papers/Zhu_Pedestrian_Attribute_Classification_2013_ICCV_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian attribute classification in surveillance: database and evaluation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td></tr><tr><td>svs</td><td>SVS</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W10/papers/Zhu_Pedestrian_Attribute_Classification_2013_ICCV_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian attribute classification in surveillance: database and evaluation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td></tr><tr><td>coco_action</td><td>COCO-a</td><td>Describing Common Human Visual Actions in Images</td><td>Describing Common Human Visual Actions in Images</td><td><a href="http://pdfs.semanticscholar.org/b38d/cf5fa5174c0d718d65cc4f3889b03c4a21df.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=describing common human visual actions in images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4946ba10a4d5a7d0a38372f23e6622bd347ae273</td></tr><tr><td>leeds_sports_pose</td><td>Leeds Sports Pose</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clustered pose and nonlinear appearance models for human pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td></tr><tr><td>stickmen_buffy</td><td>Buffy Stickmen</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clustered pose and nonlinear appearance models for human pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td></tr><tr><td>czech_news_agency</td><td>UFI</td><td>Unconstrained Facial Images: Database for Face Recognition under Real-world Conditions</td><td>Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions</td><td><a href="http://pdfs.semanticscholar.org/4b41/06614c1d553365bad75d7866bff0de6056ed.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=unconstrained facial images: database for face recognition under real-world conditions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b4106614c1d553365bad75d7866bff0de6056ed</td></tr><tr><td>cmu_pie</td><td>CMU PIE</td><td>The CMU Pose, Illumination, and Expression Database</td><td>The CMU Pose, Illumination, and Expression (PIE) Database</td><td><a href="http://pdfs.semanticscholar.org/4d42/3acc78273b75134e2afd1777ba6d3a398973.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cmu pose, illumination, and expression database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d423acc78273b75134e2afd1777ba6d3a398973</td></tr><tr><td>multi_pie</td><td>MULTIPIE</td><td>Multi-PIE</td><td>The CMU Pose, Illumination, and Expression (PIE) Database</td><td><a href="http://pdfs.semanticscholar.org/4d42/3acc78273b75134e2afd1777ba6d3a398973.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-pie&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d423acc78273b75134e2afd1777ba6d3a398973</td></tr><tr><td>3dddb_unconstrained</td><td>3D Dynamic</td><td>A 3D Dynamic Database for Unconstrained Face Recognition</td><td>A 3 D Dynamic Database for Unconstrained Face Recognition</td><td><a href="https://pdfs.semanticscholar.org/4d4b/b462c9f1d4e4ab1e4aa6a75cc0bc71b38461.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3d dynamic database for unconstrained face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461</td></tr><tr><td>texas_3dfrd</td><td>Texas 3DFRD</td><td>Texas 3D Face Recognition Database</td><td>Texas 3D Face Recognition Database</td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ssiai_may10.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=texas 3d face recognition database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d58f886f5150b2d5e48fd1b5a49e09799bf895d</td></tr><tr><td>cohn_kanade_plus</td><td>CK+</td><td>The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression</td><td>The Extended 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target="_blank">[s2]</a></td><td></td><td>4df3143922bcdf7db78eb91e6b5359d6ada004d2</td></tr><tr><td>leeds_sports_pose_extended</td><td>Leeds Sports Pose Extended</td><td>Learning Effective Human Pose Estimation from Inaccurate Annotation</td><td>Learning effective human pose estimation from inaccurate annotation</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995318" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning effective human pose estimation from inaccurate annotation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4e4746094bf60ee83e40d8597a6191e463b57f76</td></tr><tr><td>dartmouth_children</td><td>Dartmouth Children</td><td>The Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus set</td><td>The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set</td><td><a href="http://pdfs.semanticscholar.org/4e6e/e936eb50dd032f7138702fa39b7c18ee8907.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the dartmouth database of children's faces: acquisition and validation of a new face stimulus set&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4e6ee936eb50dd032f7138702fa39b7c18ee8907</td></tr><tr><td>soton</td><td>SOTON HiD</td><td>On a Large Sequence-Based Human Gait Database</td><td>On a large sequence-based human gait database</td><td><a href="http://pdfs.semanticscholar.org/4f93/cd09785c6e77bf4bc5a788e079df524c8d21.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=on a large sequence-based human gait database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4f93cd09785c6e77bf4bc5a788e079df524c8d21</td></tr><tr><td>violent_flows</td><td>Violent Flows</td><td>Violent Flows: Real-Time Detection of Violent Crowd Behavior</td><td>Violent flows: Real-time detection of violent crowd behavior</td><td><a href="http://www.wisdom.weizmann.ac.il/mathusers/kliper/Papers/violent_flows.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=violent flows: real-time detection of violent crowd behavior&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5194cbd51f9769ab25260446b4fa17204752e799</td></tr><tr><td>bp4d_plus</td><td>BP4D+</td><td>Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</td><td>Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhang_Multimodal_Spontaneous_Emotion_CVPR_2016_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multimodal spontaneous emotion corpus for human behavior analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4</td></tr><tr><td>reseed</td><td>ReSEED</td><td>ReSEED: Social Event dEtection Dataset</td><td>ReSEED: social event dEtection dataset</td><td><a href="https://pub.uni-bielefeld.de/download/2663466/2686734" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=reseed: social event detection dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>54983972aafc8e149259d913524581357b0f91c3</td></tr><tr><td>orl</td><td>ORL</td><td>Parameterisation of a Stochastic Model for Human Face Identification</td><td>Parameterisation of a stochastic model for human face identification</td><td><a href="http://pdfs.semanticscholar.org/5520/6f0b5f57ce17358999145506cd01e570358c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=parameterisation of a stochastic model for human face identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>55206f0b5f57ce17358999145506cd01e570358c</td></tr><tr><td>ifad</td><td>IFAD</td><td>Indian Face Age Database: A Database for Face Recognition with Age Variation</td><td>Indian Face Age Database : A Database for Face Recognition with Age Variation</td><td><a href="https://pdfs.semanticscholar.org/55c4/0cbcf49a0225e72d911d762c27bb1c2d14aa.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=indian face age database: a database for face recognition with age variation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>55c40cbcf49a0225e72d911d762c27bb1c2d14aa</td></tr><tr><td>data_61</td><td>Data61 Pedestrian</td><td>A Multi-Modal Graphical Model for Scene Analysis</td><td>A Multi-modal Graphical Model for Scene Analysis</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/WACV.2015.139" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a multi-modal graphical model for scene analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>563c940054e4b456661762c1ab858e6f730c3159</td></tr><tr><td>unbc_shoulder_pain</td><td>UNBC-McMaster Pain</td><td>PAINFUL DATA: The UNBC-McMaster Shoulder Pain Expression Archive Database</td><td>Painful data: The UNBC-McMaster shoulder pain expression archive database</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5771462" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=painful data: the unbc-mcmaster shoulder pain expression archive database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>56ffa7d906b08d02d6d5a12c7377a57e24ef3391</td></tr><tr><td>stanford_drone</td><td>Stanford Drone</td><td>Learning Social Etiquette: Human Trajectory Prediction In Crowded Scenes</td><td>Social LSTM: Human Trajectory Prediction in Crowded Spaces</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning social etiquette: human trajectory prediction in crowded scenes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>570f37ed63142312e6ccdf00ecc376341ec72b9f</td></tr><tr><td>ijb_c</td><td>IJB-C</td><td>IARPA Janus Benchmark C</td><td>IARPA Janus Benchmark - C: Face Dataset and Protocol</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8411217" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iarpa janus benchmark c&sort=relevance" target="_blank">[s2]</a></td><td></td><td>57178b36c21fd7f4529ac6748614bb3374714e91</td></tr><tr><td>50_people_one_question</td><td>50 People One Question</td><td>Merging Pose Estimates Across Space and Time</td><td>Merging Pose Estimates Across Space and Time</td><td><a href="http://pdfs.semanticscholar.org/63b2/f5348af0f969dfc2afb4977732393c6459ec.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=merging pose estimates across space and time&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725</td></tr><tr><td>mr2</td><td>MR2</td><td>The MR2: A multi-racial mega-resolution database of facial stimuli</td><td>The MR2: A multi-racial, mega-resolution database of facial stimuli.</td><td><a href="https://pdfs.semanticscholar.org/be5b/455abd379240460d022a0e246615b0b86c14.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mr2: a multi-racial mega-resolution database of facial stimuli&sort=relevance" target="_blank">[s2]</a></td><td></td><td>578d4ad74818086bb64f182f72e2c8bd31e3d426</td></tr><tr><td>cvc_01_barcelona</td><td>CVC-01</td><td>Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection</td><td>Adaptive Image Sampling and Windows Classification for On – board Pedestrian Detection</td><td><a href="http://pdfs.semanticscholar.org/57fe/081950f21ca03b5b375ae3e84b399c015861.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=adaptive image sampling and windows classification for on-board pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>57fe081950f21ca03b5b375ae3e84b399c015861</td></tr><tr><td>mot</td><td>MOT</td><td>Learning to associate: HybridBoosted multi-target tracker for crowded scene</td><td>Learning to associate: HybridBoosted multi-target tracker for crowded scene</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0633.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to associate: hybridboosted multi-target tracker for crowded scene&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5981e6479c3fd4e31644db35d236bfb84ae46514</td></tr><tr><td>disfa</td><td>DISFA</td><td>DISFA: A Spontaneous Facial Action Intensity Database</td><td>DISFA: A Spontaneous Facial Action Intensity Database</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/T-AFFC.2013.4" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=disfa: a spontaneous facial action intensity database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5a5f0287484f0d480fed1ce585dbf729586f0edc</td></tr><tr><td>wlfdb</td><td>WLFDB</td><td>WLFDB: Weakly Labeled Face Databases</td><td>WLFDB: Weakly Labeled Face Databases</td><td><a href="https://pdfs.semanticscholar.org/5ad4/e9f947c1653c247d418f05dad758a3f9277b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=wlfdb: weakly labeled face databases&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5ad4e9f947c1653c247d418f05dad758a3f9277b</td></tr><tr><td>coco</td><td>COCO</td><td>Microsoft COCO: Common Objects in Context</td><td>Microsoft COCO: Common Objects in Context</td><td><a href="http://pdfs.semanticscholar.org/71b7/178df5d2b112d07e45038cb5637208659ff7.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=microsoft coco: common objects in context&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5e0f8c355a37a5a89351c02f174e7a5ddcb98683</td></tr><tr><td>cityscapes</td><td>Cityscapes</td><td>The Cityscapes Dataset</td><td>The Cityscapes Dataset</td><td><a href="http://pdfs.semanticscholar.org/5ffd/74d2873b7cba2cbc5fd295cc7fbdedca22a2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cityscapes dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5ffd74d2873b7cba2cbc5fd295cc7fbdedca22a2</td></tr><tr><td>viper</td><td>VIPeR</td><td>Evaluating Appearance Models for Recognition, Reacquisition, and Tracking</td><td>Evaluating Appearance Models for Recognition , Reacquisition , and Tracking</td><td><a href="http://pdfs.semanticscholar.org/6273/b3491e94ea4dd1ce42b791d77bdc96ee73a8.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=evaluating appearance models for recognition, reacquisition, and tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6273b3491e94ea4dd1ce42b791d77bdc96ee73a8</td></tr><tr><td>caltech_10k_web_faces</td><td>Caltech 10K Web Faces</td><td>Pruning Training Sets for Learning of Object Categories</td><td>Pruning training sets for learning of object categories</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1467308" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pruning training sets for learning of object categories&sort=relevance" target="_blank">[s2]</a></td><td></td><td>636b8ffc09b1b23ff714ac8350bb35635e49fa3c</td></tr><tr><td>bfm</td><td>BFM</td><td>A 3D Face Model for Pose and Illumination Invariant Face Recognition</td><td>A 3D Face Model for Pose and Illumination Invariant Face Recognition</td><td><a href="https://pdfs.semanticscholar.org/6399/37b3a1b8bded3f7e9a40e85bd3770016cf3c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3d face model for pose and illumination invariant face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>639937b3a1b8bded3f7e9a40e85bd3770016cf3c</td></tr><tr><td>alert_airport</td><td>ALERT Airport</td><td>A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets</td><td>A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.</td><td><a href="https://arxiv.org/pdf/1605.09653.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a systematic evaluation and benchmark for person re-identification: features, metrics, and datasets&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6403117f9c005ae81f1e8e6d1302f4a045e3d99d</td></tr><tr><td>complex_activities</td><td>Ongoing Complex Activities</td><td>Recognition of Ongoing Complex Activities by Sequence Prediction over a Hierarchical Label Space</td><td>Recognition of ongoing complex activities by sequence prediction over a hierarchical label space</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/WACV.2016.7477586" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=recognition of ongoing complex activities by sequence prediction over a hierarchical label space&sort=relevance" target="_blank">[s2]</a></td><td></td><td>65355cbb581a219bd7461d48b3afd115263ea760</td></tr><tr><td>afad</td><td>AFAD</td><td>Ordinal Regression with a Multiple Output CNN for Age Estimation</td><td>Ordinal Regression with Multiple Output CNN for Age Estimation</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.532" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ordinal regression with a multiple output cnn for age estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c</td></tr><tr><td>sun_attributes</td><td>SUN</td><td>The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</td><td>The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</td><td><a href="http://www.cc.gatech.edu/~hays/papers/attribute_ijcv.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the sun attribute database: beyond categories for deeper scene understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>66e6f08873325d37e0ec20a4769ce881e04e964e</td></tr><tr><td>tud_brussels</td><td>TUD-Brussels</td><td>Multi-Cue Onboard Pedestrian Detection</td><td>Multi-cue onboard pedestrian detection</td><td><a href="https://www.mpi-inf.mpg.de/fileadmin/inf/d2/wojek/poster_cwojek_cvpr09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-cue onboard pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td></tr><tr><td>tud_motionpairs</td><td>TUD-Motionparis</td><td>Multi-Cue Onboard Pedestrian Detection</td><td>Multi-cue onboard pedestrian detection</td><td><a href="https://www.mpi-inf.mpg.de/fileadmin/inf/d2/wojek/poster_cwojek_cvpr09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-cue onboard pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td></tr><tr><td>cuhk03</td><td>CUHK03</td><td>DeepReID: Deep Filter Pairing Neural Network for Person Re-identification</td><td>DeepReID: Deep Filter Pairing Neural Network for Person Re-identification</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Li_DeepReID_Deep_Filter_2014_CVPR_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deepreid: deep filter pairing neural network for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3</td></tr><tr><td>ar_facedb</td><td>AR Face</td><td>The AR Face Database</td><td>The AR face database</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the ar face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6d96f946aaabc734af7fe3fc4454cf8547fcd5ed</td></tr><tr><td>agedb</td><td>AgeDB</td><td>AgeDB: the first manually collected, in-the-wild age database</td><td>AgeDB: The First Manually Collected, In-the-Wild Age Database</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8014984" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=agedb: the first manually collected, in-the-wild age database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dcf418c778f528b5792104760f1fbfe90c6dd6a</td></tr><tr><td>stickmen_buffy</td><td>Buffy Stickmen</td><td>Learning to Parse Images of Articulated Objects</td><td>Learning to parse images of articulated bodies</td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to parse images of articulated objects&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td>Learning to parse images of articulated bodies</td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clustered pose and nonlinear appearance models for human pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td>Learning to Parse Images of Articulated Objects</td><td>Learning to parse images of articulated bodies</td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to parse images of articulated objects&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>ward</td><td>WARD</td><td>Re-identify people in wide area camera network</td><td>Re-identify people in wide area camera network</td><td><a href="https://doi.org/10.1109/CVPRW.2012.6239203" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=re-identify people in wide area camera network&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6f3c76b7c0bd8e1d122c6ea808a271fd4749c951</td></tr><tr><td>umd_faces</td><td>UMD</td><td>The Do's and Don'ts for CNN-based Face Verification</td><td>The Do’s and Don’ts for CNN-Based Face Verification</td><td><a href="https://arxiv.org/pdf/1705.07426.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the do's and don'ts for cnn-based face verification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6</td></tr><tr><td>affectnet</td><td>AffectNet</td><td>AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild</td><td>AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild</td><td><a href="https://arxiv.org/pdf/1708.03985.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=affectnet: a database for facial expression, valence, and arousal computing in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>758d7e1be64cc668c59ef33ba8882c8597406e53</td></tr><tr><td>wildtrack</td><td>WildTrack</td><td>WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</td><td>WILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</td><td><a href="https://pdfs.semanticscholar.org/fe1c/ec4e4995b8615855572374ae3efc94949105.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=wildtrack: a multi-camera hd dataset for dense unscripted pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>77c81c13a110a341c140995bedb98101b9e84f7f</td></tr><tr><td>berkeley_pose</td><td>BPAD</td><td>Describing People: A Poselet-Based Approach to Attribute Classification</td><td>Describing people: A poselet-based approach to attribute classification</td><td><a href="http://ttic.uchicago.edu/~smaji/papers/attributes-iccv11.pdf" 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for nude detection</td><td><a href="http://ieeexplore.ieee.org/document/7077625/" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a bag-of-features approach based on hue-sift descriptor for nude detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7ace44190729927e5cb0dd5d363fcae966fe13f7</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: A Survey</td><td>Labeled Faces in the Wild : A Survey</td><td><a href="http://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: a survey&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td></tr><tr><td>nova_emotions</td><td>Novaemötions Dataset</td><td>Competitive affective gamming: Winning with a smile</td><td>Competitive affective gaming: winning with a smile</td><td><a href="http://doi.acm.org/10.1145/2502081.2502115" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=competitive affective gamming: winning with a smile&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7f4040b482d16354d5938c1d1b926b544652bf5b</td></tr><tr><td>sun_attributes</td><td>SUN</td><td>SUN Attribute Database: Discovering, Annotating, and Recognizing Scene Attributes</td><td>SUN attribute database: Discovering, annotating, and recognizing scene attributes</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6247998" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=sun attribute database: -discovering, annotating, and recognizing scene attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>833fa04463d90aab4a9fe2870d480f0b40df446e</td></tr><tr><td>svs</td><td>SVS</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W10/papers/Zhu_Pedestrian_Attribute_Classification_2013_ICCV_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian attribute classification in surveillance: database and evaluation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td></tr><tr><td>texas_3dfrd</td><td>Texas 3DFRD</td><td>Texas 3D Face Recognition Database</td><td>Anthropometric 3D Face Recognition</td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ijcv_june10.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=texas 3d face recognition database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2ce2560cf59db59ce313bbeb004e8ce55c5ce928</td></tr><tr><td>texas_3dfrd</td><td>Texas 3DFRD</td><td>Anthropometric 3D Face Recognition</td><td>Anthropometric 3D Face Recognition</td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ijcv_june10.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=anthropometric 3d face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2ce2560cf59db59ce313bbeb004e8ce55c5ce928</td></tr><tr><td>tiny_faces</td><td>TinyFace</td><td>Low-Resolution Face Recognition</td><td>Low-Resolution Face Recognition</td><td><a href="https://arxiv.org/pdf/1811.08965.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=low-resolution face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8990cdce3f917dad622e43e033db686b354d057c</td></tr><tr><td>tiny_images</td><td>Tiny Images</td><td>80 million tiny images: a large dataset for non-parametric object and scene recognition</td><td>80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition</td><td><a href="http://pdfs.semanticscholar.org/31b5/8ced31f22eab10bd3ee2d9174e7c14c27c01.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=80 million tiny images: a large dataset for non-parametric object and scene recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>31b58ced31f22eab10bd3ee2d9174e7c14c27c01</td></tr><tr><td>towncenter</td><td>TownCenter</td><td>Stable Multi-Target Tracking in Real-Time Surveillance Video</td><td>Stable multi-target tracking in real-time surveillance video</td><td><a href="http://pdfs.semanticscholar.org/9361/b784e73e9238d5cefbea5ac40d35d1e3103f.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=stable multi-target tracking in real-time surveillance video&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9361b784e73e9238d5cefbea5ac40d35d1e3103f</td></tr><tr><td>tud_brussels</td><td>TUD-Brussels</td><td>Multi-Cue Onboard Pedestrian Detection</td><td>Multi-cue onboard pedestrian detection</td><td><a href="https://www.mpi-inf.mpg.de/fileadmin/inf/d2/wojek/poster_cwojek_cvpr09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-cue onboard pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td></tr><tr><td>tud_campus</td><td>TUD-Campus</td><td>People-Tracking-by-Detection and People-Detection-by-Tracking</td><td>People-tracking-by-detection and people-detection-by-tracking</td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=people-tracking-by-detection and people-detection-by-tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td></tr><tr><td>tud_crossing</td><td>TUD-Crossing</td><td>People-Tracking-by-Detection and People-Detection-by-Tracking</td><td>People-tracking-by-detection and people-detection-by-tracking</td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=people-tracking-by-detection and people-detection-by-tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td></tr><tr><td>tud_motionpairs</td><td>TUD-Motionparis</td><td>Multi-Cue Onboard Pedestrian Detection</td><td>Multi-cue onboard pedestrian detection</td><td><a href="https://www.mpi-inf.mpg.de/fileadmin/inf/d2/wojek/poster_cwojek_cvpr09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-cue onboard pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td></tr><tr><td>tud_multiview</td><td>TUD-Multiview</td><td>Monocular 3D Pose Estimation and Tracking by Detection</td><td>Monocular 3D pose estimation and tracking by detection</td><td><a href="http://lmb.informatik.uni-freiburg.de/lectures/seminar_brox/seminar_ws1011/cvpr10_andriluka.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=monocular 3d pose estimation and tracking by detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td></tr><tr><td>tud_pedestrian</td><td>TUD-Pedestrian</td><td>People-Tracking-by-Detection and People-Detection-by-Tracking</td><td>People-tracking-by-detection and people-detection-by-tracking</td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=people-tracking-by-detection and people-detection-by-tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td></tr><tr><td>tud_stadtmitte</td><td>TUD-Stadtmitte</td><td>Monocular 3D Pose Estimation and Tracking by Detection</td><td>Monocular 3D pose estimation and tracking by detection</td><td><a href="http://lmb.informatik.uni-freiburg.de/lectures/seminar_brox/seminar_ws1011/cvpr10_andriluka.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=monocular 3d pose estimation and tracking by detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td></tr><tr><td>tvhi</td><td>TVHI</td><td>High Five: Recognising human interactions in TV shows</td><td>High Five: Recognising human interactions in TV shows</td><td><a href="http://pdfs.semanticscholar.org/3cd4/0bfa1ff193a96bde0207e5140a399476466c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=high five: recognising human interactions in tv shows&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3cd40bfa1ff193a96bde0207e5140a399476466c</td></tr><tr><td>ucf_crowd</td><td>UCF-CC-50</td><td>Multi-Source Multi-Scale Counting in Extremely Dense Crowd Images</td><td>Multi-source Multi-scale Counting in Extremely Dense Crowd Images</td><td><a href="http://www.cs.ucf.edu/~haroon/datafiles/Idrees_Counting_CVPR_2013.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-source multi-scale counting in extremely dense crowd images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>32c801cb7fbeb742edfd94cccfca4934baec71da</td></tr><tr><td>ucf_selfie</td><td>UCF Selfie</td><td>How to Take a Good Selfie?</td><td>How to Take a Good Selfie?</td><td><a href="http://doi.acm.org/10.1145/2733373.2806365" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=how to take a good selfie?&sort=relevance" target="_blank">[s2]</a></td><td></td><td>041d3eedf5e45ce5c5229f0181c5c576ed1fafd6</td></tr><tr><td>umb</td><td>UMB</td><td>UMB-DB: A Database of Partially Occluded 3D Faces</td><td>UMB-DB: A database of partially occluded 3D faces</td><td><a href="https://doi.org/10.1109/ICCVW.2011.6130509" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=umb-db: a database of partially occluded 3d faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>16e8b0a1e8451d5f697b94c0c2b32a00abee1d52</td></tr><tr><td>umd_faces</td><td>UMD</td><td>UMDFaces: An Annotated Face Dataset for Training Deep Networks</td><td>UMDFaces: An annotated face dataset for training deep networks</td><td><a href="http://arxiv.org/abs/1611.01484" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=umdfaces: an annotated face dataset for training deep networks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>31b05f65405534a696a847dd19c621b7b8588263</td></tr><tr><td>umd_faces</td><td>UMD</td><td>The Do's and Don'ts for CNN-based Face Verification</td><td>The Do’s and Don’ts for CNN-Based Face Verification</td><td><a href="https://arxiv.org/pdf/1705.07426.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the do's and don'ts for cnn-based face verification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6</td></tr><tr><td>unbc_shoulder_pain</td><td>UNBC-McMaster Pain</td><td>PAINFUL DATA: The UNBC-McMaster Shoulder Pain Expression Archive Database</td><td>Painful data: The UNBC-McMaster shoulder pain expression archive database</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5771462" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=painful data: the unbc-mcmaster shoulder pain expression archive database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>56ffa7d906b08d02d6d5a12c7377a57e24ef3391</td></tr><tr><td>used</td><td>USED Social Event Dataset</td><td>USED: A Large-scale Social Event Detection Dataset</td><td>USED: a large-scale social event detection dataset</td><td><a href="http://doi.acm.org/10.1145/2910017.2910624" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=used: a large-scale social event detection dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8627f019882b024aef92e4eb9355c499c733e5b7</td></tr><tr><td>v47</td><td>V47</td><td>Re-identification of Pedestrians with Variable Occlusion and Scale</td><td>Re-identification of pedestrians with variable occlusion and scale</td><td><a href="https://doi.org/10.1109/ICCVW.2011.6130477" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=re-identification of pedestrians with variable occlusion and scale&sort=relevance" target="_blank">[s2]</a></td><td></td><td>922e0a51a3b8c67c4c6ac09a577ff674cbd28b34</td></tr><tr><td>vadana</td><td>VADANA</td><td>VADANA: A dense dataset for facial image analysis</td><td>VADANA: A dense dataset for facial image analysis</td><td><a href="http://vims.cis.udel.edu/publications/VADANA_BeFIT2011.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vadana: a dense dataset for facial image analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4563b46d42079242f06567b3f2e2f7a80cb3befe</td></tr><tr><td>vgg_faces</td><td>VGG Face</td><td>Deep Face Recognition</td><td>Deep Face Recognition</td><td><a href="https://pdfs.semanticscholar.org/f372/ab9b3270d4e4f6a0258c83c2736c3a5c0454.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>162ea969d1929ed180cc6de9f0bf116993ff6e06</td></tr><tr><td>violent_flows</td><td>Violent Flows</td><td>Violent Flows: Real-Time Detection of Violent Crowd Behavior</td><td>Violent flows: Real-time detection of violent crowd behavior</td><td><a href="http://www.wisdom.weizmann.ac.il/mathusers/kliper/Papers/violent_flows.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=violent flows: real-time detection of violent crowd behavior&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5194cbd51f9769ab25260446b4fa17204752e799</td></tr><tr><td>viper</td><td>VIPeR</td><td>Evaluating Appearance Models for Recognition, Reacquisition, and Tracking</td><td>Evaluating Appearance Models for Recognition , Reacquisition , and Tracking</td><td><a href="http://pdfs.semanticscholar.org/6273/b3491e94ea4dd1ce42b791d77bdc96ee73a8.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=evaluating appearance models for recognition, reacquisition, and tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6273b3491e94ea4dd1ce42b791d77bdc96ee73a8</td></tr><tr><td>visual_phrases</td><td>Phrasal Recognition</td><td>Recognition using Visual Phrases</td><td>Recognition using visual phrases</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995711" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=recognition using visual phrases&sort=relevance" target="_blank">[s2]</a></td><td></td><td>e8de844fefd54541b71c9823416daa238be65546</td></tr><tr><td>vmu</td><td>VMU</td><td>Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?</td><td>Can facial cosmetics affect the matching accuracy of face recognition systems?</td><td><a href="http://www.cse.msu.edu/~climer/DantchevaChenRossFaceCosmetics_BTAS2012.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=can facial cosmetics affect the matching accuracy of face recognition systems?&sort=relevance" target="_blank">[s2]</a></td><td></td><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td></tr><tr><td>voc</td><td>VOC</td><td>The PASCAL Visual Object Classes (VOC) Challenge</td><td>The Pascal Visual Object Classes Challenge: A Retrospective</td><td><a href="http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc14.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the pascal visual object classes (voc) challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>abe9f3b91fd26fa1b50cd685c0d20debfb372f73</td></tr><tr><td>vqa</td><td>VQA</td><td>VQA: Visual Question Answering</td><td>VQA: Visual Question Answering</td><td><a href="http://arxiv.org/pdf/1505.00468v3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vqa: visual question answering&sort=relevance" target="_blank">[s2]</a></td><td></td><td>01959ef569f74c286956024866c1d107099199f7</td></tr><tr><td>ward</td><td>WARD</td><td>Re-identify people in wide area camera network</td><td>Re-identify people in wide area camera network</td><td><a href="https://doi.org/10.1109/CVPRW.2012.6239203" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=re-identify people in wide area camera network&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6f3c76b7c0bd8e1d122c6ea808a271fd4749c951</td></tr><tr><td>who_goes_there</td><td>WGT</td><td>Who Goes There? Approaches to Mapping Facial Appearance Diversity</td><td>Who goes there?: approaches to mapping facial appearance diversity</td><td><a href="http://doi.acm.org/10.1145/2996913.2996997" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=who goes there? approaches to mapping facial appearance diversity&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9b9bf5e623cb8af7407d2d2d857bc3f1b531c182</td></tr><tr><td>wildtrack</td><td>WildTrack</td><td>WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</td><td>WILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</td><td><a href="https://pdfs.semanticscholar.org/fe1c/ec4e4995b8615855572374ae3efc94949105.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=wildtrack: a multi-camera hd dataset for dense unscripted pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>77c81c13a110a341c140995bedb98101b9e84f7f</td></tr><tr><td>wlfdb</td><td></td><td>WLFDB: Weakly Labeled Face Databases</td><td>WLFDB: Weakly Labeled Face Databases</td><td><a href="https://pdfs.semanticscholar.org/5ad4/e9f947c1653c247d418f05dad758a3f9277b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=wlfdb: weakly labeled face databases&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5ad4e9f947c1653c247d418f05dad758a3f9277b</td></tr><tr><td>yale_faces</td><td>YaleFaces</td><td>Acquiring Linear Subspaces for Face Recognition under Variable Lighting</td><td>From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</td><td><a href="http://pdfs.semanticscholar.org/97bb/c2b439a79d4dc0dc7199d71ed96ad5e3fd0e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=acquiring linear subspaces for face recognition under variable lighting&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18c72175ddbb7d5956d180b65a96005c100f6014</td></tr><tr><td>yale_faces</td><td>YaleFaces</td><td>From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</td><td>From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</td><td><a href="http://pdfs.semanticscholar.org/97bb/c2b439a79d4dc0dc7199d71ed96ad5e3fd0e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=from few to many: illumination cone models for face recognition under variable lighting and pose&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18c72175ddbb7d5956d180b65a96005c100f6014</td></tr><tr><td>yawdd</td><td>YawDD</td><td>YawDD: A Yawning Detection Dataset</td><td>YawDD: a yawning detection dataset</td><td><a href="http://www.site.uottawa.ca/~shervin/pubs/CogniVue-Dataset-ACM-MMSys2014.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=yawdd: a yawning detection dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a94cae786d515d3450d48267e12ca954aab791c4</td></tr><tr><td>yfcc_100m</td><td>YFCC100M</td><td>YFCC100M: The New Data in Multimedia Research</td><td>The New Data and New Challenges in Multimedia Research</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=yfcc100m: the new data in multimedia research&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a6e695ddd07aad719001c0fc1129328452385949</td></tr><tr><td>york_3d</td><td>UOY 3D Face Database</td><td>Three-Dimensional Face Recognition: An Eigensurface Approach</td><td>Three-dimensional face recognition: an eigensurface approach</td><td><a href="http://www-users.cs.york.ac.uk/~nep/research/3Dface/tomh/3DFaceRecognition-Eigensurface-ICIP(web)2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=three-dimensional face recognition: an eigensurface approach&sort=relevance" target="_blank">[s2]</a></td><td></td><td>19d1b811df60f86cbd5e04a094b07f32fff7a32a</td></tr><tr><td>youtube_poses</td><td>YouTube Pose</td><td>Personalizing Human Video Pose Estimation</td><td>Personalizing Human Video Pose Estimation</td><td><a href="http://arxiv.org/pdf/1511.06676v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=personalizing human video pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1c2802c2199b6d15ecefe7ba0c39bfe44363de38</td></tr></table></body></html>
\ No newline at end of file +discovering, annotating, and recognizing scene attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>833fa04463d90aab4a9fe2870d480f0b40df446e</td></tr><tr><td>imdb_wiki</td><td>IMDB</td><td>DEX: Deep EXpectation of apparent age from a single image</td><td>DEX: Deep EXpectation of Apparent Age from a Single Image</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7406390" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=dex: deep expectation of apparent age from a single image&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8355d095d3534ef511a9af68a3b2893339e3f96b</td></tr><tr><td>awe_ears</td><td>AWE Ears</td><td>Ear Recognition: More Than a Survey</td><td>Ear Recognition: More Than a Survey</td><td><a href="http://pdfs.semanticscholar.org/84fe/5b4ac805af63206012d29523a1e033bc827e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ear 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target="_blank">[s2]</a></td><td></td><td>8627f019882b024aef92e4eb9355c499c733e5b7</td></tr><tr><td>tiny_faces</td><td>TinyFace</td><td>Low-Resolution Face Recognition</td><td>Low-Resolution Face Recognition</td><td><a href="https://arxiv.org/pdf/1811.08965.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=low-resolution face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8990cdce3f917dad622e43e033db686b354d057c</td></tr><tr><td>10k_US_adult_faces</td><td>10K US Adult Faces</td><td>The intrinsic memorability of face images</td><td>The intrinsic memorability of face photographs.</td><td><a href="https://pdfs.semanticscholar.org/8b2d/d5c61b23ead5ae5508bb8ce808b5ea266730.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the intrinsic memorability of face images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8b2dd5c61b23ead5ae5508bb8ce808b5ea266730</td></tr><tr><td>fei</td><td>FEI</td><td>Captura e Alinhamento de Imagens: Um Banco de Faces Brasileiro</td><td>A new ranking method for principal components analysis and its application to face image analysis</td><td><a href="https://doi.org/10.1016/j.imavis.2009.11.005" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=captura e alinhamento de imagens: um banco de faces brasileiro&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8b56e33f33e582f3e473dba573a16b598ed9bcdc</td></tr><tr><td>chalearn</td><td>ChaLearn</td><td>ChaLearn Looking at People: A Review of Events and Resources</td><td>ChaLearn looking at people: A review of events and resources</td><td><a href="https://arxiv.org/pdf/1701.02664.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=chalearn looking at people: a review of events and resources&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8d5998cd984e7cce307da7d46f155f9db99c6590</td></tr><tr><td>hda_plus</td><td>HDA+</td><td>The HDA+ data set for research on fully automated re-identification systems</td><td>The HDA+ Data Set for Research on Fully Automated Re-identification Systems</td><td><a href="http://pdfs.semanticscholar.org/8f02/ec0be21461fbcedf51d864f944cfc42c875f.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the hda+ data set for research on fully automated re-identification systems&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8f02ec0be21461fbcedf51d864f944cfc42c875f</td></tr><tr><td>morph</td><td>MORPH Commercial</td><td>MORPH: A Longitudinal Image Database of Normal Adult Age-Progression</td><td>MORPH: a longitudinal image database of normal adult age-progression</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/FGR.2006.78" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=morph: a longitudinal image database of normal adult age-progression&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td></tr><tr><td>morph_nc</td><td>MORPH Non-Commercial</td><td>MORPH: A Longitudinal Image Database of Normal Adult Age-Progression</td><td>MORPH: a longitudinal image database of normal adult age-progression</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/FGR.2006.78" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=morph: a longitudinal image database of normal adult age-progression&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td></tr><tr><td>v47</td><td>V47</td><td>Re-identification of Pedestrians with Variable Occlusion and Scale</td><td>Re-identification of pedestrians with variable occlusion and scale</td><td><a href="https://doi.org/10.1109/ICCVW.2011.6130477" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=re-identification of pedestrians with variable occlusion and scale&sort=relevance" target="_blank">[s2]</a></td><td></td><td>922e0a51a3b8c67c4c6ac09a577ff674cbd28b34</td></tr><tr><td>towncenter</td><td>TownCenter</td><td>Stable Multi-Target Tracking in Real-Time Surveillance Video</td><td>Stable multi-target tracking in real-time surveillance video</td><td><a href="http://pdfs.semanticscholar.org/9361/b784e73e9238d5cefbea5ac40d35d1e3103f.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=stable multi-target tracking in real-time surveillance video&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9361b784e73e9238d5cefbea5ac40d35d1e3103f</td></tr><tr><td>4dfab</td><td>4DFAB</td><td>4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications</td><td>4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications</td><td><a href="https://arxiv.org/pdf/1712.01443.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=4dfab: a large scale 4d facial expression database for biometric applications&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9696ad8b164f5e10fcfe23aacf74bd6168aebb15</td></tr><tr><td>ilids_vid_reid</td><td>iLIDS-VID</td><td>Person Re-Identi cation by Video Ranking</td><td>Person Re-identification by Video Ranking</td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person re-identi cation by video ranking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>98bb029afe2a1239c3fdab517323066f0957b81b</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>Person reidentification by video ranking</td><td>Person Re-identification by Video Ranking</td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person reidentification by video ranking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>98bb029afe2a1239c3fdab517323066f0957b81b</td></tr><tr><td>m2vts</td><td>m2vts</td><td>The M2VTS Multimodal Face Database (Release 1.00)</td><td>The M2VTS Multimodal Face Database (Release 1.00)</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the m2vts multimodal face database (release 1.00)&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9a9877791945c6fa4c1743ec6d3fb32570ef8481</td></tr><tr><td>who_goes_there</td><td>WGT</td><td>Who Goes There? Approaches to Mapping Facial Appearance Diversity</td><td>Who goes there?: approaches to mapping facial appearance diversity</td><td><a href="http://doi.acm.org/10.1145/2996913.2996997" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=who goes there? approaches to mapping facial appearance diversity&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9b9bf5e623cb8af7407d2d2d857bc3f1b531c182</td></tr><tr><td>precarious</td><td>Precarious</td><td>Expecting the Unexpected: Training Detectors for Unusual Pedestrians With Adversarial Imposters</td><td>Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters</td><td><a href="https://arxiv.org/pdf/1703.06283.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=expecting the unexpected: training detectors for unusual pedestrians with adversarial imposters&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9e5378e7b336c89735d3bb15cf67eff96f86d39a</td></tr><tr><td>mafl</td><td>MAFL</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td><a href="https://arxiv.org/pdf/1408.3967.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning deep representation for face alignment with auxiliary attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td></tr><tr><td>mtfl</td><td>MTFL</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td><a href="https://arxiv.org/pdf/1408.3967.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning deep representation for face alignment with auxiliary attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td></tr><tr><td>moments_in_time</td><td>Moments in Time</td><td>Moments in Time Dataset: one million videos for event understanding</td><td>Moments in Time Dataset: one million videos for event understanding</td><td><a href="https://arxiv.org/pdf/1801.03150.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=moments in time dataset: one million videos for event understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a5a44a32a91474f00a3cda671a802e87c899fbb4</td></tr><tr><td>aflw</td><td>AFLW</td><td>Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization</td><td>Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=annotated facial landmarks in the wild: a large-scale, real-world database for facial landmark localization&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td></tr><tr><td>market1203</td><td>Market 1203</td><td>Orientation Driven Bag of Appearances for Person Re-identification</td><td>Orientation Driven Bag of Appearances for Person Re-identification</td><td><a href="http://pdfs.semanticscholar.org/a7fe/834a0af614ce6b50dc093132b031dd9a856b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=orientation driven bag of appearances for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td></tr><tr><td>pku_reid</td><td>PKU-Reid</td><td>Orientation driven bag of appearances for person re-identification</td><td>Orientation Driven Bag of Appearances for Person Re-identification</td><td><a href="http://pdfs.semanticscholar.org/a7fe/834a0af614ce6b50dc093132b031dd9a856b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=orientation driven bag of appearances for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td></tr><tr><td>hi4d_adsip</td><td>Hi4D-ADSIP</td><td>Hi4D-ADSIP 3-D dynamic facial articulation database</td><td>Hi4D-ADSIP 3-D dynamic facial articulation database</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=hi4d-adsip 3-d dynamic facial articulation database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a8d0b149c2eadaa02204d3e4356fbc8eccf3b315</td></tr><tr><td>yawdd</td><td>YawDD</td><td>YawDD: A Yawning Detection Dataset</td><td>YawDD: a yawning detection dataset</td><td><a href="http://www.site.uottawa.ca/~shervin/pubs/CogniVue-Dataset-ACM-MMSys2014.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=yawdd: a yawning detection dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a94cae786d515d3450d48267e12ca954aab791c4</td></tr><tr><td>mrp_drone</td><td>MRP Drone</td><td>Investigating Open-World Person Re-identification Using a Drone</td><td>Investigating Open-World Person Re-identification Using a Drone</td><td><a href="http://pdfs.semanticscholar.org/ad01/687649d95cd5b56d7399a9603c4b8e2217d7.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=investigating open-world person re-identification using a drone&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ad01687649d95cd5b56d7399a9603c4b8e2217d7</td></tr><tr><td>put_face</td><td>Put Face</td><td>The PUT face database</td><td>The put face database</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the put face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ae0aee03d946efffdc7af2362a42d3750e7dd48a</td></tr><tr><td>m2vtsdb_extended</td><td>xm2vtsdb</td><td>XM2VTSDB: The Extended M2VTS Database</td><td>Xm2vtsdb: the Extended M2vts Database</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=xm2vtsdb: the extended m2vts database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>b62628ac06bbac998a3ab825324a41a11bc3a988</td></tr><tr><td>ifdb</td><td>IFDB</td><td>Iranian Face Database with age, pose and expression</td><td>Iranian Face Database with age, pose and expression</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iranian face database with age, pose and expression&sort=relevance" target="_blank">[s2]</a></td><td></td><td>b71d1aa90dcbe3638888725314c0d56640c1fef1</td></tr><tr><td>pornodb</td><td>Pornography DB</td><td>Pooling in Image Representation: the Visual Codeword Point of View</td><td>Pooling in image representation: The visual codeword point of view</td><td><a href="http://pdfs.semanticscholar.org/b92a/1ed9622b8268ae3ac9090e25789fc41cc9b8.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pooling in image representation: the visual codeword point of view&sort=relevance" target="_blank">[s2]</a></td><td></td><td>b92a1ed9622b8268ae3ac9090e25789fc41cc9b8</td></tr><tr><td>hda_plus</td><td>HDA+</td><td>A Multi-camera video data set for research on High-Definition surveillance</td><td>HDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance</td><td><a href="http://pdfs.semanticscholar.org/bd88/bb2e4f351352d88ee7375af834360e223498.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a multi-camera video data set for research on high-definition surveillance&sort=relevance" target="_blank">[s2]</a></td><td></td><td>bd88bb2e4f351352d88ee7375af834360e223498</td></tr><tr><td>mars</td><td>MARS</td><td>MARS: A Video Benchmark for Large-Scale Person Re-identification</td><td>MARS: A Video Benchmark for Large-Scale Person Re-Identification</td><td><a href="http://pdfs.semanticscholar.org/c038/7e788a52f10bf35d4d50659cfa515d89fbec.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=mars: a video benchmark for large-scale person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c0387e788a52f10bf35d4d50659cfa515d89fbec</td></tr><tr><td>nova_emotions</td><td>Novaemötions Dataset</td><td>Crowdsourcing facial expressions for affective-interaction</td><td>Crowdsourcing facial expressions for affective-interaction</td><td><a href="https://doi.org/10.1016/j.cviu.2016.02.001" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=crowdsourcing facial expressions for affective-interaction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c06b13d0ec3f5c43e2782cd22542588e233733c3</td></tr><tr><td>mcgill</td><td>McGill Real World</td><td>Hierarchical Temporal Graphical Model for Head Pose Estimation and Subsequent Attribute Classification in Real-World Videos</td><td>Hierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=hierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a5a3bc3e5e9753769163cb30b16dbd12e266b93e</td></tr><tr><td>qmul_surv_face</td><td>QMUL-SurvFace</td><td>Surveillance Face Recognition Challenge</td><td>Surveillance Face Recognition Challenge</td><td><a href="https://arxiv.org/pdf/1804.09691.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=surveillance face recognition challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c866a2afc871910e3282fd9498dce4ab20f6a332</td></tr><tr><td>emotio_net</td><td>EmotioNet Database</td><td>EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild</td><td>EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780969" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=emotionet: an accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c900e0ad4c95948baaf0acd8449fde26f9b4952a</td></tr><tr><td>imfdb</td><td>IMFDB</td><td>Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations</td><td>Indian Movie Face Database: A benchmark for face recognition under wide variations</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=indian movie face database: a benchmark for face recognition under wide variations&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ca3e88d87e1344d076c964ea89d91a75c417f5ee</td></tr><tr><td>b3d_ac</td><td>B3D(AC)</td><td>A 3-D Audio-Visual Corpus of Affective Communication</td><td>A 3-D Audio-Visual Corpus of Affective Communication</td><td><a href="http://files.is.tue.mpg.de/jgall/download/jgall_avcorpus_mm10.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3-d audio-visual corpus of affective communication&sort=relevance" target="_blank">[s2]</a></td><td></td><td>d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae</td></tr><tr><td>jiku_mobile</td><td>Jiku Mobile Video Dataset</td><td>The Jiku Mobile Video Dataset</td><td>The jiku mobile video dataset</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the jiku mobile video dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>d178cde92ab3dc0dd2ebee5a76a33d556c39448b</td></tr><tr><td>stair_actions</td><td>STAIR Action</td><td>STAIR Actions: A Video Dataset of Everyday Home Actions</td><td>STAIR Actions: A Video Dataset of Everyday Home Actions</td><td><a href="https://arxiv.org/pdf/1804.04326.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=stair actions: a video dataset of everyday home actions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9</td></tr><tr><td>feret</td><td>FERET</td><td>The FERET database and evaluation procedure for face-recognition algorithms</td><td>The FERET database and evaluation procedure for face-recognition algorithms</td><td><a href="http://pdfs.semanticscholar.org/dc8b/25e35a3acb812beb499844734081722319b4.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the feret database and evaluation procedure for face-recognition algorithms&sort=relevance" target="_blank">[s2]</a></td><td></td><td>dc8b25e35a3acb812beb499844734081722319b4</td></tr><tr><td>families_in_the_wild</td><td>FIW</td><td>Visual Kinship Recognition of Families in the Wild</td><td>Visual Kinship Recognition of Families in the Wild</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8337841" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=visual kinship recognition of families in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>dd65f71dac86e36eecbd3ed225d016c3336b4a13</td></tr><tr><td>scut_head</td><td>SCUT HEAD</td><td>Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture</td><td>Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture</td><td><a href="https://arxiv.org/pdf/1803.09256.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=detecting heads using feature refine net and cascaded multi-scale architecture&sort=relevance" target="_blank">[s2]</a></td><td></td><td>d3200d49a19a4a4e4e9745ee39649b65d80c834b</td></tr><tr><td>sarc3d</td><td>Sarc3D</td><td>SARC3D: a new 3D body model for People Tracking and Re-identification</td><td>SARC3D: A New 3D Body Model for People Tracking and Re-identification</td><td><a href="https://pdfs.semanticscholar.org/e27e/f52c641c2b5100a1b34fd0b819e84a31b4df.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=sarc3d: a new 3d body model for people tracking and re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>e27ef52c641c2b5100a1b34fd0b819e84a31b4df</td></tr><tr><td>fiw_300</td><td>300-W</td><td>300 faces In-the-wild challenge: Database and results</td><td>300 Faces In-The-Wild Challenge: database and results</td><td><a href="http://doi.org/10.1016/j.imavis.2016.01.002" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=300 faces in-the-wild challenge: database and results&sort=relevance" target="_blank">[s2]</a></td><td></td><td>e4754afaa15b1b53e70743880484b8d0736990ff</td></tr><tr><td>gfw</td><td>YouTube Pose</td><td>Merge or Not? Learning to Group Faces via Imitation Learning</td><td>Merge or Not? Learning to Group Faces via Imitation Learning</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=merge or not? learning to group faces via imitation learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>e58dd160a76349d46f881bd6ddbc2921f08d1050</td></tr><tr><td>visual_phrases</td><td>Phrasal Recognition</td><td>Recognition using Visual Phrases</td><td>Recognition using visual phrases</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995711" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=recognition using visual phrases&sort=relevance" target="_blank">[s2]</a></td><td></td><td>e8de844fefd54541b71c9823416daa238be65546</td></tr><tr><td>mpi_large</td><td>Large MPI Facial Expression</td><td>The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</td><td>The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</td><td><a href="http://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mpi facial expression database — a validated database of emotional and conversational facial expressions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td></tr><tr><td>mpi_small</td><td>Small MPI Facial Expression</td><td>The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</td><td>The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</td><td><a href="http://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mpi facial expression database — a validated database of emotional and conversational facial expressions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td></tr><tr><td>msmt_17</td><td>MSMT17</td><td>Person Transfer GAN to Bridge Domain Gap for Person Re-Identification</td><td>Person Transfer GAN to Bridge Domain Gap for Person Re-Identification</td><td><a href="https://arxiv.org/pdf/1711.08565.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person transfer gan to bridge domain gap for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ec792ad2433b6579f2566c932ee414111e194537</td></tr><tr><td>europersons</td><td>EuroCity Persons</td><td>The EuroCity Persons Dataset: A Novel Benchmark for Object Detection</td><td>The EuroCity Persons Dataset: A Novel Benchmark for Object Detection</td><td><a href="https://arxiv.org/pdf/1805.07193.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the eurocity persons dataset: a novel benchmark for object detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4</td></tr><tr><td>pku</td><td>PKU</td><td>Swiss-System Based Cascade Ranking for Gait-based Person Re-identification</td><td>Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification</td><td><a href="http://pdfs.semanticscholar.org/f6c8/d5e35d7e4d60a0104f233ac1a3ab757da53f.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=swiss-system based cascade ranking for gait-based person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f</td></tr><tr><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td>Pedestrian Detection: An Evaluation of the State of the Art</td><td>Pedestrian Detection: An Evaluation of the State of the Art</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5975165" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian detection: an evaluation of the state of the art&sort=relevance" target="_blank">[s2]</a></td><td></td><td>f72f6a45ee240cc99296a287ff725aaa7e7ebb35</td></tr><tr><td>iit_dehli_ear</td><td>IIT Dehli Ear</td><td>Automated human identification using ear imaging</td><td>Automated Human Identification Using Ear Imaging</td><td><a href="https://pdfs.semanticscholar.org/faf4/0ce28857aedf183e193486f5b4b0a8c478a2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automated human identification using ear imaging&sort=relevance" target="_blank">[s2]</a></td><td></td><td>faf40ce28857aedf183e193486f5b4b0a8c478a2</td></tr><tr><td>nd_2006</td><td>ND-2006</td><td>Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition</td><td>Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4401928" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=using a multi-instance enrollment representation to improve 3d face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>fd8168f1c50de85bac58a8d328df0a50248b16ae</td></tr></table></body></html>
\ No newline at end of file diff --git a/scraper/reports/paper_title_report_nonmatching.html b/scraper/reports/paper_title_report_nonmatching.html index a59cf813..d24cec59 100644 --- a/scraper/reports/paper_title_report_nonmatching.html +++ b/scraper/reports/paper_title_report_nonmatching.html @@ -1,9 +1,3 @@ -<!doctype html><html><head><meta charset='utf-8'><title>Paper Titles that do not match</title><link rel='stylesheet' href='reports.css'></head><body><h2>Paper Titles that do not match</h2><table border='1' cellpadding='3' cellspacing='3'><th>key</th><th>name</th><th>our title</th><th>found title</th><th></th><th></th><th>address</th><th>s2 id</th><tr><td>10k_US_adult_faces</td><td>10K US Adult Faces</td><td>The intrinsic memorability of face images</td><td>The intrinsic memorability of face photographs.</td><td><a href="https://pdfs.semanticscholar.org/8b2d/d5c61b23ead5ae5508bb8ce808b5ea266730.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the intrinsic memorability of face images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8b2dd5c61b23ead5ae5508bb8ce808b5ea266730</td></tr><tr><td>3dddb_unconstrained</td><td>3D Dynamic</td><td>A 3D Dynamic Database for Unconstrained Face Recognition</td><td>A 3 D Dynamic Database for Unconstrained Face Recognition</td><td><a href="https://pdfs.semanticscholar.org/4d4b/b462c9f1d4e4ab1e4aa6a75cc0bc71b38461.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3d dynamic database for unconstrained face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461</td></tr><tr><td>afad</td><td>AFAD</td><td>Ordinal Regression with a Multiple Output CNN for Age Estimation</td><td>Ordinal Regression with Multiple Output CNN for Age Estimation</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.532" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ordinal regression with a multiple output cnn for age estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c</td></tr><tr><td>affectnet</td><td>AffectNet</td><td>AffectNet: A New Database for Facial Expression, Valence, and Arousal Computation in the Wild</td><td>Skybiometry and AffectNet on Facial Emotion Recognition Using Supervised Machine Learning Algorithms</td><td><a href="http://dl.acm.org/citation.cfm?id=3232665" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=affectnet: a new database for facial expression, valence, and arousal computation in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>f152b6ee251cca940dd853c54e6a7b78fbc6b235</td></tr><tr><td>afw</td><td>AFW</td><td>Face detection, pose estimation and landmark localization in the wild</td><td>Face detection, pose estimation, and landmark localization in the wild</td><td><a href="http://vision.ics.uci.edu/papers/ZhuR_CVPR_2012/ZhuR_CVPR_2012.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face detection, pose estimation and landmark localization in the wild&sort=relevance" target="_blank">[s2]</a></td><td>University of California, Irvine</td><td>0e986f51fe45b00633de9fd0c94d082d2be51406</td></tr><tr><td>alert_airport</td><td>ALERT Airport</td><td>A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets</td><td>A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.</td><td><a href="https://arxiv.org/pdf/1605.09653.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a systematic evaluation and benchmark for person re-identification: features, metrics, and datasets&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6403117f9c005ae81f1e8e6d1302f4a045e3d99d</td></tr><tr><td>am_fed</td><td>AM-FED</td><td>Affectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”</td><td>Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"</td><td><a href="http://pdfs.semanticscholar.org/5d06/437656dd94616d7d87260d5eb77513ded30f.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=affectiva mit facial expression dataset (am-fed): naturalistic and spontaneous facial expressions collected “in the wild”&sort=relevance" target="_blank">[s2]</a></td><td></td><td>47aeb3b82f54b5ae8142b4bdda7b614433e69b9a</td></tr><tr><td>bjut_3d</td><td>BJUT-3D</td><td>The BJUT-3D Large-Scale Chinese Face Database</td><td>A novel face recognition method based on 3D face model</td><td><a href="https://doi.org/10.1109/ROBIO.2007.4522202" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the bjut-3d large-scale chinese face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1ed1a49534ad8dd00f81939449f6389cfbc25321</td></tr><tr><td>bp4d_spontanous</td><td>BP4D-Spontanous</td><td>A high resolution spontaneous 3D dynamic facial expression database</td><td>A high-resolution spontaneous 3D dynamic facial expression database</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6553788" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a high resolution spontaneous 3d dynamic facial expression database&sort=relevance" target="_blank">[s2]</a></td><td>SUNY Binghamton</td><td>b91f54e1581fbbf60392364323d00a0cd43e493c</td></tr><tr><td>brainwash</td><td>Brainwash</td><td>Brainwash dataset</td><td>Brainwash: A Data System for Feature Engineering</td><td><a href="http://pdfs.semanticscholar.org/ae44/8015b2ff2bd3b8a5c9a3266f954f5af9ffa9.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=brainwash dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>214c966d1f9c2a4b66f4535d9a0d4078e63a5867</td></tr><tr><td>camel</td><td>CAMEL</td><td>CAMEL Dataset for Visual and Thermal Infrared Multiple Object Detection and Tracking</td><td>Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis</td><td><a href="http://pdfs.semanticscholar.org/5801/690199c1917fa58c35c3dead177c0b8f9f2d.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=camel dataset for visual and thermal infrared multiple object detection and tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5801690199c1917fa58c35c3dead177c0b8f9f2d</td></tr><tr><td>casablanca</td><td>Casablanca</td><td>Context-aware {CNNs} for person head detection</td><td>Context-Aware CNNs for Person Head Detection</td><td><a href="http://arxiv.org/pdf/1511.07917v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=context-aware {cnns} for person head detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td></tr><tr><td>celeba_plus</td><td>CelebFaces+</td><td>Deep Learning Face Representation from Predicting 10,000 Classes</td><td>Learning Deep Representation for Imbalanced Classification</td><td><a href="http://personal.ie.cuhk.edu.hk/~ccloy/files/cvpr_2016_imbalanced.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep learning face representation from predicting 10,000 classes&sort=relevance" target="_blank">[s2]</a></td><td>Shenzhen Institutes of Advanced Technology</td><td>69a68f9cf874c69e2232f47808016c2736b90c35</td></tr><tr><td>cfd</td><td>CFD</td><td>The Chicago face database: A free stimulus set of faces and norming data</td><td>The Chicago face database: A free stimulus set of faces and norming data.</td><td><a href="http://pdfs.semanticscholar.org/4df3/143922bcdf7db78eb91e6b5359d6ada004d2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the chicago face database: a free stimulus set of faces and norming data&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4df3143922bcdf7db78eb91e6b5359d6ada004d2</td></tr><tr><td>cityscapes</td><td>Cityscapes</td><td>The Cityscapes Dataset</td><td>The Cityscapes Dataset for Semantic Urban Scene Understanding</td><td><a href="https://arxiv.org/pdf/1604.01685.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cityscapes dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>32cde90437ab5a70cf003ea36f66f2de0e24b3ab</td></tr><tr><td>cmu_pie</td><td>CMU PIE</td><td>The CMU Pose, Illumination, and Expression Database</td><td>The CMU Pose, Illumination, and Expression (PIE) Database</td><td><a href="http://pdfs.semanticscholar.org/4d42/3acc78273b75134e2afd1777ba6d3a398973.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cmu pose, illumination, and expression database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d423acc78273b75134e2afd1777ba6d3a398973</td></tr><tr><td>coco</td><td>COCO</td><td>Microsoft COCO: Common Objects in Context</td><td>Microsoft COCO Captions: Data Collection and Evaluation Server</td><td><a href="http://pdfs.semanticscholar.org/ba95/81c33a7eebe87c50e61763e4c8d1723538f2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=microsoft coco: common objects in context&sort=relevance" target="_blank">[s2]</a></td><td></td><td>696ca58d93f6404fea0fc75c62d1d7b378f47628</td></tr><tr><td>columbia_gaze</td><td>Columbia Gaze</td><td>Gaze Locking: Passive Eye Contact Detection for Human–Object Interaction</td><td>A 3D Morphable Eye Region Model for Gaze Estimation</td><td><a href="https://pdfs.semanticscholar.org/0d43/3b9435b874a1eea6d7999e86986c910fa285.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=gaze locking: passive eye contact detection for human–object interaction&sort=relevance" target="_blank">[s2]</a></td><td>Carnegie Mellon University</td><td>c34532fe6bfbd1e6df477c9ffdbb043b77e7804d</td></tr><tr><td>cvc_01_barcelona</td><td>CVC-01</td><td>Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection</td><td>Adaptive Image Sampling and Windows Classification for On – board Pedestrian Detection</td><td><a href="http://pdfs.semanticscholar.org/57fe/081950f21ca03b5b375ae3e84b399c015861.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=adaptive image sampling and windows classification for on-board pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>57fe081950f21ca03b5b375ae3e84b399c015861</td></tr><tr><td>d3dfacs</td><td>D3DFACS</td><td>A FACS Valid 3D Dynamic Action Unit database with Applications to 3D Dynamic Morphable Facial Modelling</td><td>A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling</td><td><a href="http://www.cs.bath.ac.uk/~dpc/D3DFACS/ICCV_final_2011.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a facs valid 3d dynamic action unit database with applications to 3d dynamic morphable facial modelling&sort=relevance" target="_blank">[s2]</a></td><td>Jacobs University</td><td>070de852bc6eb275d7ca3a9cdde8f6be8795d1a3</td></tr><tr><td>dartmouth_children</td><td>Dartmouth Children</td><td>The Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus set</td><td>The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set</td><td><a href="http://pdfs.semanticscholar.org/4e6e/e936eb50dd032f7138702fa39b7c18ee8907.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the dartmouth database of children's faces: acquisition and validation of a new face stimulus set&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4e6ee936eb50dd032f7138702fa39b7c18ee8907</td></tr><tr><td>deep_fashion</td><td>DeepFashion</td><td>DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</td><td>Fashion Landmark Detection in the Wild</td><td><a href="http://pdfs.semanticscholar.org/d8ca/e259c1c5bba0c096f480dc7322bbaebfac1a.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deepfashion: powering robust clothes recognition and retrieval with rich annotations&sort=relevance" target="_blank">[s2]</a></td><td>Chinese University of Hong Kong</td><td>4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7</td></tr><tr><td>disfa</td><td>DISFA</td><td>DISFA: A Spontaneous Facial Action Intensity Database</td><td>Extended DISFA Dataset: Investigating Posed and Spontaneous Facial Expressions</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7789672" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=disfa: a spontaneous facial action intensity database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a5acda0e8c0937bfed013e6382da127103e41395</td></tr><tr><td>expw</td><td>ExpW</td><td>Learning Social Relation Traits from Face Images</td><td>From Facial Expression Recognition to Interpersonal Relation Prediction</td><td><a href="http://arxiv.org/abs/1609.06426" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning social relation traits from face images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td></tr><tr><td>face_research_lab</td><td>Face Research Lab London</td><td>Face Research Lab London Set. figshare</td><td>Anxiety promotes memory for mood-congruent faces but does not alter loss aversion.</td><td><a href="http://pdfs.semanticscholar.org/c652/6dd3060d63a6c90e8b7ff340383c4e0e0dd8.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face research lab london set. figshare&sort=relevance" target="_blank">[s2]</a></td><td>University College London</td><td>c6526dd3060d63a6c90e8b7ff340383c4e0e0dd8</td></tr><tr><td>face_tracer</td><td>FaceTracer</td><td>FaceTracer: A Search Engine for Large Collections of Images with Faces</td><td>Face swapping: automatically replacing faces in photographs</td><td><a href="https://classes.cs.uoregon.edu/16F/cis607photo/faces.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facetracer: a search engine for large collections of images with faces&sort=relevance" target="_blank">[s2]</a></td><td>Columbia University</td><td>670637d0303a863c1548d5b19f705860a23e285c</td></tr><tr><td>fddb</td><td>FDDB</td><td>FDDB: A Benchmark for Face Detection in Unconstrained Settings</td><td>A Benchmark for Face Detection in Unconstrained Settings</td><td><a href="http://pdfs.semanticscholar.org/75da/1df4ed319926c544eefe17ec8d720feef8c0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=fddb: a benchmark for face detection in unconstrained settings&sort=relevance" target="_blank">[s2]</a></td><td>University of Massachusetts</td><td>75da1df4ed319926c544eefe17ec8d720feef8c0</td></tr><tr><td>fei</td><td>FEI</td><td>Captura e Alinhamento de Imagens: Um Banco de Faces Brasileiro</td><td>Generalização cartográfica automatizada para um banco de dados cadastral</td><td><a href="https://pdfs.semanticscholar.org/b6b1/b0632eb9d4ab1427278f5e5c46f97753c73d.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=captura e alinhamento de imagens: um banco de faces brasileiro&sort=relevance" target="_blank">[s2]</a></td><td></td><td>b6b1b0632eb9d4ab1427278f5e5c46f97753c73d</td></tr><tr><td>fiw_300</td><td>300-W</td><td>300 faces In-the-wild challenge: Database and results</td><td>A Semi-automatic Methodology for Facial Landmark Annotation</td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_cvpr_2013_amfg_w.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=300 faces in-the-wild challenge: database and results&sort=relevance" target="_blank">[s2]</a></td><td>University of Twente</td><td>013909077ad843eb6df7a3e8e290cfd5575999d2</td></tr><tr><td>fiw_300</td><td>300-W</td><td>300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge</td><td>A Semi-automatic Methodology for Facial Landmark Annotation</td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_cvpr_2013_amfg_w.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=300 faces in-the-wild challenge: the first facial landmark localization challenge&sort=relevance" target="_blank">[s2]</a></td><td>University of Twente</td><td>013909077ad843eb6df7a3e8e290cfd5575999d2</td></tr><tr><td>frav3d</td><td>FRAV3D</td><td>MULTIMODAL 2D, 2.5D & 3D FACE VERIFICATION</td><td>2D and 3D face recognition: A survey</td><td><a href="http://pdfs.semanticscholar.org/2f5d/44dc3e1b5955942133ff872ebd31716ec604.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multimodal 2d, 2.5d & 3d face verification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2f5d44dc3e1b5955942133ff872ebd31716ec604</td></tr><tr><td>frgc</td><td>FRGC</td><td>Overview of the Face Recognition Grand Challenge</td><td>Overview of the face recognition grand challenge</td><td><a href="http://www3.nd.edu/~kwb/PhillipsEtAlCVPR_2005.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=overview of the face recognition grand challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18ae7c9a4bbc832b8b14bc4122070d7939f5e00e</td></tr><tr><td>gallagher</td><td>Gallagher</td><td>Clothing Cosegmentation for Recognizing People</td><td>Clothing Cosegmentation for Shopping Images With Cluttered Background</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7423747" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clothing cosegmentation for recognizing people&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dbe8e5121c534339d6e41f8683e85f87e6abf81</td></tr><tr><td>gavab_db</td><td>Gavab</td><td>GavabDB: a 3D face database</td><td>Expression invariant 3D face recognition with a Morphable Model</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4813376" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=gavabdb: a 3d face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>42505464808dfb446f521fc6ff2cfeffd4d68ff1</td></tr><tr><td>georgia_tech_face_database</td><td>Georgia Tech Face</td><td>Maximum likelihood training of the embedded HMM for face detection and recognition</td><td>MAXIMUM LIKELIHOOD TRAINING OF THE EMBEDDED HMM FOR FACE DETECTION AND RECOGNITION Ara V. Ne an and Monson H. Hayes III Center for Signal and Image Processing School of Electrical and Computer Engineering</td><td><a href="http://pdfs.semanticscholar.org/3dc3/f0b64ef80f573e3a5f96e456e52ee980b877.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=maximum likelihood training of the embedded hmm for face detection and recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3dc3f0b64ef80f573e3a5f96e456e52ee980b877</td></tr><tr><td>graz</td><td>Graz Pedestrian</td><td>Generic Object Recognition with Boosting</td><td>Object recognition using segmentation for feature detection</td><td><a href="http://www.emt.tugraz.at/~tracking/Publications/fussenegger2004b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=generic object recognition with boosting&sort=relevance" target="_blank">[s2]</a></td><td></td><td>12ad3b5bbbf407f8e54ea692c07633d1a867c566</td></tr><tr><td>graz</td><td>Graz Pedestrian</td><td>Weak Hypotheses and Boosting for Generic Object Detection and Recognition</td><td>Object recognition using segmentation for feature detection</td><td><a href="http://www.emt.tugraz.at/~tracking/Publications/fussenegger2004b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=weak hypotheses and boosting for generic object detection and recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>12ad3b5bbbf407f8e54ea692c07633d1a867c566</td></tr><tr><td>hda_plus</td><td>HDA+</td><td>The HDA+ data set for research on fully automated re-identification systems</td><td>HDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance</td><td><a href="http://pdfs.semanticscholar.org/bd88/bb2e4f351352d88ee7375af834360e223498.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the hda+ data set for research on fully automated re-identification systems&sort=relevance" target="_blank">[s2]</a></td><td></td><td>bd88bb2e4f351352d88ee7375af834360e223498</td></tr><tr><td>hda_plus</td><td>HDA+</td><td>A Multi-camera video data set for research on High-Definition surveillance</td><td>HDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance</td><td><a href="http://pdfs.semanticscholar.org/bd88/bb2e4f351352d88ee7375af834360e223498.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a multi-camera video data set for research on high-definition surveillance&sort=relevance" target="_blank">[s2]</a></td><td></td><td>bd88bb2e4f351352d88ee7375af834360e223498</td></tr><tr><td>hi4d_adsip</td><td>Hi4D-ADSIP</td><td>Hi4D-ADSIP 3-D dynamic facial articulation database</td><td>High-resolution comprehensive 3-D dynamic database for facial articulation analysis</td><td><a href="http://www.researchgate.net/profile/Wei_Quan3/publication/221430048_High-resolution_comprehensive_3-D_dynamic_database_for_facial_articulation_analysis/links/0deec534309495805d000000.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=hi4d-adsip 3-d dynamic facial articulation database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>24830e3979d4ed01b9fd0feebf4a8fd22e0c35fd</td></tr><tr><td>hrt_transgender</td><td>HRT Transgender</td><td>Is the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender Dataset</td><td>Face recognition: A literature survey</td><td><a href="http://pdfs.semanticscholar.org/2831/2c3a47c1be3a67365700744d3d6665b86f22.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=is the eye region more reliable than the face? a preliminary study of face-based recognition on a transgender dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>28312c3a47c1be3a67365700744d3d6665b86f22</td></tr><tr><td>hrt_transgender</td><td>HRT Transgender</td><td>Investigating the Periocular-Based Face Recognition Across Gender Transformation</td><td>Face recognition: A literature survey</td><td><a href="http://pdfs.semanticscholar.org/2831/2c3a47c1be3a67365700744d3d6665b86f22.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=investigating the periocular-based face recognition across gender transformation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>28312c3a47c1be3a67365700744d3d6665b86f22</td></tr><tr><td>hrt_transgender</td><td>HRT Transgender</td><td>Face recognition across gender transformation using SVM Classifier</td><td>Face recognition: A literature survey</td><td><a href="http://pdfs.semanticscholar.org/2831/2c3a47c1be3a67365700744d3d6665b86f22.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face recognition across gender transformation using svm classifier&sort=relevance" target="_blank">[s2]</a></td><td></td><td>28312c3a47c1be3a67365700744d3d6665b86f22</td></tr><tr><td>ifad</td><td>IFAD</td><td>Indian Face Age Database: A Database for Face Recognition with Age Variation</td><td>Indian Face Age Database : A Database for Face Recognition with Age Variation</td><td><a href="https://pdfs.semanticscholar.org/55c4/0cbcf49a0225e72d911d762c27bb1c2d14aa.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=indian face age database: a database for face recognition with age variation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>55c40cbcf49a0225e72d911d762c27bb1c2d14aa</td></tr><tr><td>ifdb</td><td>IFDB</td><td>Iranian Face Database with age, pose and expression</td><td>Iranian Face Database and Evaluation with a New Detection Algorithm</td><td><a href="http://pdfs.semanticscholar.org/066d/71fcd997033dce4ca58df924397dfe0b5fd1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iranian face database with age, pose and expression&sort=relevance" target="_blank">[s2]</a></td><td></td><td>066d71fcd997033dce4ca58df924397dfe0b5fd1</td></tr><tr><td>ijb_c</td><td>IJB-C</td><td>IARPA Janus Benchmark C</td><td>IARPA Janus Benchmark - C: Face Dataset and Protocol</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8411217" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iarpa janus benchmark c&sort=relevance" target="_blank">[s2]</a></td><td></td><td>57178b36c21fd7f4529ac6748614bb3374714e91</td></tr><tr><td>ilids_mcts</td><td></td><td>Imagery Library for Intelligent Detection Systems: -The i-LIDS User Guide</td><td>Imagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=imagery library for intelligent detection systems: -the i-lids user guide&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0297448f3ed948e136bb06ceff10eccb34e5bb77</td></tr><tr><td>ilids_vid_reid</td><td>iLIDS-VID</td><td>Person Re-Identi cation by Video Ranking</td><td>Person Re-identification by Exploiting Spatio-Temporal Cues and Multi-view Metric Learning</td><td><a href="https://doi.org/10.1109/LSP.2016.2574323" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person re-identi cation by video ranking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>99eb4cea0d9bc9fe777a5c5172f8638a37a7f262</td></tr><tr><td>images_of_groups</td><td>Images of Groups</td><td>Understanding Groups of Images of People</td><td>Understanding images of groups of people</td><td><a href="http://amp.ece.cmu.edu/people/Andy/Andy_files/cvpr09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=understanding groups of images of people&sort=relevance" target="_blank">[s2]</a></td><td></td><td>21d9d0deed16f0ad62a4865e9acf0686f4f15492</td></tr><tr><td>imm_face</td><td>IMM Face Dataset</td><td>The IMM Face Database - An Annotated Dataset of 240 Face Images</td><td>Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the imm face database - an annotated dataset of 240 face images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td></tr><tr><td>jiku_mobile</td><td>Jiku Mobile Video Dataset</td><td>The Jiku Mobile Video Dataset</td><td>A Synchronization Ground Truth for the Jiku Mobile Video Dataset</td><td><a href="http://pdfs.semanticscholar.org/ad62/c6e17bc39b4dec20d32f6ac667ae42d2c118.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the jiku mobile video dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ad62c6e17bc39b4dec20d32f6ac667ae42d2c118</td></tr><tr><td>kdef</td><td>KDEF</td><td>The Karolinska Directed Emotional Faces – KDEF</td><td>Gaze fixation and the neural circuitry of face processing in autism</td><td><a href="http://doi.org/10.1038/nn1421" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the karolinska directed emotional faces – kdef&sort=relevance" target="_blank">[s2]</a></td><td></td><td>93884e46c49f7ae1c7c34046fbc28882f2bd6341</td></tr><tr><td>kin_face</td><td>UB KinFace</td><td>Genealogical Face Recognition based on UB KinFace Database</td><td>Understanding Kin Relationships in a Photo</td><td><a href="http://www1.ece.neu.edu/~yunfu/papers/Kinship-TMM.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=genealogical face recognition based on ub kinface database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7</td></tr><tr><td>kin_face</td><td>UB KinFace</td><td>Kinship Verification through Transfer Learning</td><td>Understanding Kin Relationships in a Photo</td><td><a href="http://www1.ece.neu.edu/~yunfu/papers/Kinship-TMM.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=kinship verification through transfer learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7</td></tr><tr><td>kitti</td><td>KITTI</td><td>Vision meets Robotics: The KITTI Dataset</td><td>The Role of Machine Vision for Intelligent Vehicles</td><td><a href="http://www.path.berkeley.edu/sites/default/files/my_folder_76/Pub_03.2016_Role.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vision meets robotics: the kitti dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>35ba4ebfd017a56b51e967105af9ae273c9b0178</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: A Survey</td><td>Labeled Faces in the Wild : A Survey</td><td><a href="http://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: a survey&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</td><td>Labeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments</td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: a database for studying face recognition in unconstrained environments&sort=relevance" target="_blank">[s2]</a></td><td></td><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: Updates and New Reporting Procedures</td><td>Labeled Faces in the Wild : Updates and New Reporting Procedures</td><td><a href="http://pdfs.semanticscholar.org/2d34/82dcff69c7417c7b933f22de606a0e8e42d4.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: updates and new reporting procedures&sort=relevance" target="_blank">[s2]</a></td><td>University of Massachusetts</td><td>2d3482dcff69c7417c7b933f22de606a0e8e42d4</td></tr><tr><td>lfw_a</td><td>LFW-a</td><td>Effective Unconstrained Face Recognition by - Combining Multiple Descriptors and Learned - Background Statistics</td><td>Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.230" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=effective unconstrained face recognition by - combining multiple descriptors and learned - background statistics&sort=relevance" target="_blank">[s2]</a></td><td></td><td>133f01aec1534604d184d56de866a4bd531dac87</td></tr><tr><td>m2vts</td><td>m2vts</td><td>The M2VTS Multimodal Face Database (Release 1.00)</td><td>The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</td><td><a href="https://doi.org/10.1109/TSMCA.2007.909557" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the m2vts multimodal face database (release 1.00)&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td></tr><tr><td>m2vtsdb_extended</td><td>xm2vtsdb</td><td>XM2VTSDB: The Extended M2VTS Database</td><td>Labeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments</td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=xm2vtsdb: the extended m2vts database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td></tr><tr><td>mafl</td><td>MAFL</td><td>Facial Landmark Detection by Deep Multi-task Learning</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td><a href="https://arxiv.org/pdf/1408.3967.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facial landmark detection by deep multi-task learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td></tr><tr><td>malf</td><td>MALF</td><td>Fine-grained Evaluation on Face Detection in the Wild.</td><td>Fine-grained evaluation on face detection in the wild</td><td><a href="http://www.cs.toronto.edu/~byang/papers/malf_fg15.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=fine-grained evaluation on face detection in the wild.&sort=relevance" target="_blank">[s2]</a></td><td>Chinese Academy of Sciences</td><td>45e616093a92e5f1e61a7c6037d5f637aa8964af</td></tr><tr><td>mcgill</td><td>McGill Real World</td><td>Hierarchical Temporal Graphical Model for Head Pose Estimation and Subsequent Attribute Classification in Real-World Videos</td><td>Robust semi-automatic head pose labeling for real-world face video sequences</td><td><a href="https://doi.org/10.1007/s11042-012-1352-1" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=hierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos&sort=relevance" target="_blank">[s2]</a></td><td>McGill University</td><td>c570d1247e337f91e555c3be0e8c8a5aba539d9f</td></tr><tr><td>mot</td><td>MOT</td><td>Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</td><td>Learning to associate: HybridBoosted multi-target tracker for crowded scene</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0633.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=evaluating multiple object tracking performance: the clear mot metrics&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5981e6479c3fd4e31644db35d236bfb84ae46514</td></tr><tr><td>mot</td><td>MOT</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td>Learning to associate: HybridBoosted multi-target tracker for crowded scene</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0633.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=performance measures and a data set for multi-target, multi-camera tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5981e6479c3fd4e31644db35d236bfb84ae46514</td></tr><tr><td>mr2</td><td>MR2</td><td>The MR2: A multi-racial mega-resolution database of facial stimuli</td><td>The MR2: A multi-racial, mega-resolution database of facial stimuli.</td><td><a href="https://pdfs.semanticscholar.org/be5b/455abd379240460d022a0e246615b0b86c14.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mr2: a multi-racial mega-resolution database of facial stimuli&sort=relevance" target="_blank">[s2]</a></td><td></td><td>578d4ad74818086bb64f182f72e2c8bd31e3d426</td></tr><tr><td>mtfl</td><td>MTFL</td><td>Facial Landmark Detection by Deep Multi-task Learning</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td><a href="https://arxiv.org/pdf/1408.3967.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facial landmark detection by deep multi-task learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td></tr><tr><td>muct</td><td>MUCT</td><td>The MUCT Landmarked Face Database</td><td>Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the muct landmarked face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td></tr><tr><td>multi_pie</td><td>MULTIPIE</td><td>Multi-PIE</td><td>Scheduling heterogeneous multi-cores through performance impact estimation (PIE)</td><td><a href="http://dl.acm.org/citation.cfm?id=2337184" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-pie&sort=relevance" target="_blank">[s2]</a></td><td></td><td>109df0e8e5969ddf01e073143e83599228a1163f</td></tr><tr><td>names_and_faces_news</td><td>News Dataset</td><td>Names and Faces</td><td>Names and faces in the news</td><td><a href="http://www.cs.utexas.edu/~grauman/courses/spring2007/395T/papers/berg_names_and_faces.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=names and faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2fda164863a06a92d3a910b96eef927269aeb730</td></tr><tr><td>nova_emotions</td><td>Novaemötions Dataset</td><td>Crowdsourcing facial expressions for affective-interaction</td><td>Competitive affective gaming: winning with a smile</td><td><a href="http://doi.acm.org/10.1145/2502081.2502115" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=crowdsourcing facial expressions for affective-interaction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7f4040b482d16354d5938c1d1b926b544652bf5b</td></tr><tr><td>nova_emotions</td><td>Novaemötions Dataset</td><td>Competitive affective gamming: Winning with a smile</td><td>Competitive affective gaming: winning with a smile</td><td><a href="http://doi.acm.org/10.1145/2502081.2502115" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=competitive affective gamming: winning with a smile&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7f4040b482d16354d5938c1d1b926b544652bf5b</td></tr><tr><td>pilot_parliament</td><td>PPB</td><td>Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classi cation</td><td>Summary of Research on Informant Accuracy in Network Data, 11 and on the Reverse Small World Problem</td><td><a href="http://pdfs.semanticscholar.org/fb82/681ac5d3487bd8e52dbb3d1fa220eeac855e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=gender shades: intersectional accuracy disparities in commercial gender classi cation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>fb82681ac5d3487bd8e52dbb3d1fa220eeac855e</td></tr><tr><td>put_face</td><td>Put Face</td><td>The PUT face database</td><td>Labeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments</td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the put face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td></tr><tr><td>qmul_grid</td><td>GRID</td><td>Multi-Camera Activity Correlation Analysis</td><td>Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding</td><td><a href="https://doi.org/10.1007/s11263-010-0347-5" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-camera activity correlation analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2edb87494278ad11641b6cf7a3f8996de12b8e14</td></tr><tr><td>scface</td><td>SCface</td><td>SCface – surveillance cameras face database</td><td>Large Variability Surveillance Camera Face Database</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scface – surveillance cameras face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>f3b84a03985de3890b400b68e2a92c0a00afd9d0</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</td><td>Person Re-identification by Video Ranking</td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a spatio-temporal appearance representation for video-based pedestrian re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>98bb029afe2a1239c3fdab517323066f0957b81b</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>Local descriptors encoded by Fisher vectors for person re-identification</td><td>Person Re-identification by Video Ranking</td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=local descriptors encoded by fisher vectors for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>98bb029afe2a1239c3fdab517323066f0957b81b</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>Person reidentification by video ranking</td><td>Person Re-identification by Video Ranking</td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person reidentification by video ranking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>98bb029afe2a1239c3fdab517323066f0957b81b</td></tr><tr><td>sheffield</td><td>Sheffield Face</td><td>Face Recognition: From Theory to Applications</td><td>Face Description with Local Binary Patterns: Application to Face Recognition</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.244" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face recognition: from theory to applications&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3607afdb204de9a5a9300ae98aa4635d9effcda2</td></tr><tr><td>social_relation</td><td>Social Relation</td><td>From Facial Expression Recognition to Interpersonal Relation Prediction</td><td>Learning Social Relation Traits from Face Images</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.414" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=from facial expression recognition to interpersonal relation prediction&sort=relevance" target="_blank">[s2]</a></td><td>Chinese University of Hong Kong</td><td>2a171f8d14b6b8735001a11c217af9587d095848</td></tr><tr><td>stanford_drone</td><td>Stanford Drone</td><td>Learning Social Etiquette: Human Trajectory Prediction In Crowded Scenes</td><td>Learning to Predict Human Behavior in Crowded Scenes</td><td><a href="http://pdfs.semanticscholar.org/c9bd/a86e23cab9e4f15ea0c4cbb6cc02b9dfb709.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning social etiquette: human trajectory prediction in crowded scenes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c9bda86e23cab9e4f15ea0c4cbb6cc02b9dfb709</td></tr><tr><td>stickmen_buffy</td><td>Buffy Stickmen</td><td>Learning to Parse Images of Articulated Objects</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to parse images of articulated objects&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td></tr><tr><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td>Learning to parse images of articulated bodies</td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clustered pose and nonlinear appearance models for human pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td>Learning to Parse Images of Articulated Objects</td><td>Learning to parse images of articulated bodies</td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to parse images of articulated objects&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>sun_attributes</td><td>SUN</td><td>The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</td><td>SUN attribute database: Discovering, annotating, and recognizing scene attributes</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6247998" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the sun attribute database: beyond categories for deeper scene understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>833fa04463d90aab4a9fe2870d480f0b40df446e</td></tr><tr><td>sun_attributes</td><td>SUN</td><td>SUN Attribute Database: +<!doctype html><html><head><meta charset='utf-8'><title>Paper Titles that do not match</title><link rel='stylesheet' href='reports.css'></head><body><h2>Paper Titles that do not match</h2><table border='1' cellpadding='3' cellspacing='3'><th>key</th><th>name</th><th>our title</th><th>found title</th><th></th><th></th><th>address</th><th>s2 id</th><tr><td>ilids_mcts</td><td>i-LIDS</td><td>Imagery Library for Intelligent Detection Systems: The i-LIDS User Guide</td><td>Imagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=imagery library for intelligent detection systems: the i-lids user guide&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0297448f3ed948e136bb06ceff10eccb34e5bb77</td></tr><tr><td>columbia_gaze</td><td>Columbia Gaze</td><td>Gaze Locking: Passive Eye Contact Detection for Human–Object Interaction</td><td>Gaze locking: passive eye contact detection for human-object interaction</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=gaze locking: passive eye contact detection for human–object interaction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>06f02199690961ba52997cde1527e714d2b3bf8f</td></tr><tr><td>d3dfacs</td><td>D3DFACS</td><td>A FACS Valid 3D Dynamic Action Unit database with Applications to 3D Dynamic Morphable Facial Modelling</td><td>A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling</td><td><a href="http://www.cs.bath.ac.uk/~dpc/D3DFACS/ICCV_final_2011.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a facs valid 3d dynamic action unit database with applications to 3d dynamic morphable facial modelling&sort=relevance" target="_blank">[s2]</a></td><td>Jacobs University</td><td>070de852bc6eb275d7ca3a9cdde8f6be8795d1a3</td></tr><tr><td>casablanca</td><td>Casablanca</td><td>Context-aware {CNNs} for person head detection</td><td>Context-Aware CNNs for Person Head Detection</td><td><a href="http://arxiv.org/pdf/1511.07917v1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=context-aware {cnns} for person head detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td></tr><tr><td>afw</td><td>AFW</td><td>Face detection, pose estimation and landmark localization in the wild</td><td>Face detection, pose estimation, and landmark localization in the wild</td><td><a href="http://vision.ics.uci.edu/papers/ZhuR_CVPR_2012/ZhuR_CVPR_2012.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face detection, pose estimation and landmark localization in the wild&sort=relevance" target="_blank">[s2]</a></td><td>University of California, Irvine</td><td>0e986f51fe45b00633de9fd0c94d082d2be51406</td></tr><tr><td>frgc</td><td>FRGC</td><td>Overview of the Face Recognition Grand Challenge</td><td>Overview of the face recognition grand challenge</td><td><a href="http://www3.nd.edu/~kwb/PhillipsEtAlCVPR_2005.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=overview of the face recognition grand challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18ae7c9a4bbc832b8b14bc4122070d7939f5e00e</td></tr><tr><td>images_of_groups</td><td>Images of Groups</td><td>Understanding Groups of Images of People</td><td>Understanding images of groups of people</td><td><a href="http://amp.ece.cmu.edu/people/Andy/Andy_files/cvpr09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=understanding groups of images of people&sort=relevance" target="_blank">[s2]</a></td><td></td><td>21d9d0deed16f0ad62a4865e9acf0686f4f15492</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: Updates and New Reporting Procedures</td><td>Labeled Faces in the Wild : Updates and New Reporting Procedures</td><td><a href="http://pdfs.semanticscholar.org/2d34/82dcff69c7417c7b933f22de606a0e8e42d4.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: updates and new reporting procedures&sort=relevance" target="_blank">[s2]</a></td><td>University of Massachusetts</td><td>2d3482dcff69c7417c7b933f22de606a0e8e42d4</td></tr><tr><td>names_and_faces_news</td><td>News Dataset</td><td>Names and Faces</td><td>Names and faces in the news</td><td><a href="http://www.cs.utexas.edu/~grauman/courses/spring2007/395T/papers/berg_names_and_faces.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=names and faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2fda164863a06a92d3a910b96eef927269aeb730</td></tr><tr><td>tiny_images</td><td>Tiny Images</td><td>80 million tiny images: a large dataset for non-parametric object and scene recognition</td><td>80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition</td><td><a href="http://pdfs.semanticscholar.org/31b5/8ced31f22eab10bd3ee2d9174e7c14c27c01.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=80 million tiny images: a large dataset for non-parametric object and scene recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>31b58ced31f22eab10bd3ee2d9174e7c14c27c01</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</td><td>Labeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments</td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: a database for studying face recognition in unconstrained environments&sort=relevance" target="_blank">[s2]</a></td><td></td><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td></tr><tr><td>georgia_tech_face_database</td><td>Georgia Tech Face</td><td>Maximum likelihood training of the embedded HMM for face detection and recognition</td><td>MAXIMUM LIKELIHOOD TRAINING OF THE EMBEDDED HMM FOR FACE DETECTION AND RECOGNITION Ara V. Ne an and Monson H. Hayes III Center for Signal and Image Processing School of Electrical and Computer Engineering</td><td><a href="http://pdfs.semanticscholar.org/3dc3/f0b64ef80f573e3a5f96e456e52ee980b877.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=maximum likelihood training of the embedded hmm for face detection and recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3dc3f0b64ef80f573e3a5f96e456e52ee980b877</td></tr><tr><td>malf</td><td>MALF</td><td>Fine-grained Evaluation on Face Detection in the Wild.</td><td>Fine-grained evaluation on face detection in the wild</td><td><a href="http://www.cs.toronto.edu/~byang/papers/malf_fg15.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=fine-grained evaluation on face detection in the wild.&sort=relevance" target="_blank">[s2]</a></td><td>Chinese Academy of Sciences</td><td>45e616093a92e5f1e61a7c6037d5f637aa8964af</td></tr><tr><td>am_fed</td><td>AM-FED</td><td>Affectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”</td><td>Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"</td><td><a href="http://pdfs.semanticscholar.org/5d06/437656dd94616d7d87260d5eb77513ded30f.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=affectiva mit facial expression dataset (am-fed): naturalistic and spontaneous facial expressions collected “in the wild”&sort=relevance" target="_blank">[s2]</a></td><td></td><td>47aeb3b82f54b5ae8142b4bdda7b614433e69b9a</td></tr><tr><td>cmu_pie</td><td>CMU PIE</td><td>The CMU Pose, Illumination, and Expression Database</td><td>The CMU Pose, Illumination, and Expression (PIE) Database</td><td><a href="http://pdfs.semanticscholar.org/4d42/3acc78273b75134e2afd1777ba6d3a398973.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cmu pose, illumination, and expression database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d423acc78273b75134e2afd1777ba6d3a398973</td></tr><tr><td>multi_pie</td><td>MULTIPIE</td><td>Multi-PIE</td><td>The CMU Pose, Illumination, and Expression (PIE) Database</td><td><a href="http://pdfs.semanticscholar.org/4d42/3acc78273b75134e2afd1777ba6d3a398973.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-pie&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d423acc78273b75134e2afd1777ba6d3a398973</td></tr><tr><td>3dddb_unconstrained</td><td>3D Dynamic</td><td>A 3D Dynamic Database for Unconstrained Face Recognition</td><td>A 3 D Dynamic Database for Unconstrained Face Recognition</td><td><a href="https://pdfs.semanticscholar.org/4d4b/b462c9f1d4e4ab1e4aa6a75cc0bc71b38461.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3d dynamic database for unconstrained face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461</td></tr><tr><td>cfd</td><td>CFD</td><td>The Chicago face database: A free stimulus set of faces and norming data</td><td>The Chicago face database: A free stimulus set of faces and norming data.</td><td><a href="http://pdfs.semanticscholar.org/4df3/143922bcdf7db78eb91e6b5359d6ada004d2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the chicago face database: a free stimulus set of faces and norming data&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4df3143922bcdf7db78eb91e6b5359d6ada004d2</td></tr><tr><td>dartmouth_children</td><td>Dartmouth Children</td><td>The Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus set</td><td>The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set</td><td><a href="http://pdfs.semanticscholar.org/4e6e/e936eb50dd032f7138702fa39b7c18ee8907.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the dartmouth database of children's faces: acquisition and validation of a new face stimulus set&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4e6ee936eb50dd032f7138702fa39b7c18ee8907</td></tr><tr><td>ifad</td><td>IFAD</td><td>Indian Face Age Database: A Database for Face Recognition with Age Variation</td><td>Indian Face Age Database : A Database for Face Recognition with Age Variation</td><td><a href="https://pdfs.semanticscholar.org/55c4/0cbcf49a0225e72d911d762c27bb1c2d14aa.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=indian face age database: a database for face recognition with age variation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>55c40cbcf49a0225e72d911d762c27bb1c2d14aa</td></tr><tr><td>stanford_drone</td><td>Stanford Drone</td><td>Learning Social Etiquette: Human Trajectory Prediction In Crowded Scenes</td><td>Social LSTM: Human Trajectory Prediction in Crowded Spaces</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning social etiquette: human trajectory prediction in crowded scenes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>570f37ed63142312e6ccdf00ecc376341ec72b9f</td></tr><tr><td>ijb_c</td><td>IJB-C</td><td>IARPA Janus Benchmark C</td><td>IARPA Janus Benchmark - C: Face Dataset and Protocol</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8411217" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iarpa janus benchmark c&sort=relevance" target="_blank">[s2]</a></td><td></td><td>57178b36c21fd7f4529ac6748614bb3374714e91</td></tr><tr><td>mr2</td><td>MR2</td><td>The MR2: A multi-racial mega-resolution database of facial stimuli</td><td>The MR2: A multi-racial, mega-resolution database of facial stimuli.</td><td><a href="https://pdfs.semanticscholar.org/be5b/455abd379240460d022a0e246615b0b86c14.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mr2: a multi-racial mega-resolution database of facial stimuli&sort=relevance" target="_blank">[s2]</a></td><td></td><td>578d4ad74818086bb64f182f72e2c8bd31e3d426</td></tr><tr><td>cvc_01_barcelona</td><td>CVC-01</td><td>Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection</td><td>Adaptive Image Sampling and Windows Classification for On – board Pedestrian Detection</td><td><a href="http://pdfs.semanticscholar.org/57fe/081950f21ca03b5b375ae3e84b399c015861.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=adaptive image sampling and windows classification for on-board pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>57fe081950f21ca03b5b375ae3e84b399c015861</td></tr><tr><td>viper</td><td>VIPeR</td><td>Evaluating Appearance Models for Recognition, Reacquisition, and Tracking</td><td>Evaluating Appearance Models for Recognition , Reacquisition , and Tracking</td><td><a href="http://pdfs.semanticscholar.org/6273/b3491e94ea4dd1ce42b791d77bdc96ee73a8.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=evaluating appearance models for recognition, reacquisition, and tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6273b3491e94ea4dd1ce42b791d77bdc96ee73a8</td></tr><tr><td>alert_airport</td><td>ALERT Airport</td><td>A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets</td><td>A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.</td><td><a href="https://arxiv.org/pdf/1605.09653.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a systematic evaluation and benchmark for person re-identification: features, metrics, and datasets&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6403117f9c005ae81f1e8e6d1302f4a045e3d99d</td></tr><tr><td>afad</td><td>AFAD</td><td>Ordinal Regression with a Multiple Output CNN for Age Estimation</td><td>Ordinal Regression with Multiple Output CNN for Age Estimation</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.532" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ordinal regression with a multiple output cnn for age estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c</td></tr><tr><td>stickmen_buffy</td><td>Buffy Stickmen</td><td>Learning to Parse Images of Articulated Objects</td><td>Learning to parse images of articulated bodies</td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to parse images of articulated objects&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td>Learning to parse images of articulated bodies</td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clustered pose and nonlinear appearance models for human pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td>Learning to Parse Images of Articulated Objects</td><td>Learning to parse images of articulated bodies</td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to parse images of articulated objects&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>umd_faces</td><td>UMD</td><td>The Do's and Don'ts for CNN-based Face Verification</td><td>The Do’s and Don’ts for CNN-Based Face Verification</td><td><a href="https://arxiv.org/pdf/1705.07426.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the do's and don'ts for cnn-based face verification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6</td></tr><tr><td>fddb</td><td>FDDB</td><td>FDDB: A Benchmark for Face Detection in Unconstrained Settings</td><td>A Benchmark for Face Detection in Unconstrained Settings</td><td><a href="http://pdfs.semanticscholar.org/75da/1df4ed319926c544eefe17ec8d720feef8c0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=fddb: a benchmark for face detection in unconstrained settings&sort=relevance" target="_blank">[s2]</a></td><td>University of Massachusetts</td><td>75da1df4ed319926c544eefe17ec8d720feef8c0</td></tr><tr><td>wildtrack</td><td>WildTrack</td><td>WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</td><td>WILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</td><td><a href="https://pdfs.semanticscholar.org/fe1c/ec4e4995b8615855572374ae3efc94949105.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=wildtrack: a multi-camera hd dataset for dense unscripted pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>77c81c13a110a341c140995bedb98101b9e84f7f</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: A Survey</td><td>Labeled Faces in the Wild : A Survey</td><td><a href="http://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: a survey&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td></tr><tr><td>nova_emotions</td><td>Novaemötions Dataset</td><td>Competitive affective gamming: Winning with a smile</td><td>Competitive affective gaming: winning with a smile</td><td><a href="http://doi.acm.org/10.1145/2502081.2502115" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=competitive affective gamming: winning with a smile&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7f4040b482d16354d5938c1d1b926b544652bf5b</td></tr><tr><td>sun_attributes</td><td>SUN</td><td>SUN Attribute Database: Discovering, Annotating, and Recognizing Scene Attributes</td><td>SUN attribute database: Discovering, annotating, and recognizing scene attributes</td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6247998" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=sun attribute database: -discovering, annotating, and recognizing scene attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>833fa04463d90aab4a9fe2870d480f0b40df446e</td></tr><tr><td>texas_3dfrd</td><td>Texas 3DFRD</td><td>Texas 3D Face Recognition Database</td><td>Anthropometric 3D Face Recognition</td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ijcv_june10.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=texas 3d face recognition database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2ce2560cf59db59ce313bbeb004e8ce55c5ce928</td></tr><tr><td>tiny_images</td><td>Tiny Images</td><td>80 million tiny images: a large dataset for non-parametric object and scene recognition</td><td>80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition</td><td><a href="http://pdfs.semanticscholar.org/31b5/8ced31f22eab10bd3ee2d9174e7c14c27c01.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=80 million tiny images: a large dataset for non-parametric object and scene recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>31b58ced31f22eab10bd3ee2d9174e7c14c27c01</td></tr><tr><td>umd_faces</td><td>UMD</td><td>The Do's and Don'ts for CNN-based Face Verification</td><td>The Do’s and Don’ts for CNN-Based Face Verification</td><td><a href="https://arxiv.org/pdf/1705.07426.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the do's and don'ts for cnn-based face verification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6</td></tr><tr><td>viper</td><td>VIPeR</td><td>Evaluating Appearance Models for Recognition, Reacquisition, and Tracking</td><td>Evaluating Appearance Models for Recognition , Reacquisition , and Tracking</td><td><a href="http://pdfs.semanticscholar.org/6273/b3491e94ea4dd1ce42b791d77bdc96ee73a8.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=evaluating appearance models for recognition, reacquisition, and tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6273b3491e94ea4dd1ce42b791d77bdc96ee73a8</td></tr><tr><td>voc</td><td>VOC</td><td>The PASCAL Visual Object Classes (VOC) Challenge</td><td>The Pascal Visual Object Classes Challenge: A Retrospective</td><td><a href="http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc14.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the pascal visual object classes (voc) challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>abe9f3b91fd26fa1b50cd685c0d20debfb372f73</td></tr><tr><td>who_goes_there</td><td>WGT</td><td>Who Goes There? Approaches to Mapping Facial Appearance Diversity</td><td>Who goes there?: approaches to mapping facial appearance diversity</td><td><a href="http://doi.acm.org/10.1145/2996913.2996997" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=who goes there? approaches to mapping facial appearance diversity&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9b9bf5e623cb8af7407d2d2d857bc3f1b531c182</td></tr><tr><td>wildtrack</td><td>WildTrack</td><td>WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</td><td>WILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</td><td><a href="https://pdfs.semanticscholar.org/fe1c/ec4e4995b8615855572374ae3efc94949105.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=wildtrack: a multi-camera hd dataset for dense unscripted pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>77c81c13a110a341c140995bedb98101b9e84f7f</td></tr><tr><td>yale_faces</td><td>YaleFaces</td><td>Acquiring Linear Subspaces for Face Recognition under Variable Lighting</td><td>From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</td><td><a href="http://pdfs.semanticscholar.org/97bb/c2b439a79d4dc0dc7199d71ed96ad5e3fd0e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=acquiring linear subspaces for face recognition under variable lighting&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18c72175ddbb7d5956d180b65a96005c100f6014</td></tr><tr><td>yfcc_100m</td><td>YFCC100M</td><td>YFCC100M: The New Data in Multimedia Research</td><td>The New Data and New Challenges in Multimedia Research</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=yfcc100m: the new data in multimedia research&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a6e695ddd07aad719001c0fc1129328452385949</td></tr><tr><td>youtube_makeup</td><td>YMU</td><td>Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?</td><td>Automatic facial makeup detection with application in face recognition</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6612994" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=can facial cosmetics affect the matching accuracy of face recognition systems?&sort=relevance" target="_blank">[s2]</a></td><td>West Virginia University</td><td>fcc6fe6007c322641796cb8792718641856a22a7</td></tr></table></body></html>
\ No newline at end of file +discovering, annotating, and recognizing scene attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>833fa04463d90aab4a9fe2870d480f0b40df446e</td></tr><tr><td>10k_US_adult_faces</td><td>10K US Adult Faces</td><td>The intrinsic memorability of face images</td><td>The intrinsic memorability of face photographs.</td><td><a href="https://pdfs.semanticscholar.org/8b2d/d5c61b23ead5ae5508bb8ce808b5ea266730.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the intrinsic memorability of face images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8b2dd5c61b23ead5ae5508bb8ce808b5ea266730</td></tr><tr><td>fei</td><td>FEI</td><td>Captura e Alinhamento de Imagens: Um Banco de Faces Brasileiro</td><td>A new ranking method for principal components analysis and its application to face image analysis</td><td><a href="https://doi.org/10.1016/j.imavis.2009.11.005" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=captura e alinhamento de imagens: um banco de faces brasileiro&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8b56e33f33e582f3e473dba573a16b598ed9bcdc</td></tr><tr><td>ilids_vid_reid</td><td>iLIDS-VID</td><td>Person Re-Identi cation by Video Ranking</td><td>Person Re-identification by Video Ranking</td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person re-identi cation by video ranking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>98bb029afe2a1239c3fdab517323066f0957b81b</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>Person reidentification by video ranking</td><td>Person Re-identification by Video Ranking</td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person reidentification by video ranking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>98bb029afe2a1239c3fdab517323066f0957b81b</td></tr><tr><td>who_goes_there</td><td>WGT</td><td>Who Goes There? Approaches to Mapping Facial Appearance Diversity</td><td>Who goes there?: approaches to mapping facial appearance diversity</td><td><a href="http://doi.acm.org/10.1145/2996913.2996997" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=who goes there? approaches to mapping facial appearance diversity&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9b9bf5e623cb8af7407d2d2d857bc3f1b531c182</td></tr><tr><td>bp4d_spontanous</td><td>BP4D-Spontanous</td><td>A high resolution spontaneous 3D dynamic facial expression database</td><td>A high-resolution spontaneous 3D dynamic facial expression database</td><td><a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6553788" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a high resolution spontaneous 3d dynamic facial expression database&sort=relevance" target="_blank">[s2]</a></td><td>SUNY Binghamton</td><td>b91f54e1581fbbf60392364323d00a0cd43e493c</td></tr><tr><td>hda_plus</td><td>HDA+</td><td>A Multi-camera video data set for research on High-Definition surveillance</td><td>HDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance</td><td><a href="http://pdfs.semanticscholar.org/bd88/bb2e4f351352d88ee7375af834360e223498.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a multi-camera video data set for research on high-definition surveillance&sort=relevance" target="_blank">[s2]</a></td><td></td><td>bd88bb2e4f351352d88ee7375af834360e223498</td></tr></table></body></html>
\ No newline at end of file diff --git a/scraper/s2-final-report.py b/scraper/s2-final-report.py index 489e43df..58ac481f 100644 --- a/scraper/s2-final-report.py +++ b/scraper/s2-final-report.py @@ -9,6 +9,7 @@ import subprocess from util import * DIR_PUBLIC_CITATIONS = "../site/datasets/final" +DIR_UNKNOWN_CITATIONS = "../site/datasets/unknown" @click.command() def s2_final_report(): @@ -23,15 +24,16 @@ def s2_final_report(): def process_paper(row, addresses): aggregate_citations = {} + unknown_citations = {} address = None papers = [] print(row['paper_ids']) for paper_id in row['paper_ids']: - res = process_single_paper(row, paper_id, addresses, aggregate_citations) + res = process_single_paper(row, paper_id, addresses, aggregate_citations, unknown_citations) if res: papers.append(res) - if res['address']: - address = res['address'] + if res['address']: + address = res['address'] if not len(papers): return with open('{}/{}.json'.format(DIR_PUBLIC_CITATIONS, row['key']), 'w') as f: @@ -42,8 +44,14 @@ def process_paper(row, addresses): 'additional_papers': papers[1:], 'citations': [aggregate_citations[key] for key in aggregate_citations.keys()], }, f) + with open('{}/{}.json'.format(DIR_UNKNOWN_CITATIONS, row['key']), 'w') as f: + json.dump({ + 'id': papers[0]['paper_id'], + 'paper': papers[0], + 'citations': [unknown_citations[key] for key in unknown_citations.keys()], + }, f) -def process_single_paper(row, paper_id, addresses, aggregate_citations): +def process_single_paper(row, paper_id, addresses, aggregate_citations, unknown_citations): res = { 'paper_id': '', 'key': '', @@ -60,13 +68,6 @@ def process_single_paper(row, paper_id, addresses, aggregate_citations): # 'citations_doi': 0, } - geocoded_citations = [] - unknown_citations = [] - empty_citations = [] - pdf_count = 0 - doi_count = 0 - address_count = 0 - fn = file_path('papers', paper_id, 'paper.json') with open(fn, 'r') as f: @@ -103,14 +104,16 @@ def process_single_paper(row, paper_id, addresses, aggregate_citations): citationId = cite['paperId'] if citationId in aggregate_citations: continue + elif citationId in unknown_citations: + continue seen_here = {} citation = load_paper(citationId) has_pdf = os.path.exists(file_path('pdf', citationId, 'paper.txt')) has_doi = os.path.exists(file_path('doi', citationId, 'paper.doi')) - if has_pdf: - pdf_count += 1 - if has_doi: - doi_count += 1 + # if has_pdf: + # pdf_count += 1 + # if has_doi: + # doi_count += 1 if citation is None or citation.data is None: print("Citation missing! {}".format(cite['paperId'])) continue @@ -120,7 +123,7 @@ def process_single_paper(row, paper_id, addresses, aggregate_citations): institution = '' address = None for inst in sorted(institutions, key=operator.itemgetter(1)): - address_count += 1 + # address_count += 1 institution = inst[1] next_address = addresses.findObject(institution) if next_address and next_address['address'] not in seen_here: @@ -142,21 +145,20 @@ def process_single_paper(row, paper_id, addresses, aggregate_citations): address = next_address geocoded_addresses.append(next_address) if address: - if citationId not in aggregate_citations: - aggregate_citations[citationId] = { - 'id': citationId, - 'title': citation.title, - 'addresses': geocoded_addresses, - 'year': citation.year, - 'pdf': citation.pdf_link, - } - - # res['citation_count'] = len(data['citations']) - # res['citations_geocoded'] = len(geocoded_citations) - # res['citations_unknown'] = len(unknown_citations) - # res['citations_empty'] = len(empty_citations) - # res['citations_pdf'] = pdf_count - # res['citations_doi'] = doi_count + aggregate_citations[citationId] = { + 'id': citationId, + 'title': citation.title, + 'addresses': geocoded_addresses, + 'year': citation.year, + 'pdf': citation.pdf_link, + } + else: + unknown_citations[citationId] = { + 'id': citationId, + 'title': citation.title, + 'year': citation.year, + 'pdf': citation.pdf_link, + } return res def load_ft_lookup(): @@ -179,6 +181,8 @@ def load_megapixels_lookup(): rec = {} for index, key in enumerate(keys): rec[key] = row[index] + if rec['paper_id'] == "": + continue paper_key = rec['key'] if paper_key not in lookup: rec['paper_ids'] = [] diff --git a/scraper/s2-geocode-server.py b/scraper/s2-geocode-server.py new file mode 100644 index 00000000..0b1b0937 --- /dev/null +++ b/scraper/s2-geocode-server.py @@ -0,0 +1,68 @@ +#!python + +import os +import sys +import json +import time +import argparse +from datetime import datetime +from flask import Flask, request, render_template, jsonify + +from dotenv import load_dotenv +load_dotenv() + +from util import * + +locations_worksheet = fetch_worksheet('paper_locations') + +app = Flask(__name__, static_url_path="/reports", static_folder=os.path.abspath("reports")) + +# static api route +@app.route('/', methods=['GET']) +def index(): + return app.send_static_file('geocode_papers.html') + +@app.errorhandler(404) +def page_not_found(e): + return app.send_static_file('geocode_papers.html') + +# route to get all the manually geocoded IDs (to dedupe) +# route to add a geocoding for a paper + +@app.route('/api/institutions', methods=['GET']) +def list_locations(): + addresses = AddressBook() + return jsonify({ + 'entities': addresses.entities, + 'lookup': addresses.lookup, + }) + +@app.route('/api/papers', methods=['GET']) +def list_papers(): + lookup_keys, lines = fetch_google_sheet('citation_lookup') + paper_lookup = {} + for line in lines: + paper_lookup[line[0]] = line + return jsonify({ + 'papers': paper_lookup, + }) + +@app.route('/api/address', methods=['POST']) +def add_address(): + # id, title, institution_1, institution_2, institution_3, institution_4, notes + locations_worksheet.insert_row([ + request.form['paper_id'], + request.form['title'], + request.form['institution_1'], + request.form['institution_2'], + request.form['institution_3'], + request.form['institution_4'], + request.form['notes'], + ]) + return jsonify({ + 'status': 'ok' + }) + +if __name__=="__main__": + app.run("0.0.0.0", debug=False) + diff --git a/scraper/s2-papers.py b/scraper/s2-papers.py index 9a584e29..744454b7 100644 --- a/scraper/s2-papers.py +++ b/scraper/s2-papers.py @@ -23,8 +23,16 @@ def fetch_papers(): no_location_rows = [] nonmatching_rows = [] for line in lines: - key, name, title, paper_id, is_unknown = line + # key, name, title, paper_id, is_unknown, notes = line + key = line[0] + name = line[1] + title = line[2] + paper_id = line[3] + if paper_id == '': + continue paper = fetch_paper(s2, paper_id) + if paper is None: + continue db_paper = load_paper(paper_id) pdf_link = db_paper.pdf_link if db_paper else "" diff --git a/scraper/util.py b/scraper/util.py index 2d7c2ccb..9b47510a 100644 --- a/scraper/util.py +++ b/scraper/util.py @@ -331,11 +331,11 @@ def fetch_paper(s2, paper_id): print(paper_id) paper = s2.paper(paper_id) if paper is None: - print("Got none paper??") + print("Paper not found: {}".format(paper_id)) # time.sleep(random.randint(1, 2)) paper = s2.paper(paper_id) if paper is None: - print("Paper not found") + # print("Paper not found") return None write_json(paper_fn, paper) # time.sleep(random.randint(1, 2)) @@ -343,7 +343,8 @@ def fetch_paper(s2, paper_id): def fetch_spreadsheet(): scope = ['https://spreadsheets.google.com/feeds','https://www.googleapis.com/auth/drive'] - credentials = ServiceAccountCredentials.from_json_keyfile_name('./.creds/Megapixels-ef28f91112a9.json', scope) + path = os.path.dirname(os.path.abspath(__file__)) + credentials = ServiceAccountCredentials.from_json_keyfile_name(os.path.join(path, '.creds/Megapixels-ef28f91112a9.json'), scope) docid = "1denb7TjYsN9igHyvYah7fQ0daABW32Z30lwV7QrDJQc" client = gspread.authorize(credentials) spreadsheet = client.open_by_key(docid) diff --git a/site/datasets/final/adience.csv b/site/datasets/final/adience.csv new file mode 100644 index 00000000..9c9f2b76 --- /dev/null +++ b/site/datasets/final/adience.csv @@ -0,0 +1,102 @@ +index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,Adience,adience,0.0,0.0,,,1be498d4bbc30c3bfd0029114c784bc2114d67c0,main,http://www.openu.ac.il/home/hassner/Adience/EidingerEnbarHassner_tifs.pdf,Age and Gender Estimation of Unfiltered Faces,2014 +1,Adience,adience,28.5456282,77.2731505,"IIIT Delhi, India",edu,f726738954e7055bb3615fa7e8f59f136d3e0bdc,citation,https://arxiv.org/pdf/1803.07385.pdf,Are you eligible? Predicting adulthood from face images via class specific mean autoencoder,2018 +2,Adience,adience,37.43131385,-122.16936535,Stanford University,edu,16d6737b50f969247339a6860da2109a8664198a,citation,https://pdfs.semanticscholar.org/16d6/737b50f969247339a6860da2109a8664198a.pdf,Convolutional Neural Networks for Age and Gender Classification,2016 +3,Adience,adience,40.00229045,116.32098908,Tsinghua University,edu,2149d49c84a83848d6051867290d9c8bfcef0edb,citation,https://doi.org/10.1109/TIFS.2017.2746062,Label-Sensitive Deep Metric Learning for Facial Age Estimation,2018 +4,Adience,adience,51.5217668,-0.13019072,University of London,edu,31ea88f29e7f01a9801648d808f90862e066f9ea,citation,https://arxiv.org/pdf/1605.06391.pdf,Deep Multi-task Representation Learning: A Tensor Factorisation Approach,2016 +5,Adience,adience,40.0044795,116.370238,Chinese Academy of Sciences,edu,d492dbfaa42b4f8b8a74786d7343b3be6a3e9a1d,citation,https://pdfs.semanticscholar.org/d492/dbfaa42b4f8b8a74786d7343b3be6a3e9a1d.pdf,Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation,0 +6,Adience,adience,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,d492dbfaa42b4f8b8a74786d7343b3be6a3e9a1d,citation,https://pdfs.semanticscholar.org/d492/dbfaa42b4f8b8a74786d7343b3be6a3e9a1d.pdf,Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation,0 +7,Adience,adience,34.0224149,-118.28634407,University of Southern California,edu,29f298dd5f806c99951cb434834bc8dcc765df18,citation,https://doi.org/10.1109/ICPR.2016.7899837,Computationally efficient template-based face recognition,2016 +8,Adience,adience,45.5039761,-73.5749687,McGill University,edu,ed9d11e995baeec17c5d2847ec1a8d5449254525,citation,https://pdfs.semanticscholar.org/ed9d/11e995baeec17c5d2847ec1a8d5449254525.pdf,Efficient Gender Classification Using a Deep LDA-Pruned Net,2017 +9,Adience,adience,12.9551259,77.5741985,Bangalore Institute of Technology,edu,10126b467391e153d36f1a496ef5618097775ad1,citation,https://pdfs.semanticscholar.org/1012/6b467391e153d36f1a496ef5618097775ad1.pdf,An Active Age Estimation of Facial image using Anthropometric Model and Fast ICA,2017 +10,Adience,adience,42.36782045,-71.12666653,Harvard University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 +11,Adience,adience,40.9153196,-73.1270626,Stony Brook University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 +12,Adience,adience,38.8760446,115.4973873,North China Electric Power University,edu,56f86bef26209c85f2ef66ec23b6803d12ca6cd6,citation,http://arxiv.org/abs/1710.00307,Pyramidal RoR for image classification,2017 +13,Adience,adience,40.00229045,116.32098908,Tsinghua University,edu,51f626540860ad75b68206025a45466a6d087aa6,citation,https://doi.org/10.1109/ICIP.2017.8296595,Cluster convolutional neural networks for facial age estimation,2017 +14,Adience,adience,45.5039761,-73.5749687,McGill University,edu,407bb798ab153bf6156ba2956f8cf93256b6910a,citation,http://pdfs.semanticscholar.org/407b/b798ab153bf6156ba2956f8cf93256b6910a.pdf,Fisher Pruning of Deep Nets for Facial Trait Classification,2018 +15,Adience,adience,39.2899685,-76.62196103,University of Maryland,edu,81fc86e86980a32c47410f0ba7b17665048141ec,citation,http://pdfs.semanticscholar.org/81fc/86e86980a32c47410f0ba7b17665048141ec.pdf,Segment-based Methods for Facial Attribute Detection from Partial Faces,2018 +16,Adience,adience,22.304572,114.17976285,Hong Kong Polytechnic University,edu,dc2f16f967eac710cb9b7553093e9c977e5b761d,citation,https://doi.org/10.1109/ICPR.2016.7900141,Learning a lightweight deep convolutional network for joint age and gender recognition,2016 +17,Adience,adience,23.09461185,113.28788994,Sun Yat-Sen University,edu,dc2f16f967eac710cb9b7553093e9c977e5b761d,citation,https://doi.org/10.1109/ICPR.2016.7900141,Learning a lightweight deep convolutional network for joint age and gender recognition,2016 +18,Adience,adience,39.65404635,-79.96475355,West Virginia University,edu,7a65fc9e78eff3ab6062707deaadde024d2fad40,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Zhu_A_Study_on_ICCV_2015_paper.pdf,A Study on Apparent Age Estimation,2015 +19,Adience,adience,42.357757,-83.06286711,Wayne State University,edu,28d99dc2d673d62118658f8375b414e5192eac6f,citation,http://www.cs.wayne.edu/~mdong/cvpr17.pdf,Using Ranking-CNN for Age Estimation,2017 +20,Adience,adience,25.0410728,121.6147562,Institute of Information Science,edu,0951f42abbf649bb564a21d4ff5dddf9a5ea54d9,citation,https://arxiv.org/pdf/1806.02023.pdf,Joint Estimation of Age and Gender from Unconstrained Face Images Using Lightweight Multi-Task CNN for Mobile Applications,2018 +21,Adience,adience,34.0224149,-118.28634407,University of Southern California,edu,eb6ee56e085ebf473da990d032a4249437a3e462,citation,http://www-scf.usc.edu/~chuntinh/doc/Age_Gender_Classification_APSIPA_2017.pdf,Age/gender classification with whole-component convolutional neural networks (WC-CNN),2017 +22,Adience,adience,32.77824165,34.99565673,Open University of Israel,edu,0a34fe39e9938ae8c813a81ae6d2d3a325600e5c,citation,https://arxiv.org/pdf/1708.07517.pdf,FacePoseNet: Making a Case for Landmark-Free Face Alignment,2017 +23,Adience,adience,40.51865195,-74.44099801,State University of New Jersey,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791154,A cascaded convolutional neural network for age estimation of unconstrained faces,2016 +24,Adience,adience,39.2899685,-76.62196103,University of Maryland,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791154,A cascaded convolutional neural network for age estimation of unconstrained faces,2016 +25,Adience,adience,32.8536333,-117.2035286,Kyung Hee University,edu,9d4692e243e25eb465a0480376beb60a5d2f0f13,citation,https://doi.org/10.1109/ICCE.2016.7430617,Positional Ternary Pattern (PTP): An edge based image descriptor for human age recognition,2016 +26,Adience,adience,1.340216,103.965089,Singapore University of Technology and Design,edu,00823e6c0b6f1cf22897b8d0b2596743723ec51c,citation,https://arxiv.org/pdf/1708.07689.pdf,Understanding and Comparing Deep Neural Networks for Age and Gender Classification,2017 +27,Adience,adience,45.47567215,9.23336232,Università degli Studi di Milano,edu,a713a01971e73d0c3118d0409dc7699a24f521d6,citation,https://doi.org/10.1109/SSCI.2017.8285381,Age estimation based on face images and pre-trained convolutional neural networks,2017 +28,Adience,adience,37.2830003,127.04548469,Ajou University,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018 +29,Adience,adience,37.403917,127.159786,Korea Electronics Technology Institute,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018 +30,Adience,adience,37.26728,126.9841151,Seoul National University,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018 +31,Adience,adience,49.2767454,-122.91777375,Simon Fraser University,edu,975978ee6a32383d6f4f026b944099e7739e5890,citation,https://pdfs.semanticscholar.org/9759/78ee6a32383d6f4f026b944099e7739e5890.pdf,Privacy-Preserving Age Estimation for Content Rating,2018 +32,Adience,adience,49.8091536,-97.13304179,University of Manitoba,edu,975978ee6a32383d6f4f026b944099e7739e5890,citation,https://pdfs.semanticscholar.org/9759/78ee6a32383d6f4f026b944099e7739e5890.pdf,Privacy-Preserving Age Estimation for Content Rating,2018 +33,Adience,adience,33.7774349,-84.3973208,"College of Computing, Georgia Tech",edu,58df849378fbcfb6b1a8ebddfbe4caa450226b9d,citation,https://doi.org/10.1109/ICIP.2017.8296770,Head pose estimation using learned discretization,2017 +34,Adience,adience,39.95472495,-75.15346905,Temple University,edu,58df849378fbcfb6b1a8ebddfbe4caa450226b9d,citation,https://doi.org/10.1109/ICIP.2017.8296770,Head pose estimation using learned discretization,2017 +35,Adience,adience,36.1017956,-79.501733,Elon University,edu,58df849378fbcfb6b1a8ebddfbe4caa450226b9d,citation,https://doi.org/10.1109/ICIP.2017.8296770,Head pose estimation using learned discretization,2017 +36,Adience,adience,23.7289899,90.3982682,Institute of Information Technology,edu,2e58ec57d71b2b2a3e71086234dd7037559cc17e,citation,https://pdfs.semanticscholar.org/2e58/ec57d71b2b2a3e71086234dd7037559cc17e.pdf,A Gender Recognition System from Facial Image,2018 +37,Adience,adience,23.7316957,90.3965275,University of Dhaka,edu,2e58ec57d71b2b2a3e71086234dd7037559cc17e,citation,https://pdfs.semanticscholar.org/2e58/ec57d71b2b2a3e71086234dd7037559cc17e.pdf,A Gender Recognition System from Facial Image,2018 +38,Adience,adience,37.98782705,23.73179733,National Technical University of Athens,edu,bd572e9cbec095bcf5700cb7cd73d1cdc2fe02f4,citation,http://pdfs.semanticscholar.org/bd57/2e9cbec095bcf5700cb7cd73d1cdc2fe02f4.pdf,Deep Learning for Computer Vision: A Brief Review,2018 +39,Adience,adience,47.00646895,-120.5367304,Central Washington University,edu,56c2fb2438f32529aec604e6fc3b06a595ddbfcc,citation,http://pdfs.semanticscholar.org/56c2/fb2438f32529aec604e6fc3b06a595ddbfcc.pdf,Comparison of Recent Machine Learning Techniques for Gender Recognition from Facial Images,2016 +40,Adience,adience,32.77824165,34.99565673,Open University of Israel,edu,c75e6ce54caf17b2780b4b53f8d29086b391e839,citation,https://arxiv.org/pdf/1802.00542.pdf,"ExpNet: Landmark-Free, Deep, 3D Facial Expressions",2018 +41,Adience,adience,31.83907195,117.26420748,University of Science and Technology of China,edu,47cd161546c59ab1e05f8841b82e985f72e5ddcb,citation,https://doi.org/10.1109/ICIP.2017.8296552,Gender classification in live videos,2017 +42,Adience,adience,25.0410728,121.6147562,Institute of Information Science,edu,1862f2df2e278505c9ca970f9c5a25ea3aeb9686,citation,https://pdfs.semanticscholar.org/1862/f2df2e278505c9ca970f9c5a25ea3aeb9686.pdf,Merging Deep Neural Networks for Mobile Devices,0 +43,Adience,adience,45.42580475,-75.68740118,University of Ottawa,edu,16820ccfb626dcdc893cc7735784aed9f63cbb70,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W12/papers/Azarmehr_Real-Time_Embedded_Age_2015_CVPR_paper.pdf,Real-time embedded age and gender classification in unconstrained video,2015 +44,Adience,adience,37.26728,126.9841151,Seoul National University,edu,282503fa0285240ef42b5b4c74ae0590fe169211,citation,http://pdfs.semanticscholar.org/2825/03fa0285240ef42b5b4c74ae0590fe169211.pdf,Feeding Hand-Crafted Features for Enhancing the Performance of Convolutional Neural Networks,2018 +45,Adience,adience,32.8536333,-117.2035286,Kyung Hee University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +46,Adience,adience,24.7246403,46.62335012,King Saud University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +47,Adience,adience,23.7289899,90.3982682,Institute of Information Technology,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +48,Adience,adience,53.21967825,6.56251482,University of Groningen,edu,361c9ba853c7d69058ddc0f32cdbe94fbc2166d5,citation,http://pdfs.semanticscholar.org/361c/9ba853c7d69058ddc0f32cdbe94fbc2166d5.pdf,Deep Reinforcement Learning of Video Games,2017 +49,Adience,adience,41.1664858,-73.1920564,University of Bridgeport,edu,ac9a331327cceda4e23f9873f387c9fd161fad76,citation,http://pdfs.semanticscholar.org/ac9a/331327cceda4e23f9873f387c9fd161fad76.pdf,Deep Convolutional Neural Network for Age Estimation based on VGG-Face Model,2017 +50,Adience,adience,53.21967825,6.56251482,University of Groningen,edu,4ff4c27e47b0aa80d6383427642bb8ee9d01c0ac,citation,https://doi.org/10.1109/SSCI.2015.37,Deep Convolutional Neural Networks and Support Vector Machines for Gender Recognition,2015 +51,Adience,adience,40.4319722,-86.92389368,Purdue University,edu,6193c833ad25ac27abbde1a31c1cabe56ce1515b,citation,https://pdfs.semanticscholar.org/5f25/7ca18a92c3595db3bda3224927ec494003a5.pdf,Trojaning Attack on Neural Networks,2018 +52,Adience,adience,40.4319722,-86.92389368,Purdue University,edu,b18858ad6ec88d8b443dffd3e944e653178bc28b,citation,http://pdfs.semanticscholar.org/b188/58ad6ec88d8b443dffd3e944e653178bc28b.pdf,Trojaning Attack on Neural Networks,2017 +53,Adience,adience,40.9153196,-73.1270626,Stony Brook University,edu,25bf288b2d896f3c9dab7e7c3e9f9302e7d6806b,citation,http://pdfs.semanticscholar.org/25bf/288b2d896f3c9dab7e7c3e9f9302e7d6806b.pdf,Neural Networks with Smooth Adaptive Activation Functions for Regression,2016 +54,Adience,adience,40.9153196,-73.1270626,Stony Brook University,edu,1190cba0cae3c8bb81bf80d6a0a83ae8c41240bc,citation,https://pdfs.semanticscholar.org/1190/cba0cae3c8bb81bf80d6a0a83ae8c41240bc.pdf,Squared Earth Mover ’ s Distance Loss for Training Deep Neural Networks on Ordered-Classes,2017 +55,Adience,adience,40.9153196,-73.1270626,Stony Brook University,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,http://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017 +56,Adience,adience,40.90826665,-73.11520891,Stony Brook University Hospital,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,http://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017 +57,Adience,adience,45.5039761,-73.5749687,McGill University,edu,13719bbb4bb8bbe0cbcdad009243a926d93be433,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w6/papers/Tian_Deep_LDA-Pruned_Nets_CVPR_2017_paper.pdf,Deep LDA-Pruned Nets for Efficient Facial Gender Classification,2017 +58,Adience,adience,41.10427915,29.02231159,Istanbul Technical University,edu,fd53be2e0a9f33080a9db4b5a5e416e24ae8e198,citation,https://arxiv.org/pdf/1606.02909.pdf,Apparent Age Estimation Using Ensemble of Deep Learning Models,2016 +59,Adience,adience,47.3804685,8.5430355,"Disney Research, Zurich",edu,017e94ad51c9be864b98c9b75582753ce6ee134f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7892240,Rapid one-shot acquisition of dynamic VR avatars,2017 +60,Adience,adience,34.1579742,-118.2894729,"Disney Research, UK",company,017e94ad51c9be864b98c9b75582753ce6ee134f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7892240,Rapid one-shot acquisition of dynamic VR avatars,2017 +61,Adience,adience,34.1619174,-118.2883702,Walt Disney Imagineering,company,017e94ad51c9be864b98c9b75582753ce6ee134f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7892240,Rapid one-shot acquisition of dynamic VR avatars,2017 +62,Adience,adience,49.2593879,-122.9151893,"AltumView Systems Inc., Burnaby, BC, Canada",company,b44f03b5fa8c6275238c2d13345652e6ff7e6ea9,citation,https://doi.org/10.1109/GlobalSIP.2017.8309138,Lapped convolutional neural networks for embedded systems,2017 +63,Adience,adience,37.2830003,127.04548469,Ajou University,edu,24286ef164f0e12c3e9590ec7f636871ba253026,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369721,Age and gender classification using wide convolutional neural network and Gabor filter,2018 +64,Adience,adience,37.26728,126.9841151,Seoul National University,edu,24286ef164f0e12c3e9590ec7f636871ba253026,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369721,Age and gender classification using wide convolutional neural network and Gabor filter,2018 +65,Adience,adience,47.6543238,-122.30800894,University of Washington,edu,96e0cfcd81cdeb8282e29ef9ec9962b125f379b0,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.527,The MegaFace Benchmark: 1 Million Faces for Recognition at Scale,2016 +66,Adience,adience,65.0592157,25.46632601,University of Oulu,edu,1fe121925668743762ce9f6e157081e087171f4c,citation,https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W02/papers/Ylioinas_Unsupervised_Learning_of_2015_CVPR_paper.pdf,Unsupervised learning of overcomplete face descriptors,2015 +67,Adience,adience,23.0886214,-82.4481944,"Advanced Technologies Application Center, Havana, Cuba",edu,c5eba789aeb41904aa1b03fad1dc7cea5d0cd3b6,citation,https://doi.org/10.1109/BTAS.2017.8272773,Age and gender classification using local appearance descriptors from facial components,2017 +68,Adience,adience,40.7240176,8.5578947,University of Sassari,edu,c5eba789aeb41904aa1b03fad1dc7cea5d0cd3b6,citation,https://doi.org/10.1109/BTAS.2017.8272773,Age and gender classification using local appearance descriptors from facial components,2017 +69,Adience,adience,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,cb27b45329d61f5f95ed213798d4b2a615e76be2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8329236,Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion,2018 +70,Adience,adience,37.2520226,127.0555019,"Samsung SAIT, Korea",company,cb27b45329d61f5f95ed213798d4b2a615e76be2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8329236,Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion,2018 +71,Adience,adience,22.42031295,114.20788644,Chinese University of Hong Kong,edu,d80a3d1f3a438e02a6685e66ee908446766fefa9,citation,https://arxiv.org/pdf/1708.09687.pdf,Quantifying Facial Age by Posterior of Age Comparisons,2017 +72,Adience,adience,42.357757,-83.06286711,Wayne State University,edu,4f1249369127cc2e2894f6b2f1052d399794919a,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8239663,Deep Age Estimation: From Classification to Ranking,2018 +73,Adience,adience,35.2742655,137.01327841,Chubu University,edu,5fb5d9389e2a2a4302c81bcfc068a4c8d4efe70c,citation,http://pdfs.semanticscholar.org/5fb5/d9389e2a2a4302c81bcfc068a4c8d4efe70c.pdf,Multiple Facial Attributes Estimation Based on Weighted Heterogeneous Learning,2016 +74,Adience,adience,1.3484104,103.68297965,Nanyang Technological University,edu,d0471d5907d6557cf081edf4c7c2296c3c221a38,citation,https://pdfs.semanticscholar.org/d047/1d5907d6557cf081edf4c7c2296c3c221a38.pdf,A Constrained Deep Neural Network for Ordinal Regression,0 +75,Adience,adience,41.3868913,2.16352385,University of Barcelona,edu,500fbe18afd44312738cab91b4689c12b4e0eeee,citation,http://www.maia.ub.es/~sergio/linked/ijcnn_age_and_cultural_2015.pdf,ChaLearn looking at people 2015 new competitions: Age estimation and cultural event recognition,2015 +76,Adience,adience,45.4312742,12.3265377,University of Venezia,edu,500fbe18afd44312738cab91b4689c12b4e0eeee,citation,http://www.maia.ub.es/~sergio/linked/ijcnn_age_and_cultural_2015.pdf,ChaLearn looking at people 2015 new competitions: Age estimation and cultural event recognition,2015 +77,Adience,adience,40.47913175,-74.43168868,Rutgers University,edu,31f1e711fcf82c855f27396f181bf5e565a2f58d,citation,http://doi.ieeecomputersociety.org/10.1109/ICCVW.2015.54,Unconstrained Age Estimation with Deep Convolutional Neural Networks,2015 +78,Adience,adience,39.2899685,-76.62196103,University of Maryland,edu,31f1e711fcf82c855f27396f181bf5e565a2f58d,citation,http://doi.ieeecomputersociety.org/10.1109/ICCVW.2015.54,Unconstrained Age Estimation with Deep Convolutional Neural Networks,2015 +79,Adience,adience,32.77824165,34.99565673,Open University of Israel,edu,62e913431bcef5983955e9ca160b91bb19d9de42,citation,http://pdfs.semanticscholar.org/62e9/13431bcef5983955e9ca160b91bb19d9de42.pdf,Facial Landmark Detection with Tweaked Convolutional Neural Networks,2015 +80,Adience,adience,34.67567405,33.04577648,Cyprus University of Technology,edu,9f3c9e41f46df9c94d714b1f080dafad6b4de1de,citation,https://doi.org/10.1109/ICT.2017.7998260,On the detection of images containing child-pornographic material,2017 +81,Adience,adience,51.49887085,-0.17560797,Imperial College London,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +82,Adience,adience,51.59029705,-0.22963221,Middlesex University,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +83,Adience,adience,37.5600406,126.9369248,Yonsei University,edu,fde41dc4ec6ac6474194b99e05b43dd6a6c4f06f,citation,https://arxiv.org/pdf/1809.01990.pdf,Multi-Expert Gender Classification on Age Group by Integrating Deep Neural Networks,2018 +84,Adience,adience,23.143197,113.34009651,South China Normal University,edu,dc6ad30c7a4bc79bb06b4725b16e202d3d7d8935,citation,http://doi.org/10.1007/s11042-017-4646-5,Age classification with deep learning face representation,2017 +85,Adience,adience,23.0502042,113.39880323,South China University of Technology,edu,dc6ad30c7a4bc79bb06b4725b16e202d3d7d8935,citation,http://doi.org/10.1007/s11042-017-4646-5,Age classification with deep learning face representation,2017 +86,Adience,adience,34.2152538,117.1398541,China University of Mining and Technology,edu,bc6a7390135bf127b93b90a21b1fdebbfb56ad30,citation,https://doi.org/10.1109/TIFS.2017.2766039,Bimodal Vein Data Mining via Cross-Selected-Domain Knowledge Transfer,2018 +87,Adience,adience,31.2284923,121.40211389,East China Normal University,edu,bc6a7390135bf127b93b90a21b1fdebbfb56ad30,citation,https://doi.org/10.1109/TIFS.2017.2766039,Bimodal Vein Data Mining via Cross-Selected-Domain Knowledge Transfer,2018 +88,Adience,adience,25.0410728,121.6147562,Institute of Information Science,edu,337dd4aaca2c5f9b5d2de8e0e2401b5a8feb9958,citation,https://arxiv.org/pdf/1810.11160.pdf,Data-specific Adaptive Threshold for Face Recognition and Authentication,2018 +89,Adience,adience,28.3656193,75.5834953,"Central Electronics Research Institute, Pilani, India",edu,1aeef2ab062c27e0dbba481047e818d4c471ca57,citation,https://doi.org/10.1109/ICACCI.2015.7275860,Analyzing impact of image scaling algorithms on viola-jones face detection framework,2015 +90,Adience,adience,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,2911e7f0fb6803851b0eddf8067a6fc06e8eadd6,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Jung_Joint_Fine-Tuning_in_ICCV_2015_paper.pdf,Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition,2015 +91,Adience,adience,23.7317915,90.3805625,Dhaka University,edu,026e4ee480475e63ae68570d73388f8dfd4b4cde,citation,http://pdfs.semanticscholar.org/026e/4ee480475e63ae68570d73388f8dfd4b4cde.pdf,Evaluating gender portrayal in Bangladeshi TV,2017 +92,Adience,adience,40.0505672,-75.37109326,Eastern University,edu,026e4ee480475e63ae68570d73388f8dfd4b4cde,citation,http://pdfs.semanticscholar.org/026e/4ee480475e63ae68570d73388f8dfd4b4cde.pdf,Evaluating gender portrayal in Bangladeshi TV,2017 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Barcelona,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0 +97,Adience,adience,31.76909325,117.17795091,Anhui University,edu,b910590a0eb191d03e1aedb3d55c905129e92e6b,citation,http://doi.acm.org/10.1145/2808492.2808570,Robust gender classification on unconstrained face images,2015 +98,Adience,adience,40.0044795,116.370238,Chinese Academy of Sciences,edu,b910590a0eb191d03e1aedb3d55c905129e92e6b,citation,http://doi.acm.org/10.1145/2808492.2808570,Robust gender classification on unconstrained face images,2015 +99,Adience,adience,43.7743911,-79.50481085,York University,edu,ffe4bb47ec15f768e1744bdf530d5796ba56cfc1,citation,https://arxiv.org/pdf/1706.04277.pdf,AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces,2017 +100,Adience,adience,27.18794105,31.17009498,Assiut University,edu,ffe4bb47ec15f768e1744bdf530d5796ba56cfc1,citation,https://arxiv.org/pdf/1706.04277.pdf,AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces,2017 diff --git a/site/datasets/final/aflw.csv b/site/datasets/final/aflw.csv new file mode 100644 index 00000000..29cfe134 --- /dev/null +++ b/site/datasets/final/aflw.csv @@ -0,0 +1,212 @@ +index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,AFLW,aflw,0.0,0.0,,,a74251efa970b92925b89eeef50a5e37d9281ad0,main,http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf,"Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization",2011 +1,AFLW,aflw,42.2942142,-83.71003894,University of Michigan,edu,860588fafcc80c823e66429fadd7e816721da42a,citation,https://arxiv.org/pdf/1804.04412.pdf,Unsupervised Discovery of Object Landmarks as Structural Representations,2018 +2,AFLW,aflw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,433a6d6d2a3ed8a6502982dccc992f91d665b9b3,citation,http://pdfs.semanticscholar.org/433a/6d6d2a3ed8a6502982dccc992f91d665b9b3.pdf,Transferring Landmark Annotations for Cross-Dataset Face Alignment,2014 +3,AFLW,aflw,40.00229045,116.32098908,Tsinghua University,edu,433a6d6d2a3ed8a6502982dccc992f91d665b9b3,citation,http://pdfs.semanticscholar.org/433a/6d6d2a3ed8a6502982dccc992f91d665b9b3.pdf,Transferring Landmark Annotations for Cross-Dataset Face Alignment,2014 +4,AFLW,aflw,-27.47715625,153.02841004,Queensland University of Technology,edu,6342a4c54835c1e14159495373ab18b4233d2d9b,citation,http://pdfs.semanticscholar.org/6342/a4c54835c1e14159495373ab18b4233d2d9b.pdf,Towards Pose-robust Face Recognition on Video,2014 +5,AFLW,aflw,39.993008,116.329882,SenseTime,company,38183fe28add21693729ddeaf3c8a90a2d5caea3,citation,http://arxiv.org/abs/1706.09876,Scale-Aware Face Detection,2017 +6,AFLW,aflw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,2c17d36bab56083293456fe14ceff5497cc97d75,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhu_Unconstrained_Face_Alignment_CVPR_2016_paper.pdf,Unconstrained Face Alignment via Cascaded Compositional Learning,2016 +7,AFLW,aflw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,2c17d36bab56083293456fe14ceff5497cc97d75,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhu_Unconstrained_Face_Alignment_CVPR_2016_paper.pdf,Unconstrained Face Alignment via Cascaded Compositional Learning,2016 +8,AFLW,aflw,47.05821,15.46019568,Graz University of Technology,edu,4ab10174a4f98f7e2da7cf6ccfeb9bc64c8e7da8,citation,http://pdfs.semanticscholar.org/4ab1/0174a4f98f7e2da7cf6ccfeb9bc64c8e7da8.pdf,Efficient Metric Learning for Real-World Face Recognition,2013 +9,AFLW,aflw,22.53521465,113.9315911,Shenzhen University,edu,32ecbbd76fdce249f9109594eee2d52a1cafdfc7,citation,http://pdfs.semanticscholar.org/32ec/bbd76fdce249f9109594eee2d52a1cafdfc7.pdf,Object Specific Deep Learning Feature and Its Application to Face Detection,2016 +10,AFLW,aflw,52.9387428,-1.20029569,University of Nottingham,edu,32ecbbd76fdce249f9109594eee2d52a1cafdfc7,citation,http://pdfs.semanticscholar.org/32ec/bbd76fdce249f9109594eee2d52a1cafdfc7.pdf,Object Specific Deep Learning Feature and Its Application to Face Detection,2016 +11,AFLW,aflw,40.0044795,116.370238,Chinese Academy of Sciences,edu,4e6c17966efae956133bf8f22edeffc24a0470c1,citation,http://pdfs.semanticscholar.org/4e6c/17966efae956133bf8f22edeffc24a0470c1.pdf,Face Classification: A Specialized Benchmark Study,2016 +12,AFLW,aflw,22.15263985,113.56803206,Macau University of Science and Technology,edu,4e6c17966efae956133bf8f22edeffc24a0470c1,citation,http://pdfs.semanticscholar.org/4e6c/17966efae956133bf8f22edeffc24a0470c1.pdf,Face Classification: A Specialized Benchmark Study,2016 +13,AFLW,aflw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,4e6c17966efae956133bf8f22edeffc24a0470c1,citation,http://pdfs.semanticscholar.org/4e6c/17966efae956133bf8f22edeffc24a0470c1.pdf,Face Classification: A Specialized Benchmark Study,2016 +14,AFLW,aflw,37.4102193,-122.05965487,Carnegie Mellon University,edu,f1b4583c576d6d8c661b4b2c82bdebf3ba3d7e53,citation,https://arxiv.org/pdf/1707.05653.pdf,Faster than Real-Time Facial Alignment: A 3D Spatial Transformer Network Approach in Unconstrained Poses,2017 +15,AFLW,aflw,29.6328784,-82.3490133,University of Florida,edu,441bf5f7fe7d1a3939d8b200eca9b4bb619449a9,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W08/papers/Sundararajan_Head_Pose_Estimation_2015_CVPR_paper.pdf,Head pose estimation in the wild using approximate view manifolds,2015 +16,AFLW,aflw,37.4102193,-122.05965487,Carnegie Mellon 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York,edu,c146aa6d56233ce700032f1cb179700778557601,citation,https://arxiv.org/pdf/1708.07199.pdf,3D Morphable Models as Spatial Transformer Networks,2017 +21,AFLW,aflw,51.24303255,-0.59001382,University of Surrey,edu,438e7999c937b94f0f6384dbeaa3febff6d283b6,citation,https://arxiv.org/pdf/1705.02402v2.pdf,"Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild",2017 +22,AFLW,aflw,31.4854255,120.2739581,Jiangnan University,edu,438e7999c937b94f0f6384dbeaa3febff6d283b6,citation,https://arxiv.org/pdf/1705.02402v2.pdf,"Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild",2017 +23,AFLW,aflw,37.4102193,-122.05965487,Carnegie Mellon University,edu,b1fdd4ae17d82612cefd4e78b690847b071379d3,citation,https://pdfs.semanticscholar.org/4fc5/416b6c7173d3462e5be796bda3ad8d5645a1.pdf,Supervised Descent Method,2015 +24,AFLW,aflw,40.0044795,116.370238,Chinese Academy of 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Oxford,edu,8d9ffe9f7bf1ff3ecc320afe50a92a867a12aeb7,citation,https://arxiv.org/pdf/1809.02169.pdf,Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings,2018 +66,AFLW,aflw,38.99203005,-76.9461029,University of Maryland College Park,edu,f7824758800a7b1a386db5bd35f84c81454d017a,citation,https://arxiv.org/pdf/1702.05085.pdf,KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors,2017 +67,AFLW,aflw,17.4454957,78.34854698,International Institute of Information Technology,edu,156cd2a0e2c378e4c3649a1d046cd080d3338bca,citation,http://pdfs.semanticscholar.org/156c/d2a0e2c378e4c3649a1d046cd080d3338bca.pdf,Exemplar based approaches on Face Fiducial Detection and Frontalization,2017 +68,AFLW,aflw,39.7275037,39.47127034,Firat University,edu,5cfbeae360398de9e20e4165485837bd42b93217,citation,http://pdfs.semanticscholar.org/5cfb/eae360398de9e20e4165485837bd42b93217.pdf,Comparison Of Hog (Histogram of Oriented Gradients) and Haar Cascade Algorithms with a Convolutional Neural Network Based Face Detection Approaches,2017 +69,AFLW,aflw,29.5084174,106.57858552,Chongqing University,edu,a065080353d18809b2597246bb0b48316234c29a,citation,http://pdfs.semanticscholar.org/a065/080353d18809b2597246bb0b48316234c29a.pdf,FHEDN: A based on context modeling Feature Hierarchy Encoder-Decoder Network for face detection,2017 +70,AFLW,aflw,52.22165395,21.00735776,Warsaw University of Technology,edu,f27b8b8f2059248f77258cf8595e9434cf0b0228,citation,https://arxiv.org/pdf/1706.01789.pdf,Deep Alignment Network: A Convolutional Neural Network for Robust Face Alignment,2017 +71,AFLW,aflw,53.46600455,-2.23300881,University of Manchester,edu,68c1090f912b69b76437644dd16922909dd40d60,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6987312,Robust and Accurate Shape Model Matching Using Random Forest Regression-Voting,2012 +72,AFLW,aflw,32.77824165,34.99565673,Open University of Israel,edu,62e913431bcef5983955e9ca160b91bb19d9de42,citation,http://pdfs.semanticscholar.org/62e9/13431bcef5983955e9ca160b91bb19d9de42.pdf,Facial Landmark Detection with Tweaked Convolutional Neural Networks,2015 +73,AFLW,aflw,50.0764296,14.41802312,Czech Technical University,edu,f4ba07d2ae6c9673502daf50ee751a5e9262848f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7284810,Real-time multi-view facial landmark detector learned by the structured output SVM,2015 +74,AFLW,aflw,35.6924853,139.7582533,"National Institute of Informatics, Japan",edu,f4ba07d2ae6c9673502daf50ee751a5e9262848f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7284810,Real-time multi-view facial landmark detector learned by the structured output SVM,2015 +75,AFLW,aflw,40.00229045,116.32098908,Tsinghua University,edu,204f1cf56794bb23f9516b5f225a6ae00d3d30b8,citation,https://doi.org/10.1109/JSYST.2015.2418680,An AdaBoost-Based Face Detection System Using Parallel Configurable 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Facial Keypoint Detection,2017 +156,AFLW,aflw,51.49887085,-0.17560797,Imperial College London,edu,38cbb500823057613494bacd0078aa0e57b30af8,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2017.252,Deep Face Deblurring,2017 +157,AFLW,aflw,22.2081469,114.25964115,University of Hong Kong,edu,fb87045600da73b07f0757f345a937b1c8097463,citation,https://pdfs.semanticscholar.org/5c54/2fef80a35a4f930e5c82040b52c58e96ce87.pdf,Reflective Regression of 2D-3D Face Shape Across Large Pose,2016 +158,AFLW,aflw,52.2380139,6.8566761,University of Twente,edu,71b07c537a9e188b850192131bfe31ef206a39a0,citation,http://pdfs.semanticscholar.org/71b0/7c537a9e188b850192131bfe31ef206a39a0.pdf,300 Faces In-The-Wild Challenge: database and results,2016 +159,AFLW,aflw,35.6924853,139.7582533,"National Institute of Informatics, Japan",edu,4dd71a097e6b3cd379d8c802460667ee0cbc8463,citation,http://www.dgcv.nii.ac.jp/Publications/Papers/2015/BWILD2015.pdf,Real-time multi-view facial landmark detector learned by 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University,edu,cc70fb1ab585378c79a2ab94776723e597afe379,citation,https://doi.org/10.1109/ICIP.2017.8297067,Detect face in the wild using CNN cascade with feature aggregation at multi-resolution,2017 +165,AFLW,aflw,51.49887085,-0.17560797,Imperial College London,edu,59d8fa6fd91cdb72cd0fa74c04016d79ef5a752b,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Zafeiriou_The_Menpo_Facial_CVPR_2017_paper.pdf,The Menpo Facial Landmark Localisation Challenge: A Step Towards the Solution,2017 +166,AFLW,aflw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,2f61d91033a06dd904ff9d1765d57e5b4d7f57a6,citation,https://doi.org/10.1109/ICIP.2016.7532953,FCFD: Teach the machine to accomplish face detection step by step,2016 +167,AFLW,aflw,40.47913175,-74.43168868,Rutgers University,edu,04ff69aa20da4eeccdabbe127e3641b8e6502ec0,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w28/papers/Peng_Sequential_Face_Alignment_CVPR_2016_paper.pdf,Sequential Face Alignment via Person-Specific Modeling in the Wild,2016 +168,AFLW,aflw,32.7283683,-97.11201835,University of Texas at Arlington,edu,04ff69aa20da4eeccdabbe127e3641b8e6502ec0,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w28/papers/Peng_Sequential_Face_Alignment_CVPR_2016_paper.pdf,Sequential Face Alignment via Person-Specific Modeling in the Wild,2016 +169,AFLW,aflw,31.2284923,121.40211389,East China Normal University,edu,83295bce2340cb87901499cff492ae6ff3365475,citation,https://arxiv.org/pdf/1808.01558.pdf,Deep Multi-Center Learning for Face Alignment,2018 +170,AFLW,aflw,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,83295bce2340cb87901499cff492ae6ff3365475,citation,https://arxiv.org/pdf/1808.01558.pdf,Deep Multi-Center Learning for Face Alignment,2018 +171,AFLW,aflw,46.0658836,11.1159894,University of Trento,edu,f201baf618574108bcee50e9a8b65f5174d832ee,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8031057,Viewpoint-Consistent 3D Face Alignment,2018 +172,AFLW,aflw,13.65450525,100.49423171,Robotics Institute,edu,f201baf618574108bcee50e9a8b65f5174d832ee,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8031057,Viewpoint-Consistent 3D Face Alignment,2018 +173,AFLW,aflw,40.0044795,116.370238,Chinese Academy of Sciences,edu,4c6233765b5f83333f6c675d3389bbbf503805e3,citation,https://perceptual.mpi-inf.mpg.de/files/2015/03/Yan_Vis13.pdf,Real-time high performance deformable model for face detection in the wild,2013 +174,AFLW,aflw,40.51865195,-74.44099801,State University of New Jersey,edu,02820c1491b10a1ff486fed32c269e4077c36551,citation,https://arxiv.org/pdf/1610.07930v1.pdf,Active user authentication for smartphones: A challenge data set and benchmark results,2016 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Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge,2013 +206,AFLW,aflw,52.9387428,-1.20029569,University of Nottingham,edu,4cd0da974af9356027a31b8485a34a24b57b8b90,citation,https://arxiv.org/pdf/1703.00862v2.pdf,Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources,2017 +207,AFLW,aflw,41.70456775,-86.23822026,University of Notre Dame,edu,17479e015a2dcf15d40190e06419a135b66da4e0,citation,https://arxiv.org/pdf/1610.08119.pdf,Predicting First Impressions With Deep Learning,2017 +208,AFLW,aflw,30.274084,120.15507,Alibaba,company,89497854eada7e32f06aa8f3c0ceedc0e91ecfef,citation,https://doi.org/10.1109/TIP.2017.2784571,Deep Context-Sensitive Facial Landmark Detection With Tree-Structured Modeling,2018 +209,AFLW,aflw,30.19331415,120.11930822,Zhejiang University,edu,89497854eada7e32f06aa8f3c0ceedc0e91ecfef,citation,https://doi.org/10.1109/TIP.2017.2784571,Deep Context-Sensitive Facial Landmark Detection With Tree-Structured Modeling,2018 +210,AFLW,aflw,32.77824165,34.99565673,Open University of Israel,edu,0a34fe39e9938ae8c813a81ae6d2d3a325600e5c,citation,https://arxiv.org/pdf/1708.07517.pdf,FacePoseNet: Making a Case for Landmark-Free Face Alignment,2017 diff --git a/site/datasets/final/casia_webface.csv b/site/datasets/final/casia_webface.csv new file mode 100644 index 00000000..2cbffd5d --- /dev/null +++ b/site/datasets/final/casia_webface.csv @@ -0,0 +1,312 @@ +index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,CASIA Webface,casia_webface,0.0,0.0,,,853bd61bc48a431b9b1c7cab10c603830c488e39,main,http://pdfs.semanticscholar.org/b8a2/0ed7e74325da76d7183d1ab77b082a92b447.pdf,Learning Face Representation from Scratch,2014 +1,CASIA Webface,casia_webface,39.2899685,-76.62196103,University of Maryland,edu,def2983576001bac7d6461d78451159800938112,citation,https://arxiv.org/pdf/1705.07426.pdf,The Do’s and Don’ts for CNN-Based Face Verification,2017 +2,CASIA 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Maryland,edu,5865b6d83ba6dbbf9167f1481e9339c2ef1d1f6b,citation,https://doi.org/10.1109/ICPR.2016.7900278,Regularized metric adaptation for unconstrained face verification,2016 +91,CASIA Webface,casia_webface,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,e9c008d31da38d9eef67a28d2c77cb7daec941fb,citation,https://arxiv.org/pdf/1708.03769.pdf,Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation,2017 +92,CASIA Webface,casia_webface,23.09461185,113.28788994,Sun Yat-Sen University,edu,c675534be881e59a78a5986b8fb4e649ddd2abbe,citation,https://doi.org/10.1109/ICIP.2017.8296548,Face recognition by landmark pooling-based CNN with concentrate loss,2017 +93,CASIA Webface,casia_webface,40.51865195,-74.44099801,State University of New Jersey,edu,ea03a569272d329090fe60d6bff8d119e18057d7,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7532906,Fisher vector encoded deep convolutional features for unconstrained face verification,2016 +94,CASIA Webface,casia_webface,39.2899685,-76.62196103,University of Maryland,edu,ea03a569272d329090fe60d6bff8d119e18057d7,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7532906,Fisher vector encoded deep convolutional features for unconstrained face verification,2016 +95,CASIA Webface,casia_webface,40.0044795,116.370238,Chinese Academy of Sciences,edu,a73405038fdc0d8bf986539ef755a80ebd341e97,citation,https://doi.org/10.1109/TIP.2017.2698918,Conditional High-Order Boltzmann Machines for Supervised Relation Learning,2017 +96,CASIA Webface,casia_webface,36.20304395,117.05842113,Tianjin University,edu,5180df9d5eb26283fb737f491623395304d57497,citation,https://arxiv.org/pdf/1804.10899.pdf,Scalable Angular Discriminative Deep Metric Learning for Face Recognition,2018 +97,CASIA Webface,casia_webface,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,3ac3a714042d3ebc159546c26321a1f8f4f5f80c,citation,http://dl.acm.org/citation.cfm?id=3025149,Clustering lightened deep representation for large scale face identification,2017 +98,CASIA Webface,casia_webface,37.26728,126.9841151,Seoul National University,edu,282503fa0285240ef42b5b4c74ae0590fe169211,citation,http://pdfs.semanticscholar.org/2825/03fa0285240ef42b5b4c74ae0590fe169211.pdf,Feeding Hand-Crafted Features for Enhancing the Performance of Convolutional Neural Networks,2018 +99,CASIA Webface,casia_webface,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,29db046dd1f8100b279c3f5f5c5ef19bdbf5af9a,citation,https://arxiv.org/pdf/1706.04717.pdf,Recent Progress of Face Image Synthesis,2017 +100,CASIA Webface,casia_webface,39.2899685,-76.62196103,University of Maryland,edu,4f7b92bd678772552b3c3edfc9a7c5c4a8c60a8e,citation,https://pdfs.semanticscholar.org/4f7b/92bd678772552b3c3edfc9a7c5c4a8c60a8e.pdf,Deep Density Clustering of Unconstrained Faces,0 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unconstrained face images,2015 +105,CASIA Webface,casia_webface,40.0044795,116.370238,Chinese Academy of Sciences,edu,b910590a0eb191d03e1aedb3d55c905129e92e6b,citation,http://doi.acm.org/10.1145/2808492.2808570,Robust gender classification on unconstrained face images,2015 +106,CASIA Webface,casia_webface,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,b8bcf9c773da1c5ee76db4bf750c9ff5d159f1a0,citation,http://doi.acm.org/10.1145/2911996.2911999,Homemade TS-Net for Automatic Face Recognition,2016 +107,CASIA Webface,casia_webface,39.86742125,32.73519072,Hacettepe University,edu,9865fe20df8fe11717d92b5ea63469f59cf1635a,citation,https://arxiv.org/pdf/1805.07566.pdf,Wildest Faces: Face Detection and Recognition in Violent Settings,2018 +108,CASIA Webface,casia_webface,39.87549675,32.78553506,Middle East Technical University,edu,9865fe20df8fe11717d92b5ea63469f59cf1635a,citation,https://arxiv.org/pdf/1805.07566.pdf,Wildest Faces: Face Detection and Recognition in Violent Settings,2018 +109,CASIA Webface,casia_webface,46.0501558,14.46907327,University of Ljubljana,edu,69adbfa7b0b886caac15ebe53b89adce390598a3,citation,https://arxiv.org/pdf/1805.10938.pdf,Face hallucination using cascaded super-resolution and identity priors,2018 +110,CASIA Webface,casia_webface,41.70456775,-86.23822026,University of Notre Dame,edu,69adbfa7b0b886caac15ebe53b89adce390598a3,citation,https://arxiv.org/pdf/1805.10938.pdf,Face hallucination using cascaded super-resolution and identity priors,2018 +111,CASIA Webface,casia_webface,42.3383668,-71.08793524,Northeastern University,edu,feea73095b1be0cbae1ad7af8ba2c4fb6f316d35,citation,http://dl.acm.org/citation.cfm?id=3126693,Deep Face Recognition with Center Invariant Loss,2017 +112,CASIA Webface,casia_webface,35.9990522,-78.9290629,Duke University,edu,3f0c51989c516a7c5dee7dec4d7fb474ae6c28d9,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.720,Not Afraid of the Dark: NIR-VIS Face Recognition via Cross-Spectral Hallucination and Low-Rank Embedding,2017 +113,CASIA Webface,casia_webface,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,91a4ebf1ca0314a74c436729700ef09bddaa6222,citation,https://arxiv.org/pdf/1808.01338.pdf,Detailed Human Avatars from Monocular Video,2018 +114,CASIA Webface,casia_webface,47.5612651,7.5752961,University of Basel,edu,0081e2188c8f34fcea3e23c49fb3e17883b33551,citation,http://pdfs.semanticscholar.org/0081/e2188c8f34fcea3e23c49fb3e17883b33551.pdf,Training Deep Face Recognition Systems with Synthetic Data,2018 +115,CASIA Webface,casia_webface,24.4399419,118.09301781,Xiamen University,edu,57ba4b6de23a6fc9d45ff052ed2563e5de00b968,citation,https://doi.org/10.1109/ICIP.2017.8296993,An efficient deep neural networks training framework for robust face recognition,2017 +116,CASIA Webface,casia_webface,40.0044795,116.370238,Chinese Academy of Sciences,edu,43fe03ec1acb6ea9d05d2b22eeddb2631bd30437,citation,https://doi.org/10.1109/ICIP.2017.8296394,Weakly supervised multiscale-inception learning for web-scale face recognition,2017 +117,CASIA Webface,casia_webface,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,0be418e63d111e3b94813875f75909e4dc27d13a,citation,https://doi.org/10.1109/ICB.2016.7550057,Fine-grained LFW database,2016 +118,CASIA Webface,casia_webface,40.51865195,-74.44099801,State University of New Jersey,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791154,A cascaded convolutional neural network for age estimation of unconstrained faces,2016 +119,CASIA Webface,casia_webface,39.2899685,-76.62196103,University of Maryland,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791154,A cascaded convolutional neural network for age estimation of unconstrained faces,2016 +120,CASIA Webface,casia_webface,42.718568,-84.47791571,Michigan State University,edu,450c6a57f19f5aa45626bb08d7d5d6acdb863b4b,citation,https://arxiv.org/pdf/1805.00611.pdf,Towards Interpretable Face Recognition,2018 +121,CASIA Webface,casia_webface,30.2931534,120.1620458,Zhejiang University of Technology,edu,cb9921d5fc4ffa50be537332e111f03d74622442,citation,https://doi.org/10.1007/978-3-319-46654-5_79,Face Occlusion Detection Using Cascaded Convolutional Neural Network,2016 +122,CASIA Webface,casia_webface,29.6328784,-82.3490133,University of Florida,edu,291de30ceecb5dcf0644c35e2b5935d341ea148b,citation,https://arxiv.org/pdf/1810.00024.pdf,Explainable Black-Box Attacks Against Model-based Authentication,2018 +123,CASIA Webface,casia_webface,42.3383668,-71.08793524,Northeastern University,edu,3f540faf85e1f8de6ce04fb37e556700b67e4ad3,citation,http://pdfs.semanticscholar.org/3f54/0faf85e1f8de6ce04fb37e556700b67e4ad3.pdf,Face Verification with Multi-Task and Multi-Scale Feature Fusion,2017 +124,CASIA Webface,casia_webface,29.7207902,-95.34406271,University of Houston,edu,8334da483f1986aea87b62028672836cb3dc6205,citation,https://arxiv.org/pdf/1805.06306.pdf,Fully Associative Patch-Based 1-to-N Matcher for Face Recognition,2018 +125,CASIA Webface,casia_webface,39.2899685,-76.62196103,University of Maryland,edu,0019925779bff96448f0c75492717e4473f88377,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w3/papers/Reale_Deep_Heterogeneous_Face_CVPR_2017_paper.pdf,Deep Heterogeneous Face Recognition Networks Based on Cross-Modal Distillation and an Equitable Distance Metric,2017 +126,CASIA Webface,casia_webface,45.7413921,126.62552755,Harbin Institute of Technology,edu,05455f5e3c3989be4991cb74b73cdfd0d6522622,citation,https://arxiv.org/pdf/1804.04829.pdf,Learning Warped Guidance for Blind Face Restoration,2018 +127,CASIA Webface,casia_webface,23.09461185,113.28788994,Sun Yat-Sen University,edu,05455f5e3c3989be4991cb74b73cdfd0d6522622,citation,https://arxiv.org/pdf/1804.04829.pdf,Learning Warped Guidance for Blind Face Restoration,2018 +128,CASIA Webface,casia_webface,38.0333742,-84.5017758,University of Kentucky,edu,05455f5e3c3989be4991cb74b73cdfd0d6522622,citation,https://arxiv.org/pdf/1804.04829.pdf,Learning Warped Guidance for Blind Face Restoration,2018 +129,CASIA Webface,casia_webface,51.49887085,-0.17560797,Imperial College London,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +130,CASIA Webface,casia_webface,51.59029705,-0.22963221,Middlesex University,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +131,CASIA Webface,casia_webface,25.01682835,121.53846924,National Taiwan University,edu,81884e1de00e59f24bc20254584d73a1a1806933,citation,https://arxiv.org/pdf/1811.02328.pdf,Super-Identity Convolutional Neural Network for Face Hallucination,2018 +132,CASIA Webface,casia_webface,39.993008,116.329882,SenseTime,company,81884e1de00e59f24bc20254584d73a1a1806933,citation,https://arxiv.org/pdf/1811.02328.pdf,Super-Identity Convolutional Neural Network for Face Hallucination,2018 +133,CASIA Webface,casia_webface,30.284151,-97.73195598,University of Texas at Austin,edu,81884e1de00e59f24bc20254584d73a1a1806933,citation,https://arxiv.org/pdf/1811.02328.pdf,Super-Identity Convolutional Neural Network for Face Hallucination,2018 +134,CASIA Webface,casia_webface,47.6543238,-122.30800894,University of Washington,edu,405526dfc79de98f5bf3c97bf4aa9a287700f15d,citation,http://pdfs.semanticscholar.org/8a6c/57fcd99a77982ec754e0b97fd67519ccb60c.pdf,MegaFace: A Million Faces for Recognition at Scale,2015 +135,CASIA Webface,casia_webface,32.77824165,34.99565673,Open University of Israel,edu,582edc19f2b1ab2ac6883426f147196c8306685a,citation,http://pdfs.semanticscholar.org/be6c/db7b181e73f546d43cf2ab6bc7181d7d619b.pdf,Do We Really Need to Collect Millions of Faces for Effective Face Recognition?,2016 +136,CASIA Webface,casia_webface,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,076d3fc800d882445c11b9af466c3af7d2afc64f,citation,http://slsp.kaist.ac.kr/paperdata/Face_attribute_classification.pdf,Face attribute classification using attribute-aware correlation map and gated convolutional neural networks,2015 +137,CASIA Webface,casia_webface,31.83907195,117.26420748,University of Science and Technology of China,edu,e1256ff535bf4c024dd62faeb2418d48674ddfa2,citation,https://arxiv.org/pdf/1803.11182.pdf,Towards Open-Set Identity Preserving Face Synthesis,2018 +138,CASIA Webface,casia_webface,36.383765,127.36694,"Electronics and Telecommunications Research Institute, Daejeon, Korea",edu,77c5437107f8138d48cb7e10b2b286fa51473678,citation,https://doi.org/10.1109/URAI.2016.7734005,A pseudo ensemble convolutional neural networks,2016 +139,CASIA Webface,casia_webface,36.3851395,127.3683413,"University of Science and Technology, Korea",edu,77c5437107f8138d48cb7e10b2b286fa51473678,citation,https://doi.org/10.1109/URAI.2016.7734005,A pseudo ensemble convolutional neural networks,2016 +140,CASIA Webface,casia_webface,40.00229045,116.32098908,Tsinghua University,edu,93eb3963bc20e28af26c53ef3bce1e76b15e3209,citation,https://doi.org/10.1109/ICIP.2017.8296992,Occlusion robust face recognition based on mask learning,2017 +141,CASIA Webface,casia_webface,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,16b9d258547f1eccdb32111c9f45e2e4bbee79af,citation,https://arxiv.org/pdf/1704.06369.pdf,NormFace: L2 Hypersphere Embedding for Face Verification,2017 +142,CASIA Webface,casia_webface,39.94976005,116.33629046,Beijing Jiaotong University,edu,7e2cfbfd43045fbd6aabd9a45090a5716fc4e179,citation,https://arxiv.org/pdf/1808.00435.pdf,Global Norm-Aware Pooling for Pose-Robust Face Recognition at Low False Positive Rate,2018 +143,CASIA Webface,casia_webface,40.0044795,116.370238,Chinese Academy of Sciences,edu,99facca6fc50cc30f13b7b6dd49ace24bc94f702,citation,https://arxiv.org/pdf/1609.03892.pdf,VIPLFaceNet: an open source deep face recognition SDK,2016 +144,CASIA Webface,casia_webface,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,99facca6fc50cc30f13b7b6dd49ace24bc94f702,citation,https://arxiv.org/pdf/1609.03892.pdf,VIPLFaceNet: an open source deep face recognition SDK,2016 +145,CASIA Webface,casia_webface,25.01682835,121.53846924,National Taiwan University,edu,b50edfea790f86373407a964b4255bf8e436d377,citation,http://doi.acm.org/10.1145/3136755.3143008,Group emotion recognition with individual facial emotion CNNs and global image based CNNs,2017 +146,CASIA Webface,casia_webface,1.2962018,103.77689944,National University of Singapore,edu,c17c7b201cfd0bcd75441afeaa734544c6ca3416,citation,https://doi.org/10.1109/TCSVT.2016.2587389,Layerwise Class-Aware Convolutional Neural Network,2017 +147,CASIA Webface,casia_webface,32.0575279,118.78682252,Southeast University,edu,c17c7b201cfd0bcd75441afeaa734544c6ca3416,citation,https://doi.org/10.1109/TCSVT.2016.2587389,Layerwise Class-Aware Convolutional Neural Network,2017 +148,CASIA Webface,casia_webface,22.3386304,114.2620337,Hong Kong University of Science and Technology,edu,585260468d023ffc95f0e539c3fa87254c28510b,citation,http://pdfs.semanticscholar.org/5852/60468d023ffc95f0e539c3fa87254c28510b.pdf,Cardea: Context-Aware Visual Privacy Protection from Pervasive Cameras,2016 +149,CASIA Webface,casia_webface,39.65404635,-79.96475355,West Virginia University,edu,3b9b200e76a35178da940279d566bbb7dfebb787,citation,http://pdfs.semanticscholar.org/3b9b/200e76a35178da940279d566bbb7dfebb787.pdf,Learning Channel Inter-dependencies at Multiple Scales on Dense Networks for Face Recognition,2017 +150,CASIA Webface,casia_webface,39.65404635,-79.96475355,West Virginia University,edu,8bfada57140aa1aa22a575e960c2a71140083293,citation,http://pdfs.semanticscholar.org/8bfa/da57140aa1aa22a575e960c2a71140083293.pdf,Can we match Ultraviolet Face Images against their Visible Counterparts?,2015 +151,CASIA Webface,casia_webface,40.0044795,116.370238,Chinese Academy of Sciences,edu,72a7eb68f0955564e1ceafa75aeeb6b5bbb14e7e,citation,https://pdfs.semanticscholar.org/72a7/eb68f0955564e1ceafa75aeeb6b5bbb14e7e.pdf,Face Recognition with Contrastive Convolution,2018 +152,CASIA Webface,casia_webface,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,72a7eb68f0955564e1ceafa75aeeb6b5bbb14e7e,citation,https://pdfs.semanticscholar.org/72a7/eb68f0955564e1ceafa75aeeb6b5bbb14e7e.pdf,Face Recognition with Contrastive Convolution,2018 +153,CASIA Webface,casia_webface,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,44b827df6c433ca49bcf44f9f3ebfdc0774ee952,citation,https://doi.org/10.1109/LSP.2017.2726105,Deep Correlation Feature Learning for Face Verification in the Wild,2017 +154,CASIA Webface,casia_webface,22.42031295,114.20788644,Chinese University of Hong Kong,edu,58d76380d194248b3bb291b8c7c5137a0a376897,citation,https://pdfs.semanticscholar.org/58d7/6380d194248b3bb291b8c7c5137a0a376897.pdf,FaceID-GAN : Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis,2018 +155,CASIA Webface,casia_webface,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,58d76380d194248b3bb291b8c7c5137a0a376897,citation,https://pdfs.semanticscholar.org/58d7/6380d194248b3bb291b8c7c5137a0a376897.pdf,FaceID-GAN : Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis,2018 +156,CASIA Webface,casia_webface,39.2899685,-76.62196103,University of Maryland,edu,b6f758be954d34817d4ebaa22b30c63a4b8ddb35,citation,http://arxiv.org/abs/1703.04835,A Proximity-Aware Hierarchical Clustering of Faces,2017 +157,CASIA Webface,casia_webface,28.2290209,112.99483204,"National University of Defense Technology, China",edu,511a8cdf2127ef8aa07cbdf9660fe9e0e2dfbde7,citation,https://pdfs.semanticscholar.org/511a/8cdf2127ef8aa07cbdf9660fe9e0e2dfbde7.pdf,A Community Detection Approach to Cleaning Extremely Large Face Database,2018 +158,CASIA Webface,casia_webface,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,cdef0eaff4a3c168290d238999fc066ebc3a93e8,citation,https://arxiv.org/pdf/1707.07391.pdf,Contrastive-center loss for deep neural networks,2017 +159,CASIA Webface,casia_webface,30.2810654,120.02139087,"Alibaba Group, Hangzhou, China",edu,1e62ca5845a6f0492574a5da049e9b43dbeadb1b,citation,https://doi.org/10.1109/LSP.2016.2637400,Cross-Modality Face Recognition via Heterogeneous Joint Bayesian,2017 +160,CASIA 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Webface,casia_webface,33.776033,-84.39884086,Georgia Institute of Technology,edu,93af36da08bf99e68c9b0d36e141ed8154455ac2,citation,https://pdfs.semanticscholar.org/93af/36da08bf99e68c9b0d36e141ed8154455ac2.pdf,A Dditive M Argin S Oftmax for F Ace V Erification,2018 +200,CASIA Webface,casia_webface,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,93af36da08bf99e68c9b0d36e141ed8154455ac2,citation,https://pdfs.semanticscholar.org/93af/36da08bf99e68c9b0d36e141ed8154455ac2.pdf,A Dditive M Argin S Oftmax for F Ace V Erification,2018 +201,CASIA Webface,casia_webface,31.30104395,121.50045497,Fudan University,edu,5a259f2f5337435f841d39dada832ab24e7b3325,citation,http://doi.acm.org/10.1145/2964284.2984059,Face Recognition via Active Annotation and Learning,2016 +202,CASIA Webface,casia_webface,40.0044795,116.370238,Chinese Academy of Sciences,edu,5a259f2f5337435f841d39dada832ab24e7b3325,citation,http://doi.acm.org/10.1145/2964284.2984059,Face Recognition via 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China,edu,3107316f243233d45e3c7e5972517d1ed4991f91,citation,http://arxiv.org/abs/1703.10155,CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training,2017 +285,CASIA Webface,casia_webface,46.010737,8.958109,University of Lugano,edu,cae41c3d5508f57421faf672ee1bea0da4be66e0,citation,https://doi.org/10.1109/ICPR.2016.7900298,Palmprint recognition via discriminative index learning,2016 +286,CASIA Webface,casia_webface,39.2899685,-76.62196103,University of Maryland,edu,a3201e955d6607d383332f3a12a7befa08c5a18c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7900276,VLAD encoded Deep Convolutional features for unconstrained face verification,2016 +287,CASIA Webface,casia_webface,40.47913175,-74.43168868,Rutgers University,edu,a3201e955d6607d383332f3a12a7befa08c5a18c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7900276,VLAD encoded Deep Convolutional features for unconstrained face verification,2016 +288,CASIA 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Landmark Extraction,2016 +53,COFW,cofw,3.12267405,101.65356103,University of Malaya,edu,deb89950939ae9847f0a1a4bb198e6dbfed62778,citation,https://doi.org/10.1109/LSP.2016.2543019,Accurate Facial Landmark Extraction,2016 +54,COFW,cofw,37.4102193,-122.05965487,Carnegie Mellon University,edu,78598e7005f7c96d64cc47ff47e6f13ae52245b8,citation,https://arxiv.org/pdf/1708.00370.pdf,Hand2Face: Automatic synthesis and recognition of hand over face occlusions,2017 +55,COFW,cofw,28.59899755,-81.19712501,University of Central Florida,edu,78598e7005f7c96d64cc47ff47e6f13ae52245b8,citation,https://arxiv.org/pdf/1708.00370.pdf,Hand2Face: Automatic synthesis and recognition of hand over face occlusions,2017 +56,COFW,cofw,52.17638955,0.14308882,University of Cambridge,edu,9901f473aeea177a55e58bac8fd4f1b086e575a4,citation,https://arxiv.org/pdf/1509.04954.pdf,Human and sheep facial landmarks localisation by triplet interpolated features,2016 +57,COFW,cofw,40.00229045,116.32098908,Tsinghua 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Sciences,edu,a820941eaf03077d68536732a4d5f28d94b5864a,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zhang_Leveraging_Datasets_With_ICCV_2015_paper.pdf,Leveraging Datasets with Varying Annotations for Face Alignment via Deep Regression Network,2015 +191,COFW,cofw,34.0687788,-118.4450094,"University of California, Los Angeles",edu,195d331c958f2da3431f37a344559f9bce09c0f7,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/ext/2B_066_ext.pdf,Parsing occluded people by flexible compositions,2015 +192,COFW,cofw,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,5f448ab700528888019542e6fea1d1e0db6c35f2,citation,https://doi.org/10.1109/LSP.2016.2533721,Transferred Deep Convolutional Neural Network Features for Extensive Facial Landmark Localization,2016 +193,COFW,cofw,31.846918,117.29053367,Hefei University of 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University,edu,7ef44b7c2b5533d00001ae81f9293bdb592f1146,citation,https://pdfs.semanticscholar.org/7ef4/4b7c2b5533d00001ae81f9293bdb592f1146.pdf,Détection des émotions à partir de vidéos dans un environnement non contrôlé Detection of emotions from video in non-controlled environment,2003 +31,FERET,feret,-27.5533975,153.05336234,Griffith University,edu,6e968f74fd6b4b3b172c787f298b3d4746ec5cc9,citation,http://www.ict.griffith.edu.au/~junzhou/papers/C_DICTA_2013_C.pdf,A 3D Polygonal Line Chains Matching Method for Face Recognition,2013 +32,FERET,feret,39.2899685,-76.62196103,University of Maryland,edu,3a1c3307f57ef09577ac0dc8cd8b090a4fe8091f,citation,http://pdfs.semanticscholar.org/3a1c/3307f57ef09577ac0dc8cd8b090a4fe8091f.pdf,Thermal-to-visible face recognition using partial least squares.,2015 +33,FERET,feret,51.24303255,-0.59001382,University of Surrey,edu,81a8b2e55bcea9d9b26e67fcbb5a30ca8a8defc3,citation,http://multispectral-imagery-lab.sandbox.wvu.edu/files/d/337b61b4-b6af-4c96-8314-c282ebebf299/databasesizeeffectsonperformancesmartcardfaceverification.pdf,Database size effects on performance on a smart card face verification system,2006 +34,FERET,feret,41.10427915,29.02231159,Istanbul Technical University,edu,b8b0f0ca35cb02334aaa3192559fb35f0c90f8fa,citation,http://pdfs.semanticscholar.org/b8b0/f0ca35cb02334aaa3192559fb35f0c90f8fa.pdf,Face Recognition in Low-resolution Images by Using Local Zernike Moments,2014 +35,FERET,feret,1.29500195,103.84909214,Singapore Management University,edu,76d1c6c6b67e67ced1f19a89a5034dafc9599f25,citation,http://doi.acm.org/10.1145/2590296.2590315,Understanding OSN-based facial disclosure against face authentication systems,2014 +36,FERET,feret,50.89273635,-1.39464295,University of Southampton,edu,8a12edaf81fd38f81057cf9577c822eb09ff6fc1,citation,http://pdfs.semanticscholar.org/8a12/edaf81fd38f81057cf9577c822eb09ff6fc1.pdf,Measuring and mitigating targeted biometric impersonation,2014 +37,FERET,feret,65.0592157,25.46632601,University of Oulu,edu,8a12edaf81fd38f81057cf9577c822eb09ff6fc1,citation,http://pdfs.semanticscholar.org/8a12/edaf81fd38f81057cf9577c822eb09ff6fc1.pdf,Measuring and mitigating targeted biometric impersonation,2014 +38,FERET,feret,1.3484104,103.68297965,Nanyang Technological University,edu,4b86e711658003a600666d3ccfa4a9905463df1c,citation,https://pdfs.semanticscholar.org/4b86/e711658003a600666d3ccfa4a9905463df1c.pdf,Fusion of Appearance Image and Passive Stereo Depth Map for Face Recognition Based on the Bilateral 2DLDA,2007 +39,FERET,feret,40.7286484,-73.9956863,Courant Institute of Mathematical Sciences,edu,4b8d80f91d271f61b26db5ad627e24e59955c56a,citation,http://pdfs.semanticscholar.org/4b8d/80f91d271f61b26db5ad627e24e59955c56a.pdf,Learning Long-Range Vision for an Offroad Robot,2008 +40,FERET,feret,40.72925325,-73.99625394,New York University,edu,4b8d80f91d271f61b26db5ad627e24e59955c56a,citation,http://pdfs.semanticscholar.org/4b8d/80f91d271f61b26db5ad627e24e59955c56a.pdf,Learning Long-Range Vision for an Offroad Robot,2008 +41,FERET,feret,51.24303255,-0.59001382,University of Surrey,edu,7af15295224c3ad69d56f17ff635763dd008a8a4,citation,http://pdfs.semanticscholar.org/7af1/5295224c3ad69d56f17ff635763dd008a8a4.pdf,Learning Support Vectors for Face Authentication: Sensitivity to Mis-Registrations,2007 +42,FERET,feret,50.0764296,14.41802312,Czech Technical University,edu,7af15295224c3ad69d56f17ff635763dd008a8a4,citation,http://pdfs.semanticscholar.org/7af1/5295224c3ad69d56f17ff635763dd008a8a4.pdf,Learning Support Vectors for Face Authentication: Sensitivity to Mis-Registrations,2007 +43,FERET,feret,40.00229045,116.32098908,Tsinghua University,edu,5ea51401eea9a50a16bd17471bfd559d2d989760,citation,http://pdfs.semanticscholar.org/5ea5/1401eea9a50a16bd17471bfd559d2d989760.pdf,Robust Face Alignment Based on Hierarchical Classifier Network,2006 +44,FERET,feret,40.0044795,116.370238,Chinese Academy of Sciences,edu,71644fab2275cfd6a8f770a26aba4e6228e85dec,citation,http://www.jdl.ac.cn/doc/2011/20131910365517756_2012_eccv_mnkan_mvda.pdf,Multi-View Discriminant Analysis,2012 +45,FERET,feret,31.4006332,74.2137296,"COMSATS Institute of Information Technology, Lahore",edu,280bc9751593897091015aaf2cab39805768b463,citation,http://pdfs.semanticscholar.org/280b/c9751593897091015aaf2cab39805768b463.pdf,Gender Perception From Faces Using Boosted LBPH (Local Binary Patten Histograms),2013 +46,FERET,feret,34.0687788,-118.4450094,"University of California, Los Angeles",edu,23b80dc704e25cf52b5a14935002fc083ce9c317,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2007.383035,Learning Generative Models via Discriminative Approaches,2007 +47,FERET,feret,45.42580475,-75.68740118,University of Ottawa,edu,857ad04fca2740b016f0066b152bd1fa1171483f,citation,http://pdfs.semanticscholar.org/857a/d04fca2740b016f0066b152bd1fa1171483f.pdf,Sample Images can be Independently Restored from Face Recognition Templates,2003 +48,FERET,feret,8.76554685,77.65100445,Manonmaniam Sundaranar University,edu,87b81c8821a2cb9cdf26c75c1531717cab4b942f,citation,http://pdfs.semanticscholar.org/87b8/1c8821a2cb9cdf26c75c1531717cab4b942f.pdf,Face Detection with Facial Features and Gender Classification Based On Support Vector Machine,2010 +49,FERET,feret,43.66333345,-79.39769975,University of Toronto,edu,099ce5cb6f42bff5ad117852d62c5a07e6407b8a,citation,https://pdfs.semanticscholar.org/099c/e5cb6f42bff5ad117852d62c5a07e6407b8a.pdf,Spectral Methods for Multi-Scale Feature Extraction and Data Clustering,0 +50,FERET,feret,34.0224149,-118.28634407,University of Southern California,edu,21358489b5ce0e94ff37792a8a5eea198e7272f3,citation,http://pdfs.semanticscholar.org/c0cc/2073cad539d979fc6f860177b531b45fafc1.pdf,Face Inpainting with Local Linear Representations,2004 +51,FERET,feret,61.44964205,23.85877462,Tampere University of Technology,edu,dc4e4b9c507e8be2d832faf64e5a2e8887115265,citation,https://pdfs.semanticscholar.org/dc4e/4b9c507e8be2d832faf64e5a2e8887115265.pdf,Face Retrieval Based on Robust Local Features and Statistical-Structural Learning Approach,2008 +52,FERET,feret,37.3219575,127.1250723,Dankook University,edu,891d435fd1a070bb66225abfd62b2e2c5350e87c,citation,https://pdfs.semanticscholar.org/891d/435fd1a070bb66225abfd62b2e2c5350e87c.pdf,Selective Feature Generation Method for Classification of Low-dimensional Data,2018 +53,FERET,feret,32.8536333,-117.2035286,Kyung Hee University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for 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Kong,edu,55498d89f9eb0c9df9760f5e0e47a15ae7e92f25,citation,http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/264.pdf,Learning-based face hallucination in DCT domain,2008 +61,FERET,feret,40.0044795,116.370238,Chinese Academy of Sciences,edu,f12813073a7f894f82fe2b166893424edba7dc79,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2008.4587375,Unified Principal Component Analysis with generalized Covariance Matrix for face recognition,2008 +62,FERET,feret,45.7413921,126.62552755,Harbin Institute of Technology,edu,f12813073a7f894f82fe2b166893424edba7dc79,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2008.4587375,Unified Principal Component Analysis with generalized Covariance Matrix for face recognition,2008 +63,FERET,feret,39.87549675,32.78553506,Middle East Technical University,edu,946c2036c940e77260ade031ba413ec9f2435985,citation,http://pdfs.semanticscholar.org/946c/2036c940e77260ade031ba413ec9f2435985.pdf,PCA for Gender Estimation: Which Eigenvectors Contribute?,2002 +64,FERET,feret,36.1017956,-79.501733,Elon University,edu,a129c30b176820bf7f4756b4b4efc92d2a83f190,citation,https://pdfs.semanticscholar.org/a129/c30b176820bf7f4756b4b4efc92d2a83f190.pdf,Older adults' associative memory is modified by manner of presentation at encoding and retrieval.,2018 +65,FERET,feret,13.01119095,74.79498825,"National Institute of Technology, Karnataka",edu,e1fac9e9427499d3758213daf1c781b9a42a3420,citation,https://pdfs.semanticscholar.org/7c90/60a809bd28ef61421588f48e33f6eae6ddfd.pdf,Face Image Retrieval Based on Probe Sketch Using SIFT Feature Descriptors,2012 +66,FERET,feret,35.9542493,-83.9307395,University of Tennessee,edu,7735f63e5790006cb3d989c8c19910e40200abfc,citation,http://pdfs.semanticscholar.org/7735/f63e5790006cb3d989c8c19910e40200abfc.pdf,Multispectral Imaging For Face Recognition Over Varying Illumination,2008 +67,FERET,feret,32.0565957,118.77408833,Nanjing University,edu,f909d04c809013b930bafca12c0f9a8192df9d92,citation,http://pdfs.semanticscholar.org/f909/d04c809013b930bafca12c0f9a8192df9d92.pdf,Single Image Subspace for Face Recognition,2007 +68,FERET,feret,22.3386304,114.2620337,Hong Kong University of Science and Technology,edu,33abfe693258a4e00467494b11ee4d523379ab6b,citation,http://www.cse.ust.hk/~dyyeung/paper/pdf/yeung.icip2006a.pdf,Local Discriminant Embedding with Tensor Representation,2006 +69,FERET,feret,38.0333742,-84.5017758,University of Kentucky,edu,a1997d89f544cc862c63a972ef364b2ff38982e9,citation,https://pdfs.semanticscholar.org/a199/7d89f544cc862c63a972ef364b2ff38982e9.pdf,Can SNOMED CT Changes Be Used as a Surrogate Standard for Evaluating the Performance of Its Auditing Methods?,2017 +70,FERET,feret,-33.3578899,151.37834708,University of Newcastle,edu,aeb64f88302b9d4d23ee13ece5c9842dd43dc37f,citation,https://pdfs.semanticscholar.org/aeb6/4f88302b9d4d23ee13ece5c9842dd43dc37f.pdf,Recollection and confidence in two-alternative forced choice episodic recognition,2009 +71,FERET,feret,51.49887085,-0.17560797,Imperial College London,edu,e392816ec3e0b131bbab06431ac85b14afa7d656,citation,http://pdfs.semanticscholar.org/e392/816ec3e0b131bbab06431ac85b14afa7d656.pdf,A Simple and Efficient Supervised Method for Spatially Weighted PCA in Face Image Analysis,2010 +72,FERET,feret,34.1235825,108.83546,Xidian University,edu,3e76496aa3840bca2974d6d087bfa4267a390768,citation,https://pdfs.semanticscholar.org/3e76/496aa3840bca2974d6d087bfa4267a390768.pdf,Dictionary Learning in Optimal Metric Subspace,2018 +73,FERET,feret,39.9808333,116.34101249,Beihang University,edu,3e76496aa3840bca2974d6d087bfa4267a390768,citation,https://pdfs.semanticscholar.org/3e76/496aa3840bca2974d6d087bfa4267a390768.pdf,Dictionary Learning in Optimal Metric Subspace,2018 +74,FERET,feret,39.2899685,-76.62196103,University of Maryland,edu,355af3c3adbb17d25f0d2a4193e3daadffc0d4e8,citation,http://pdfs.semanticscholar.org/355a/f3c3adbb17d25f0d2a4193e3daadffc0d4e8.pdf,Pattern recognition: Historical perspective and future directions,2000 +75,FERET,feret,38.83133325,-77.30798839,George Mason University,edu,355af3c3adbb17d25f0d2a4193e3daadffc0d4e8,citation,http://pdfs.semanticscholar.org/355a/f3c3adbb17d25f0d2a4193e3daadffc0d4e8.pdf,Pattern recognition: Historical perspective and future directions,2000 +76,FERET,feret,40.8927159,29.37863323,Sabanci University,edu,1e6d1e811da743df02481bca1a7bdaa73b809913,citation,http://research.sabanciuniv.edu/608/1/3011800001159.pdf,Multimodal person recognition for human-vehicle interaction,2006 +77,FERET,feret,50.7338124,7.1022465,University of Bonn,edu,f4aafb50c93c5ad3e5c4696ed24b063a1932915a,citation,http://pdfs.semanticscholar.org/f4aa/fb50c93c5ad3e5c4696ed24b063a1932915a.pdf,What would you look like in Springfield? Linear Transformations between High-Dimensional Spaces,2011 +78,FERET,feret,45.7413921,126.62552755,Harbin Institute of Technology,edu,10156890bc53cb6be97bd144a68fde693bf13612,citation,http://pdfs.semanticscholar.org/1015/6890bc53cb6be97bd144a68fde693bf13612.pdf,Face Recognition Using Sparse Representation-Based Classification on K-Nearest Subspace,2013 +79,FERET,feret,45.42580475,-75.68740118,University of Ottawa,edu,16820ccfb626dcdc893cc7735784aed9f63cbb70,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W12/papers/Azarmehr_Real-Time_Embedded_Age_2015_CVPR_paper.pdf,Real-time embedded age and gender classification in unconstrained video,2015 +80,FERET,feret,42.3504253,-71.10056114,Boston University,edu,966b76acfa75253679b1a82ecc5a68e523f5c0c9,citation,http://pdfs.semanticscholar.org/f204/2494d5666e436f5e96ff5e0cd3b5f5e5485b.pdf,Preference suppression caused by misattribution of task-irrelevant subliminal motion.,2012 +81,FERET,feret,40.8419836,-73.94368971,Columbia University,edu,0c7f27d23a162d4f3896325d147f412c40160b52,citation,http://pdfs.semanticscholar.org/0c7f/27d23a162d4f3896325d147f412c40160b52.pdf,Models and Algorithms for Vision through the Atmosphere,2003 +82,FERET,feret,40.47913175,-74.43168868,Rutgers University,edu,6069b4bc1a21341b77b49f01341c238c770d52e0,citation,http://pdfs.semanticscholar.org/b02b/50ed995fe526208b1577b9d7ef6262bf3ecf.pdf,Comparing Kernel-based Learning Methods for Face Recognition,2003 +83,FERET,feret,51.49887085,-0.17560797,Imperial College London,edu,af31ef1e81c1132f186d7aebb141d7f59a815010,citation,http://cas.ee.ic.ac.uk/people/ccb98/papers/LiuGlobalSIP13.pdf,Domain-specific progressive sampling of face images,2013 +84,FERET,feret,51.5073219,-0.1276474,"London, United Kingdom",edu,af31ef1e81c1132f186d7aebb141d7f59a815010,citation,http://cas.ee.ic.ac.uk/people/ccb98/papers/LiuGlobalSIP13.pdf,Domain-specific progressive sampling of face images,2013 +85,FERET,feret,39.1254938,-77.22293475,National Institute of Standards and Technology,edu,07f31bef7a7035792e3791473b3c58d03928abbf,citation,https://doi.org/10.1016/j.imavis.2016.08.004,Lessons from collecting a million biometric samples,2015 +86,FERET,feret,41.70456775,-86.23822026,University of Notre Dame,edu,07f31bef7a7035792e3791473b3c58d03928abbf,citation,https://doi.org/10.1016/j.imavis.2016.08.004,Lessons from collecting a million biometric samples,2015 +87,FERET,feret,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,d275714c323dd4e400e8003fa8c33070f8ea03d1,citation,https://pdfs.semanticscholar.org/d275/714c323dd4e400e8003fa8c33070f8ea03d1.pdf,"White Fear, Dehumanization and Low Empathy: a Lethal Combination for Shooting Biases by Yara Mekawi",2014 +88,FERET,feret,40.0044795,116.370238,Chinese Academy of Sciences,edu,1a5a79b4937b89420049bc279a7b7f765d143881,citation,http://pdfs.semanticscholar.org/1a5a/79b4937b89420049bc279a7b7f765d143881.pdf,Are Rich People Perceived as More Trustworthy? Perceived Socioeconomic Status Modulates Judgments of Trustworthiness and Trust Behavior Based on Facial Appearance,2018 +89,FERET,feret,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,1a5a79b4937b89420049bc279a7b7f765d143881,citation,http://pdfs.semanticscholar.org/1a5a/79b4937b89420049bc279a7b7f765d143881.pdf,Are Rich People Perceived as More Trustworthy? Perceived Socioeconomic Status Modulates Judgments of Trustworthiness and Trust Behavior Based on Facial Appearance,2018 +90,FERET,feret,37.548215,-77.45306424,Virginia Commonwealth University,edu,1a5a79b4937b89420049bc279a7b7f765d143881,citation,http://pdfs.semanticscholar.org/1a5a/79b4937b89420049bc279a7b7f765d143881.pdf,Are Rich People Perceived as More Trustworthy? Perceived Socioeconomic Status Modulates Judgments of Trustworthiness and Trust Behavior Based on Facial Appearance,2018 +91,FERET,feret,39.1254938,-77.22293475,National Institute of Standards and Technology,edu,88ee6d0b8342852a5bd55864dc7a1c8452c10bbf,citation,http://pdfs.semanticscholar.org/88ee/6d0b8342852a5bd55864dc7a1c8452c10bbf.pdf,Support Vector Machines Applied to Face Recognition,1998 +92,FERET,feret,32.0565957,118.77408833,Nanjing University,edu,59f83e94a7f52cbb728d434426f6fe85f756259c,citation,https://pdfs.semanticscholar.org/59f8/3e94a7f52cbb728d434426f6fe85f756259c.pdf,An Improved Illumination Normalization Approach based on Wavelet Tranform for Face Recognition from Single Training Image Per Person,2010 +93,FERET,feret,40.0044795,116.370238,Chinese Academy of Sciences,edu,5d1c4e93e32ee686234c5aae7f38025523993c8c,citation,http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/4989d539.pdf,Towards Pose Robust Face Recognition,2013 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University,edu,63a584487beb7382cad8ed70020f108ded5bf076,citation,https://pdfs.semanticscholar.org/2bb3/4f45b1f0ae2b602a6f25f1966cd0f84e3f5f.pdf,Face Detection and Modeling for Recognition,2002 +127,FERET,feret,22.5611537,88.41310194,Jadavpur University,edu,5e6c23d2e2f92a90bd35bdbc937b2d7d95ee2d55,citation,http://pdfs.semanticscholar.org/5e6c/23d2e2f92a90bd35bdbc937b2d7d95ee2d55.pdf,Fusion of Wavelet Coefficients from Visual and Thermal Face Images for Human Face Recognition - A Comparative Study,2006 +128,FERET,feret,40.0044795,116.370238,Chinese Academy of Sciences,edu,c03e01717b2d93f04cce9b5fd2dcfd1143bcc180,citation,http://pdfs.semanticscholar.org/c03e/01717b2d93f04cce9b5fd2dcfd1143bcc180.pdf,Locality-Constrained Active Appearance Model,2012 +129,FERET,feret,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,c03e01717b2d93f04cce9b5fd2dcfd1143bcc180,citation,http://pdfs.semanticscholar.org/c03e/01717b2d93f04cce9b5fd2dcfd1143bcc180.pdf,Locality-Constrained Active 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Automatic detection of spontaneous asymmetric smiles to understand viewer experience,2013 +137,FERET,feret,32.8536333,-117.2035286,Kyung Hee University,edu,bf0836e5c10add0b13005990ba019a9c4b744b06,citation,https://doi.org/10.1109/TCE.2009.5373791,An enhanced independent component-based human facial expression recognition from video,2009 +138,FERET,feret,42.718568,-84.47791571,Michigan State University,edu,051f03bc25ec633592aa2ff5db1d416b705eac6c,citation,http://www.cse.msu.edu/biometrics/Publications/Face/LiaoJain_PartialFR_AlignmentFreeApproach_ICJB11.pdf,Partial face recognition: An alignment free approach,2011 +139,FERET,feret,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,aba31184783150c723805831cde0f22fe257b835,citation,http://pdfs.semanticscholar.org/aba3/1184783150c723805831cde0f22fe257b835.pdf,Contribution of Non-scrambled Chroma Information in Privacy-Protected Face Images to Privacy Leakage,2011 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Rome,edu,757e4cb981e807d83539d9982ad325331cb59b16,citation,http://pdfs.semanticscholar.org/757e/4cb981e807d83539d9982ad325331cb59b16.pdf,Demographics versus Biometric Automatic Interoperability,2013 +144,FERET,feret,49.2622421,-123.2450052,University of Perugia,edu,67c08e2b8b918a61dcbd0d4c63a74b89b833d259,citation,http://pdfs.semanticscholar.org/67c0/8e2b8b918a61dcbd0d4c63a74b89b833d259.pdf,Multi-class texture analysis in colorectal cancer histology,2016 +145,FERET,feret,38.83133325,-77.30798839,George Mason University,edu,ac942c4870e55fe1d9822d62edcdb685d41cd2bf,citation,http://pdfs.semanticscholar.org/ac94/2c4870e55fe1d9822d62edcdb685d41cd2bf.pdf,Pose Discriminiation and Eye Detection Using Support Vector Machines (SVM),1998 +146,FERET,feret,44.97308605,-93.23708813,University of Minnesota,edu,ac942c4870e55fe1d9822d62edcdb685d41cd2bf,citation,http://pdfs.semanticscholar.org/ac94/2c4870e55fe1d9822d62edcdb685d41cd2bf.pdf,Pose Discriminiation and Eye Detection Using Support Vector Machines (SVM),1998 +147,FERET,feret,51.4584837,-2.6097752,University of Bristol,edu,a632ebe6f1e7d9b2b652b0186abef8db218037f3,citation,http://pdfs.semanticscholar.org/a632/ebe6f1e7d9b2b652b0186abef8db218037f3.pdf,Subliminally and Supraliminally Acquired Long-Term Memories Jointly Bias Delayed Decisions,2017 +148,FERET,feret,32.8536333,-117.2035286,Kyung Hee University,edu,027f769aed0cfcb3169ef60f182ce1decc0e99eb,citation,http://www.ijicic.org/10-12018-1.pdf,Local Directional Pattern (LDP) for face recognition,2010 +149,FERET,feret,32.0565957,118.77408833,Nanjing University,edu,edd6ed94207ab614c71ac0591d304a708d708e7b,citation,http://doi.org/10.1016/j.neucom.2012.02.001,Reconstructive discriminant analysis: A feature extraction method induced from linear regression classification,2012 +150,FERET,feret,42.718568,-84.47791571,Michigan State University,edu,5dbf772b98cb944befa9cf01ec5d15da713a338b,citation,http://pdfs.semanticscholar.org/9d82/44d5a32ecc314860c1d673d687df28f77d84.pdf,Face 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Telecommunications,edu,9729930ab0f9cbcd07f1105bc69c540330cda50a,citation,https://doi.org/10.1109/ACCESS.2017.2749331,Compressing Fisher Vector for Robust Face Recognition,2017 +158,FERET,feret,31.32235655,121.38400941,Shanghai University,edu,459eb3cfd9b52a0d416571e4bc4e75f979f4b901,citation,https://doi.org/10.1109/ROBIO.2015.7418998,Vision development of humanoid head robot SHFR-III,2015 +159,FERET,feret,34.8452999,48.5596212,Islamic Azad University,edu,14b2dff604f148c4e5b54aa25fbecbf7f9071205,citation,http://www.iranprc.org/pdf/paper/2011-06.pdf,A new preselection method for face recognition in JPEG domain based on face segmentation,2011 +160,FERET,feret,47.5612651,7.5752961,University of Basel,edu,ff47698be7313005d0ea0fe0cc72c13f2f4b092a,citation,http://pdfs.semanticscholar.org/ff47/698be7313005d0ea0fe0cc72c13f2f4b092a.pdf,Caring or daring? Exploring the impact of facial masculinity/femininity and gender category information on first impressions,2017 +161,FERET,feret,35.84658875,127.1350133,Chonbuk National University,edu,0c6a18b0cee01038eb1f9373c369835b236373ae,citation,https://doi.org/10.1007/s11042-017-4359-9,Learning warps based similarity for pose-unconstrained face recognition,2017 +162,FERET,feret,40.72925325,-73.99625394,New York University,edu,54e6343f4368d9e5468c3e83b6eeb3a58a3c7555,citation,http://pdfs.semanticscholar.org/54e6/343f4368d9e5468c3e83b6eeb3a58a3c7555.pdf,Reconstructing Perceived and Retrieved Faces from Activity Patterns in Lateral Parietal Cortex.,2016 +163,FERET,feret,38.8833413,-77.1045977,DARPA,mil,00d6e5a1b347463f6aeb08a10cd912273c9d1347,citation,http://pdfs.semanticscholar.org/00d6/e5a1b347463f6aeb08a10cd912273c9d1347.pdf,Face Recognition Vendor Test 2002 : Evaluation Report,2003 +164,FERET,feret,39.1254938,-77.22293475,National Institute of Standards and 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algorithms,2011 +186,FERET,feret,49.25839375,-123.24658161,University of British Columbia,edu,4bc55ffc2f53801267ca1767028515be6e84f551,citation,http://pdfs.semanticscholar.org/4bc5/5ffc2f53801267ca1767028515be6e84f551.pdf,The Decision to Engage Cognitive Control Is Driven by Expected Reward-Value: Neural and Behavioral Evidence,2012 +187,FERET,feret,28.54632595,77.27325504,Indian Institute of Technology Delhi,edu,0fae5d9d2764a8d6ea691b9835d497dd680bbccd,citation,http://pdfs.semanticscholar.org/0fae/5d9d2764a8d6ea691b9835d497dd680bbccd.pdf,Face Recognition using Canonical Correlation Analysis,2006 +188,FERET,feret,22.42031295,114.20788644,Chinese University of Hong Kong,edu,71ed20748c919cd261024b146992ced4c9c2157b,citation,http://mmlab.ie.cuhk.edu.hk/archive/2006/01640756.pdf,Learning Semantic Patterns with Discriminant Localized Binary Projections,2006 +189,FERET,feret,39.977217,116.337632,Microsoft Research 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variations on face images,2014 +211,FERET,feret,-35.2776999,149.118527,Australian National University,edu,826f1ac8ef16abd893062fdf5058a09881aed516,citation,https://arxiv.org/pdf/1801.02279.pdf,Identity-Preserving Face Recovery from Portraits,2018 +212,FERET,feret,36.20304395,117.05842113,Tianjin University,edu,1d5219687b9e63767f19cd804147c256c5a5a3bc,citation,https://pdfs.semanticscholar.org/1d52/19687b9e63767f19cd804147c256c5a5a3bc.pdf,Patch-based locality-enhanced collaborative representation for face recognition,2015 +213,FERET,feret,43.66333345,-79.39769975,University of Toronto,edu,c423b0a0b7232a5cd0c3f4c75164923a3f04cdcd,citation,http://pdfs.semanticscholar.org/c423/b0a0b7232a5cd0c3f4c75164923a3f04cdcd.pdf,Kernel Discriminant Learning with Application to Face Recognition,2004 +214,FERET,feret,38.83133325,-77.30798839,George Mason University,edu,a1c1970f7c728cc96aea798d65d38df7c9ea61dc,citation,http://pdfs.semanticscholar.org/a1c1/970f7c728cc96aea798d65d38df7c9ea61dc.pdf,Eye 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Basel,edu,916498961a51f56a592c3551b0acc25978571fa7,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2011.6126275,Optimal landmark detection using shape models and branch and bound,2011 +226,FERET,feret,35.9023226,14.4834189,University of Malta,edu,4efd58102ff46b7435c9ec6d4fc3dd21d93b15b4,citation,https://doi.org/10.1109/TIFS.2017.2788002,"Matching Software-Generated Sketches to Face Photographs With a Very Deep CNN, Morphed Faces, and Transfer Learning",2018 +227,FERET,feret,24.7246403,46.62335012,King Saud University,edu,e26a7e343fe109e2b52d1eeea5b02dae836f3502,citation,https://doi.org/10.1109/ACCESS.2017.2676238,Facial Expression Recognition Utilizing Local Direction-Based Robust Features and Deep Belief Network,2017 +228,FERET,feret,59.93891665,10.72170765,University of Oslo,edu,e26a7e343fe109e2b52d1eeea5b02dae836f3502,citation,https://doi.org/10.1109/ACCESS.2017.2676238,Facial Expression Recognition Utilizing Local Direction-Based Robust Features and Deep Belief 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Columbia,edu,2cae2ca6221fbfa9655e41ac52e54631ada7ad2c,citation,http://pdfs.semanticscholar.org/ffd6/14925a326efcb27ef52accd5638a912b4792.pdf,Electoral College and Direct Popular Vote for Multi-Candidate Election,2010 +316,FERET,feret,34.2249827,-77.86907744,University of North Carolina at Wilmington,edu,328bfd1d0229bc4973277f893abd1eb288159fc9,citation,http://pdfs.semanticscholar.org/328b/fd1d0229bc4973277f893abd1eb288159fc9.pdf,A review of the literature on the aging adult skull and face: implications for forensic science research and applications.,2007 +317,FERET,feret,32.87935255,-117.23110049,"University of California, San Diego",edu,18b4e9e51ee14c9d816358fbe1af29f0771b7916,citation,http://pdfs.semanticscholar.org/18b4/e9e51ee14c9d816358fbe1af29f0771b7916.pdf,Intelligent environments and active camera networks,2000 +318,FERET,feret,40.8722825,-73.89489171,City University of New 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Technology,edu,4fb9f05dc03eb4983d8f9a815745bb47970f1b93,citation,http://pdfs.semanticscholar.org/f4ee/4f7ac7585f7ea0db3b27c5ad016dbfb0feac.pdf,"On Robust Face Recognition via Sparse Encoding: the Good, the Bad, and the Ugly",2013 +327,FERET,feret,52.9387428,-1.20029569,University of Nottingham,edu,b9df25cc4be2f703b059da93823bad6e8e8c0659,citation,http://pdfs.semanticscholar.org/b9df/25cc4be2f703b059da93823bad6e8e8c0659.pdf,Local Gabor Binary Pattern Whitened PCA: A Novel Approach for Face Recognition from Single Image Per Person,2009 +328,FERET,feret,24.4399419,118.09301781,Xiamen University,edu,57ba4b6de23a6fc9d45ff052ed2563e5de00b968,citation,https://doi.org/10.1109/ICIP.2017.8296993,An efficient deep neural networks training framework for robust face recognition,2017 +329,FERET,feret,32.7283683,-97.11201835,University of Texas at Arlington,edu,90bd16caa44086db6f0e4bbc1dde7063cb71b7b8,citation,http://www.kdd.org/kdd2016/papers/files/rfp1162-wangA.pdf,Structured Doubly Stochastic 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Sciences,edu,9e1c3c7f1dce662a877727a821bdf41c5cd906bb,citation,http://pdfs.semanticscholar.org/9e1c/3c7f1dce662a877727a821bdf41c5cd906bb.pdf,Learning Disentangling and Fusing Networks for Face Completion Under Structured Occlusions,2017 +608,FERET,feret,42.718568,-84.47791571,Michigan State University,edu,4308f53244bbb6a1e22ba1d39e079e5065a51364,citation,http://pdfs.semanticscholar.org/4308/f53244bbb6a1e22ba1d39e079e5065a51364.pdf,Ethnicity Identification from Face Images,2004 +609,FERET,feret,33.30715065,-111.67653157,Arizona State University,edu,5b1f3a60518c3a552de09ed51646764551f4cb84,citation,http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/WorkShops/data/papers/121.pdf,Multiple cue integration in transductive confidence machines for head pose classification,2008 +610,FERET,feret,3.06405715,101.6005974,Monash University Malaysia,edu,a96a7a381872ae40179ded0d79f905da0455d9d1,citation,http://pdfs.semanticscholar.org/a96a/7a381872ae40179ded0d79f905da0455d9d1.pdf,Segmentation of Saimaa 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Representation Learning for Unconstrained Face Identification,2017 +34,IJB-A,ijb_c,-35.23656905,149.08446994,University of Canberra,edu,0c1d85a197a1f5b7376652a485523e616a406273,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.169,Joint Registration and Representation Learning for Unconstrained Face Identification,2017 +35,IJB-A,ijb_c,32.77824165,34.99565673,Open University of Israel,edu,c75e6ce54caf17b2780b4b53f8d29086b391e839,citation,https://arxiv.org/pdf/1802.00542.pdf,"ExpNet: Landmark-Free, Deep, 3D Facial Expressions",2018 +36,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,450c6a57f19f5aa45626bb08d7d5d6acdb863b4b,citation,https://arxiv.org/pdf/1805.00611.pdf,Towards Interpretable Face Recognition,2018 +37,IJB-A,ijb_c,51.7534538,-1.25400997,University of Oxford,edu,30180f66d5b4b7c0367e4b43e2b55367b72d6d2a,citation,http://www.robots.ox.ac.uk/~vgg/publications/2017/Crosswhite17/crosswhite17.pdf,Template Adaptation for Face Verification and Identification,2017 +38,IJB-A,ijb_c,29.7207902,-95.34406271,University of Houston,edu,8334da483f1986aea87b62028672836cb3dc6205,citation,https://arxiv.org/pdf/1805.06306.pdf,Fully Associative Patch-Based 1-to-N Matcher for Face Recognition,2018 +39,IJB-A,ijb_c,-33.8809651,151.20107299,University of Technology Sydney,edu,3b64efa817fd609d525c7244a0e00f98feacc8b4,citation,http://doi.acm.org/10.1145/2845089,A Comprehensive Survey on Pose-Invariant Face Recognition,2016 +40,IJB-A,ijb_c,40.9153196,-73.1270626,Stony Brook University,edu,6fbb179a4ad39790f4558dd32316b9f2818cd106,citation,http://pdfs.semanticscholar.org/6fbb/179a4ad39790f4558dd32316b9f2818cd106.pdf,Input Aggregated Network for Face Video Representation,2016 +41,IJB-A,ijb_c,38.8920756,-104.79716389,"University of Colorado, Colorado Springs",edu,d4f1eb008eb80595bcfdac368e23ae9754e1e745,citation,https://arxiv.org/pdf/1708.02337.pdf,Unconstrained Face Detection and Open-Set Face Recognition Challenge,2017 +42,IJB-A,ijb_c,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018 +43,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018 +44,IJB-A,ijb_c,34.0224149,-118.28634407,University of Southern California,edu,d28d32af7ef9889ef9cb877345a90ea85e70f7f1,citation,http://doi.ieeecomputersociety.org/10.1109/FG.2017.84,Local-Global Landmark Confidences for Face Recognition,2017 +45,IJB-A,ijb_c,37.4102193,-122.05965487,Carnegie Mellon University,edu,d28d32af7ef9889ef9cb877345a90ea85e70f7f1,citation,http://doi.ieeecomputersociety.org/10.1109/FG.2017.84,Local-Global Landmark Confidences for Face Recognition,2017 +46,IJB-A,ijb_c,51.5247272,-0.03931035,Queen Mary University of London,edu,a29566375836f37173ccaffa47dea25eb1240187,citation,https://arxiv.org/pdf/1809.09409.pdf,Vehicle Re-Identification in Context,2018 +47,IJB-A,ijb_c,34.0224149,-118.28634407,University of Southern California,edu,29f298dd5f806c99951cb434834bc8dcc765df18,citation,https://doi.org/10.1109/ICPR.2016.7899837,Computationally efficient template-based face recognition,2016 +48,IJB-A,ijb_c,51.49887085,-0.17560797,Imperial College London,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +49,IJB-A,ijb_c,51.59029705,-0.22963221,Middlesex University,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +50,IJB-A,ijb_c,50.8142701,8.771435,Philipps-Universität Marburg,edu,5981c309bd0ffd849c51b1d8a2ccc481a8ec2f5c,citation,https://doi.org/10.1109/ICT.2017.7998256,SmartFace: Efficient face detection on smartphones for wireless on-demand emergency networks,2017 +51,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,a2b4a6c6b32900a066d0257ae6d4526db872afe2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8272466,Learning Face Image Quality From Human Assessments,2018 +52,IJB-A,ijb_c,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,3dfb822e16328e0f98a47209d7ecd242e4211f82,citation,https://arxiv.org/pdf/1708.08197.pdf,Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments,2017 +53,IJB-A,ijb_c,47.6423318,-122.1369302,Microsoft,company,291265db88023e92bb8c8e6390438e5da148e8f5,citation,http://pdfs.semanticscholar.org/4603/cb8e05258bb0572ae912ad20903b8f99f4b1.pdf,MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition,2016 +54,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,d29eec5e047560627c16803029d2eb8a4e61da75,citation,http://pdfs.semanticscholar.org/d29e/ec5e047560627c16803029d2eb8a4e61da75.pdf,Feature Transfer Learning for Deep Face Recognition with Long-Tail Data,2018 +55,IJB-A,ijb_c,36.20304395,117.05842113,Tianjin University,edu,5180df9d5eb26283fb737f491623395304d57497,citation,https://arxiv.org/pdf/1804.10899.pdf,Scalable Angular Discriminative Deep Metric Learning for Face Recognition,2018 +56,IJB-A,ijb_c,22.42031295,114.20788644,Chinese University of Hong Kong,edu,abdd17e411a7bfe043f280abd4e560a04ab6e992,citation,https://arxiv.org/pdf/1803.00839.pdf,Pose-Robust Face Recognition via Deep Residual Equivariant Mapping,2018 +57,IJB-A,ijb_c,28.5456282,77.2731505,"IIIT Delhi, India",edu,3cf1f89d73ca4b25399c237ed3e664a55cd273a2,citation,https://arxiv.org/pdf/1710.02914.pdf,Face Sketch Matching via Coupled Deep Transform Learning,2017 +58,IJB-A,ijb_c,-27.49741805,153.01316956,University of Queensland,edu,f27fd2a1bc229c773238f1912db94991b8bf389a,citation,https://doi.org/10.1109/IVCNZ.2016.7804414,How do you develop a face detector for the unconstrained environment?,2016 +59,IJB-A,ijb_c,39.86742125,32.73519072,Hacettepe University,edu,9865fe20df8fe11717d92b5ea63469f59cf1635a,citation,https://arxiv.org/pdf/1805.07566.pdf,Wildest Faces: Face Detection and Recognition in Violent Settings,2018 +60,IJB-A,ijb_c,39.87549675,32.78553506,Middle East Technical University,edu,9865fe20df8fe11717d92b5ea63469f59cf1635a,citation,https://arxiv.org/pdf/1805.07566.pdf,Wildest Faces: Face Detection and Recognition in Violent Settings,2018 +61,IJB-A,ijb_c,28.2290209,112.99483204,"National University of Defense Technology, China",edu,c1cc2a2a1ab66f6c9c6fabe28be45d1440a57c3d,citation,https://pdfs.semanticscholar.org/aae7/a5182e59f44b7bb49f61999181ce011f800b.pdf,Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis,2017 +62,IJB-A,ijb_c,1.2962018,103.77689944,National University of Singapore,edu,c1cc2a2a1ab66f6c9c6fabe28be45d1440a57c3d,citation,https://pdfs.semanticscholar.org/aae7/a5182e59f44b7bb49f61999181ce011f800b.pdf,Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis,2017 +63,IJB-A,ijb_c,17.4454957,78.34854698,International Institute of Information Technology,edu,f5eb411217f729ad7ae84bfd4aeb3dedb850206a,citation,https://pdfs.semanticscholar.org/f5eb/411217f729ad7ae84bfd4aeb3dedb850206a.pdf,Tackling Low Resolution for Better Scene Understanding,2018 +64,IJB-A,ijb_c,40.51865195,-74.44099801,State University of New Jersey,edu,96e731e82b817c95d4ce48b9e6b08d2394937cf8,citation,http://arxiv.org/pdf/1508.01722v2.pdf,Unconstrained face verification using deep CNN features,2016 +65,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,96e731e82b817c95d4ce48b9e6b08d2394937cf8,citation,http://arxiv.org/pdf/1508.01722v2.pdf,Unconstrained face verification using deep CNN features,2016 +66,IJB-A,ijb_c,32.77824165,34.99565673,Open University of Israel,edu,870433ba89d8cab1656e57ac78f1c26f4998edfb,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.163,Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network,2017 +67,IJB-A,ijb_c,55.6801502,12.572327,University of Copenhagen,edu,3dfd94d3fad7e17f52a8ae815eb9cc5471172bc0,citation,http://pdfs.semanticscholar.org/3dfd/94d3fad7e17f52a8ae815eb9cc5471172bc0.pdf,Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions,2018 +68,IJB-A,ijb_c,35.9023226,14.4834189,University of Malta,edu,3dfd94d3fad7e17f52a8ae815eb9cc5471172bc0,citation,http://pdfs.semanticscholar.org/3dfd/94d3fad7e17f52a8ae815eb9cc5471172bc0.pdf,Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions,2018 +69,IJB-A,ijb_c,34.0224149,-118.28634407,University of Southern California,edu,6341274aca0c2977c3e1575378f4f2126aa9b050,citation,http://arxiv.org/pdf/1609.03536v1.pdf,A multi-scale cascade fully convolutional network face detector,2016 +70,IJB-A,ijb_c,41.70456775,-86.23822026,University of Notre Dame,edu,17479e015a2dcf15d40190e06419a135b66da4e0,citation,https://arxiv.org/pdf/1610.08119.pdf,Predicting First Impressions With Deep Learning,2017 +71,IJB-A,ijb_c,37.4102193,-122.05965487,Carnegie Mellon University,edu,a0b1990dd2b4cd87e4fd60912cc1552c34792770,citation,https://pdfs.semanticscholar.org/a0b1/990dd2b4cd87e4fd60912cc1552c34792770.pdf,Deep Constrained Local Models for Facial Landmark Detection,2016 +72,IJB-A,ijb_c,30.642769,104.06751175,"Sichuan University, Chengdu",edu,772474b5b0c90629f4d9c223fd9c1ef45e1b1e66,citation,https://doi.org/10.1109/BTAS.2017.8272716,Multi-dim: A multi-dimensional face database towards the application of 3D technology in real-world scenarios,2017 +73,IJB-A,ijb_c,38.8920756,-104.79716389,"University of Colorado, Colorado Springs",edu,4b3f425274b0c2297d136f8833a31866db2f2aec,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2017.85,Toward Open-Set Face Recognition,2017 +74,IJB-A,ijb_c,56.46255985,84.95565495,Tomsk Polytechnic University,edu,17ded725602b4329b1c494bfa41527482bf83a6f,citation,http://pdfs.semanticscholar.org/cb10/434a5d68ffbe9ed0498771192564ecae8894.pdf,Compact Convolutional Neural Network Cascade for Face Detection,2015 +75,IJB-A,ijb_c,37.3351908,-121.88126008,San Jose State University,edu,14b016c7a87d142f4b9a0e6dc470dcfc073af517,citation,http://ws680.nist.gov/publication/get_pdf.cfm?pub_id=918912,Modest proposals for improving biometric recognition papers,2015 +76,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,93420d9212dd15b3ef37f566e4d57e76bb2fab2f,citation,https://arxiv.org/pdf/1611.00851.pdf,An All-In-One Convolutional Neural Network for Face Analysis,2017 +77,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,def2983576001bac7d6461d78451159800938112,citation,https://arxiv.org/pdf/1705.07426.pdf,The Do’s and Don’ts for CNN-Based Face Verification,2017 +78,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,4b605e6a9362485bfe69950432fa1f896e7d19bf,citation,http://biometrics.cse.msu.edu/Publications/Face/BlantonAllenMillerKalkaJain_CVPRWB2016_HID.pdf,A Comparison of Human and Automated Face Verification Accuracy on Unconstrained Image Sets,2016 +79,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,8d3e95c31c93548b8c71dbeee2e9f7180067a888,citation,https://doi.org/10.1109/ICPR.2016.7899841,Template regularized sparse coding for face verification,2016 +80,IJB-A,ijb_c,42.8271556,-73.8780481,GE Global Research,company,8d3e95c31c93548b8c71dbeee2e9f7180067a888,citation,https://doi.org/10.1109/ICPR.2016.7899841,Template regularized sparse coding for face verification,2016 +81,IJB-A,ijb_c,25.0410728,121.6147562,Institute of Information Science,edu,337dd4aaca2c5f9b5d2de8e0e2401b5a8feb9958,citation,https://arxiv.org/pdf/1810.11160.pdf,Data-specific Adaptive Threshold for Face Recognition and Authentication,2018 +82,IJB-A,ijb_c,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,0aeb5020003e0c89219031b51bd30ff1bceea363,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.525,Sparsifying Neural Network Connections for Face Recognition,2016 +83,IJB-A,ijb_c,22.42031295,114.20788644,Chinese University of Hong Kong,edu,0aeb5020003e0c89219031b51bd30ff1bceea363,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.525,Sparsifying Neural Network Connections for Face Recognition,2016 +84,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,99daa2839213f904e279aec7cef26c1dfb768c43,citation,https://arxiv.org/pdf/1805.02283.pdf,DocFace: Matching ID Document Photos to Selfies,2018 +85,IJB-A,ijb_c,43.7776426,11.259765,University of Florence,edu,71ca8b6e84c17b3e68f980bfb8cddc837100f8bf,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7899774,Effective 3D based frontalization for unconstrained face recognition,2016 +86,IJB-A,ijb_c,51.49887085,-0.17560797,Imperial College London,edu,c43ed9b34cad1a3976bac7979808eb038d88af84,citation,https://arxiv.org/pdf/1804.03675.pdf,Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model,2018 +87,IJB-A,ijb_c,51.24303255,-0.59001382,University of Surrey,edu,c43ed9b34cad1a3976bac7979808eb038d88af84,citation,https://arxiv.org/pdf/1804.03675.pdf,Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model,2018 +88,IJB-A,ijb_c,37.3936717,-122.0807262,Facebook,company,628a3f027b7646f398c68a680add48c7969ab1d9,citation,https://pdfs.semanticscholar.org/628a/3f027b7646f398c68a680add48c7969ab1d9.pdf,Plan for Final Year Project : HKU-Face : A Large Scale Dataset for Deep Face Recognition,2017 +89,IJB-A,ijb_c,40.2773077,-7.5095801,University of Beira Interior,edu,61262450d4d814865a4f9a84299c24daa493f66e,citation,http://doi.org/10.1007/s10462-016-9474-x,Biometric recognition in surveillance scenarios: a survey,2016 +90,IJB-A,ijb_c,-31.95040445,115.79790037,University of Western Australia,edu,626913b8fcbbaee8932997d6c4a78fe1ce646127,citation,https://arxiv.org/pdf/1711.05942.pdf,Learning from Millions of 3D Scans for Large-scale 3D Face Recognition,2017 +91,IJB-A,ijb_c,35.9023226,14.4834189,University of Malta,edu,4efd58102ff46b7435c9ec6d4fc3dd21d93b15b4,citation,https://doi.org/10.1109/TIFS.2017.2788002,"Matching Software-Generated Sketches to Face Photographs With a Very Deep CNN, Morphed Faces, and Transfer Learning",2018 +92,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,b6f758be954d34817d4ebaa22b30c63a4b8ddb35,citation,http://arxiv.org/abs/1703.04835,A Proximity-Aware Hierarchical Clustering of Faces,2017 +93,IJB-A,ijb_c,32.77824165,34.99565673,Open University of Israel,edu,0a34fe39e9938ae8c813a81ae6d2d3a325600e5c,citation,https://arxiv.org/pdf/1708.07517.pdf,FacePoseNet: Making a Case for Landmark-Free Face Alignment,2017 +94,IJB-A,ijb_c,40.2773077,-7.5095801,University of Beira Interior,edu,84ae55603bffda40c225fe93029d39f04793e01f,citation,https://doi.org/10.1109/ICB.2016.7550066,ICB-RW 2016: International challenge on biometric recognition in the wild,2016 +95,IJB-A,ijb_c,41.70456775,-86.23822026,University of Notre Dame,edu,73ea06787925157df519a15ee01cc3dc1982a7e0,citation,https://arxiv.org/pdf/1811.01474.pdf,Fast Face Image Synthesis with Minimal Training,2018 +96,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,c6382de52636705be5898017f2f8ed7c70d7ae96,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139089,Unconstrained face detection: State of the art baseline and challenges,2015 +97,IJB-A,ijb_c,38.95187,-77.363259,"Noblis, Falls Church, VA, U.S.A.",company,c6382de52636705be5898017f2f8ed7c70d7ae96,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139089,Unconstrained face detection: State of the art baseline and challenges,2015 +98,IJB-A,ijb_c,40.47913175,-74.43168868,Rutgers University,edu,eee06d68497be8bf3a8aba4fde42a13aa090b301,citation,https://arxiv.org/pdf/1806.11191.pdf,CR-GAN: Learning Complete Representations for Multi-view Generation,2018 +99,IJB-A,ijb_c,35.3103441,-80.73261617,University of North Carolina at Charlotte,edu,eee06d68497be8bf3a8aba4fde42a13aa090b301,citation,https://arxiv.org/pdf/1806.11191.pdf,CR-GAN: Learning Complete Representations for Multi-view Generation,2018 +100,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,a3201e955d6607d383332f3a12a7befa08c5a18c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7900276,VLAD encoded Deep Convolutional features for unconstrained face verification,2016 +101,IJB-A,ijb_c,40.47913175,-74.43168868,Rutgers University,edu,a3201e955d6607d383332f3a12a7befa08c5a18c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7900276,VLAD encoded Deep Convolutional features for unconstrained face verification,2016 +102,IJB-A,ijb_c,22.42031295,114.20788644,Chinese University of Hong Kong,edu,52d7eb0fbc3522434c13cc247549f74bb9609c5d,citation,https://arxiv.org/pdf/1511.06523.pdf,WIDER FACE: A Face Detection Benchmark,2016 +103,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,19458454308a9f56b7de76bf7d8ff8eaa52b0173,citation,https://pdfs.semanticscholar.org/1945/8454308a9f56b7de76bf7d8ff8eaa52b0173.pdf,Deep Features for Recognizing Disguised Faces in the Wild,0 +104,IJB-A,ijb_c,43.7776426,11.259765,University of 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(UNICAMP),edu,b161d261fabb507803a9e5834571d56a3b87d147,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8122913,Gender recognition from face images using a geometric descriptor,2017 +14,Images of Groups,images_of_groups,40.9153196,-73.1270626,Stony Brook University,edu,1190cba0cae3c8bb81bf80d6a0a83ae8c41240bc,citation,https://pdfs.semanticscholar.org/1190/cba0cae3c8bb81bf80d6a0a83ae8c41240bc.pdf,Squared Earth Mover ’ s Distance Loss for Training Deep Neural Networks on Ordered-Classes,2017 +15,Images of Groups,images_of_groups,24.94314825,121.36862979,National Taipei University,edu,30cc1ddd7a9b4878cca7783a59086bdc49dc4044,citation,https://doi.org/10.1007/s11042-015-2599-0,Intensity contrast masks for gender classification,2015 +16,Images of Groups,images_of_groups,-35.2776999,149.118527,Australian National University,edu,49e541e0bbc7a082e5c952fc70716e66e5713080,citation,http://ieeexplore.ieee.org/document/6460925/,Group expression intensity estimation in videos via Gaussian Processes,2012 +17,Images of Groups,images_of_groups,50.3755269,-4.13937687,Plymouth University,edu,8bed7ff2f75d956652320270eaf331e1f73efb35,citation,https://arxiv.org/pdf/1709.03820.pdf,Emotion recognition in the wild using deep neural networks and Bayesian classifiers,2017 +18,Images of Groups,images_of_groups,39.3650216,16.2257177,University of Calabria,edu,8bed7ff2f75d956652320270eaf331e1f73efb35,citation,https://arxiv.org/pdf/1709.03820.pdf,Emotion recognition in the wild using deep neural networks and Bayesian classifiers,2017 +19,Images of Groups,images_of_groups,51.7534538,-1.25400997,University of Oxford,edu,0be8b12f194fb604be69c139a195799e8ab53fd3,citation,http://www.robots.ox.ac.uk/~vgg/publications/2014/Hoai14/poster.pdf,Talking Heads: Detecting Humans and Recognizing Their Interactions,2014 +20,Images of Groups,images_of_groups,-35.2776999,149.118527,Australian National University,edu,0d3068b352c3733c9e1cc75e449bf7df1f7b10a4,citation,http://doi.ieeecomputersociety.org/10.1109/ACII.2013.111,Context Based Facial Expression Analysis in the Wild,2013 +21,Images of Groups,images_of_groups,45.42580475,-75.68740118,University of Ottawa,edu,16820ccfb626dcdc893cc7735784aed9f63cbb70,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W12/papers/Azarmehr_Real-Time_Embedded_Age_2015_CVPR_paper.pdf,Real-time embedded age and gender classification in unconstrained video,2015 +22,Images of Groups,images_of_groups,51.5247272,-0.03931035,Queen Mary University of London,edu,fcc82154067dfe778423c2df4ed69f0bec6e1534,citation,https://pdfs.semanticscholar.org/fcc8/2154067dfe778423c2df4ed69f0bec6e1534.pdf,Automatic Analysis of Affect and Membership in Group Settings,2017 +23,Images of Groups,images_of_groups,52.17638955,0.14308882,University of Cambridge,edu,fcc82154067dfe778423c2df4ed69f0bec6e1534,citation,https://pdfs.semanticscholar.org/fcc8/2154067dfe778423c2df4ed69f0bec6e1534.pdf,Automatic Analysis of Affect and Membership in Group Settings,2017 +24,Images of Groups,images_of_groups,30.284151,-97.73195598,University of Texas at Austin,edu,45513d0f2f5c0dac5b61f9ff76c7e46cce62f402,citation,http://pdfs.semanticscholar.org/4551/3d0f2f5c0dac5b61f9ff76c7e46cce62f402.pdf,Face Discovery with Social Context,2011 +25,Images of Groups,images_of_groups,37.26728,126.9841151,Seoul National University,edu,282503fa0285240ef42b5b4c74ae0590fe169211,citation,http://pdfs.semanticscholar.org/2825/03fa0285240ef42b5b4c74ae0590fe169211.pdf,Feeding Hand-Crafted Features for Enhancing the Performance of Convolutional Neural Networks,2018 +26,Images of Groups,images_of_groups,-35.2776999,149.118527,Australian National University,edu,1ab881ec87167af9071b2ad8ff6d4ce3eee38477,citation,http://pdfs.semanticscholar.org/1ab8/81ec87167af9071b2ad8ff6d4ce3eee38477.pdf,Finding Happiest Moments in a Social Context,2012 +27,Images of Groups,images_of_groups,-35.23656905,149.08446994,University of Canberra,edu,1ab881ec87167af9071b2ad8ff6d4ce3eee38477,citation,http://pdfs.semanticscholar.org/1ab8/81ec87167af9071b2ad8ff6d4ce3eee38477.pdf,Finding Happiest Moments in a Social Context,2012 +28,Images of Groups,images_of_groups,-35.23656905,149.08446994,University of Canberra,edu,572dbaee6648eefa4c9de9b42551204b985ff863,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7163151,The more the merrier: Analysing the affect of a group of people in images,2015 +29,Images of Groups,images_of_groups,32.87935255,-117.23110049,"University of California, San Diego",edu,572dbaee6648eefa4c9de9b42551204b985ff863,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7163151,The more the merrier: Analysing the affect of a group of people in images,2015 +30,Images of Groups,images_of_groups,46.0658836,11.1159894,University of Trento,edu,572dbaee6648eefa4c9de9b42551204b985ff863,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7163151,The more the merrier: Analysing the affect of a group of people in images,2015 +31,Images of Groups,images_of_groups,35.90503535,-79.04775327,University of North Carolina,edu,dbf6d2619bd41ce4c36488e15d114a2da31b51c9,citation,https://arxiv.org/pdf/1810.00028.pdf,Data-Driven Modeling of Group Entitativity in Virtual Environments,2018 +32,Images of Groups,images_of_groups,39.2899685,-76.62196103,University of Maryland,edu,dbf6d2619bd41ce4c36488e15d114a2da31b51c9,citation,https://arxiv.org/pdf/1810.00028.pdf,Data-Driven Modeling of Group Entitativity in Virtual Environments,2018 +33,Images of Groups,images_of_groups,37.4102193,-122.05965487,Carnegie Mellon University,edu,b593f13f974cf444a5781bbd487e1c69e056a1f7,citation,https://pdfs.semanticscholar.org/b593/f13f974cf444a5781bbd487e1c69e056a1f7.pdf,Query Image Query Image Retrievals Retrievals Transferred Poses Transferred Poses,2018 +34,Images of Groups,images_of_groups,43.7776426,11.259765,University of Florence,edu,02cc96ad997102b7c55e177ac876db3b91b4e72c,citation,http://www.micc.unifi.it/wp-content/uploads/2015/12/2015_museum-visitors-dataset.pdf,"MuseumVisitors: A dataset for pedestrian and group detection, gaze estimation and behavior understanding",2015 +35,Images of Groups,images_of_groups,40.8419836,-73.94368971,Columbia University,edu,02cc96ad997102b7c55e177ac876db3b91b4e72c,citation,http://www.micc.unifi.it/wp-content/uploads/2015/12/2015_museum-visitors-dataset.pdf,"MuseumVisitors: A dataset for pedestrian and group detection, gaze estimation and behavior understanding",2015 +36,Images of Groups,images_of_groups,58.38131405,26.72078081,University of Tartu,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0 +37,Images of Groups,images_of_groups,41.3868913,2.16352385,University of Barcelona,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0 +38,Images of Groups,images_of_groups,51.49887085,-0.17560797,Imperial College London,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +39,Images of Groups,images_of_groups,51.59029705,-0.22963221,Middlesex University,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +40,Images of Groups,images_of_groups,39.2899685,-76.62196103,University of Maryland,edu,3b092733f428b12f1f920638f868ed1e8663fe57,citation,http://www.math.jhu.edu/~data/RamaPapers/PerformanceBounds.pdf,On the size of Convolutional Neural Networks and generalization performance,2016 +41,Images of Groups,images_of_groups,33.6431901,-117.84016494,"University of California, Irvine",edu,3991223b1dc3b87883cec7af97cf56534178f74a,citation,http://doi.acm.org/10.1145/2461466.2461469,A unified framework for context assisted face clustering,2013 +42,Images of Groups,images_of_groups,65.0592157,25.46632601,University of Oulu,edu,1e516273554d87bbe1902fa0298179c493299035,citation,http://www.ee.oulu.fi/~hadid/Age-ICPR2012.pdf,Age Classification in Unconstrained Conditions Using LBP Variants,2012 +43,Images of Groups,images_of_groups,50.89273635,-1.39464295,University of Southampton,edu,fd67d0efbd94c9d8f9d2f0a972edd7320bc7604f,citation,http://pdfs.semanticscholar.org/fd67/d0efbd94c9d8f9d2f0a972edd7320bc7604f.pdf,Real-Time Semantic Clothing Segmentation,2012 +44,Images of Groups,images_of_groups,47.6543238,-122.30800894,University of Washington,edu,f2c30594d917ea915028668bc2a481371a72a14d,citation,http://pdfs.semanticscholar.org/f2c3/0594d917ea915028668bc2a481371a72a14d.pdf,Scene Understanding Using Internet Photo Collections,2010 +45,Images of Groups,images_of_groups,40.47913175,-74.43168868,Rutgers University,edu,31f1e711fcf82c855f27396f181bf5e565a2f58d,citation,http://doi.ieeecomputersociety.org/10.1109/ICCVW.2015.54,Unconstrained Age Estimation with Deep Convolutional Neural Networks,2015 +46,Images of Groups,images_of_groups,39.2899685,-76.62196103,University of Maryland,edu,31f1e711fcf82c855f27396f181bf5e565a2f58d,citation,http://doi.ieeecomputersociety.org/10.1109/ICCVW.2015.54,Unconstrained Age Estimation with Deep Convolutional Neural Networks,2015 +47,Images of Groups,images_of_groups,35.93006535,-84.31240032,Oak Ridge National Laboratory,edu,2cf3564d7421b661e84251d280d159d4b3ebb336,citation,https://doi.org/10.1109/BTAS.2014.6996287,Discriminating projections for estimating face age in wild images,2014 +48,Images of Groups,images_of_groups,34.2239869,-77.8701325,"UNCW, USA",edu,2cf3564d7421b661e84251d280d159d4b3ebb336,citation,https://doi.org/10.1109/BTAS.2014.6996287,Discriminating projections for estimating face age in wild images,2014 +49,Images of Groups,images_of_groups,34.2249827,-77.86907744,University of North Carolina at Wilmington,edu,2cf3564d7421b661e84251d280d159d4b3ebb336,citation,https://doi.org/10.1109/BTAS.2014.6996287,Discriminating projections for estimating face age in wild images,2014 +50,Images of Groups,images_of_groups,41.3868913,2.16352385,University of Barcelona,edu,500fbe18afd44312738cab91b4689c12b4e0eeee,citation,http://www.maia.ub.es/~sergio/linked/ijcnn_age_and_cultural_2015.pdf,ChaLearn looking at people 2015 new competitions: Age estimation and cultural event recognition,2015 +51,Images of Groups,images_of_groups,45.4312742,12.3265377,University of Venezia,edu,500fbe18afd44312738cab91b4689c12b4e0eeee,citation,http://www.maia.ub.es/~sergio/linked/ijcnn_age_and_cultural_2015.pdf,ChaLearn looking at people 2015 new competitions: Age estimation and cultural event recognition,2015 +52,Images of Groups,images_of_groups,42.4505507,-76.4783513,Cornell University,edu,0d57d3d2d04fc96d731cac99a7a8ef79050dac75,citation,http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/Workshops/4990a269.pdf,Not Everybody's Special: Using Neighbors in Referring Expressions with Uncertain Attributes,2013 +53,Images of Groups,images_of_groups,42.4505507,-76.4783513,Cornell University,edu,fbc9ba70e36768efff130c7d970ce52810b044ff,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6738500,Face-graph matching for classifying groups of people,2013 +54,Images of Groups,images_of_groups,37.43131385,-122.16936535,Stanford University,edu,fbc9ba70e36768efff130c7d970ce52810b044ff,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6738500,Face-graph matching for classifying groups of people,2013 +55,Images of Groups,images_of_groups,1.340216,103.965089,Singapore University of Technology and Design,edu,00823e6c0b6f1cf22897b8d0b2596743723ec51c,citation,https://arxiv.org/pdf/1708.07689.pdf,Understanding and Comparing Deep Neural Networks for Age and Gender Classification,2017 +56,Images of Groups,images_of_groups,41.1664858,-73.1920564,University of Bridgeport,edu,ac9a331327cceda4e23f9873f387c9fd161fad76,citation,http://pdfs.semanticscholar.org/ac9a/331327cceda4e23f9873f387c9fd161fad76.pdf,Deep Convolutional Neural Network for Age Estimation based on VGG-Face Model,2017 +57,Images of Groups,images_of_groups,42.4505507,-76.4783513,Cornell University,edu,5aad56cfa2bac5d6635df4184047e809f8fecca2,citation,http://chenlab.ece.cornell.edu/people/Amir/publications/picture_password.pdf,A visual dictionary attack on Picture Passwords,2013 +58,Images of Groups,images_of_groups,42.9336278,-78.88394479,SUNY Buffalo,edu,4793f11fbca4a7dba898b9fff68f70d868e2497c,citation,http://pdfs.semanticscholar.org/4793/f11fbca4a7dba898b9fff68f70d868e2497c.pdf,Kinship Verification through Transfer Learning,2011 +59,Images of Groups,images_of_groups,37.4102193,-122.05965487,Carnegie Mellon University,edu,eddc4989cdb20c8cdfb22e989bdb2cb9031d0439,citation,https://arxiv.org/pdf/1804.03080.pdf,Binge Watching: Scaling Affordance Learning from Sitcoms,2017 +60,Images of Groups,images_of_groups,42.3383668,-71.08793524,Northeastern University,edu,090e4713bcccff52dcd0c01169591affd2af7e76,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Shao_What_Do_You_2013_ICCV_paper.pdf,What Do You Do? Occupation Recognition in a Photo via Social Context,2013 +61,Images of Groups,images_of_groups,53.21967825,6.56251482,University of Groningen,edu,4ff4c27e47b0aa80d6383427642bb8ee9d01c0ac,citation,https://doi.org/10.1109/SSCI.2015.37,Deep Convolutional Neural Networks and Support Vector Machines for Gender Recognition,2015 +62,Images of Groups,images_of_groups,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,291f527598c589fb0519f890f1beb2749082ddfd,citation,http://pdfs.semanticscholar.org/3215/ceb94227451a958bcf6b1205c710d17e53f5.pdf,Seeing People in Social Context: Recognizing People and Social Relationships,2010 +63,Images of Groups,images_of_groups,42.4505507,-76.4783513,Cornell University,edu,28d06fd508d6f14cd15f251518b36da17909b79e,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Chen_Whats_in_a_2013_CVPR_paper.pdf,What's in a Name? First Names as Facial Attributes,2013 +64,Images of Groups,images_of_groups,37.43131385,-122.16936535,Stanford University,edu,28d06fd508d6f14cd15f251518b36da17909b79e,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Chen_Whats_in_a_2013_CVPR_paper.pdf,What's in a Name? First Names as Facial Attributes,2013 +65,Images of Groups,images_of_groups,47.0570222,21.922709,Queen Mary University,edu,34022637860443c052375c45c4f700afcb438cd0,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.185,Automatic Recognition of Emotions and Membership in Group Videos,2016 +66,Images of Groups,images_of_groups,52.17638955,0.14308882,University of Cambridge,edu,34022637860443c052375c45c4f700afcb438cd0,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.185,Automatic Recognition of Emotions and Membership in Group Videos,2016 +67,Images of Groups,images_of_groups,38.8964679,-104.8050594,University of Colorado at Colorado Springs,edu,e3e2c106ccbd668fb9fca851498c662add257036,citation,http://www.vast.uccs.edu/~tboult/PAPERS/BTAS13-Sapkota-et-al-Ensembles.pdf,"Appearance, context and co-occurrence ensembles for identity recognition in personal photo collections",2013 +68,Images of Groups,images_of_groups,25.01682835,121.53846924,National Taiwan University,edu,8ba67f45fbb1ce47a90df38f21834db37c840079,citation,http://www.cmlab.csie.ntu.edu.tw/~yanying/paper/dsp006-chen.pdf,People search and activity mining in large-scale community-contributed photos,2012 +69,Images of Groups,images_of_groups,1.2962018,103.77689944,National University of Singapore,edu,a5219fff98dfe3ec81dee95c4ead69a8e24cc802,citation,https://arxiv.org/pdf/1708.00634.pdf,Dual-Glance Model for Deciphering Social Relationships,2017 +70,Images of Groups,images_of_groups,44.97308605,-93.23708813,University of Minnesota,edu,a5219fff98dfe3ec81dee95c4ead69a8e24cc802,citation,https://arxiv.org/pdf/1708.00634.pdf,Dual-Glance Model for Deciphering Social Relationships,2017 +71,Images of Groups,images_of_groups,40.742252,-74.0270949,Stevens Institute of Technology,edu,1e1d7cbbef67e9e042a3a0a9a1bcefcc4a9adacf,citation,http://personal.stevens.edu/~hli18//data/papers/CVPR2016_CameraReady.pdf,A Multi-level Contextual Model for Person Recognition in Photo Albums,2016 +72,Images of Groups,images_of_groups,-34.9189226,138.60423668,University of Adelaide,edu,3d24b386d003bee176a942c26336dbe8f427aadd,citation,http://arxiv.org/abs/1611.09967,Sequential Person Recognition in Photo Albums with a Recurrent Network,2017 +73,Images of Groups,images_of_groups,37.43131385,-122.16936535,Stanford University,edu,111ae23b60284927f2545dfc59b0147bb3423792,citation,https://pdfs.semanticscholar.org/111a/e23b60284927f2545dfc59b0147bb3423792.pdf,Classroom Data Collection and Analysis using Computer Vision,2016 +74,Images of Groups,images_of_groups,51.99882735,4.37396037,Delft University of Technology,edu,dfbf941adeea19f5dff4a70a466ddd1b77f3b727,citation,https://pdfs.semanticscholar.org/dfbf/941adeea19f5dff4a70a466ddd1b77f3b727.pdf,Models for supervised learning in sequence data,2018 +75,Images of Groups,images_of_groups,40.8419836,-73.94368971,Columbia University,edu,774cbb45968607a027ae4729077734db000a1ec5,citation,http://pdfs.semanticscholar.org/774c/bb45968607a027ae4729077734db000a1ec5.pdf,From Bikers to Surfers: Visual Recognition of Urban Tribes,2013 +76,Images of Groups,images_of_groups,32.87935255,-117.23110049,"University of California, San Diego",edu,774cbb45968607a027ae4729077734db000a1ec5,citation,http://pdfs.semanticscholar.org/774c/bb45968607a027ae4729077734db000a1ec5.pdf,From Bikers to Surfers: Visual Recognition of Urban Tribes,2013 +77,Images of Groups,images_of_groups,47.6543238,-122.30800894,University of Washington,edu,5b2bc289b607ca1a0634555158464f28fe68a6d3,citation,http://vision.ics.uci.edu/papers/GargRSS_CVPR_2011/GargRSS_CVPR_2011.pdf,Where's Waldo: Matching people in images of crowds,2011 +78,Images of Groups,images_of_groups,42.4505507,-76.4783513,Cornell University,edu,5b2bc289b607ca1a0634555158464f28fe68a6d3,citation,http://vision.ics.uci.edu/papers/GargRSS_CVPR_2011/GargRSS_CVPR_2011.pdf,Where's Waldo: Matching people in images of crowds,2011 +79,Images of Groups,images_of_groups,42.4505507,-76.4783513,Cornell University,edu,b185f0a39384ceb3c4923196aeed6d68830a069f,citation,http://pdfs.semanticscholar.org/b185/f0a39384ceb3c4923196aeed6d68830a069f.pdf,Describing Clothing by Semantic Attributes,2012 +80,Images of Groups,images_of_groups,37.43131385,-122.16936535,Stanford University,edu,b185f0a39384ceb3c4923196aeed6d68830a069f,citation,http://pdfs.semanticscholar.org/b185/f0a39384ceb3c4923196aeed6d68830a069f.pdf,Describing Clothing by Semantic Attributes,2012 +81,Images of Groups,images_of_groups,42.4505507,-76.4783513,Cornell University,edu,14c37ea85ba8d74d053a34aedd7e484659fd54d4,citation,http://users.ece.cmu.edu/~dbatra/publications/assets/opd_cvpr10.pdf,Beyond trees: MRF inference via outer-planar decomposition,2010 +82,Images of Groups,images_of_groups,65.0592157,25.46632601,University of Oulu,edu,8d95317d0e366cecae1dd3f7c1ba69fe3fc4a8e0,citation,http://pdfs.semanticscholar.org/f7c7/f4494f73f2fe845be3b82ee711bc00be7508.pdf,Riesz-based Volume Local Binary Pattern and A Novel Group Expression Model for Group Happiness Intensity Analysis,2015 +83,Images of Groups,images_of_groups,-35.23656905,149.08446994,University of Canberra,edu,8d95317d0e366cecae1dd3f7c1ba69fe3fc4a8e0,citation,http://pdfs.semanticscholar.org/f7c7/f4494f73f2fe845be3b82ee711bc00be7508.pdf,Riesz-based Volume Local Binary Pattern and A Novel Group Expression Model for Group Happiness Intensity Analysis,2015 +84,Images of Groups,images_of_groups,-35.2776999,149.118527,Australian National University,edu,8d95317d0e366cecae1dd3f7c1ba69fe3fc4a8e0,citation,http://pdfs.semanticscholar.org/f7c7/f4494f73f2fe845be3b82ee711bc00be7508.pdf,Riesz-based Volume Local Binary Pattern and A Novel Group Expression Model for Group Happiness Intensity Analysis,2015 +85,Images of Groups,images_of_groups,45.42580475,-75.68740118,University of Ottawa,edu,65293ecf6a4c5ab037a2afb4a9a1def95e194e5f,citation,http://pdfs.semanticscholar.org/6529/3ecf6a4c5ab037a2afb4a9a1def95e194e5f.pdf,"Face , Age and Gender Recognition using Local Descriptors",2014 +86,Images of Groups,images_of_groups,32.8536333,-117.2035286,Kyung Hee University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +87,Images of Groups,images_of_groups,24.7246403,46.62335012,King Saud University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +88,Images of Groups,images_of_groups,23.7289899,90.3982682,Institute of Information Technology,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +89,Images of Groups,images_of_groups,42.9336278,-78.88394479,SUNY Buffalo,edu,2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d,citation,https://doi.org/10.1109/CVPRW.2011.5981801,Genealogical face recognition based on UB KinFace database,2011 +90,Images of Groups,images_of_groups,35.9042272,-78.85565763,"IBM Research, North Carolina",company,aea50d3414ecb20dc2ba77b0277d0df59bde2c2c,citation,http://pdfs.semanticscholar.org/aea5/0d3414ecb20dc2ba77b0277d0df59bde2c2c.pdf,The #selfiestation: Design and Use of a Kiosk for Taking Selfies in the Enterprise,2015 +91,Images of Groups,images_of_groups,40.00229045,116.32098908,Tsinghua University,edu,0a0d5283439f088c158fcec732e2593bb3cd57ad,citation,http://media.cs.tsinghua.edu.cn/~ahz/papers/whoblockswho_iccv2011_final.pdf,Who Blocks Who: Simultaneous clothing segmentation for grouping images,2011 +92,Images of Groups,images_of_groups,42.36782045,-71.12666653,Harvard University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 +93,Images of Groups,images_of_groups,40.9153196,-73.1270626,Stony Brook University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 +94,Images of Groups,images_of_groups,51.5217668,-0.13019072,University of London,edu,b9eb55c2c573e2fffd686b00a39185f0142ef816,citation,http://elvera.nue.tu-berlin.de/files/1241Ramzan2010.pdf,The participation payoff: challenges and opportunities for multimedia access in networked communities,2010 +95,Images of Groups,images_of_groups,51.99882735,4.37396037,Delft University of Technology,edu,b9eb55c2c573e2fffd686b00a39185f0142ef816,citation,http://elvera.nue.tu-berlin.de/files/1241Ramzan2010.pdf,The participation payoff: challenges and opportunities for multimedia access in networked communities,2010 +96,Images of Groups,images_of_groups,1.2962018,103.77689944,National University of Singapore,edu,cc3ef62b4a7eb6c4e45302deb89df2e547b6efcc,citation,http://pdfs.semanticscholar.org/cc3e/f62b4a7eb6c4e45302deb89df2e547b6efcc.pdf,Creating Picture Legends for Group Photos,2012 +97,Images of Groups,images_of_groups,37.4585796,-122.17560525,SRI International,edu,683f5c838ea2c9c50f3f5c5fa064c00868751733,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Chakraborty_3D_Visual_Proxemics_2013_CVPR_paper.pdf,3D Visual Proxemics: Recognizing Human Interactions in 3D from a Single Image,2013 +98,Images of Groups,images_of_groups,40.51865195,-74.44099801,State University of New Jersey,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791154,A cascaded convolutional neural network for age estimation of unconstrained faces,2016 +99,Images of Groups,images_of_groups,39.2899685,-76.62196103,University of Maryland,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791154,A cascaded convolutional neural network for age estimation of unconstrained faces,2016 +100,Images of Groups,images_of_groups,31.2284923,121.40211389,East China Normal University,edu,5364e58ba1f4cdfcffb247c2421e8f56a75fad8d,citation,https://doi.org/10.1109/VCIP.2017.8305113,Facial age estimation through self-paced learning,2017 +101,Images of Groups,images_of_groups,42.3383668,-71.08793524,Northeastern University,edu,c9f588d295437009994ddaabb64fd4e4c499b294,citation,http://pdfs.semanticscholar.org/c9f5/88d295437009994ddaabb64fd4e4c499b294.pdf,Predicting Professions through 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Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition,2018 +10,IMDB,imdb_wiki,55.91029135,-3.32345777,Heriot-Watt University,edu,2cdc40f20b70ca44d9fd8e7716080ee05ca7924a,citation,http://pdfs.semanticscholar.org/2cdc/40f20b70ca44d9fd8e7716080ee05ca7924a.pdf,Real-time Convolutional Neural Networks for Emotion and Gender Classification,2017 +11,IMDB,imdb_wiki,56.45796755,-2.98214831,University of Dundee,edu,d5b0e73b584be507198b6665bcddeba92b62e1e5,citation,http://pdfs.semanticscholar.org/d5b0/e73b584be507198b6665bcddeba92b62e1e5.pdf,Multi-Region Ensemble Convolutional Neural Networks for High-Accuracy Age Estimation,2017 +12,IMDB,imdb_wiki,22.15263985,113.56803206,Macau University of Science and Technology,edu,d5b0e73b584be507198b6665bcddeba92b62e1e5,citation,http://pdfs.semanticscholar.org/d5b0/e73b584be507198b6665bcddeba92b62e1e5.pdf,Multi-Region Ensemble Convolutional Neural Networks for High-Accuracy Age Estimation,2017 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Telecommunications,edu,39c10888a470b92b917788c57a6fd154c97b421c,citation,https://doi.org/10.1109/VCIP.2017.8305036,Joint multi-feature fusion and attribute relationships for facial attribute prediction,2017 +30,IMDB,imdb_wiki,51.7534538,-1.25400997,University of Oxford,edu,eb027969f9310e0ae941e2adee2d42cdf07d938c,citation,https://arxiv.org/pdf/1710.08092.pdf,VGGFace2: A Dataset for Recognising Faces across Pose and Age,2018 +31,IMDB,imdb_wiki,45.5039761,-73.5749687,McGill University,edu,ed9d11e995baeec17c5d2847ec1a8d5449254525,citation,https://pdfs.semanticscholar.org/ed9d/11e995baeec17c5d2847ec1a8d5449254525.pdf,Efficient Gender Classification Using a Deep LDA-Pruned Net,2017 +32,IMDB,imdb_wiki,31.32235655,121.38400941,Shanghai University,edu,d454ad60b061c1a1450810a0f335fafbfeceeccc,citation,https://arxiv.org/pdf/1712.07195.pdf,Deep Regression Forests for Age Estimation,2017 +33,IMDB,imdb_wiki,40.0044795,116.370238,Chinese Academy of 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unconstrained faces,2016 +37,IMDB,imdb_wiki,37.2830003,127.04548469,Ajou University,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018 +38,IMDB,imdb_wiki,37.403917,127.159786,Korea Electronics Technology Institute,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018 +39,IMDB,imdb_wiki,37.26728,126.9841151,Seoul National University,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018 +40,IMDB,imdb_wiki,1.2962018,103.77689944,National University of Singapore,edu,5f94969b9491db552ffebc5911a45def99026afe,citation,https://pdfs.semanticscholar.org/5f94/969b9491db552ffebc5911a45def99026afe.pdf,Multimodal Learning and Reasoning for Visual Question Answering,2017 +41,IMDB,imdb_wiki,42.357757,-83.06286711,Wayne State University,edu,28d99dc2d673d62118658f8375b414e5192eac6f,citation,http://www.cs.wayne.edu/~mdong/cvpr17.pdf,Using Ranking-CNN for Age Estimation,2017 +42,IMDB,imdb_wiki,49.2767454,-122.91777375,Simon Fraser University,edu,975978ee6a32383d6f4f026b944099e7739e5890,citation,https://pdfs.semanticscholar.org/9759/78ee6a32383d6f4f026b944099e7739e5890.pdf,Privacy-Preserving Age Estimation for Content Rating,2018 +43,IMDB,imdb_wiki,49.8091536,-97.13304179,University of Manitoba,edu,975978ee6a32383d6f4f026b944099e7739e5890,citation,https://pdfs.semanticscholar.org/9759/78ee6a32383d6f4f026b944099e7739e5890.pdf,Privacy-Preserving Age Estimation for Content Rating,2018 +44,IMDB,imdb_wiki,43.66333345,-79.39769975,University of Toronto,edu,36a3a96ef54000a0cd63de867a5eb7e84396de09,citation,http://www.cs.toronto.edu/~guerzhoy/oriviz/crv17.pdf,Automatic Photo Orientation Detection with Convolutional Neural Networks,2017 +45,IMDB,imdb_wiki,31.32235655,121.38400941,Shanghai University,edu,5f0d4a0b5f72d8700cdf8cb179263a8fa866b59b,citation,https://pdfs.semanticscholar.org/5f0d/4a0b5f72d8700cdf8cb179263a8fa866b59b.pdf,Memo No . 85 06 / 2018 Deep Regression Forests for Age Estimation,2018 +46,IMDB,imdb_wiki,51.5247272,-0.03931035,Queen Mary University of London,edu,6cefb70f4668ee6c0bf0c18ea36fd49dd60e8365,citation,http://pdfs.semanticscholar.org/6cef/b70f4668ee6c0bf0c18ea36fd49dd60e8365.pdf,Privacy-Preserving Deep Inference for Rich User Data on The Cloud,2017 +47,IMDB,imdb_wiki,35.7036227,51.35125097,Sharif University of Technology,edu,6cefb70f4668ee6c0bf0c18ea36fd49dd60e8365,citation,http://pdfs.semanticscholar.org/6cef/b70f4668ee6c0bf0c18ea36fd49dd60e8365.pdf,Privacy-Preserving Deep Inference for Rich User Data on The Cloud,2017 +48,IMDB,imdb_wiki,51.99882735,4.37396037,Delft University of Technology,edu,dfbf941adeea19f5dff4a70a466ddd1b77f3b727,citation,https://pdfs.semanticscholar.org/dfbf/941adeea19f5dff4a70a466ddd1b77f3b727.pdf,Models for supervised learning in sequence data,2018 +49,IMDB,imdb_wiki,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,cb27b45329d61f5f95ed213798d4b2a615e76be2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8329236,Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion,2018 +50,IMDB,imdb_wiki,37.2520226,127.0555019,"Samsung SAIT, Korea",company,cb27b45329d61f5f95ed213798d4b2a615e76be2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8329236,Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion,2018 +51,IMDB,imdb_wiki,35.9042272,-78.85565763,"IBM Research, North Carolina",company,00a967cb2d18e1394226ad37930524a31351f6cf,citation,https://arxiv.org/pdf/1611.05377v1.pdf,Fully-Adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification,2017 +52,IMDB,imdb_wiki,12.9803537,77.6975101,"Samsung R&D Institute, Bangalore, India",company,cf736f596bf881ca97ec4b29776baaa493b9d50e,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7952629,Low Dimensional Deep Features for facial landmark alignment,2017 +53,IMDB,imdb_wiki,-35.0636071,147.3552234,Charles Sturt University,edu,2e231f1e7e641dd3619bec59e14d02e91360ac01,citation,https://arxiv.org/pdf/1807.10421.pdf,Fusion Network for Face-Based Age Estimation,2018 +54,IMDB,imdb_wiki,51.3791442,-2.3252332,University of Bath,edu,2e231f1e7e641dd3619bec59e14d02e91360ac01,citation,https://arxiv.org/pdf/1807.10421.pdf,Fusion Network for Face-Based Age Estimation,2018 +55,IMDB,imdb_wiki,1.340216,103.965089,Singapore University of Technology and Design,edu,00823e6c0b6f1cf22897b8d0b2596743723ec51c,citation,https://arxiv.org/pdf/1708.07689.pdf,Understanding and Comparing Deep Neural Networks for Age and Gender Classification,2017 +56,IMDB,imdb_wiki,31.2284923,121.40211389,East China Normal University,edu,5364e58ba1f4cdfcffb247c2421e8f56a75fad8d,citation,https://doi.org/10.1109/VCIP.2017.8305113,Facial age estimation through self-paced learning,2017 +57,IMDB,imdb_wiki,61.44964205,23.85877462,Tampere University of Technology,edu,7f21a7441c6ded38008c1fd0b91bdd54425d3f80,citation,https://arxiv.org/pdf/1809.05474.pdf,Real Time System for Facial Analysis,2018 +58,IMDB,imdb_wiki,55.94951105,-3.19534913,University of Edinburgh,edu,f5fae7810a33ed67852ad6a3e0144cb278b24b41,citation,https://pdfs.semanticscholar.org/f5fa/e7810a33ed67852ad6a3e0144cb278b24b41.pdf,Multilingual Gender Classification with Multi-view Deep Learning: Notebook for PAN at CLEF 2018,2018 +59,IMDB,imdb_wiki,40.9153196,-73.1270626,Stony Brook University,edu,1190cba0cae3c8bb81bf80d6a0a83ae8c41240bc,citation,https://pdfs.semanticscholar.org/1190/cba0cae3c8bb81bf80d6a0a83ae8c41240bc.pdf,Squared Earth Mover ’ s Distance Loss for Training Deep Neural Networks on Ordered-Classes,2017 +60,IMDB,imdb_wiki,26.88111275,112.62850666,Hunan University,edu,86d0127e1fd04c3d8ea78401c838af621647dc95,citation,https://arxiv.org/pdf/1804.02810.pdf,A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction,2018 +61,IMDB,imdb_wiki,28.2290209,112.99483204,"National University of Defense Technology, China",edu,86d0127e1fd04c3d8ea78401c838af621647dc95,citation,https://arxiv.org/pdf/1804.02810.pdf,A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction,2018 +62,IMDB,imdb_wiki,29.58333105,-98.61944505,University of Texas at San Antonio,edu,86d0127e1fd04c3d8ea78401c838af621647dc95,citation,https://arxiv.org/pdf/1804.02810.pdf,A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction,2018 +63,IMDB,imdb_wiki,22.42031295,114.20788644,Chinese University of Hong Kong,edu,d80a3d1f3a438e02a6685e66ee908446766fefa9,citation,https://arxiv.org/pdf/1708.09687.pdf,Quantifying Facial Age by Posterior of Age Comparisons,2017 +64,IMDB,imdb_wiki,51.49887085,-0.17560797,Imperial College London,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +65,IMDB,imdb_wiki,51.59029705,-0.22963221,Middlesex University,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +66,IMDB,imdb_wiki,40.00229045,116.32098908,Tsinghua University,edu,493c8591d6a1bef5d7b84164a73761cefb9f5a25,citation,http://dl.acm.org/citation.cfm?id=3159691,User Profiling through Deep Multimodal Fusion,2018 +67,IMDB,imdb_wiki,47.6543238,-122.30800894,University of Washington,edu,493c8591d6a1bef5d7b84164a73761cefb9f5a25,citation,http://dl.acm.org/citation.cfm?id=3159691,User Profiling through Deep Multimodal Fusion,2018 +68,IMDB,imdb_wiki,30.44235995,-84.29747867,Florida State University,edu,b8c08c1330779283b3fbf06d133faf8bd55ea941,citation,https://arxiv.org/pdf/1803.11521.pdf,Online Regression with Feature Selection in Stochastic Data Streams,2018 +69,IMDB,imdb_wiki,30.44235995,-84.29747867,Florida State University,edu,1cfca6b71b0ead87bbb79a8614ddec3a10100faa,citation,https://arxiv.org/pdf/1809.05465.pdf,Are screening methods useful in feature selection? An empirical study,2018 +70,IMDB,imdb_wiki,51.49887085,-0.17560797,Imperial College London,edu,a06b6d30e2b31dc600f622ab15afe5e2929581a7,citation,https://ibug.doc.ic.ac.uk/media/uploads/documents/2209.pdf,Robust Joint and Individual Variance Explained,2017 +71,IMDB,imdb_wiki,51.59029705,-0.22963221,Middlesex University,edu,a06b6d30e2b31dc600f622ab15afe5e2929581a7,citation,https://ibug.doc.ic.ac.uk/media/uploads/documents/2209.pdf,Robust Joint and Individual Variance Explained,2017 +72,IMDB,imdb_wiki,32.0575279,118.78682252,Southeast University,edu,8ff8c64288a2f7e4e8bf8fda865820b04ab3dbe8,citation,https://pdfs.semanticscholar.org/0056/92b9fa6728df3a7f14578c43410867bba425.pdf,Age Estimation Using Expectation of Label Distribution Learning,2018 +73,IMDB,imdb_wiki,32.0565957,118.77408833,Nanjing University,edu,8ff8c64288a2f7e4e8bf8fda865820b04ab3dbe8,citation,https://pdfs.semanticscholar.org/0056/92b9fa6728df3a7f14578c43410867bba425.pdf,Age Estimation Using Expectation of Label Distribution Learning,2018 +74,IMDB,imdb_wiki,58.38131405,26.72078081,University of Tartu,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0 +75,IMDB,imdb_wiki,41.3868913,2.16352385,University of Barcelona,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0 +76,IMDB,imdb_wiki,35.9542493,-83.9307395,University of Tennessee,edu,7fab17ef7e25626643f1d55257a3e13348e435bd,citation,https://arxiv.org/pdf/1702.08423.pdf,Age Progression/Regression by Conditional Adversarial Autoencoder,2017 +77,IMDB,imdb_wiki,37.4102193,-122.05965487,Carnegie Mellon University,edu,ec05078be14a11157ac0e1c6b430ac886124589b,citation,http://pdfs.semanticscholar.org/ec05/078be14a11157ac0e1c6b430ac886124589b.pdf,Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches,2018 +78,IMDB,imdb_wiki,45.57022705,-122.63709346,Concordia University,edu,ec05078be14a11157ac0e1c6b430ac886124589b,citation,http://pdfs.semanticscholar.org/ec05/078be14a11157ac0e1c6b430ac886124589b.pdf,Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches,2018 +79,IMDB,imdb_wiki,40.00229045,116.32098908,Tsinghua University,edu,2149d49c84a83848d6051867290d9c8bfcef0edb,citation,https://doi.org/10.1109/TIFS.2017.2746062,Label-Sensitive Deep Metric Learning for Facial Age Estimation,2018 +80,IMDB,imdb_wiki,42.36782045,-71.12666653,Harvard University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 +81,IMDB,imdb_wiki,40.9153196,-73.1270626,Stony Brook University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 +82,IMDB,imdb_wiki,46.0658836,11.1159894,University of Trento,edu,df31e9c882dfb3ea5a3abe3b139ceacb1d90a302,citation,https://arxiv.org/pdf/1808.09211.pdf,DeepGUM: Learning Deep Robust Regression with a Gaussian-Uniform Mixture Model,2018 +83,IMDB,imdb_wiki,51.7534538,-1.25400997,University of Oxford,edu,523854a7d8755e944bd50217c14481fe1329a969,citation,https://arxiv.org/pdf/1808.00380.pdf,A Differentially Private Kernel Two-Sample Test,2018 +84,IMDB,imdb_wiki,51.49887085,-0.17560797,Imperial College London,edu,9b0489f2d5739213ef8c3e2e18739c4353c3a3b7,citation,http://pdfs.semanticscholar.org/9b04/89f2d5739213ef8c3e2e18739c4353c3a3b7.pdf,Visual Data Augmentation through Learning,2018 +85,IMDB,imdb_wiki,51.59029705,-0.22963221,Middlesex University,edu,9b0489f2d5739213ef8c3e2e18739c4353c3a3b7,citation,http://pdfs.semanticscholar.org/9b04/89f2d5739213ef8c3e2e18739c4353c3a3b7.pdf,Visual Data Augmentation through Learning,2018 +86,IMDB,imdb_wiki,40.00229045,116.32098908,Tsinghua University,edu,51f626540860ad75b68206025a45466a6d087aa6,citation,https://doi.org/10.1109/ICIP.2017.8296595,Cluster convolutional neural networks for facial age estimation,2017 +87,IMDB,imdb_wiki,49.2593879,-122.9151893,"AltumView Systems Inc., Burnaby, BC, Canada",company,b44f03b5fa8c6275238c2d13345652e6ff7e6ea9,citation,https://doi.org/10.1109/GlobalSIP.2017.8309138,Lapped convolutional neural networks for embedded systems,2017 +88,IMDB,imdb_wiki,39.2899685,-76.62196103,University of Maryland,edu,93420d9212dd15b3ef37f566e4d57e76bb2fab2f,citation,https://arxiv.org/pdf/1611.00851.pdf,An All-In-One Convolutional Neural Network for Face Analysis,2017 +89,IMDB,imdb_wiki,22.15263985,113.56803206,Macau University of Science and Technology,edu,56f231fc40424ed9a7c93cbc9f5a99d022e1d242,citation,http://pdfs.semanticscholar.org/d060/f2f3641c6a89ade021eea749414a5c6b443f.pdf,Age Estimation Based on a Single Network with Soft Softmax of Aging Modeling,2016 +90,IMDB,imdb_wiki,40.0044795,116.370238,Chinese Academy of Sciences,edu,56f231fc40424ed9a7c93cbc9f5a99d022e1d242,citation,http://pdfs.semanticscholar.org/d060/f2f3641c6a89ade021eea749414a5c6b443f.pdf,Age Estimation Based on a Single Network with Soft Softmax of Aging Modeling,2016 +91,IMDB,imdb_wiki,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,56f231fc40424ed9a7c93cbc9f5a99d022e1d242,citation,http://pdfs.semanticscholar.org/d060/f2f3641c6a89ade021eea749414a5c6b443f.pdf,Age Estimation Based on a Single Network with Soft Softmax of Aging Modeling,2016 +92,IMDB,imdb_wiki,43.614386,7.071125,EURECOM,edu,1648cf24c042122af2f429641ba9599a2187d605,citation,https://doi.org/10.1109/BTAS.2017.8272698,Boosting cross-age face verification via generative age normalization,2017 +93,IMDB,imdb_wiki,21.003952,105.84360183,Hanoi University of Science and Technology,edu,ca37933b6297cdca211aa7250cbe6b59f8be40e5,citation,http://doi.acm.org/10.1145/3155133.3155207,"Multi-task learning for smile detection, emotion recognition and gender classification",2017 +94,IMDB,imdb_wiki,51.49887085,-0.17560797,Imperial College London,edu,cf2002fac81ccdccdadb5cc43f7b1cd30882d2c2,citation,https://arxiv.org/pdf/1803.09546.pdf,Calibrated Prediction Intervals for Neural Network Regressors,2018 +95,IMDB,imdb_wiki,51.7534538,-1.25400997,University of Oxford,edu,75f9d3533f175943e33c9155f4038488f32a24bc,citation,https://arxiv.org/pdf/1811.06817.pdf,Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control,2018 +96,IMDB,imdb_wiki,32.8536333,-117.2035286,Kyung Hee University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +97,IMDB,imdb_wiki,24.7246403,46.62335012,King Saud University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +98,IMDB,imdb_wiki,23.7289899,90.3982682,Institute of Information Technology,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +99,IMDB,imdb_wiki,28.5456282,77.2731505,"IIIT Delhi, India",edu,f726738954e7055bb3615fa7e8f59f136d3e0bdc,citation,https://arxiv.org/pdf/1803.07385.pdf,Are you eligible? Predicting adulthood from face images via class specific mean autoencoder,2018 +100,IMDB,imdb_wiki,42.0551164,-87.67581113,Northwestern University,edu,c1586ee25e660f31cba0ca9ba5bf39ffcc020aab,citation,https://arxiv.org/pdf/1807.06708.pdf,A Modulation Module for Multi-task Learning with Applications in Image Retrieval,2018 +101,IMDB,imdb_wiki,37.4102193,-122.05965487,Carnegie Mellon University,edu,c1586ee25e660f31cba0ca9ba5bf39ffcc020aab,citation,https://arxiv.org/pdf/1807.06708.pdf,A Modulation Module for Multi-task Learning with Applications in Image Retrieval,2018 +102,IMDB,imdb_wiki,30.04287695,31.23664139,American University in Cairo,edu,3a2c90e0963bfb07fc7cd1b5061383e9a99c39d2,citation,https://arxiv.org/pdf/1710.03804.pdf,End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies,2017 +103,IMDB,imdb_wiki,31.83907195,117.26420748,University of Science and Technology of China,edu,47cd161546c59ab1e05f8841b82e985f72e5ddcb,citation,https://doi.org/10.1109/ICIP.2017.8296552,Gender classification in live videos,2017 +104,IMDB,imdb_wiki,39.2899685,-76.62196103,University of Maryland,edu,1491d0938bb4183bd19f2fee3b61997e1918160d,citation,https://arxiv.org/pdf/1807.00453.pdf,Elastic Neural Networks: A Scalable Framework for Embedded Computer Vision,2018 +105,IMDB,imdb_wiki,30.44235995,-84.29747867,Florida State University,edu,b88bace97d214d279e3a2053ccff0b6425295708,citation,https://arxiv.org/pdf/1803.11521.pdf,A Novel Framework for Online Supervised Learning with Feature Selection,2018 +106,IMDB,imdb_wiki,61.44964205,23.85877462,Tampere University of Technology,edu,b20cfbb2348984b4e25b6b9174f3c7b65b6aed9e,citation,http://pdfs.semanticscholar.org/b20c/fbb2348984b4e25b6b9174f3c7b65b6aed9e.pdf,Learning with Ambiguous Label Distribution for Apparent Age Estimation,2016 +107,IMDB,imdb_wiki,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,f3ec43a7b22f6e5414fec473acda8ffd843e7baf,citation,https://arxiv.org/pdf/1809.07447.pdf,A Coupled Evolutionary Network for Age Estimation,2018 +108,IMDB,imdb_wiki,39.94976005,116.33629046,Beijing Jiaotong University,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017 +109,IMDB,imdb_wiki,43.1576969,-77.58829158,University of Rochester,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017 +110,IMDB,imdb_wiki,1.2962018,103.77689944,National University of Singapore,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017 +111,IMDB,imdb_wiki,31.846918,117.29053367,Hefei University of Technology,edu,dc9c0527f8d4461b1742cccc7317fec8dd96d81a,citation,https://arxiv.org/pdf/1805.08373.pdf,Speeding-Up Age Estimation in Intelligent Demographics System via Network Optimization,2018 +112,IMDB,imdb_wiki,1.3484104,103.68297965,Nanyang Technological University,edu,dc9c0527f8d4461b1742cccc7317fec8dd96d81a,citation,https://arxiv.org/pdf/1805.08373.pdf,Speeding-Up Age Estimation in Intelligent Demographics System via Network Optimization,2018 +113,IMDB,imdb_wiki,43.614386,7.071125,EURECOM,edu,f7b422df567ce9813926461251517761e3e6cda0,citation,https://arxiv.org/pdf/1702.01983.pdf,Face aging with conditional generative adversarial networks,2017 +114,IMDB,imdb_wiki,21.003952,105.84360183,Hanoi University of Science and Technology,edu,68573e296f069071d071fc158e974e8bc70c893f,citation,https://pdfs.semanticscholar.org/6857/3e296f069071d071fc158e974e8bc70c893f.pdf,"Effective Deep Multi-source Multi-task Learning Frameworks for Smile Detection, Emotion Recognition and Gender Classification",2018 +115,IMDB,imdb_wiki,46.0658836,11.1159894,University of Trento,edu,cb43519894258b125624dc0df655ab5357b1e42f,citation,https://arxiv.org/pdf/1802.00237.pdf,Face Aging with Contextual Generative Adversarial Nets,2017 +116,IMDB,imdb_wiki,1.2962018,103.77689944,National University of Singapore,edu,cb43519894258b125624dc0df655ab5357b1e42f,citation,https://arxiv.org/pdf/1802.00237.pdf,Face Aging with Contextual Generative Adversarial Nets,2017 +117,IMDB,imdb_wiki,43.614386,7.071125,EURECOM,edu,70569810e46f476515fce80a602a210f8d9a2b95,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.105,Apparent Age Estimation from Face Images Combining General and Children-Specialized Deep Learning Models,2016 +118,IMDB,imdb_wiki,43.614386,7.071125,EURECOM,edu,f519723238701849f1160d5a9cedebd31017da89,citation,http://pdfs.semanticscholar.org/f519/723238701849f1160d5a9cedebd31017da89.pdf,Impact of 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University,edu,4b519e2e88ccd45718b0fc65bfd82ebe103902f7,citation,http://biometrics.cse.msu.edu/Publications/Face/LiParkJain_DiscriminativeModelAgeInvariantFR_TIFS11.pdf,A Discriminative Model for Age Invariant Face Recognition,2011 +75,MORPH Commercial,morph,42.718568,-84.47791571,Michigan State University,edu,4b519e2e88ccd45718b0fc65bfd82ebe103902f7,citation,http://biometrics.cse.msu.edu/Publications/Face/LiParkJain_DiscriminativeModelAgeInvariantFR_TIFS11.pdf,A Discriminative Model for Age Invariant Face Recognition,2011 +76,MORPH Commercial,morph,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,4b519e2e88ccd45718b0fc65bfd82ebe103902f7,citation,http://biometrics.cse.msu.edu/Publications/Face/LiParkJain_DiscriminativeModelAgeInvariantFR_TIFS11.pdf,A Discriminative Model for Age Invariant Face Recognition,2011 +77,MORPH Commercial,morph,23.09461185,113.28788994,Sun Yat-Sen 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estimation,2010 +92,MORPH Commercial,morph,34.2249827,-77.86907744,University of North Carolina at Wilmington,edu,97c59db934ff85c60c460a4591106682b5ab9caa,citation,https://doi.org/10.1109/BTAS.2012.6374568,Extremely dense face registration: Comparing automatic landmarking algorithms for general and ethno-gender models,2012 +93,MORPH Commercial,morph,43.2213516,-75.4085577,"Air Force Research Lab, Rome, NY",mil,834736698f2cc5c221c22369abe95515243a9fc3,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6996249,GARP-face: Balancing privacy protection and utility preservation in face de-identification,2014 +94,MORPH Commercial,morph,39.95472495,-75.15346905,Temple University,edu,834736698f2cc5c221c22369abe95515243a9fc3,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6996249,GARP-face: Balancing privacy protection and utility preservation in face de-identification,2014 +95,MORPH Commercial,morph,32.0575279,118.78682252,Southeast 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Technology,edu,3cc46bf79fb9225cf308815c7d41c8dd5625cc29,citation,http://poseidon.csd.auth.gr/papers/PUBLISHED/CONFERENCE/pdf/2016/Pantraki2016.pdf,Age interval and gender prediction using PARAFAC2 applied to speech utterances,2016 +132,MORPH Commercial,morph,23.09461185,113.28788994,Sun Yat-Sen University,edu,189e5a2fa51ed471c0e7227d82dffb52736070d8,citation,https://doi.org/10.1109/ICIP.2017.8296995,Cross-age face recognition using reference coding with kernel direct discriminant analysis,2017 +133,MORPH Commercial,morph,42.357757,-83.06286711,Wayne State University,edu,4f1249369127cc2e2894f6b2f1052d399794919a,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8239663,Deep Age Estimation: From Classification to Ranking,2018 +134,MORPH Commercial,morph,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,cd6aaa37fffd0b5c2320f386be322b8adaa1cc68,citation,https://arxiv.org/pdf/1804.06655.pdf,Deep Face Recognition: A Survey,2018 +135,MORPH 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Rome,edu,62750d78e819d745b9200b0c5c35fcae6fb9f404,citation,http://doi.org/10.1007/s11042-016-4085-8,Leveraging implicit demographic information for face recognition using a multi-expert system,2016 +146,MORPH Commercial,morph,40.845492,14.2578058,University of Naples Federico II,edu,62750d78e819d745b9200b0c5c35fcae6fb9f404,citation,http://doi.org/10.1007/s11042-016-4085-8,Leveraging implicit demographic information for face recognition using a multi-expert system,2016 +147,MORPH Commercial,morph,25.01353105,121.54173736,National Taiwan University of Science and Technology,edu,e4c3587392d477b7594086c6f28a00a826abf004,citation,https://doi.org/10.1109/ICIP.2017.8296998,Face recognition by facial attribute assisted network,2017 +148,MORPH Commercial,morph,39.9922379,116.30393816,Peking University,edu,c4ca092972abb74ee1c20b7cae6e69c654479e2c,citation,https://doi.org/10.1109/ICIP.2016.7532960,Linear canonical correlation analysis based ranking approach for facial age estimation,2016 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University,edu,55bc7abcef8266d76667896bbc652d081d00f797,citation,http://www.cse.msu.edu/~rossarun/pubs/ChenCosmeticsGenderAge_VISAPP2014.pdf,Impact of facial cosmetics on automatic gender and age estimation algorithms,2014 +244,MORPH Commercial,morph,42.718568,-84.47791571,Michigan State University,edu,55bc7abcef8266d76667896bbc652d081d00f797,citation,http://www.cse.msu.edu/~rossarun/pubs/ChenCosmeticsGenderAge_VISAPP2014.pdf,Impact of facial cosmetics on automatic gender and age estimation algorithms,2014 +245,MORPH Commercial,morph,39.65404635,-79.96475355,West Virginia University,edu,7a65fc9e78eff3ab6062707deaadde024d2fad40,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Zhu_A_Study_on_ICCV_2015_paper.pdf,A Study on Apparent Age Estimation,2015 +246,MORPH Commercial,morph,42.357757,-83.06286711,Wayne State University,edu,28d99dc2d673d62118658f8375b414e5192eac6f,citation,http://www.cs.wayne.edu/~mdong/cvpr17.pdf,Using Ranking-CNN for Age Estimation,2017 +247,MORPH Commercial,morph,37.4102193,-122.05965487,Carnegie Mellon University,edu,ec05078be14a11157ac0e1c6b430ac886124589b,citation,http://pdfs.semanticscholar.org/ec05/078be14a11157ac0e1c6b430ac886124589b.pdf,Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches,2018 +248,MORPH Commercial,morph,45.57022705,-122.63709346,Concordia University,edu,ec05078be14a11157ac0e1c6b430ac886124589b,citation,http://pdfs.semanticscholar.org/ec05/078be14a11157ac0e1c6b430ac886124589b.pdf,Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches,2018 +249,MORPH Commercial,morph,46.5190557,6.5667576,"Swiss Federal Institute of Technology Lausanne, Switzerland",edu,d7a84db2a1bf7b97657b0250f354f249394dd700,citation,https://doi.org/10.1109/ICIP.2010.5653518,Global and local feature based multi-classifier A-stack model for aging face identification,2010 +250,MORPH Commercial,morph,39.65404635,-79.96475355,West Virginia 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Predicting adulthood from face images via class specific mean autoencoder,2018 +254,MORPH Commercial,morph,1.2962018,103.77689944,National University of Singapore,edu,b9d68dbeb8e5fdc5984b49a317ea6798b378e5ae,citation,http://doi.acm.org/10.1145/2733373.2807962,What Shall I Look Like after N Years?,2015 +255,MORPH Commercial,morph,32.0565957,118.77408833,Nanjing University,edu,b9d68dbeb8e5fdc5984b49a317ea6798b378e5ae,citation,http://doi.acm.org/10.1145/2733373.2807962,What Shall I Look Like after N Years?,2015 +256,MORPH Commercial,morph,45.42580475,-75.68740118,University of Ottawa,edu,16820ccfb626dcdc893cc7735784aed9f63cbb70,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W12/papers/Azarmehr_Real-Time_Embedded_Age_2015_CVPR_paper.pdf,Real-time embedded age and gender classification in unconstrained video,2015 +257,MORPH Commercial,morph,35.0274996,135.78154513,University of Caen,edu,0ad8149318912b5449085187eb3521786a37bc78,citation,http://arxiv.org/abs/1604.02975,CP-mtML: Coupled Projection Multi-Task Metric Learning for Large Scale Face Retrieval,2016 +258,MORPH Commercial,morph,51.44415765,7.26096541,Ruhr-University Bochum,edu,7e1ea2679a110241ed0dd38ff45cd4dfeb7a8e83,citation,http://pdfs.semanticscholar.org/7e1e/a2679a110241ed0dd38ff45cd4dfeb7a8e83.pdf,Extensions of Hierarchical Slow Feature Analysis for Efficient Classification and Regression on High-Dimensional Data,2017 +259,MORPH Commercial,morph,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,2e27667421a7eeab278e0b761db4d2c725683c3f,citation,https://doi.org/10.1007/s11042-013-1815-z,Effective human age estimation using a two-stage approach based on Lie Algebrized Gaussians feature,2013 +260,MORPH Commercial,morph,32.0565957,118.77408833,Nanjing University,edu,0c741fa0966ba3ee4fc326e919bf2f9456d0cd74,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.51,Facial Age Estimation by Learning from Label Distributions,2010 +261,MORPH Commercial,morph,32.0575279,118.78682252,Southeast University,edu,0c741fa0966ba3ee4fc326e919bf2f9456d0cd74,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.51,Facial Age Estimation by Learning from Label Distributions,2010 +262,MORPH Commercial,morph,-37.78397455,144.95867433,Monash University,edu,0c741fa0966ba3ee4fc326e919bf2f9456d0cd74,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.51,Facial Age Estimation by Learning from Label Distributions,2010 +263,MORPH Commercial,morph,1.2962018,103.77689944,National University of Singapore,edu,fca9ebaa30d69ccec8bb577c31693c936c869e72,citation,https://arxiv.org/pdf/1809.00338.pdf,Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition,2018 +264,MORPH Commercial,morph,40.0044795,116.370238,Chinese Academy of Sciences,edu,fca9ebaa30d69ccec8bb577c31693c936c869e72,citation,https://arxiv.org/pdf/1809.00338.pdf,Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition,2018 +265,MORPH Commercial,morph,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,cfdc4d0f8e1b4b9ced35317d12b4229f2e3311ab,citation,https://pdfs.semanticscholar.org/cfdc/4d0f8e1b4b9ced35317d12b4229f2e3311ab.pdf,Quaero at TRECVID 2010: Semantic Indexing,2010 +266,MORPH Commercial,morph,42.718568,-84.47791571,Michigan State University,edu,02d650d8a3a9daaba523433fbe93705df0a7f4b1,citation,http://pdfs.semanticscholar.org/02d6/50d8a3a9daaba523433fbe93705df0a7f4b1.pdf,How Does Aging Affect Facial Components?,2012 +267,MORPH Commercial,morph,34.67567405,33.04577648,Cyprus University of Technology,edu,70db3a0d2ca8a797153cc68506b8650908cb0ada,citation,http://pdfs.semanticscholar.org/70db/3a0d2ca8a797153cc68506b8650908cb0ada.pdf,An Overview of Research 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analysis of factors influencing the performance of facial age progression,2016 +272,MORPH Commercial,morph,-35.0636071,147.3552234,Charles Sturt University,edu,2e231f1e7e641dd3619bec59e14d02e91360ac01,citation,https://arxiv.org/pdf/1807.10421.pdf,Fusion Network for Face-Based Age Estimation,2018 +273,MORPH Commercial,morph,51.3791442,-2.3252332,University of Bath,edu,2e231f1e7e641dd3619bec59e14d02e91360ac01,citation,https://arxiv.org/pdf/1807.10421.pdf,Fusion Network for Face-Based Age Estimation,2018 +274,MORPH Commercial,morph,40.0044795,116.370238,Chinese Academy of Sciences,edu,56359d2b4508cc267d185c1d6d310a1c4c2cc8c2,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2015.7298618,Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition,2015 +275,MORPH Commercial,morph,39.9041999,116.4073963,Chinese Academy of Science,edu,56359d2b4508cc267d185c1d6d310a1c4c2cc8c2,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2015.7298618,Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition,2015 +276,MORPH Commercial,morph,1.2962018,103.77689944,National University of Singapore,edu,56359d2b4508cc267d185c1d6d310a1c4c2cc8c2,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2015.7298618,Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition,2015 +277,MORPH Commercial,morph,32.0565957,118.77408833,Nanjing University,edu,a6e43b73f9f87588783988333997a81b4487e2d5,citation,http://pdfs.semanticscholar.org/a6e4/3b73f9f87588783988333997a81b4487e2d5.pdf,Facial Age Estimation by Total Ordering Preserving Projection,2016 +278,MORPH Commercial,morph,1.2988926,103.7873107,"Institution for Infocomm Research, Singapore",edu,8229f2735a0db0ad41f4d7252129311f06959907,citation,https://doi.org/10.1109/TIP.2011.2106794,Active Learning for Solving the Incomplete Data Problem in Facial Age Classification by the Furthest Nearest-Neighbor Criterion,2011 +279,MORPH Commercial,morph,1.3484104,103.68297965,Nanyang Technological University,edu,8229f2735a0db0ad41f4d7252129311f06959907,citation,https://doi.org/10.1109/TIP.2011.2106794,Active Learning for Solving the Incomplete Data Problem in Facial Age Classification by the Furthest Nearest-Neighbor Criterion,2011 +280,MORPH Commercial,morph,39.2899685,-76.62196103,University of Maryland,edu,963a004e208ce4bd26fa79a570af61d31651b3c3,citation,https://doi.org/10.1016/j.jvlc.2009.01.011,Computational methods for modeling facial aging: A survey,2009 +281,MORPH Commercial,morph,40.48256135,-3.6906079,Universidad Autonoma de Madrid,edu,4b5ff8c67f3496a414f94e35cb35a601ec98e5cf,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6547306,Understanding the discrimination power of facial regions in forensic casework,2013 +282,MORPH Commercial,morph,40.4445565,-3.7122785,"Dirección General de la Guardia Civil, Madrid, Spain",edu,4b5ff8c67f3496a414f94e35cb35a601ec98e5cf,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6547306,Understanding the discrimination power of facial regions in forensic casework,2013 +283,MORPH Commercial,morph,-37.8087465,144.9638875,RMIT University,edu,c49075ead6eb07ede5ada4fe372899bd0cfb83ac,citation,https://doi.org/10.1109/ICSPCS.2015.7391782,Multi-stage classification network for automatic age estimation from facial images,2015 +284,MORPH Commercial,morph,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,00301c250d667700276b1e573640ff2fd7be574d,citation,https://doi.org/10.1109/BTAS.2014.6996242,Establishing a test set and initial comparisons for quantitatively evaluating synthetic age progression for adult aging,2014 diff --git a/site/datasets/final/morph_nc.csv b/site/datasets/final/morph_nc.csv new file mode 100644 index 00000000..6ff0320b --- 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China,edu,ddd0f1c53f76d7fc20e11b7e33bbdc0437516d2b,citation,https://doi.org/10.1109/ICDSP.2016.7868598,Deep learning-based learning to rank with ties for image re-ranking,2016 +4,MORPH Non-Commercial,morph_nc,25.0410728,121.6147562,Institute of Information Science,edu,4c71b0cdb6b80889b976e8eb4457942bd4dd7b66,citation,https://doi.org/10.1109/TIP.2014.2387379,A Learning Framework for Age Rank Estimation Based on Face Images With Scattering Transform,2015 +5,MORPH Non-Commercial,morph_nc,51.0267513,-1.3972576,"IBM Hursley Labs, UK",company,7123e510dea783035b02f6c35e35a1a09677c5ab,citation,https://doi.org/10.1109/ICPR.2016.7900297,Back to the future: A fully automatic method for robust age progression,2016 +6,MORPH Non-Commercial,morph_nc,35.9042272,-78.85565763,"IBM Research, North Carolina",company,7123e510dea783035b02f6c35e35a1a09677c5ab,citation,https://doi.org/10.1109/ICPR.2016.7900297,Back to the future: A fully automatic method for robust age progression,2016 +7,MORPH Non-Commercial,morph_nc,51.49887085,-0.17560797,Imperial College London,edu,7123e510dea783035b02f6c35e35a1a09677c5ab,citation,https://doi.org/10.1109/ICPR.2016.7900297,Back to the future: A fully automatic method for robust age progression,2016 +8,MORPH Non-Commercial,morph_nc,35.5167538,139.48342251,Tokyo Institute of Technology,edu,3083d2c6d4f456e01cbb72930dc2207af98a6244,citation,http://pdfs.semanticscholar.org/3083/d2c6d4f456e01cbb72930dc2207af98a6244.pdf,Perceived Age Estimation from Face Images,2011 +9,MORPH Non-Commercial,morph_nc,41.3868913,2.16352385,University of Barcelona,edu,500fbe18afd44312738cab91b4689c12b4e0eeee,citation,http://www.maia.ub.es/~sergio/linked/ijcnn_age_and_cultural_2015.pdf,ChaLearn looking at people 2015 new competitions: Age estimation and cultural event recognition,2015 +10,MORPH Non-Commercial,morph_nc,45.4312742,12.3265377,University of Venezia,edu,500fbe18afd44312738cab91b4689c12b4e0eeee,citation,http://www.maia.ub.es/~sergio/linked/ijcnn_age_and_cultural_2015.pdf,ChaLearn looking at people 2015 new competitions: Age estimation and cultural event recognition,2015 +11,MORPH Non-Commercial,morph_nc,41.10427915,29.02231159,Istanbul Technical University,edu,fd53be2e0a9f33080a9db4b5a5e416e24ae8e198,citation,https://arxiv.org/pdf/1606.02909.pdf,Apparent Age Estimation Using Ensemble of Deep Learning Models,2016 +12,MORPH Non-Commercial,morph_nc,40.6341322,-8.6599726,"University of Beira Interior, Portugal",edu,81c21f4aafab39b7f5965829ec9e0f828d6a6182,citation,https://doi.org/10.1109/BTAS.2015.7358744,Acquiring high-resolution face images in outdoor environments: A master-slave calibration algorithm,2015 +13,MORPH Non-Commercial,morph_nc,42.36782045,-71.12666653,Harvard University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 +14,MORPH Non-Commercial,morph_nc,40.9153196,-73.1270626,Stony Brook University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 +15,MORPH Non-Commercial,morph_nc,40.47913175,-74.43168868,Rutgers University,edu,31f1e711fcf82c855f27396f181bf5e565a2f58d,citation,http://doi.ieeecomputersociety.org/10.1109/ICCVW.2015.54,Unconstrained Age Estimation with Deep Convolutional Neural Networks,2015 +16,MORPH Non-Commercial,morph_nc,39.2899685,-76.62196103,University of Maryland,edu,31f1e711fcf82c855f27396f181bf5e565a2f58d,citation,http://doi.ieeecomputersociety.org/10.1109/ICCVW.2015.54,Unconstrained Age Estimation with Deep Convolutional Neural Networks,2015 +17,MORPH Non-Commercial,morph_nc,39.65404635,-79.96475355,West Virginia University,edu,af12a79892bd030c19dfea392f7a7ccb0e7ebb72,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6247972,A study on human age estimation under facial expression changes,2012 +18,MORPH Non-Commercial,morph_nc,23.09461185,113.28788994,Sun Yat-Sen University,edu,2d7c2c015053fff5300515a7addcd74b523f3f66,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8323422,Age-Related Factor Guided Joint Task Modeling Convolutional Neural Network for Cross-Age Face Recognition,2018 +19,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,b234d429c9ea682e54fca52f4b889b3170f65ffc,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2012.22,A Concatenational Graph Evolution Aging Model,2012 +20,MORPH Non-Commercial,morph_nc,39.9922379,116.30393816,Peking University,edu,b234d429c9ea682e54fca52f4b889b3170f65ffc,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2012.22,A Concatenational Graph Evolution Aging Model,2012 +21,MORPH Non-Commercial,morph_nc,40.00229045,116.32098908,Tsinghua University,edu,b234d429c9ea682e54fca52f4b889b3170f65ffc,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2012.22,A Concatenational Graph Evolution Aging Model,2012 +22,MORPH Non-Commercial,morph_nc,30.19331415,120.11930822,Zhejiang University,edu,ee65cee5151928c63d3ef36fcbb582fabb2b6d2c,citation,https://doi.org/10.1109/LSP.2016.2602538,Structure-Aware Slow Feature Analysis for Age Estimation,2016 +23,MORPH Non-Commercial,morph_nc,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,ee65cee5151928c63d3ef36fcbb582fabb2b6d2c,citation,https://doi.org/10.1109/LSP.2016.2602538,Structure-Aware Slow Feature Analysis for Age Estimation,2016 +24,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,288964068cd87d97a98b8bc927d6e0d2349458a2,citation,https://pdfs.semanticscholar.org/2889/64068cd87d97a98b8bc927d6e0d2349458a2.pdf,Mean-Variance Loss for Deep Age Estimation from a Face,0 +25,MORPH Non-Commercial,morph_nc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,288964068cd87d97a98b8bc927d6e0d2349458a2,citation,https://pdfs.semanticscholar.org/2889/64068cd87d97a98b8bc927d6e0d2349458a2.pdf,Mean-Variance Loss for Deep Age Estimation from a Face,0 +26,MORPH Non-Commercial,morph_nc,39.65404635,-79.96475355,West Virginia University,edu,cd63759842a56bd2ede3999f6e11a74ccbec318b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995404,Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression,2011 +27,MORPH Non-Commercial,morph_nc,28.5456282,77.2731505,"IIIT Delhi, India",edu,ffc81ced9ee8223ab0adb18817321cbee99606e6,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791157,A multibiometrics-based CAPTCHA for improved online security,2016 +28,MORPH Non-Commercial,morph_nc,39.65404635,-79.96475355,West Virginia University,edu,ffc81ced9ee8223ab0adb18817321cbee99606e6,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791157,A multibiometrics-based CAPTCHA for improved online security,2016 +29,MORPH Non-Commercial,morph_nc,41.25713055,-72.9896696,Yale University,edu,df7312cbabb7d75d915ba0d91dea77100ded5c56,citation,https://arxiv.org/pdf/1811.06446.pdf,Preliminary Studies on a Large Face Database,2018 +30,MORPH Non-Commercial,morph_nc,29.6328784,-82.3490133,University of Florida,edu,df7312cbabb7d75d915ba0d91dea77100ded5c56,citation,https://arxiv.org/pdf/1811.06446.pdf,Preliminary Studies on a Large Face Database,2018 +31,MORPH Non-Commercial,morph_nc,31.83907195,117.26420748,University of Science and Technology of China,edu,56c700693b63e3da3b985777da6d9256e2e0dc21,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1A_079.pdf,Global refinement of random forest,2015 +32,MORPH Non-Commercial,morph_nc,40.00229045,116.32098908,Tsinghua University,edu,1e344b99583b782e3eaf152cdfa15f217b781181,citation,http://doi.acm.org/10.1145/2499788.2499789,A new biologically inspired active appearance model for face age estimation by using local ordinal ranking,2013 +33,MORPH Non-Commercial,morph_nc,39.94976005,116.33629046,Beijing Jiaotong University,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017 +34,MORPH Non-Commercial,morph_nc,43.1576969,-77.58829158,University of Rochester,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017 +35,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017 +36,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,bd8b7599acf53e3053aa27cfd522764e28474e57,citation,http://www.jdl.ac.cn/doc/2009/iccv09_Learning%20Long%20Term%20Face%20Aging%20Patterns%20from%20Partially%20Dense%20Aging%20Databases.pdf,Learning long term face aging patterns from partially dense aging databases,2009 +37,MORPH Non-Commercial,morph_nc,39.9922379,116.30393816,Peking University,edu,bd8b7599acf53e3053aa27cfd522764e28474e57,citation,http://www.jdl.ac.cn/doc/2009/iccv09_Learning%20Long%20Term%20Face%20Aging%20Patterns%20from%20Partially%20Dense%20Aging%20Databases.pdf,Learning long term face aging patterns from partially dense aging databases,2009 +38,MORPH Non-Commercial,morph_nc,43.614386,7.071125,EURECOM,edu,70569810e46f476515fce80a602a210f8d9a2b95,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.105,Apparent Age Estimation from Face Images Combining General and Children-Specialized Deep Learning Models,2016 +39,MORPH Non-Commercial,morph_nc,39.9213097,32.7988233,"TOBB Economy and Technology University, Ankara, Turkey",edu,cc1ed45b02d7fffb42a0fd8cffe5f11792b6ea74,citation,https://doi.org/10.1109/SIU.2016.7495874,Analysis of the effect of image resolution on automatic face gender and age classification,2016 +40,MORPH Non-Commercial,morph_nc,-33.91758275,151.23124025,University of New South Wales,edu,29631ca6cff21c9199c70bcdbbcd5f812d331a96,citation,http://pdfs.semanticscholar.org/2963/1ca6cff21c9199c70bcdbbcd5f812d331a96.pdf,Error Rates in Users of Automatic Face Recognition Software,2015 +41,MORPH Non-Commercial,morph_nc,-33.88890695,151.18943366,University of Sydney,edu,29631ca6cff21c9199c70bcdbbcd5f812d331a96,citation,http://pdfs.semanticscholar.org/2963/1ca6cff21c9199c70bcdbbcd5f812d331a96.pdf,Error Rates in Users of Automatic Face Recognition Software,2015 +42,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,1a53ca294bbe5923c46a339955e8207907e9c8c6,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7273870,What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics,2016 +43,MORPH Non-Commercial,morph_nc,43.614386,7.071125,EURECOM,edu,1a53ca294bbe5923c46a339955e8207907e9c8c6,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7273870,What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics,2016 +44,MORPH Non-Commercial,morph_nc,40.4319722,-86.92389368,Purdue University,edu,c7c53d75f6e963b403057d8ba5952e4974a779ad,citation,https://pdfs.semanticscholar.org/c7c5/3d75f6e963b403057d8ba5952e4974a779ad.pdf,Aging effects in automated face recognition,2018 +45,MORPH Non-Commercial,morph_nc,41.02451875,28.97697953,Bahçeşehir University,edu,0c2370e156a4eb8d84a5fdb049c5a894c3431f1c,citation,https://doi.org/10.1109/CIBIM.2014.7015437,Biometric template update under facial aging,2014 +46,MORPH Non-Commercial,morph_nc,53.22853665,-0.54873472,University of Lincoln,edu,0c2370e156a4eb8d84a5fdb049c5a894c3431f1c,citation,https://doi.org/10.1109/CIBIM.2014.7015437,Biometric template update under facial aging,2014 +47,MORPH Non-Commercial,morph_nc,46.0810723,13.2119474,University of Udine,edu,0c2370e156a4eb8d84a5fdb049c5a894c3431f1c,citation,https://doi.org/10.1109/CIBIM.2014.7015437,Biometric template update under facial aging,2014 +48,MORPH Non-Commercial,morph_nc,25.0410728,121.6147562,Institute of Information Science,edu,1c17450c4d616e1e1eece248c42eba4f87de9e0d,citation,http://pdfs.semanticscholar.org/d269/39a00a8d3964de612cd3faa86764343d5622.pdf,Automatic Age Estimation from Face Images via Deep Ranking,2015 +49,MORPH Non-Commercial,morph_nc,43.47061295,-80.54724732,University of Waterloo,edu,f2902f5956d7e2dca536d9131d4334f85f52f783,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6460191,Facial age estimation using Clustered Multi-task Support Vector Regression Machine,2012 +50,MORPH Non-Commercial,morph_nc,39.65404635,-79.96475355,West Virginia University,edu,ba2bbef34f05551291410103e3de9e82fdf9dddd,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Guo_A_Study_on_2014_CVPR_paper.pdf,A Study on Cross-Population Age Estimation,2014 +51,MORPH Non-Commercial,morph_nc,31.32235655,121.38400941,Shanghai University,edu,d454ad60b061c1a1450810a0f335fafbfeceeccc,citation,https://arxiv.org/pdf/1712.07195.pdf,Deep Regression Forests for Age Estimation,2017 +52,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,ad2cb5c255e555d9767d526721a4c7053fa2ac58,citation,https://arxiv.org/pdf/1711.03990.pdf,Longitudinal Study of Child Face Recognition,2018 +53,MORPH Non-Commercial,morph_nc,39.95472495,-75.15346905,Temple University,edu,0cf2eecf20cfbcb7f153713479e3206670ea0e9c,citation,https://arxiv.org/pdf/1806.08906.pdf,Privacy-Protective-GAN for Face De-identification,2018 +54,MORPH Non-Commercial,morph_nc,31.32235655,121.38400941,Shanghai University,edu,c0b02be66a5a1907e8cfb8117de50f80b90a65a8,citation,http://doi.acm.org/10.1145/2808492.2808523,Manifold learning in sparse selected feature subspaces,2015 +55,MORPH Non-Commercial,morph_nc,47.6423318,-122.1369302,Microsoft,company,ff012c56b9b1de969328dacd13e26b7138ff298b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7762921,Facial Age Estimation With Age Difference,2017 +56,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,ff012c56b9b1de969328dacd13e26b7138ff298b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7762921,Facial Age Estimation With Age Difference,2017 +57,MORPH Non-Commercial,morph_nc,31.846918,117.29053367,Hefei University of Technology,edu,ff012c56b9b1de969328dacd13e26b7138ff298b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7762921,Facial Age Estimation With Age Difference,2017 +58,MORPH Non-Commercial,morph_nc,1.3484104,103.68297965,Nanyang Technological University,edu,ff012c56b9b1de969328dacd13e26b7138ff298b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7762921,Facial Age Estimation With Age Difference,2017 +59,MORPH Non-Commercial,morph_nc,40.00229045,116.32098908,Tsinghua University,edu,2149d49c84a83848d6051867290d9c8bfcef0edb,citation,https://doi.org/10.1109/TIFS.2017.2746062,Label-Sensitive Deep Metric Learning for Facial Age Estimation,2018 +60,MORPH Non-Commercial,morph_nc,25.0410728,121.6147562,Institute of Information Science,edu,c44c84540db1c38ace232ef34b03bda1c81ba039,citation,http://pdfs.semanticscholar.org/c44c/84540db1c38ace232ef34b03bda1c81ba039.pdf,Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval,2014 +61,MORPH Non-Commercial,morph_nc,25.01682835,121.53846924,National Taiwan University,edu,c44c84540db1c38ace232ef34b03bda1c81ba039,citation,http://pdfs.semanticscholar.org/c44c/84540db1c38ace232ef34b03bda1c81ba039.pdf,Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval,2014 +62,MORPH Non-Commercial,morph_nc,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018 +63,MORPH Non-Commercial,morph_nc,39.2899685,-76.62196103,University of Maryland,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018 +64,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,2f2406551c693d616a840719ae1e6ea448e2f5d3,citation,http://biometrics.cse.msu.edu/Presentations/CharlesOtto_ICB13_AgeEstimationFaceImages_HumanVsMachinePerformance.pdf,Age estimation from face images: Human vs. machine performance,2013 +65,MORPH Non-Commercial,morph_nc,1.3037257,103.7737763,"Advanced Digital Sciences Center, Singapore",edu,15fbb5fc3bdd692a6b2dd737cce7f39f7c89a25c,citation,https://doi.org/10.1109/TMM.2011.2167317,Web Image and Video Mining Towards Universal and Robust Age Estimator,2011 +66,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,15fbb5fc3bdd692a6b2dd737cce7f39f7c89a25c,citation,https://doi.org/10.1109/TMM.2011.2167317,Web Image and Video Mining Towards Universal and Robust Age Estimator,2011 +67,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,b446bcd7fb78adfe346cf7a01a38e4f43760f363,citation,http://pdfs.semanticscholar.org/b446/bcd7fb78adfe346cf7a01a38e4f43760f363.pdf,To appear in ICB 2018 Longitudinal Study of Child Face Recognition,2017 +68,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,c035c193eed5d72c7f187f0bc880a17d217dada0,citation,http://pdfs.semanticscholar.org/c035/c193eed5d72c7f187f0bc880a17d217dada0.pdf,"Local Gradient Gabor Pattern (LGGP) with Applications in Face Recognition, Cross-spectral Matching and Soft Biometrics",2013 +69,MORPH Non-Commercial,morph_nc,39.65404635,-79.96475355,West Virginia University,edu,c035c193eed5d72c7f187f0bc880a17d217dada0,citation,http://pdfs.semanticscholar.org/c035/c193eed5d72c7f187f0bc880a17d217dada0.pdf,"Local Gradient Gabor Pattern (LGGP) with Applications in Face Recognition, Cross-spectral Matching and Soft Biometrics",2013 +70,MORPH Non-Commercial,morph_nc,34.66869155,-82.83743476,Clemson University,edu,c907104680ad53bdc673f2648d713e4d26335825,citation,http://doi.acm.org/10.1145/3077286.3077304,Dataset and Metrics for Adult Age-Progression Evaluation,2017 +71,MORPH Non-Commercial,morph_nc,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,c907104680ad53bdc673f2648d713e4d26335825,citation,http://doi.acm.org/10.1145/3077286.3077304,Dataset and Metrics for Adult Age-Progression Evaluation,2017 +72,MORPH Non-Commercial,morph_nc,37.5600406,126.9369248,Yonsei University,edu,fde41dc4ec6ac6474194b99e05b43dd6a6c4f06f,citation,https://arxiv.org/pdf/1809.01990.pdf,Multi-Expert Gender Classification on Age Group by Integrating Deep Neural Networks,2018 +73,MORPH Non-Commercial,morph_nc,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,31a36014354ee7c89aa6d94e656db77922b180a5,citation,http://doi.acm.org/10.1145/2304496.2304509,An interactive tool for extremely dense landmarking of faces,2012 +74,MORPH Non-Commercial,morph_nc,37.5901411,127.0362318,Korea University,edu,4b519e2e88ccd45718b0fc65bfd82ebe103902f7,citation,http://biometrics.cse.msu.edu/Publications/Face/LiParkJain_DiscriminativeModelAgeInvariantFR_TIFS11.pdf,A Discriminative Model for Age Invariant Face Recognition,2011 +75,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,4b519e2e88ccd45718b0fc65bfd82ebe103902f7,citation,http://biometrics.cse.msu.edu/Publications/Face/LiParkJain_DiscriminativeModelAgeInvariantFR_TIFS11.pdf,A 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USA",company,604a281100784b4d5bc1a6db993d423abc5dc8f0,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5353681,Face Verification Across Age Progression Using Discriminative Methods,2010 +80,MORPH Non-Commercial,morph_nc,39.2899685,-76.62196103,University of Maryland,edu,604a281100784b4d5bc1a6db993d423abc5dc8f0,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5353681,Face Verification Across Age Progression Using Discriminative Methods,2010 +81,MORPH Non-Commercial,morph_nc,39.95472495,-75.15346905,Temple University,edu,604a281100784b4d5bc1a6db993d423abc5dc8f0,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5353681,Face Verification Across Age Progression Using Discriminative Methods,2010 +82,MORPH Non-Commercial,morph_nc,51.2975344,1.07296165,University of Kent,edu,6486b36c6f7fd7675257d26e896223a02a1881d9,citation,https://doi.org/10.1109/THMS.2014.2376874,Selective Review and Analysis of Aging Effects in Biometric System Implementation,2015 +83,MORPH Non-Commercial,morph_nc,22.42031295,114.20788644,Chinese University of Hong Kong,edu,16bce9f940bb01aa5ec961892cc021d4664eb9e4,citation,http://www.cise.ufl.edu/~dihong/assets/TIST-2014-10-0214.R2.pdf,Mutual Component Analysis for Heterogeneous Face Recognition,2016 +84,MORPH Non-Commercial,morph_nc,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,16bce9f940bb01aa5ec961892cc021d4664eb9e4,citation,http://www.cise.ufl.edu/~dihong/assets/TIST-2014-10-0214.R2.pdf,Mutual Component Analysis for Heterogeneous Face Recognition,2016 +85,MORPH Non-Commercial,morph_nc,34.67567405,33.04577648,Cyprus University of Technology,edu,9d3aa3b7d392fad596b067b13b9e42443bbc377c,citation,http://pdfs.semanticscholar.org/9d3a/a3b7d392fad596b067b13b9e42443bbc377c.pdf,Facial Biometric Templates and Aging: Problems and Challenges for Artificial Intelligence,2009 +86,MORPH Non-Commercial,morph_nc,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,217a21d60bb777d15cd9328970cab563d70b5d23,citation,http://www.cise.ufl.edu/~dihong/assets/iccv2013.pdf,Hidden Factor Analysis for Age Invariant Face Recognition,2013 +87,MORPH Non-Commercial,morph_nc,22.42031295,114.20788644,Chinese University of Hong Kong,edu,217a21d60bb777d15cd9328970cab563d70b5d23,citation,http://www.cise.ufl.edu/~dihong/assets/iccv2013.pdf,Hidden Factor Analysis for Age Invariant Face Recognition,2013 +88,MORPH Non-Commercial,morph_nc,32.0565957,118.77408833,Nanjing University,edu,b1bb517bd87a1212174033fc786b2237844b04e6,citation,https://doi.org/10.1016/j.neucom.2015.03.078,Cumulative attribute relation regularization learning for human age estimation,2015 +89,MORPH Non-Commercial,morph_nc,40.8419836,-73.94368971,Columbia University,edu,a0dc68c546e0fc72eb0d9ca822cf0c9ccb4b4c4f,citation,http://www.cs.columbia.edu/~neeraj/base/papers/nk_ijcb2011_fusion.pdf,Fusing with context: A Bayesian approach to combining descriptive attributes,2011 +90,MORPH Non-Commercial,morph_nc,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,a0dc68c546e0fc72eb0d9ca822cf0c9ccb4b4c4f,citation,http://www.cs.columbia.edu/~neeraj/base/papers/nk_ijcb2011_fusion.pdf,Fusing with context: A Bayesian approach to combining descriptive attributes,2011 +91,MORPH Non-Commercial,morph_nc,1.3484104,103.68297965,Nanyang Technological University,edu,d119443de1d75cad384d897c2ed5a7b9c1661d98,citation,https://doi.org/10.1109/ICIP.2010.5650873,Cost-sensitive subspace learning for human age estimation,2010 +92,MORPH Non-Commercial,morph_nc,34.2249827,-77.86907744,University of North Carolina at Wilmington,edu,97c59db934ff85c60c460a4591106682b5ab9caa,citation,https://doi.org/10.1109/BTAS.2012.6374568,Extremely dense face registration: Comparing automatic landmarking algorithms for general and ethno-gender models,2012 +93,MORPH Non-Commercial,morph_nc,43.2213516,-75.4085577,"Air Force Research Lab, Rome, 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University,edu,2050847bc7a1a0453891f03aeeb4643e360fde7d,citation,https://cvhci.anthropomatik.kit.edu/~mtapaswi/papers/ICMR2015.pdf,Accio: A Data Set for Face Track Retrieval in Movies Across Age,2015 +129,MORPH Non-Commercial,morph_nc,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,2050847bc7a1a0453891f03aeeb4643e360fde7d,citation,https://cvhci.anthropomatik.kit.edu/~mtapaswi/papers/ICMR2015.pdf,Accio: A Data Set for Face Track Retrieval in Movies Across Age,2015 +130,MORPH Non-Commercial,morph_nc,40.62984145,22.9588935,Aristotle University of Thessaloniki,edu,3cc46bf79fb9225cf308815c7d41c8dd5625cc29,citation,http://poseidon.csd.auth.gr/papers/PUBLISHED/CONFERENCE/pdf/2016/Pantraki2016.pdf,Age interval and gender prediction using PARAFAC2 applied to speech utterances,2016 +131,MORPH Non-Commercial,morph_nc,34.67567405,33.04577648,Cyprus University of Technology,edu,3cc46bf79fb9225cf308815c7d41c8dd5625cc29,citation,http://poseidon.csd.auth.gr/papers/PUBLISHED/CONFERENCE/pdf/2016/Pantraki2016.pdf,Age interval and gender prediction using PARAFAC2 applied to speech utterances,2016 +132,MORPH Non-Commercial,morph_nc,23.09461185,113.28788994,Sun Yat-Sen University,edu,189e5a2fa51ed471c0e7227d82dffb52736070d8,citation,https://doi.org/10.1109/ICIP.2017.8296995,Cross-age face recognition using reference coding with kernel direct discriminant analysis,2017 +133,MORPH Non-Commercial,morph_nc,42.357757,-83.06286711,Wayne State University,edu,4f1249369127cc2e2894f6b2f1052d399794919a,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8239663,Deep Age Estimation: From Classification to Ranking,2018 +134,MORPH Non-Commercial,morph_nc,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,cd6aaa37fffd0b5c2320f386be322b8adaa1cc68,citation,https://arxiv.org/pdf/1804.06655.pdf,Deep Face Recognition: A Survey,2018 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information for face recognition using a multi-expert system,2016 +145,MORPH Non-Commercial,morph_nc,41.9037626,12.5144384,Sapienza University of Rome,edu,62750d78e819d745b9200b0c5c35fcae6fb9f404,citation,http://doi.org/10.1007/s11042-016-4085-8,Leveraging implicit demographic information for face recognition using a multi-expert system,2016 +146,MORPH Non-Commercial,morph_nc,40.845492,14.2578058,University of Naples Federico II,edu,62750d78e819d745b9200b0c5c35fcae6fb9f404,citation,http://doi.org/10.1007/s11042-016-4085-8,Leveraging implicit demographic information for face recognition using a multi-expert system,2016 +147,MORPH Non-Commercial,morph_nc,25.01353105,121.54173736,National Taiwan University of Science and Technology,edu,e4c3587392d477b7594086c6f28a00a826abf004,citation,https://doi.org/10.1109/ICIP.2017.8296998,Face recognition by facial attribute assisted network,2017 +148,MORPH Non-Commercial,morph_nc,39.9922379,116.30393816,Peking University,edu,c4ca092972abb74ee1c20b7cae6e69c654479e2c,citation,https://doi.org/10.1109/ICIP.2016.7532960,Linear canonical correlation analysis based ranking approach for facial age estimation,2016 +149,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,575141e42740564f64d9be8ab88d495192f5b3bc,citation,http://pdfs.semanticscholar.org/5751/41e42740564f64d9be8ab88d495192f5b3bc.pdf,Age Estimation Based on Multi-Region Convolutional Neural Network,2016 +150,MORPH Non-Commercial,morph_nc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,575141e42740564f64d9be8ab88d495192f5b3bc,citation,http://pdfs.semanticscholar.org/5751/41e42740564f64d9be8ab88d495192f5b3bc.pdf,Age Estimation Based on Multi-Region Convolutional Neural Network,2016 +151,MORPH Non-Commercial,morph_nc,56.66340325,12.87929727,Halmstad University,edu,555f75077a02f33a05841f9b63a1388ec5fbcba5,citation,https://arxiv.org/pdf/1810.03360.pdf,A Survey on Periocular Biometrics 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Korea",edu,abbc6dcbd032ff80e0535850f1bc27c4610b0d45,citation,https://doi.org/10.1109/ICIP.2015.7350983,Facial age estimation via extended curvature Gabor filter,2015 +159,MORPH Non-Commercial,morph_nc,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,abbc6dcbd032ff80e0535850f1bc27c4610b0d45,citation,https://doi.org/10.1109/ICIP.2015.7350983,Facial age estimation via extended curvature Gabor filter,2015 +160,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,989332c5f1b22604d6bb1f78e606cb6b1f694e1a,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Wang_Recurrent_Face_Aging_CVPR_2016_paper.pdf,Recurrent Face Aging,2016 +161,MORPH Non-Commercial,morph_nc,32.0575279,118.78682252,Southeast University,edu,989332c5f1b22604d6bb1f78e606cb6b1f694e1a,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Wang_Recurrent_Face_Aging_CVPR_2016_paper.pdf,Recurrent Face Aging,2016 +162,MORPH 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Lausanne",edu,6c6f0e806e4e286f3b18b934f42c72b67030ce17,citation,https://doi.org/10.1109/FG.2011.5771345,Combination of age and head pose for adult face verification,2011 +166,MORPH Non-Commercial,morph_nc,52.6221571,1.2409136,University of East Anglia,edu,05a0d04693b2a51a8131d195c68ad9f5818b2ce1,citation,http://pdfs.semanticscholar.org/05a0/d04693b2a51a8131d195c68ad9f5818b2ce1.pdf,Dual-reference Face Retrieval: What Does He/She Look Like at Age 'X'?,2017 +167,MORPH Non-Commercial,morph_nc,40.44415295,-79.96243993,University of Pittsburgh,edu,05a0d04693b2a51a8131d195c68ad9f5818b2ce1,citation,http://pdfs.semanticscholar.org/05a0/d04693b2a51a8131d195c68ad9f5818b2ce1.pdf,Dual-reference Face Retrieval: What Does He/She Look Like at Age 'X'?,2017 +168,MORPH Non-Commercial,morph_nc,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,387b54cf6c186c12d83f95df6bd458c5eb1254ee,citation,https://doi.org/10.1109/VCIP.2017.8305123,Deep probabilities for age estimation,2017 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Norway",edu,0647c9d56cf11215894d57d677997826b22f6a13,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8401557,Transgender face recognition with off-the-shelf pre-trained CNNs: A comprehensive study,2018 +173,MORPH Non-Commercial,morph_nc,52.3553655,4.9501644,University of Amsterdam,edu,935a7793cbb8f102924fa34fce1049727de865c2,citation,https://doi.org/10.1109/ICIP.2015.7351554,Age estimation under changes in image quality: An experimental study,2015 +174,MORPH Non-Commercial,morph_nc,40.01407945,-105.26695944,"University of Colorado, Boulder",edu,4aabd6db4594212019c9af89b3e66f39f3108aac,citation,http://pdfs.semanticscholar.org/4aab/d6db4594212019c9af89b3e66f39f3108aac.pdf,The Mere Exposure Effect and Classical Conditioning,2015 +175,MORPH Non-Commercial,morph_nc,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,73d15a975b0595e0cc2e0981a9396a89c474dc7e,citation,https://arxiv.org/pdf/1811.03680.pdf,Gender Effect on Face Recognition for a Large Longitudinal 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Singapore,edu,8cffe360a05085d4bcba111a3a3cd113d96c0369,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2011.6126248,Learning universal multi-view age estimator using video context,2011 +180,MORPH Non-Commercial,morph_nc,23.143197,113.34009651,South China Normal University,edu,dc6ad30c7a4bc79bb06b4725b16e202d3d7d8935,citation,http://doi.org/10.1007/s11042-017-4646-5,Age classification with deep learning face representation,2017 +181,MORPH Non-Commercial,morph_nc,23.0502042,113.39880323,South China University of Technology,edu,dc6ad30c7a4bc79bb06b4725b16e202d3d7d8935,citation,http://doi.org/10.1007/s11042-017-4646-5,Age classification with deep learning face representation,2017 +182,MORPH Non-Commercial,morph_nc,50.0764296,14.41802312,Czech Technical University,edu,023ed32ac3ea6029f09b8c582efbe3866de7d00a,citation,http://pdfs.semanticscholar.org/023e/d32ac3ea6029f09b8c582efbe3866de7d00a.pdf,Discriminative learning from partially annotated examples,2016 +183,MORPH 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University,edu,e22adcd2a6a7544f017ec875ce8f89d5c59e09c8,citation,https://arxiv.org/pdf/1807.11936.pdf,Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers,2018 +191,MORPH Non-Commercial,morph_nc,25.01682835,121.53846924,National Taiwan University,edu,6ab33fa51467595f18a7a22f1d356323876f8262,citation,http://www.iis.sinica.edu.tw/~kuangyu/OHRank_files/0523.pdf,Ordinal hyperplanes ranker with cost sensitivities for age estimation,2011 +192,MORPH Non-Commercial,morph_nc,25.0410728,121.6147562,Institute of Information Science,edu,6ab33fa51467595f18a7a22f1d356323876f8262,citation,http://www.iis.sinica.edu.tw/~kuangyu/OHRank_files/0523.pdf,Ordinal hyperplanes ranker with cost sensitivities for age estimation,2011 +193,MORPH Non-Commercial,morph_nc,25.0411727,121.6146518,"Academia Sinica, Taiwan",edu,6ab33fa51467595f18a7a22f1d356323876f8262,citation,http://www.iis.sinica.edu.tw/~kuangyu/OHRank_files/0523.pdf,Ordinal hyperplanes ranker with cost 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University,edu,fcb97ede372c5bddde7a61924ac2fd29788c82ce,citation,https://doi.org/10.1109/TSMCC.2012.2192727,Ordinary Preserving Manifold Analysis for Human Age and Head Pose Estimation,2013 +198,MORPH Non-Commercial,morph_nc,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,cb27b45329d61f5f95ed213798d4b2a615e76be2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8329236,Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion,2018 +199,MORPH Non-Commercial,morph_nc,37.2520226,127.0555019,"Samsung SAIT, Korea",company,cb27b45329d61f5f95ed213798d4b2a615e76be2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8329236,Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion,2018 +200,MORPH Non-Commercial,morph_nc,35.14479945,33.90492318,Eastern Mediterranean University,edu,c5421a18583f629b49ca20577022f201692c4f5d,citation,http://pdfs.semanticscholar.org/c542/1a18583f629b49ca20577022f201692c4f5d.pdf,Facial Age Classification using Subpattern-based Approaches,2011 +201,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,68c4a1d438ea1c6dfba92e3aee08d48f8e7f7090,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Liu_AgeNet_Deeply_Learned_ICCV_2015_paper.pdf,AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation,2015 +202,MORPH Non-Commercial,morph_nc,31.32235655,121.38400941,Shanghai University,edu,5f0d4a0b5f72d8700cdf8cb179263a8fa866b59b,citation,https://pdfs.semanticscholar.org/5f0d/4a0b5f72d8700cdf8cb179263a8fa866b59b.pdf,Memo No . 85 06 / 2018 Deep Regression Forests for Age Estimation,2018 +203,MORPH Non-Commercial,morph_nc,24.96841805,121.19139696,National Central University,edu,c58ece1a3fa23608f022e424ec5a93cddda31308,citation,https://doi.org/10.1109/JSYST.2014.2325957,Extraction of Visual Facial Features for Health Management,2016 +204,MORPH Non-Commercial,morph_nc,50.0764296,14.41802312,Czech Technical University,edu,56e25358ebfaf8a8b3c7c33ed007e24f026065d0,citation,https://doi.org/10.1007/s10994-015-5541-9,V-shaped interval insensitive loss for ordinal classification,2015 +205,MORPH Non-Commercial,morph_nc,5.7648848,102.6281702,"University Sultan Zainal Abidin, Malaysia",edu,3337cfc3de2c16dee6f7cbeda5f263409a9ad81e,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8398675,Age prediction on face features via multiple classifiers,2018 +206,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,2836d68c86f29bb87537ea6066d508fde838ad71,citation,http://arxiv.org/pdf/1510.06503v1.pdf,Personalized Age Progression with Aging Dictionary,2015 +207,MORPH Non-Commercial,morph_nc,32.0565957,118.77408833,Nanjing University,edu,2836d68c86f29bb87537ea6066d508fde838ad71,citation,http://arxiv.org/pdf/1510.06503v1.pdf,Personalized Age Progression with Aging Dictionary,2015 +208,MORPH Non-Commercial,morph_nc,22.42031295,114.20788644,Chinese University of Hong Kong,edu,55966926e7c28b1eee1c7eb7a0b11b10605a1af0,citation,http://pdfs.semanticscholar.org/baa8/bdeb5aa545af5b5f43efaf9dda08490da0bc.pdf,Surpassing Human-Level Face Verification Performance on LFW with GaussianFace,2015 +209,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,d492dbfaa42b4f8b8a74786d7343b3be6a3e9a1d,citation,https://pdfs.semanticscholar.org/d492/dbfaa42b4f8b8a74786d7343b3be6a3e9a1d.pdf,Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation,0 +210,MORPH Non-Commercial,morph_nc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,d492dbfaa42b4f8b8a74786d7343b3be6a3e9a1d,citation,https://pdfs.semanticscholar.org/d492/dbfaa42b4f8b8a74786d7343b3be6a3e9a1d.pdf,Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation,0 +211,MORPH Non-Commercial,morph_nc,34.67567405,33.04577648,Cyprus University of Technology,edu,fa518a033b1f6299d1826389bd1520cf52291b56,citation,https://pdfs.semanticscholar.org/fa51/8a033b1f6299d1826389bd1520cf52291b56.pdf,Facial Age Simulation using Age-specific 3D Models and Recursive PCA,2013 +212,MORPH Non-Commercial,morph_nc,38.83133325,-77.30798839,George Mason University,edu,1c147261f5ab1b8ee0a54021a3168fa191096df8,citation,http://pdfs.semanticscholar.org/1c14/7261f5ab1b8ee0a54021a3168fa191096df8.pdf,Face Recognition across Time Lapse Using Convolutional Neural Networks,2016 +213,MORPH Non-Commercial,morph_nc,32.05765485,118.7550004,HoHai University,edu,b84b7b035c574727e4c30889e973423fe15560d7,citation,http://pdfs.semanticscholar.org/b84b/7b035c574727e4c30889e973423fe15560d7.pdf,Human Age Estimation Using Ranking SVM,2012 +214,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,b84b7b035c574727e4c30889e973423fe15560d7,citation,http://pdfs.semanticscholar.org/b84b/7b035c574727e4c30889e973423fe15560d7.pdf,Human Age Estimation Using Ranking SVM,2012 +215,MORPH Non-Commercial,morph_nc,39.6810328,-75.7540184,University of Delaware,edu,19da9f3532c2e525bf92668198b8afec14f9efea,citation,http://pdfs.semanticscholar.org/19da/9f3532c2e525bf92668198b8afec14f9efea.pdf,Challenge: Face verification across age progression using real-world data,2011 +216,MORPH Non-Commercial,morph_nc,39.95472495,-75.15346905,Temple University,edu,f24e379e942e134d41c4acec444ecf02b9d0d3a9,citation,http://pdfs.semanticscholar.org/f24e/379e942e134d41c4acec444ecf02b9d0d3a9.pdf,Analysis of Facial Images across Age Progression 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in score-age space using single reference image template,2010 +221,MORPH Non-Commercial,morph_nc,38.88140235,121.52281098,Dalian University of Technology,edu,ed70d1a9435c0b32c0c75c1a062f4f07556f7016,citation,https://doi.org/10.1109/ICIP.2015.7350774,Correlated warped Gaussian processes for gender-specific age estimation,2015 +222,MORPH Non-Commercial,morph_nc,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,ed70d1a9435c0b32c0c75c1a062f4f07556f7016,citation,https://doi.org/10.1109/ICIP.2015.7350774,Correlated warped Gaussian processes for gender-specific age estimation,2015 +223,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,0e5557a0cc58194ad53fab5dd6f4d4195d19ce4e,citation,https://doi.org/10.1109/TMM.2015.2500730,Deep Aging Face Verification With Large Gaps,2016 +224,MORPH Non-Commercial,morph_nc,51.52344665,-0.25973535,"North Acton, London",edu,0e5557a0cc58194ad53fab5dd6f4d4195d19ce4e,citation,https://doi.org/10.1109/TMM.2015.2500730,Deep Aging Face Verification With Large Gaps,2016 +225,MORPH Non-Commercial,morph_nc,31.846918,117.29053367,Hefei University of Technology,edu,0e5557a0cc58194ad53fab5dd6f4d4195d19ce4e,citation,https://doi.org/10.1109/TMM.2015.2500730,Deep Aging Face Verification With Large Gaps,2016 +226,MORPH Non-Commercial,morph_nc,29.58333105,-98.61944505,University of Texas at San Antonio,edu,f2896dd2701fbb3564492a12c64f11a5ad456a67,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5495414,Cross-database age estimation based on transfer learning,2010 +227,MORPH Non-Commercial,morph_nc,34.1235825,108.83546,Xidian University,edu,f2896dd2701fbb3564492a12c64f11a5ad456a67,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5495414,Cross-database age estimation based on transfer learning,2010 +228,MORPH Non-Commercial,morph_nc,56.66340325,12.87929727,Halmstad University,edu,9cda3e56cec21bd8f91f7acfcefc04ac10973966,citation,https://doi.org/10.1109/IWBF.2016.7449688,"Periocular biometrics: databases, algorithms and directions",2016 +229,MORPH Non-Commercial,morph_nc,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,13aef395f426ca8bd93640c9c3f848398b189874,citation,https://pdfs.semanticscholar.org/13ae/f395f426ca8bd93640c9c3f848398b189874.pdf,1 Image Preprocessing and Complete 2 DPCA with Feature Extraction for Gender Recognition NSF REU 2017 : Statistical Learning and Data Mining,2017 +230,MORPH Non-Commercial,morph_nc,24.7925484,120.9951183,National Tsing Hua University,edu,cfa40560fa74b2fb5c26bdd6ea7c610ba5130e2f,citation,https://doi.org/10.1109/TIFS.2013.2286265,Subspace Learning for Facial Age Estimation Via Pairwise Age Ranking,2013 +231,MORPH Non-Commercial,morph_nc,58.38131405,26.72078081,University of Tartu,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0 +232,MORPH Non-Commercial,morph_nc,41.3868913,2.16352385,University of Barcelona,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0 +233,MORPH Non-Commercial,morph_nc,39.65404635,-79.96475355,West Virginia University,edu,86a8b3d0f753cb49ac3250fa14d277983e30a4b7,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2013.75,Exploiting Unlabeled Ages for Aging Pattern Analysis on a Large Database,2013 +234,MORPH Non-Commercial,morph_nc,34.2239869,-77.8701325,"UNCW, USA",edu,2b5cb5466eecb131f06a8100dcaf0c7a0e30d391,citation,http://doi.acm.org/10.1145/1924559.1924608,A comparative study of active appearance model annotation schemes for the face,2010 +235,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,fc798314994bf94d1cde8d615ba4d5e61b6268b6,citation,http://pdfs.semanticscholar.org/fc79/8314994bf94d1cde8d615ba4d5e61b6268b6.pdf,"Face Recognition : face in video , age invariance , and facial marks",2009 +236,MORPH Non-Commercial,morph_nc,24.12084345,120.67571165,National Chung Hsing University,edu,635d2696aa597a278dd6563f079be06aa76a33c0,citation,https://doi.org/10.1109/ICIP.2016.7532429,Age estimation via fusion of multiple binary age grouping systems,2016 +237,MORPH Non-Commercial,morph_nc,25.01682835,121.53846924,National Taiwan University,edu,635d2696aa597a278dd6563f079be06aa76a33c0,citation,https://doi.org/10.1109/ICIP.2016.7532429,Age estimation via fusion of multiple binary age grouping systems,2016 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University,edu,7a65fc9e78eff3ab6062707deaadde024d2fad40,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Zhu_A_Study_on_ICCV_2015_paper.pdf,A Study on Apparent Age Estimation,2015 +246,MORPH Non-Commercial,morph_nc,42.357757,-83.06286711,Wayne State University,edu,28d99dc2d673d62118658f8375b414e5192eac6f,citation,http://www.cs.wayne.edu/~mdong/cvpr17.pdf,Using Ranking-CNN for Age Estimation,2017 +247,MORPH Non-Commercial,morph_nc,37.4102193,-122.05965487,Carnegie Mellon University,edu,ec05078be14a11157ac0e1c6b430ac886124589b,citation,http://pdfs.semanticscholar.org/ec05/078be14a11157ac0e1c6b430ac886124589b.pdf,Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches,2018 +248,MORPH Non-Commercial,morph_nc,45.57022705,-122.63709346,Concordia University,edu,ec05078be14a11157ac0e1c6b430ac886124589b,citation,http://pdfs.semanticscholar.org/ec05/078be14a11157ac0e1c6b430ac886124589b.pdf,Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches,2018 +249,MORPH Non-Commercial,morph_nc,46.5190557,6.5667576,"Swiss Federal Institute of Technology Lausanne, Switzerland",edu,d7a84db2a1bf7b97657b0250f354f249394dd700,citation,https://doi.org/10.1109/ICIP.2010.5653518,Global and local feature based multi-classifier A-stack model for aging face identification,2010 +250,MORPH Non-Commercial,morph_nc,39.65404635,-79.96475355,West Virginia University,edu,d3c004125c71942846a9b32ae565c5216c068d1e,citation,http://pdfs.semanticscholar.org/d3c0/04125c71942846a9b32ae565c5216c068d1e.pdf,Recognizing Age-Separated Face Images: Humans and Machines,2014 +251,MORPH Non-Commercial,morph_nc,52.3553655,4.9501644,University of Amsterdam,edu,999289b0ef76c4c6daa16a4f42df056bf3d68377,citation,http://pdfs.semanticscholar.org/9992/89b0ef76c4c6daa16a4f42df056bf3d68377.pdf,The Role of Color and Contrast in Facial Age Estimation,2014 +252,MORPH Non-Commercial,morph_nc,51.99882735,4.37396037,Delft University of Technology,edu,999289b0ef76c4c6daa16a4f42df056bf3d68377,citation,http://pdfs.semanticscholar.org/9992/89b0ef76c4c6daa16a4f42df056bf3d68377.pdf,The Role of Color and Contrast in Facial Age Estimation,2014 +253,MORPH Non-Commercial,morph_nc,28.5456282,77.2731505,"IIIT Delhi, India",edu,f726738954e7055bb3615fa7e8f59f136d3e0bdc,citation,https://arxiv.org/pdf/1803.07385.pdf,Are you eligible? Predicting adulthood from face images via class specific mean autoencoder,2018 +254,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,b9d68dbeb8e5fdc5984b49a317ea6798b378e5ae,citation,http://doi.acm.org/10.1145/2733373.2807962,What Shall I Look Like after N Years?,2015 +255,MORPH Non-Commercial,morph_nc,32.0565957,118.77408833,Nanjing University,edu,b9d68dbeb8e5fdc5984b49a317ea6798b378e5ae,citation,http://doi.acm.org/10.1145/2733373.2807962,What Shall I Look Like after N Years?,2015 +256,MORPH Non-Commercial,morph_nc,45.42580475,-75.68740118,University of Ottawa,edu,16820ccfb626dcdc893cc7735784aed9f63cbb70,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W12/papers/Azarmehr_Real-Time_Embedded_Age_2015_CVPR_paper.pdf,Real-time embedded age and gender classification in unconstrained video,2015 +257,MORPH Non-Commercial,morph_nc,35.0274996,135.78154513,University of Caen,edu,0ad8149318912b5449085187eb3521786a37bc78,citation,http://arxiv.org/abs/1604.02975,CP-mtML: Coupled Projection Multi-Task Metric Learning for Large Scale Face Retrieval,2016 +258,MORPH Non-Commercial,morph_nc,51.44415765,7.26096541,Ruhr-University Bochum,edu,7e1ea2679a110241ed0dd38ff45cd4dfeb7a8e83,citation,http://pdfs.semanticscholar.org/7e1e/a2679a110241ed0dd38ff45cd4dfeb7a8e83.pdf,Extensions of Hierarchical Slow Feature Analysis for Efficient Classification and Regression on High-Dimensional Data,2017 +259,MORPH Non-Commercial,morph_nc,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,2e27667421a7eeab278e0b761db4d2c725683c3f,citation,https://doi.org/10.1007/s11042-013-1815-z,Effective human age estimation using a two-stage approach based on Lie Algebrized Gaussians feature,2013 +260,MORPH Non-Commercial,morph_nc,32.0565957,118.77408833,Nanjing University,edu,0c741fa0966ba3ee4fc326e919bf2f9456d0cd74,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.51,Facial Age Estimation by Learning from Label Distributions,2010 +261,MORPH Non-Commercial,morph_nc,32.0575279,118.78682252,Southeast University,edu,0c741fa0966ba3ee4fc326e919bf2f9456d0cd74,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.51,Facial Age Estimation by Learning from Label Distributions,2010 +262,MORPH Non-Commercial,morph_nc,-37.78397455,144.95867433,Monash University,edu,0c741fa0966ba3ee4fc326e919bf2f9456d0cd74,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.51,Facial Age Estimation by Learning from Label Distributions,2010 +263,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,fca9ebaa30d69ccec8bb577c31693c936c869e72,citation,https://arxiv.org/pdf/1809.00338.pdf,Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition,2018 +264,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,fca9ebaa30d69ccec8bb577c31693c936c869e72,citation,https://arxiv.org/pdf/1809.00338.pdf,Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition,2018 +265,MORPH Non-Commercial,morph_nc,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,cfdc4d0f8e1b4b9ced35317d12b4229f2e3311ab,citation,https://pdfs.semanticscholar.org/cfdc/4d0f8e1b4b9ced35317d12b4229f2e3311ab.pdf,Quaero at TRECVID 2010: Semantic Indexing,2010 +266,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,02d650d8a3a9daaba523433fbe93705df0a7f4b1,citation,http://pdfs.semanticscholar.org/02d6/50d8a3a9daaba523433fbe93705df0a7f4b1.pdf,How Does Aging Affect Facial Components?,2012 +267,MORPH Non-Commercial,morph_nc,34.67567405,33.04577648,Cyprus University of Technology,edu,70db3a0d2ca8a797153cc68506b8650908cb0ada,citation,http://pdfs.semanticscholar.org/70db/3a0d2ca8a797153cc68506b8650908cb0ada.pdf,An Overview of Research Activities in Facial Age Estimation Using the FG-NET Aging Database,2014 +268,MORPH Non-Commercial,morph_nc,22.5447154,113.9357164,Tencent,company,a2d1818eb461564a5153c74028e53856cf0b40fd,citation,https://arxiv.org/pdf/1810.07599.pdf,Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition,2018 +269,MORPH Non-Commercial,morph_nc,57.6252103,39.8845656,Yaroslavl State University,edu,05318a267226f6d855d83e9338eaa9e718b2a8dd,citation,https://fruct.org/publications/fruct16/files/Khr.pdf,Age estimation from face images: challenging problem for audience measurement systems,2014 +270,MORPH Non-Commercial,morph_nc,41.5381124,2.4447406,"EUP Mataró, Spain",edu,1f5725a4a2eb6cdaefccbc20dccadf893936df12,citation,https://doi.org/10.1109/CCST.2012.6393544,On the relevance of age in handwritten biometric recognition,2012 +271,MORPH Non-Commercial,morph_nc,34.67567405,33.04577648,Cyprus University of Technology,edu,876583a059154def7a4bc503b21542f80859affd,citation,https://doi.org/10.1109/IWBF.2016.7449697,On the analysis of factors influencing the performance of facial age progression,2016 +272,MORPH Non-Commercial,morph_nc,-35.0636071,147.3552234,Charles Sturt University,edu,2e231f1e7e641dd3619bec59e14d02e91360ac01,citation,https://arxiv.org/pdf/1807.10421.pdf,Fusion Network for Face-Based Age Estimation,2018 +273,MORPH Non-Commercial,morph_nc,51.3791442,-2.3252332,University of Bath,edu,2e231f1e7e641dd3619bec59e14d02e91360ac01,citation,https://arxiv.org/pdf/1807.10421.pdf,Fusion Network for Face-Based Age Estimation,2018 +274,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,56359d2b4508cc267d185c1d6d310a1c4c2cc8c2,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2015.7298618,Shape driven kernel adaptation in Convolutional Neural Network for robust facial 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Research, Singapore",edu,8229f2735a0db0ad41f4d7252129311f06959907,citation,https://doi.org/10.1109/TIP.2011.2106794,Active Learning for Solving the Incomplete Data Problem in Facial Age Classification by the Furthest Nearest-Neighbor Criterion,2011 +279,MORPH Non-Commercial,morph_nc,1.3484104,103.68297965,Nanyang Technological University,edu,8229f2735a0db0ad41f4d7252129311f06959907,citation,https://doi.org/10.1109/TIP.2011.2106794,Active Learning for Solving the Incomplete Data Problem in Facial Age Classification by the Furthest Nearest-Neighbor Criterion,2011 +280,MORPH Non-Commercial,morph_nc,39.2899685,-76.62196103,University of Maryland,edu,963a004e208ce4bd26fa79a570af61d31651b3c3,citation,https://doi.org/10.1016/j.jvlc.2009.01.011,Computational methods for modeling facial aging: A survey,2009 +281,MORPH Non-Commercial,morph_nc,40.48256135,-3.6906079,Universidad Autonoma de Madrid,edu,4b5ff8c67f3496a414f94e35cb35a601ec98e5cf,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6547306,Understanding the discrimination power of facial regions in forensic casework,2013 +282,MORPH Non-Commercial,morph_nc,40.4445565,-3.7122785,"Dirección General de la Guardia Civil, Madrid, Spain",edu,4b5ff8c67f3496a414f94e35cb35a601ec98e5cf,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6547306,Understanding the discrimination power of facial regions in forensic casework,2013 +283,MORPH Non-Commercial,morph_nc,-37.8087465,144.9638875,RMIT University,edu,c49075ead6eb07ede5ada4fe372899bd0cfb83ac,citation,https://doi.org/10.1109/ICSPCS.2015.7391782,Multi-stage classification network for automatic age estimation from facial images,2015 +284,MORPH Non-Commercial,morph_nc,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,00301c250d667700276b1e573640ff2fd7be574d,citation,https://doi.org/10.1109/BTAS.2014.6996242,Establishing a test set 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Faces across Pose and Age,2018 +81,MsCeleb,msceleb,42.3383668,-71.08793524,Northeastern University,edu,feea73095b1be0cbae1ad7af8ba2c4fb6f316d35,citation,http://dl.acm.org/citation.cfm?id=3126693,Deep Face Recognition with Center Invariant Loss,2017 +82,MsCeleb,msceleb,22.42031295,114.20788644,Chinese University of Hong Kong,edu,2296d79753118cfcd0fecefece301557f4cb66e2,citation,https://arxiv.org/pdf/1804.03487.pdf,Exploring Disentangled Feature Representation Beyond Face Identification,2018 +83,MsCeleb,msceleb,39.993008,116.329882,SenseTime,company,2296d79753118cfcd0fecefece301557f4cb66e2,citation,https://arxiv.org/pdf/1804.03487.pdf,Exploring Disentangled Feature Representation Beyond Face Identification,2018 +84,MsCeleb,msceleb,28.2290209,112.99483204,"National University of Defense Technology, China",edu,511a8cdf2127ef8aa07cbdf9660fe9e0e2dfbde7,citation,https://pdfs.semanticscholar.org/511a/8cdf2127ef8aa07cbdf9660fe9e0e2dfbde7.pdf,A Community Detection Approach to Cleaning Extremely 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Sciences,edu,01dfd60c0851c4e5a99176e99aa369e1b5f606b7,citation,https://arxiv.org/pdf/1809.01936.pdf,Disentangled Variational Representation for Heterogeneous Face Recognition,2018 +93,MsCeleb,msceleb,39.329053,-76.619425,Johns Hopkins University,edu,01dfd60c0851c4e5a99176e99aa369e1b5f606b7,citation,https://arxiv.org/pdf/1809.01936.pdf,Disentangled Variational Representation for Heterogeneous Face Recognition,2018 +94,MsCeleb,msceleb,32.0575279,118.78682252,Southeast University,edu,8ff8c64288a2f7e4e8bf8fda865820b04ab3dbe8,citation,https://pdfs.semanticscholar.org/0056/92b9fa6728df3a7f14578c43410867bba425.pdf,Age Estimation Using Expectation of Label Distribution Learning,2018 +95,MsCeleb,msceleb,32.0565957,118.77408833,Nanjing University,edu,8ff8c64288a2f7e4e8bf8fda865820b04ab3dbe8,citation,https://pdfs.semanticscholar.org/0056/92b9fa6728df3a7f14578c43410867bba425.pdf,Age Estimation Using Expectation of Label Distribution Learning,2018 +96,MsCeleb,msceleb,1.3484104,103.68297965,Nanyang 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Maryland,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018 +101,MsCeleb,msceleb,37.4102193,-122.05965487,Carnegie Mellon University,edu,d35534f3f59631951011539da2fe83f2844ca245,citation,https://arxiv.org/pdf/1705.07904.pdf,Semantically Decomposing the Latent Spaces of Generative Adversarial Networks,2017 +102,MsCeleb,msceleb,37.43131385,-122.16936535,Stanford University,edu,d35534f3f59631951011539da2fe83f2844ca245,citation,https://arxiv.org/pdf/1705.07904.pdf,Semantically Decomposing the Latent Spaces of Generative Adversarial Networks,2017 +103,MsCeleb,msceleb,32.87935255,-117.23110049,"University of California, San Diego",edu,d35534f3f59631951011539da2fe83f2844ca245,citation,https://arxiv.org/pdf/1705.07904.pdf,Semantically Decomposing the Latent Spaces of Generative Adversarial Networks,2017 +104,MsCeleb,msceleb,37.3936717,-122.0807262,Facebook,company,628a3f027b7646f398c68a680add48c7969ab1d9,citation,https://pdfs.semanticscholar.org/628a/3f027b7646f398c68a680add48c7969ab1d9.pdf,Plan for Final Year Project : HKU-Face : A Large Scale Dataset for Deep Face Recognition,2017 +105,MsCeleb,msceleb,51.7534538,-1.25400997,University of Oxford,edu,313d5eba97fe064bdc1f00b7587a4b3543ef712a,citation,https://pdfs.semanticscholar.org/cb7f/93467b0ec1afd43d995e511f5d7bf052a5af.pdf,Compact Deep Aggregation for Set Retrieval,2018 +106,MsCeleb,msceleb,42.718568,-84.47791571,Michigan State University,edu,b446bcd7fb78adfe346cf7a01a38e4f43760f363,citation,http://pdfs.semanticscholar.org/b446/bcd7fb78adfe346cf7a01a38e4f43760f363.pdf,To appear in ICB 2018 Longitudinal Study of Child Face Recognition,2017 +107,MsCeleb,msceleb,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,cd6aaa37fffd0b5c2320f386be322b8adaa1cc68,citation,https://arxiv.org/pdf/1804.06655.pdf,Deep Face Recognition: A Survey,2018 +108,MsCeleb,msceleb,22.42031295,114.20788644,Chinese University of Hong Kong,edu,831b4d8b0c0173b0bac0e328e844a0fbafae6639,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018 +109,MsCeleb,msceleb,1.3484104,103.68297965,Nanyang Technological University,edu,831b4d8b0c0173b0bac0e328e844a0fbafae6639,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018 +110,MsCeleb,msceleb,29.7207902,-95.34406271,University of Houston,edu,3cb2841302af1fb9656f144abc79d4f3d0b27380,citation,https://pdfs.semanticscholar.org/3cb2/841302af1fb9656f144abc79d4f3d0b27380.pdf,When 3 D-Aided 2 D Face Recognition Meets Deep Learning : An extended UR 2 D for Pose-Invariant Face Recognition,2017 +111,MsCeleb,msceleb,39.2899685,-76.62196103,University of Maryland,edu,23dd8d17ce09c22d367e4d62c1ccf507bcbc64da,citation,https://pdfs.semanticscholar.org/23dd/8d17ce09c22d367e4d62c1ccf507bcbc64da.pdf,Deep Density Clustering of Unconstrained Faces ( Supplementary Material ),2018 diff --git a/site/datasets/final/pipa.csv b/site/datasets/final/pipa.csv new file mode 100644 index 00000000..c68e70b6 --- /dev/null +++ b/site/datasets/final/pipa.csv @@ -0,0 +1,37 @@ +index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,PIPA,pipa,0.0,0.0,,,0a85bdff552615643dd74646ac881862a7c7072d,main,https://doi.org/10.1109/CVPR.2015.7299113,Beyond frontal faces: Improving Person Recognition using multiple cues,2015 +1,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,bfc04ce7752fac884cf5a78b30ededfd5a0ad109,citation,https://arxiv.org/pdf/1804.04779.pdf,A Hybrid Model for Identity Obfuscation by Face Replacement,2018 +2,PIPA,pipa,28.59899755,-81.19712501,University of Central Florida,edu,2b339ece73e3787f445c5b92078e8f82c9b1c522,citation,http://pdfs.semanticscholar.org/7a2e/e06aaa3f342937225272951c0b6dd4309a7a.pdf,"Human Re-identification in Crowd Videos Using Personal, Social and Environmental Constraints",2016 +3,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,bc27434e376db89fe0e6ef2d2fabc100d2575ec6,citation,https://arxiv.org/pdf/1607.08438.pdf,Faceless Person Recognition; Privacy Implications in Social Media,2016 +4,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,0c59071ddd33849bd431165bc2d21bbe165a81e0,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Oh_Person_Recognition_in_ICCV_2015_paper.pdf,Person Recognition in Personal Photo Collections,2015 +5,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,23429ef60e7a9c0e2f4d81ed1b4e47cc2616522f,citation,https://arxiv.org/pdf/1704.06456.pdf,A Domain Based Approach to Social Relation Recognition,2017 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Sydney,edu,0b84f07af44f964817675ad961def8a51406dd2e,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.357,Person Re-identification in the Wild,2017 +14,PIPA,pipa,17.4454957,78.34854698,International Institute of Information Technology,edu,eb8a3948c4be0d23eb7326d27f2271be893b3409,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7914701,A Probabilistic Approach to People-Centric Photo Selection and Sequencing,2017 +15,PIPA,pipa,1.3037257,103.7737763,University of Illinois’ Advanced Digital Sciences Center,edu,eb8a3948c4be0d23eb7326d27f2271be893b3409,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7914701,A Probabilistic Approach to People-Centric Photo Selection and Sequencing,2017 +16,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,3e0a1884448bfd7f416c6a45dfcdfc9f2e617268,citation,https://arxiv.org/pdf/1805.05838.pdf,Understanding and Controlling User Linkability in Decentralized Learning,2018 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Singapore,edu,b5968e7bb23f5f03213178c22fd2e47af3afa04c,citation,https://arxiv.org/pdf/1705.07206.pdf,Multiple-Human Parsing in the Wild,2017 +25,PIPA,pipa,42.4505507,-76.4783513,Cornell University,edu,537328af75f50d49696972a6c34bca97c14bc762,citation,https://arxiv.org/pdf/1805.04049.pdf,Exploiting Unintended Feature Leakage in Collaborative Learning,2018 +26,PIPA,pipa,22.42031295,114.20788644,Chinese University of Hong Kong,edu,1c9efb6c895917174ac6ccc3bae191152f90c625,citation,https://arxiv.org/pdf/1806.03084.pdf,Unifying Identification and Context Learning for Person Recognition,2018 +27,PIPA,pipa,37.21872455,-80.42542519,Virginia Polytechnic Institute and State University,edu,6d8eef8f8d6cd8436c55018e6ca5c5907b31ac19,citation,http://pdfs.semanticscholar.org/6d8e/ef8f8d6cd8436c55018e6ca5c5907b31ac19.pdf,Understanding Representations and Reducing their Redundancy in Deep Networks,2016 +28,PIPA,pipa,17.4454957,78.34854698,International Institute of Information 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+32,PIPA,pipa,1.2962018,103.77689944,National University of Singapore,edu,5f771fed91c8e4b666489ba2384d0705bcf75030,citation,https://arxiv.org/pdf/1804.03287.pdf,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018 +33,PIPA,pipa,22.42031295,114.20788644,Chinese University of Hong Kong,edu,d949fadc9b6c5c8b067fa42265ad30945f9caa99,citation,https://arxiv.org/pdf/1710.00870.pdf,Rethinking Feature Discrimination and Polymerization for Large-scale Recognition,2017 +34,PIPA,pipa,-34.9189226,138.60423668,University of Adelaide,edu,3d24b386d003bee176a942c26336dbe8f427aadd,citation,http://arxiv.org/abs/1611.09967,Sequential Person Recognition in Photo Albums with a Recurrent Network,2017 +35,PIPA,pipa,42.4505507,-76.4783513,Cornell University,edu,8bdf6f03bde08c424c214188b35be8b2dec7cdea,citation,https://arxiv.org/pdf/1805.04049.pdf,Inference Attacks Against Collaborative Learning,2018 diff --git a/site/datasets/final/umd_faces.csv b/site/datasets/final/umd_faces.csv new file mode 100644 index 00000000..53788401 --- /dev/null +++ b/site/datasets/final/umd_faces.csv @@ -0,0 +1,34 @@ +index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,UMD,umd_faces,0.0,0.0,,,31b05f65405534a696a847dd19c621b7b8588263,main,http://arxiv.org/abs/1611.01484,UMDFaces: An annotated face dataset for training deep networks,2017 +1,UMD,umd_faces,39.2899685,-76.62196103,University of Maryland,edu,19458454308a9f56b7de76bf7d8ff8eaa52b0173,citation,https://pdfs.semanticscholar.org/1945/8454308a9f56b7de76bf7d8ff8eaa52b0173.pdf,Deep Features for Recognizing Disguised Faces in the Wild,0 +2,UMD,umd_faces,28.2290209,112.99483204,"National University of Defense Technology, China",edu,511a8cdf2127ef8aa07cbdf9660fe9e0e2dfbde7,citation,https://pdfs.semanticscholar.org/511a/8cdf2127ef8aa07cbdf9660fe9e0e2dfbde7.pdf,A Community Detection Approach to Cleaning Extremely Large Face Database,2018 +3,UMD,umd_faces,25.01682835,121.53846924,National Taiwan University,edu,81884e1de00e59f24bc20254584d73a1a1806933,citation,https://arxiv.org/pdf/1811.02328.pdf,Super-Identity Convolutional Neural Network for Face Hallucination,2018 +4,UMD,umd_faces,39.993008,116.329882,SenseTime,company,81884e1de00e59f24bc20254584d73a1a1806933,citation,https://arxiv.org/pdf/1811.02328.pdf,Super-Identity Convolutional Neural Network for Face Hallucination,2018 +5,UMD,umd_faces,30.284151,-97.73195598,University of Texas at Austin,edu,81884e1de00e59f24bc20254584d73a1a1806933,citation,https://arxiv.org/pdf/1811.02328.pdf,Super-Identity Convolutional Neural Network for Face Hallucination,2018 +6,UMD,umd_faces,45.7835966,4.7678948,École Centrale de Lyon,edu,486840f4f524e97f692a7f6b42cd19019ee71533,citation,https://arxiv.org/pdf/1703.08388v2.pdf,DeepVisage: Making Face Recognition Simple Yet With Powerful Generalization Skills,2017 +7,UMD,umd_faces,48.832493,2.267474,Safran Identity and Security,company,486840f4f524e97f692a7f6b42cd19019ee71533,citation,https://arxiv.org/pdf/1703.08388v2.pdf,DeepVisage: Making Face Recognition Simple Yet With Powerful Generalization Skills,2017 +8,UMD,umd_faces,41.70456775,-86.23822026,University of Notre Dame,edu,73ea06787925157df519a15ee01cc3dc1982a7e0,citation,https://arxiv.org/pdf/1811.01474.pdf,Fast Face Image Synthesis with Minimal Training,2018 +9,UMD,umd_faces,30.40550035,-91.18620474,Louisiana State University,edu,9f65319b8a33c8ec11da2f034731d928bf92e29d,citation,http://pdfs.semanticscholar.org/9f65/319b8a33c8ec11da2f034731d928bf92e29d.pdf,Taking Roll: a Pipeline for Face Recognition,2018 +10,UMD,umd_faces,51.24303255,-0.59001382,University of Surrey,edu,ed07856461da6c7afa4f1782b5b607b45eebe9f6,citation,https://pdfs.semanticscholar.org/ed07/856461da6c7afa4f1782b5b607b45eebe9f6.pdf,D Morphable Models as Spatial Transformer Networks,2017 +11,UMD,umd_faces,53.94540365,-1.03138878,University of York,edu,ed07856461da6c7afa4f1782b5b607b45eebe9f6,citation,https://pdfs.semanticscholar.org/ed07/856461da6c7afa4f1782b5b607b45eebe9f6.pdf,D Morphable Models as Spatial Transformer Networks,2017 +12,UMD,umd_faces,53.94540365,-1.03138878,University of York,edu,6a4419ce2338ea30a570cf45624741b754fa52cb,citation,https://arxiv.org/pdf/1804.02541.pdf,Statistical transformer networks: learning shape and appearance models via self supervision,2018 +13,UMD,umd_faces,51.49887085,-0.17560797,Imperial College London,edu,809ea255d144cff780300440d0f22c96e98abd53,citation,http://pdfs.semanticscholar.org/809e/a255d144cff780300440d0f22c96e98abd53.pdf,ArcFace: Additive Angular Margin Loss for Deep Face Recognition,2018 +14,UMD,umd_faces,39.2899685,-76.62196103,University of Maryland,edu,def2983576001bac7d6461d78451159800938112,citation,https://arxiv.org/pdf/1705.07426.pdf,The Do’s and Don’ts for CNN-Based Face Verification,2017 +15,UMD,umd_faces,43.7776426,11.259765,University of Florence,edu,746c0205fdf191a737df7af000eaec9409ede73f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8423119,Investigating Nuisances in DCNN-Based Face Recognition,2018 +16,UMD,umd_faces,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018 +17,UMD,umd_faces,39.2899685,-76.62196103,University of Maryland,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018 +18,UMD,umd_faces,51.24303255,-0.59001382,University of Surrey,edu,c146aa6d56233ce700032f1cb179700778557601,citation,https://arxiv.org/pdf/1708.07199.pdf,3D Morphable Models as Spatial Transformer Networks,2017 +19,UMD,umd_faces,53.94540365,-1.03138878,University of York,edu,c146aa6d56233ce700032f1cb179700778557601,citation,https://arxiv.org/pdf/1708.07199.pdf,3D Morphable Models as Spatial Transformer Networks,2017 +20,UMD,umd_faces,25.01682835,121.53846924,National Taiwan University,edu,17423fe480b109e1d924314c1dddb11b084e8a42,citation,https://pdfs.semanticscholar.org/1742/3fe480b109e1d924314c1dddb11b084e8a42.pdf,Deep Disguised Faces Recognition,0 +21,UMD,umd_faces,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,7a131fafa7058fb75fdca32d0529bc7cb50429bd,citation,https://arxiv.org/pdf/1704.04086.pdf,Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis,2017 +22,UMD,umd_faces,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,cd6aaa37fffd0b5c2320f386be322b8adaa1cc68,citation,https://arxiv.org/pdf/1804.06655.pdf,Deep Face Recognition: A Survey,2018 +23,UMD,umd_faces,30.274084,120.15507,Alibaba,company,89497854eada7e32f06aa8f3c0ceedc0e91ecfef,citation,https://doi.org/10.1109/TIP.2017.2784571,Deep Context-Sensitive Facial Landmark Detection With Tree-Structured Modeling,2018 +24,UMD,umd_faces,30.19331415,120.11930822,Zhejiang University,edu,89497854eada7e32f06aa8f3c0ceedc0e91ecfef,citation,https://doi.org/10.1109/TIP.2017.2784571,Deep Context-Sensitive Facial Landmark Detection With Tree-Structured Modeling,2018 +25,UMD,umd_faces,38.8920756,-104.79716389,"University of Colorado, Colorado Springs",edu,d4f1eb008eb80595bcfdac368e23ae9754e1e745,citation,https://arxiv.org/pdf/1708.02337.pdf,Unconstrained Face Detection and Open-Set Face Recognition Challenge,2017 +26,UMD,umd_faces,51.7534538,-1.25400997,University of Oxford,edu,eb027969f9310e0ae941e2adee2d42cdf07d938c,citation,https://arxiv.org/pdf/1710.08092.pdf,VGGFace2: A Dataset for Recognising Faces across Pose and Age,2018 +27,UMD,umd_faces,51.49887085,-0.17560797,Imperial College London,edu,c43ed9b34cad1a3976bac7979808eb038d88af84,citation,https://arxiv.org/pdf/1804.03675.pdf,Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model,2018 +28,UMD,umd_faces,51.24303255,-0.59001382,University of Surrey,edu,c43ed9b34cad1a3976bac7979808eb038d88af84,citation,https://arxiv.org/pdf/1804.03675.pdf,Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model,2018 +29,UMD,umd_faces,41.70456775,-86.23822026,University of Notre Dame,edu,e64c166dc5bb33bc61462a8b5ac92edb24d905a1,citation,https://arxiv.org/pdf/1811.01474.pdf,Fast Face Image Synthesis with Minimal Training.,2018 +30,UMD,umd_faces,51.7534538,-1.25400997,University of Oxford,edu,70c59dc3470ae867016f6ab0e008ac8ba03774a1,citation,https://arxiv.org/pdf/1710.08092.pdf,VGGFace2: A Dataset for Recognising Faces across Pose and Age,2018 +31,UMD,umd_faces,38.99203005,-76.9461029,University of Maryland College Park,edu,3d2891950f1b76f783a9ba77b3c55b8e68b95fbe,citation,https://arxiv.org/pdf/1802.06713.pdf,Disentangling 3D Pose in a Dendritic CNN for Unconstrained 2D Face Alignment,2018 +32,UMD,umd_faces,51.49887085,-0.17560797,Imperial College London,edu,1929863fff917ee7f6dc428fc1ce732777668eca,citation,https://arxiv.org/pdf/1712.04695.pdf,UV-GAN: Adversarial Facial UV Map Completion for Pose-Invariant Face Recognition,2018 diff --git a/site/datasets/final/voc.csv b/site/datasets/final/voc.csv new file mode 100644 index 00000000..9400d7d6 --- /dev/null +++ b/site/datasets/final/voc.csv @@ -0,0 +1,401 @@ +index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,VOC,voc,0.0,0.0,,,abe9f3b91fd26fa1b50cd685c0d20debfb372f73,main,http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc14.pdf,The Pascal Visual Object Classes Challenge: A Retrospective,2014 +1,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,ed2f711cf9bcd9d7ab039d746af109ed9573421a,citation,https://pdfs.semanticscholar.org/ed2f/711cf9bcd9d7ab039d746af109ed9573421a.pdf,Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks,2018 +2,VOC,voc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,ed2f711cf9bcd9d7ab039d746af109ed9573421a,citation,https://pdfs.semanticscholar.org/ed2f/711cf9bcd9d7ab039d746af109ed9573421a.pdf,Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks,2018 +3,VOC,voc,13.0222347,77.56718325,Indian Institute of Science Bangalore,edu,a6ac6463b5c89ac9eb013c978f213b309cc6a5c7,citation,https://arxiv.org/pdf/1808.01134.pdf,iSPA-Net: Iterative Semantic Pose Alignment Network,2018 +4,VOC,voc,42.3583961,-71.09567788,MIT,edu,aaf4d938f2e66d158d5e635a9c1d279cdc7639c0,citation,http://pdfs.semanticscholar.org/aaf4/d938f2e66d158d5e635a9c1d279cdc7639c0.pdf,Toward visual understanding of everyday object,2015 +5,VOC,voc,42.2942142,-83.71003894,University of Michigan,edu,74dbcc09a3456ddacf5cece640b84045ebdf6be1,citation,https://arxiv.org/pdf/1810.05162.pdf,Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation,2018 +6,VOC,voc,49.2767454,-122.91777375,Simon Fraser University,edu,74dbcc09a3456ddacf5cece640b84045ebdf6be1,citation,https://arxiv.org/pdf/1810.05162.pdf,Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation,2018 +7,VOC,voc,46.109237,7.08453549,IDIAP Research Institute,edu,dedc7b080b8e13d72f8dc33e248e7637d191fdbf,citation,http://pdfs.semanticscholar.org/dedc/7b080b8e13d72f8dc33e248e7637d191fdbf.pdf,Beyond Dataset Bias: Multi-task Unaligned Shared Knowledge Transfer,2012 +8,VOC,voc,52.17638955,0.14308882,University of Cambridge,edu,dedc7b080b8e13d72f8dc33e248e7637d191fdbf,citation,http://pdfs.semanticscholar.org/dedc/7b080b8e13d72f8dc33e248e7637d191fdbf.pdf,Beyond Dataset Bias: Multi-task Unaligned Shared Knowledge Transfer,2012 +9,VOC,voc,39.00041165,-77.10327775,National Institutes of Health,edu,18c57ddc9c0164ee792661f43a5578f7a00d0330,citation,https://arxiv.org/pdf/1705.02315v2.pdf,ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases,2017 +10,VOC,voc,37.40253645,-122.11655107,Toyota Research Institute,edu,a825680aeb853fc34c65b5844c4c4391148f18c3,citation,https://arxiv.org/pdf/1711.10006.pdf,SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again,2017 +11,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,f249c266321d661ae398c26ddb8c7409f6455ba1,citation,https://pdfs.semanticscholar.org/f249/c266321d661ae398c26ddb8c7409f6455ba1.pdf,Revisiting Faster R-CNN: A Deeper Look at Region Proposal Network,2017 +12,VOC,voc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,7fa5ede4a34dbe604ce317d529eed78db6642bc0,citation,https://arxiv.org/pdf/1709.01829.pdf,Soft Proposal Networks for Weakly Supervised Object Localization,2017 +13,VOC,voc,35.9990522,-78.9290629,Duke University,edu,7fa5ede4a34dbe604ce317d529eed78db6642bc0,citation,https://arxiv.org/pdf/1709.01829.pdf,Soft Proposal Networks for Weakly Supervised Object Localization,2017 +14,VOC,voc,42.3583961,-71.09567788,MIT,edu,05fdd29536d55fe3ad00689b6f60ada8bc761e91,citation,http://people.csail.mit.edu/torralba/publications/ihog_iccv.pdf,HOGgles: Visualizing Object Detection Features,2013 +15,VOC,voc,24.7925484,120.9951183,National Tsing Hua University,edu,394bf41cd8578ec10cd34452c688c3e3de1c16a7,citation,https://pdfs.semanticscholar.org/394b/f41cd8578ec10cd34452c688c3e3de1c16a7.pdf,Multi-view to Novel View: Synthesizing Novel Views With Self-learned Confidence,2018 +16,VOC,voc,22.42031295,114.20788644,Chinese University of Hong Kong,edu,2453dd38cde21f3248b55d281405f11d58168fa9,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.342,Multi-scale Patch Aggregation (MPA) for Simultaneous Detection and Segmentation,2016 +17,VOC,voc,50.7791703,6.06728733,RWTH Aachen University,edu,ccb9ffa26b28dffc4f7d613821d1a9f0d60ea3f4,citation,https://arxiv.org/pdf/1706.09364.pdf,Online Adaptation of Convolutional Neural Networks for Video Object Segmentation,2017 +18,VOC,voc,39.87549675,32.78553506,Middle East Technical University,edu,d38af10096aa90dfccd7e4cec9757900bf6958bd,citation,https://arxiv.org/pdf/1807.04067.pdf,MultiPoseNet: Fast Multi-Person Pose Estimation Using Pose Residual Network,2018 +19,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,8c1e828a4826a1fb3eb47ee432f5333b974fa141,citation,http://pdfs.semanticscholar.org/8c1e/828a4826a1fb3eb47ee432f5333b974fa141.pdf,Spatial Graph for Image Classification,2012 +20,VOC,voc,38.88140235,121.52281098,Dalian University of Technology,edu,2a31b4bf2a294b6e67956a6cd5ed6d875af548e0,citation,https://arxiv.org/pdf/1710.01020.pdf,Learning Affinity via Spatial Propagation Networks,2017 +21,VOC,voc,39.2899685,-76.62196103,University of Maryland,edu,0790c400bfe6fbefe88ef7791476e1abf1952089,citation,https://arxiv.org/pdf/1511.04067v1.pdf,Deep Gaussian Conditional Random Field Network: A Model-Based Deep Network for Discriminative Denoising,2016 +22,VOC,voc,41.3868913,2.16352385,University of Barcelona,edu,442cf9b24661c9ea5c2a1dcabd4a5b8af1cd89da,citation,https://arxiv.org/pdf/1806.10805.pdf,Beyond One-hot Encoding: lower dimensional target embedding,2018 +23,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,04eda7eee3e0282de50e54554f50870dd17defa1,citation,https://arxiv.org/pdf/1705.08280v1.pdf,How Hard Can It Be? Estimating the Difficulty of Visual Search in an Image,2016 +24,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,90a4125974564a5ab6c2ce2ff685fc36e9cf0680,citation,https://arxiv.org/pdf/1703.08448.pdf,Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach,2017 +25,VOC,voc,39.94976005,116.33629046,Beijing Jiaotong University,edu,90a4125974564a5ab6c2ce2ff685fc36e9cf0680,citation,https://arxiv.org/pdf/1703.08448.pdf,Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach,2017 +26,VOC,voc,39.9922379,116.30393816,Peking University,edu,c3dd6c1ddbb9cfcc1bed6383ffaa0b1ce4d13625,citation,https://arxiv.org/pdf/1807.01544.pdf,TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes,2018 +27,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,2976605dc3b73377696537291d45f09f1ab1fbf5,citation,http://www.ri.cmu.edu/pub_files/2016/6/multi-task.pdf,Cross-Stitch Networks for Multi-task Learning,2016 +28,VOC,voc,28.54632595,77.27325504,Indian Institute of Technology Delhi,edu,25e9a2ec45c34d4610359196dc505a72c3833336,citation,http://pdfs.semanticscholar.org/25e9/a2ec45c34d4610359196dc505a72c3833336.pdf,Benchmarking KAZE and MCM for Multiclass Classification,2015 +29,VOC,voc,39.9808333,116.34101249,Beihang University,edu,935e639bebf905af2e35e8b1e7aa0538d7122185,citation,https://arxiv.org/pdf/1808.00313.pdf,A Network Structure to Explicitly Reduce Confusion Errors in Semantic Segmentation,2018 +30,VOC,voc,39.8011499,140.0459116,Akita Prefectural University,edu,211435a4e14d00f4aaed191acfb548185ee800b9,citation,http://pdfs.semanticscholar.org/2114/35a4e14d00f4aaed191acfb548185ee800b9.pdf,Visual Saliency Based Multiple Objects Segmentation and its Parallel Implementation for Real-Time Vision Processing,2015 +31,VOC,voc,49.25839375,-123.24658161,University of British Columbia,edu,9fae24003bbedecdb617f9779215d79d06b90dd8,citation,https://arxiv.org/pdf/1807.09856.pdf,Where Are the Blobs: Counting by Localization with Point Supervision,2018 +32,VOC,voc,40.72925325,-73.99625394,New York University,edu,c45681fa9d9c36a6a196017ef283ac38904f91bb,citation,https://arxiv.org/pdf/1711.07377.pdf,Pixel-wise object tracking,2017 +33,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,45f858f9e8d7713f60f52618e54089ba68dfcd6d,citation,http://openaccess.thecvf.com/content_ICCV_2017/papers/Sigurdsson_What_Actions_Are_ICCV_2017_paper.pdf,What Actions are Needed for Understanding Human Actions in Videos?,2017 +34,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,57bd01c042a5f64659b3a9f91c048b8594f762f6,citation,http://pdfs.semanticscholar.org/57bd/01c042a5f64659b3a9f91c048b8594f762f6.pdf,Advances in fine-grained visual categorization,2015 +35,VOC,voc,31.30104395,121.50045497,Fudan University,edu,9716416a15e79a36e3481bcdad79cdc905603e6d,citation,https://arxiv.org/pdf/1808.07016.pdf,Gaussian Word Embedding with a Wasserstein Distance Loss,2017 +36,VOC,voc,32.0565957,118.77408833,Nanjing University,edu,97265d64859e06900c11ae5bb5f03f3bd265f858,citation,https://arxiv.org/pdf/1612.01082.pdf,Multilabel Image Classification With Regional Latent Semantic Dependencies,2018 +37,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,97265d64859e06900c11ae5bb5f03f3bd265f858,citation,https://arxiv.org/pdf/1612.01082.pdf,Multilabel Image Classification With Regional Latent Semantic Dependencies,2018 +38,VOC,voc,-33.8809651,151.20107299,University of Technology Sydney,edu,97265d64859e06900c11ae5bb5f03f3bd265f858,citation,https://arxiv.org/pdf/1612.01082.pdf,Multilabel Image Classification With Regional Latent Semantic Dependencies,2018 +39,VOC,voc,42.3583961,-71.09567788,MIT,edu,a19904e76b5ded44e6aeb9af85997d160de6bb22,citation,http://pdfs.semanticscholar.org/a199/04e76b5ded44e6aeb9af85997d160de6bb22.pdf,TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation,2018 +40,VOC,voc,47.05821,15.46019568,Graz University of Technology,edu,96a9ca7a8366ae0efe6b58a515d15b44776faf6e,citation,https://arxiv.org/pdf/1609.00129.pdf,Grid Loss: Detecting Occluded Faces,2016 +41,VOC,voc,47.05821,15.46019568,Graz University of Technology,edu,513b8dc73a9fbc467e1ac130fe8c842b5839ca51,citation,http://pdfs.semanticscholar.org/513b/8dc73a9fbc467e1ac130fe8c842b5839ca51.pdf,Dissertation Scalable Visual Navigation for Micro Aerial Vehicles using Geometric Prior Knowledge,2013 +42,VOC,voc,37.8687126,-122.25586815,"University of California, Berkeley",edu,0ee3aa2a78f9680bb65a823bd9195c879572ec1c,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Dubey_What_Makes_an_ICCV_2015_paper.pdf,What Makes an Object Memorable?,2015 +43,VOC,voc,42.3583961,-71.09567788,MIT,edu,0ee3aa2a78f9680bb65a823bd9195c879572ec1c,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Dubey_What_Makes_an_ICCV_2015_paper.pdf,What Makes an Object Memorable?,2015 +44,VOC,voc,37.36566745,-120.42158888,"University of California, Merced",edu,0ee3aa2a78f9680bb65a823bd9195c879572ec1c,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Dubey_What_Makes_an_ICCV_2015_paper.pdf,What Makes an Object Memorable?,2015 +45,VOC,voc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,a776acc53591c3eb0b53501d9758d984e2e52a97,citation,https://arxiv.org/pdf/1804.00880.pdf,Weakly Supervised Instance Segmentation using Class Peak Response,2018 +46,VOC,voc,35.9990522,-78.9290629,Duke University,edu,a776acc53591c3eb0b53501d9758d984e2e52a97,citation,https://arxiv.org/pdf/1804.00880.pdf,Weakly Supervised Instance Segmentation using Class Peak Response,2018 +47,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,423b941641728a21e37f41359a691815cdd84ceb,citation,http://arxiv.org/abs/1511.04517,Reversible Recursive Instance-Level Object Segmentation,2016 +48,VOC,voc,47.6423318,-122.1369302,Microsoft,company,666939690c564641b864eed0d60a410b31e49f80,citation,http://pdfs.semanticscholar.org/6669/39690c564641b864eed0d60a410b31e49f80.pdf,What Visual Attributes Characterize an Object Class?,2014 +49,VOC,voc,43.7776426,11.259765,University of Florence,edu,51e8e8c4cac8260ef21c25f9f2a0a68aedbc6d58,citation,https://arxiv.org/pdf/1704.02518.pdf,Deep Generative Adversarial Compression Artifact Removal,2017 +50,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,3b01a839d174dad6f2635cff7ebe7e1aaad701a4,citation,http://pdfs.semanticscholar.org/3b01/a839d174dad6f2635cff7ebe7e1aaad701a4.pdf,Image Co-localization by Mimicking a Good Detector's Confidence Score Distribution,2016 +51,VOC,voc,31.83907195,117.26420748,University of Science and Technology of China,edu,d467035d83fb4e86c4a47b2ca87894388deb8c44,citation,https://pdfs.semanticscholar.org/d467/035d83fb4e86c4a47b2ca87894388deb8c44.pdf,Relief R-CNN : Utilizing Convolutional Feature Interrelationship for Object Detection,2016 +52,VOC,voc,30.284151,-97.73195598,University of Texas at Austin,edu,264a2b946fae4af23c646cc08fc56947b5be82cf,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2015.7301302,Robust object recognition in RGB-D egocentric videos based on Sparse Affine Hull Kernel,2015 +53,VOC,voc,34.0687788,-118.4450094,"University of California, Los Angeles",edu,480888bad59b314236f2d947ebf308ae146c98e4,citation,https://arxiv.org/pdf/1511.06881.pdf,Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net,2016 +54,VOC,voc,25.01682835,121.53846924,National Taiwan University,edu,a1ee55d529e04a80f4eae3b30d0961a985a64fa4,citation,http://www.cs.utexas.edu/~ycsu/publications/mm029-su.pdf,Enabling low bitrate mobile visual recognition: a performance versus bandwidth evaluation,2013 +55,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,0cd736baf31dceea1cc39ac72e00b65587f5fb9e,citation,http://pdfs.semanticscholar.org/4ad0/b6f189718a7287c6e7b90eb05331e56db334.pdf,Learning Hash Functions Using Column Generation,2013 +56,VOC,voc,39.2899685,-76.62196103,University of Maryland,edu,6424574cb92b316928c37232869bfadcb5b4c20f,citation,https://arxiv.org/pdf/1711.05282.pdf,C-WSL: Count-Guided Weakly Supervised Localization,2018 +57,VOC,voc,47.6543238,-122.30800894,University of Washington,edu,51eba481dac6b229a7490f650dff7b17ce05df73,citation,http://grail.cs.washington.edu/wp-content/uploads/2016/09/yatskar2016srv.pdf,Situation Recognition: Visual Semantic Role Labeling for Image Understanding,2016 +58,VOC,voc,47.3764534,8.54770931,ETH Zürich,edu,961a5d5750f18e91e28a767b3cb234a77aac8305,citation,http://pdfs.semanticscholar.org/961a/5d5750f18e91e28a767b3cb234a77aac8305.pdf,Face Detection without Bells and Whistles,2014 +59,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,0c05f60998628884a9ac60116453f1a91bcd9dda,citation,http://pdfs.semanticscholar.org/7b19/80d4ac1730fd0145202a8cb125bf05d96f01.pdf,Optimizing Open-Ended Crowdsourcing: The Next Frontier in Crowdsourced Data Management,2016 +60,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,efa2aacb0fbee857015fad1dba72767f56be6f39,citation,https://pdfs.semanticscholar.org/efa2/aacb0fbee857015fad1dba72767f56be6f39.pdf,Aggregating Crowdsourced Image Segmentations,2018 +61,VOC,voc,37.3936717,-122.0807262,Facebook,company,efa2aacb0fbee857015fad1dba72767f56be6f39,citation,https://pdfs.semanticscholar.org/efa2/aacb0fbee857015fad1dba72767f56be6f39.pdf,Aggregating Crowdsourced Image Segmentations,2018 +62,VOC,voc,34.0687788,-118.4450094,"University of California, Los Angeles",edu,17113b0f647ce05b2e50d1d40c856370f94da7de,citation,http://pdfs.semanticscholar.org/1711/3b0f647ce05b2e50d1d40c856370f94da7de.pdf,Zoom Better to See Clearer: Human Part Segmentation with Auto Zoom Net,2015 +63,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,549d55a06c5402696e063ce36b411f341a64f8a9,citation,http://arxiv.org/pdf/1511.06078v1.pdf,Learning Deep Structure-Preserving Image-Text Embeddings,2016 +64,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,549d55a06c5402696e063ce36b411f341a64f8a9,citation,http://arxiv.org/pdf/1511.06078v1.pdf,Learning Deep Structure-Preserving Image-Text Embeddings,2016 +65,VOC,voc,35.9020448,139.93622009,University of Tokyo,edu,44bfa5311f0921664e9036f63cadd71049a35f35,citation,https://pdfs.semanticscholar.org/44bf/a5311f0921664e9036f63cadd71049a35f35.pdf,Faster R-CNN-Based Glomerular Detection in Multistained Human Whole Slide Images,2018 +66,VOC,voc,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,133f1f2679892d408420d8092283539010723359,citation,http://arxiv.org/pdf/1502.05082v3.pdf,What Makes for Effective Detection Proposals?,2016 +67,VOC,voc,60.18558755,24.8242733,Aalto University,edu,98d04187f091f402a90a6a9a2108393ca5f91563,citation,https://arxiv.org/pdf/1807.09828.pdf,ADVIO: An Authentic Dataset for Visual-Inertial Odometry,2018 +68,VOC,voc,61.44964205,23.85877462,Tampere University of Technology,edu,98d04187f091f402a90a6a9a2108393ca5f91563,citation,https://arxiv.org/pdf/1807.09828.pdf,ADVIO: An Authentic Dataset for Visual-Inertial Odometry,2018 +69,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,f8015e31d1421f6aee5e17fc3907070b8e0a5e59,citation,http://pdfs.semanticscholar.org/f801/5e31d1421f6aee5e17fc3907070b8e0a5e59.pdf,Towards Usable Multimedia Event Detection from Web Videos,2016 +70,VOC,voc,34.0224149,-118.28634407,University of Southern California,edu,6b9e8acef979c13fa9ecc8fe9b635b312fedbcbe,citation,https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Chang_Multiple_Structured-Instance_Learning_2014_CVPR_paper.pdf,Multiple Structured-Instance Learning for Semantic Segmentation with Uncertain Training Data,2014 +71,VOC,voc,51.4584837,-2.6097752,University of Bristol,edu,72fd97d21d6465d4bb407b6f8f3accd4419a2fb4,citation,https://pdfs.semanticscholar.org/384a/ea88ffd79295c99bcb80552f8655dbb87509.pdf,Automated Identification of Individual Great White Sharks from Unrestricted Fin Imagery,2015 +72,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,62b83bf64f200ebb9fa16dfb7108b85e390b2207,citation,https://arxiv.org/pdf/1807.11236.pdf,Semantic Labeling in Very High Resolution Images via a Self-Cascaded Convolutional Neural Network,2018 +73,VOC,voc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,62b83bf64f200ebb9fa16dfb7108b85e390b2207,citation,https://arxiv.org/pdf/1807.11236.pdf,Semantic Labeling in Very High Resolution Images via a Self-Cascaded Convolutional Neural Network,2018 +74,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,2577211aeaaa1f2245ddc379564813bee3d46c06,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Misra_Seeing_Through_the_CVPR_2016_paper.pdf,Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels,2016 +75,VOC,voc,47.6423318,-122.1369302,Microsoft,company,2577211aeaaa1f2245ddc379564813bee3d46c06,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Misra_Seeing_Through_the_CVPR_2016_paper.pdf,Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels,2016 +76,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,3900fb44902396f94fb070be41199b4beecc9081,citation,https://arxiv.org/pdf/1612.02101.pdf,Bottom-Up Top-Down Cues for Weakly-Supervised Semantic Segmentation,2017 +77,VOC,voc,34.0687788,-118.4450094,"University of California, Los Angeles",edu,32c45df9e11e6751bcea1b928f398f6c134d22c6,citation,http://pdfs.semanticscholar.org/32c4/5df9e11e6751bcea1b928f398f6c134d22c6.pdf,Towards Unified Object Detection and Semantic Segmentation,2014 +78,VOC,voc,42.36782045,-71.12666653,Harvard University,edu,2bcd59835528c583bb5b310522a5ba6e99c58b15,citation,http://pdfs.semanticscholar.org/c0ef/596a212d0e40c79c6760673fe122e517b43c.pdf,Multi-class Open Set Recognition Using Probability of Inclusion,2014 +79,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,3920a205990abc7883c70cc96a0410a2d056c2a8,citation,http://groups.inf.ed.ac.uk/calvin/Publications/papazoglouICCV2013-camera-ready.pdf,Fast Object Segmentation in Unconstrained Video,2013 +80,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,b6810adcfd507b2e019ebc8afe4f44f953faf946,citation,https://pdfs.semanticscholar.org/b681/0adcfd507b2e019ebc8afe4f44f953faf946.pdf,ML-LocNet: Improving Object Localization with Multi-view Learning Network,2018 +81,VOC,voc,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,b6810adcfd507b2e019ebc8afe4f44f953faf946,citation,https://pdfs.semanticscholar.org/b681/0adcfd507b2e019ebc8afe4f44f953faf946.pdf,ML-LocNet: Improving Object Localization with Multi-view Learning Network,2018 +82,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,0e08cf0b19f0600dadce0f6694420d643ea9828b,citation,http://openaccess.thecvf.com/content_iccv_2015/papers/Humayun_The_Middle_Child_ICCV_2015_paper.pdf,The Middle Child Problem: Revisiting Parametric Min-Cut and Seeds for Object Proposals,2015 +83,VOC,voc,45.5198289,-122.67797964,Oregon State University,edu,0e08cf0b19f0600dadce0f6694420d643ea9828b,citation,http://openaccess.thecvf.com/content_iccv_2015/papers/Humayun_The_Middle_Child_ICCV_2015_paper.pdf,The Middle Child Problem: Revisiting Parametric Min-Cut and Seeds for Object Proposals,2015 +84,VOC,voc,30.19331415,120.11930822,Zhejiang University,edu,81bf7a4b8b3c21d42cb82f946f762c94031e11b8,citation,https://pdfs.semanticscholar.org/81bf/7a4b8b3c21d42cb82f946f762c94031e11b8.pdf,Segmentation of Nerve on Ultrasound Images Using Deep Adversarial Network,2017 +85,VOC,voc,52.4107358,-4.05295501,Aberystwyth University,edu,30d8fbb9345cdf1096635af7d39a9b04af9b72f9,citation,https://pdfs.semanticscholar.org/30d8/fbb9345cdf1096635af7d39a9b04af9b72f9.pdf,Watching plants grow - a position paper on computer vision and Arabidopsis thaliana,2017 +86,VOC,voc,43.66333345,-79.39769975,University of Toronto,edu,87204e4e1a96b8f59cb91828199dacd192292231,citation,http://pdfs.semanticscholar.org/8720/4e4e1a96b8f59cb91828199dacd192292231.pdf,Towards Real-Time Detection and Tracking of Basketball Players using Deep Neural Networks,2017 +87,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,30a4637cbc461838c151073b265fb08e00492ff4,citation,http://faculty.ucmerced.edu/mhyang/papers/cvpr16_object_localization.pdf,Weakly Supervised Object Localization with Progressive Domain Adaptation,2016 +88,VOC,voc,50.7338124,7.1022465,University of Bonn,edu,606cfdcc43203351dbb944a3bb3719695e557e37,citation,https://pdfs.semanticscholar.org/606c/fdcc43203351dbb944a3bb3719695e557e37.pdf,Ex Paucis Plura : Learning Affordance Segmentation from Very Few Examples,2018 +89,VOC,voc,39.2899685,-76.62196103,University of Maryland,edu,47b6cd69c0746688f6e17b37d73fa12422826dbc,citation,http://pdfs.semanticscholar.org/47b6/cd69c0746688f6e17b37d73fa12422826dbc.pdf,Self corrective Perturbations for Semantic Segmentation and Classification,2017 +90,VOC,voc,38.99203005,-76.9461029,University of Maryland College Park,edu,47b6cd69c0746688f6e17b37d73fa12422826dbc,citation,http://pdfs.semanticscholar.org/47b6/cd69c0746688f6e17b37d73fa12422826dbc.pdf,Self corrective Perturbations for Semantic Segmentation and Classification,2017 +91,VOC,voc,42.8298248,-73.87719385,GE Global Research Center,edu,47b6cd69c0746688f6e17b37d73fa12422826dbc,citation,http://pdfs.semanticscholar.org/47b6/cd69c0746688f6e17b37d73fa12422826dbc.pdf,Self corrective Perturbations for Semantic Segmentation and Classification,2017 +92,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,14421119527aa5882e1552a651fbd2d73bc94637,citation,http://pdfs.semanticscholar.org/9b81/86b6bc1e05d7a473d2afebc8a12698d88691.pdf,Searching for objects driven by context,2012 +93,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,14421119527aa5882e1552a651fbd2d73bc94637,citation,http://pdfs.semanticscholar.org/9b81/86b6bc1e05d7a473d2afebc8a12698d88691.pdf,Searching for objects driven by context,2012 +94,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,3410a1489d04ec6fcfbb3d76d39055117931ccf0,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2013.126,Learning Collections of Part Models for Object Recognition,2013 +95,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,69b647afe6526256a93033eac14ce470204e7bae,citation,http://pdfs.semanticscholar.org/d7dd/4fb9074db71ebf9155d64b439102d4c7b0c5.pdf,Training Deep Neural Networks via Direct Loss Minimization,2016 +96,VOC,voc,43.66333345,-79.39769975,University of Toronto,edu,69b647afe6526256a93033eac14ce470204e7bae,citation,http://pdfs.semanticscholar.org/d7dd/4fb9074db71ebf9155d64b439102d4c7b0c5.pdf,Training Deep Neural Networks via Direct Loss Minimization,2016 +97,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,81825711c2aaa1b9d3ead1a300e71c4353a41382,citation,https://arxiv.org/pdf/1607.03476.pdf,End-to-end training of object class detectors for mean average precision,2016 +98,VOC,voc,39.993008,116.329882,SenseTime,company,2ce073da76e6ed87eda2da08da0e00f4f060f1a6,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.78,Deep Saliency with Encoded Low Level Distance Map and High Level Features,2016 +99,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,2313c827d3cb9a291b6a00d015c29580862bbdcc,citation,https://arxiv.org/pdf/1808.03575.pdf,Weakly- and Semi-supervised Panoptic Segmentation,2018 +100,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,839a2155995acc0a053a326e283be12068b35cb8,citation,http://pdfs.semanticscholar.org/839a/2155995acc0a053a326e283be12068b35cb8.pdf,Handcrafted Local Features are Convolutional Neural Networks,2015 +101,VOC,voc,32.0565957,118.77408833,Nanjing University,edu,634e02d6107529d672cbbdf5b97990966e289829,citation,https://arxiv.org/pdf/1802.05394.pdf,Cost-Effective Training of Deep CNNs with Active Model Adaptation,2018 +102,VOC,voc,56.45796755,-2.98214831,University of Dundee,edu,d0137881f6c791997337b9cc7f1efbd61977270d,citation,http://pdfs.semanticscholar.org/d013/7881f6c791997337b9cc7f1efbd61977270d.pdf,"University of Dundee An automated pattern recognition system for classifying indirect immunofluorescence images for HEp-2 cells and specimens Manivannan,",2016 +103,VOC,voc,42.2942142,-83.71003894,University of Michigan,edu,ed173a39f4cd980eef319116b6ba39cec1b37c42,citation,https://arxiv.org/pdf/1611.05424.pdf,Associative Embedding: End-to-End Learning for Joint Detection and Grouping,2017 +104,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,ed173a39f4cd980eef319116b6ba39cec1b37c42,citation,https://arxiv.org/pdf/1611.05424.pdf,Associative Embedding: End-to-End Learning for Joint Detection and Grouping,2017 +105,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,84cf838be40e2ab05732fbefbb93ccb2afb0cb48,citation,http://pdfs.semanticscholar.org/84cf/838be40e2ab05732fbefbb93ccb2afb0cb48.pdf,Recognizing Handwritten Characters,2016 +106,VOC,voc,37.26728,126.9841151,Seoul National University,edu,b082f440ee91e2751701401919584203b37e1e1a,citation,https://pdfs.semanticscholar.org/303c/28f1ba643a7cd88255cc379e79052fb7e7b1.pdf,SeedNet : Automatic Seed Generation with Deep Reinforcement Learning for Robust Interactive Segmentation,2018 +107,VOC,voc,22.2081469,114.25964115,University of Hong Kong,edu,6008213e4270e88cb414459de759c961469b92dd,citation,https://arxiv.org/pdf/1802.09129.pdf,"Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning",2018 +108,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,90b4470032f2796a347a0080bcd833c2db0e8bf0,citation,https://arxiv.org/pdf/1807.07760.pdf,Improving Image Clustering With Multiple Pretrained CNN Feature Extractors,2018 +109,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,beecaf2d6e9d102b6b2459ea38e15179a4b55ffd,citation,https://arxiv.org/pdf/1611.09587.pdf,Surveillance Video Parsing with Single Frame Supervision,2017 +110,VOC,voc,41.3868913,2.16352385,University of Barcelona,edu,0fb8317a8bf5feaf297af8e9b94c50c5ed0e8277,citation,http://pdfs.semanticscholar.org/0fb8/317a8bf5feaf297af8e9b94c50c5ed0e8277.pdf,Detecting Hands in Egocentric Videos: Towards Action Recognition,2017 +111,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,0e0179eb4b43016691f0f1473a08089dda21f8f0,citation,http://pdfs.semanticscholar.org/0e01/79eb4b43016691f0f1473a08089dda21f8f0.pdf,The Art of Detection,2016 +112,VOC,voc,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,135c957f6a80f250507c7707479e584c288f430f,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2014.498,Image-Based Synthesis and Re-synthesis of Viewpoints Guided by 3D Models,2014 +113,VOC,voc,39.00041165,-77.10327775,National Institutes of Health,edu,c72b063e23b8b45b57a42ebc2f9714297c539a6f,citation,https://arxiv.org/pdf/1801.04334.pdf,TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays,2018 +114,VOC,voc,36.05238585,140.11852361,National Institute of Advanced Industrial Science and Technology,edu,061ffd3967540424ac4e4066f4a605d8318bab90,citation,https://staff.aist.go.jp/takumi.kobayashi/publication/2014/CVPR2014.pdf,Dirichlet-Based Histogram Feature Transform for Image Classification,2014 +115,VOC,voc,42.3583961,-71.09567788,MIT,edu,1a2e9a56e5f71bf95a2f68b6e67e2aaa1c6bf91e,citation,http://pdfs.semanticscholar.org/1a2e/9a56e5f71bf95a2f68b6e67e2aaa1c6bf91e.pdf,FPM: Fine Pose Parts-Based Model with 3D CAD Models,2014 +116,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,c6f58adf4a5ee8499cbc9b9bc1e6f1c39f1f8eae,citation,https://pdfs.semanticscholar.org/c6f5/8adf4a5ee8499cbc9b9bc1e6f1c39f1f8eae.pdf,Earn to P Ay a Ttention,2018 +117,VOC,voc,32.87935255,-117.23110049,"University of California, San Diego",edu,3c8db2ca155ce4e15ec8a2c4c4b979de654fb296,citation,http://pages.ucsd.edu/~ztu/publication/iccv15_hed.pdf,Holistically-Nested Edge Detection,2015 +118,VOC,voc,59.34986645,18.07063213,"KTH Royal Institute of Technology, Stockholm",edu,8ccd6aaf1ee4b66c13fffbf560e3920f9bdf5f10,citation,http://pdfs.semanticscholar.org/8ccd/6aaf1ee4b66c13fffbf560e3920f9bdf5f10.pdf,A multitask deep learning model for real-time deployment in embedded systems,2017 +119,VOC,voc,53.5238572,-113.52282665,University of Alberta,edu,b4f5cf797a1c857f32e5740d53d9990bc925af2b,citation,https://pdfs.semanticscholar.org/b4f5/cf797a1c857f32e5740d53d9990bc925af2b.pdf,Review of Segmentation with Deep Learning and Discover Its Application in Ultrasound Images,2018 +120,VOC,voc,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,3bad18554678ab46bbbf9de41d36423bc8083c83,citation,http://arxiv.org/pdf/1511.07803v1.pdf,Weakly Supervised Object Boundaries,2016 +121,VOC,voc,24.7925484,120.9951183,National Tsing Hua University,edu,07191c2047b5b643dd72a0583c1d537ba59f977a,citation,http://pdfs.semanticscholar.org/0719/1c2047b5b643dd72a0583c1d537ba59f977a.pdf,Interactive Segmentation from 1-Bit Feedback,2016 +122,VOC,voc,37.26728,126.9841151,Seoul National University,edu,ae6e8851dfd9c97e37e1cbd61b21cc54d5e2b9c7,citation,https://arxiv.org/pdf/1802.04977.pdf,Paraphrasing Complex Network: Network Compression via Factor Transfer,2018 +123,VOC,voc,37.26728,126.9841151,Seoul National University,edu,5375a3344017d9502ebb4170325435de3da1fa16,citation,https://doi.org/10.1007/978-3-642-37447-0,Computer Vision – ACCV 2012,2012 +124,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,5375a3344017d9502ebb4170325435de3da1fa16,citation,https://doi.org/10.1007/978-3-642-37447-0,Computer Vision – ACCV 2012,2012 +125,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,5375a3344017d9502ebb4170325435de3da1fa16,citation,https://doi.org/10.1007/978-3-642-37447-0,Computer Vision – ACCV 2012,2012 +126,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,fdfd57d4721174eba288e501c0c120ad076cdca8,citation,https://arxiv.org/pdf/1704.07129.pdf,An Analysis of Action Recognition Datasets for Language and Vision Tasks,2017 +127,VOC,voc,32.0565957,118.77408833,Nanjing University,edu,ec83c63e28ae2a658bc76a6750e078c3a54b9760,citation,https://arxiv.org/pdf/1705.02758.pdf,Deep Descriptor Transforming for Image Co-Localization,2017 +128,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,ec83c63e28ae2a658bc76a6750e078c3a54b9760,citation,https://arxiv.org/pdf/1705.02758.pdf,Deep Descriptor Transforming for Image Co-Localization,2017 +129,VOC,voc,59.34986645,18.07063213,"KTH Royal Institute of Technology, Stockholm",edu,b1177aad0db8bd6b605ffe0d68addaf97b1f9a6b,citation,https://pdfs.semanticscholar.org/5035/733022916db7e5965c565327e169da1e2f39.pdf,Visual Representations and Models: From Latent SVM to Deep Learning,2016 +130,VOC,voc,31.83907195,117.26420748,University of Science and Technology of China,edu,a5ae7d662ed086bc5b0c9a2c1dc54fcb23635000,citation,https://pdfs.semanticscholar.org/a5ae/7d662ed086bc5b0c9a2c1dc54fcb23635000.pdf,Relief R-CNN : Utilizing Convolutional Feature Interrelationship for Fast Object Detection Deployment,2016 +131,VOC,voc,22.53521465,113.9315911,Shenzhen University,edu,a5ae7d662ed086bc5b0c9a2c1dc54fcb23635000,citation,https://pdfs.semanticscholar.org/a5ae/7d662ed086bc5b0c9a2c1dc54fcb23635000.pdf,Relief R-CNN : Utilizing Convolutional Feature Interrelationship for Fast Object Detection Deployment,2016 +132,VOC,voc,53.38522185,-6.25740874,Dublin City University,edu,9528e2e8c20517ab916f803c0371abb4f0ed488b,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Pan_Shallow_and_Deep_CVPR_2016_paper.pdf,Shallow and Deep Convolutional Networks for Saliency Prediction,2016 +133,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,e2272f50ffa33b8e41509e4b795ad5a4eb27bb46,citation,https://arxiv.org/pdf/1607.07671.pdf,Region-based semantic segmentation with end-to-end training,2016 +134,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,b8d61dc56a4112e0317c6a7323417ee649476148,citation,https://arxiv.org/pdf/1807.05636.pdf,Cross Pixel Optical Flow Similarity for Self-Supervised Learning,2018 +135,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,db0a4af734dab1854c2e8dfe499fe0e353226e45,citation,https://pdfs.semanticscholar.org/db0a/4af734dab1854c2e8dfe499fe0e353226e45.pdf,Hot Anchors: A Heuristic Anchors Sampling Method in RCNN-Based Object Detection,2018 +136,VOC,voc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,db0a4af734dab1854c2e8dfe499fe0e353226e45,citation,https://pdfs.semanticscholar.org/db0a/4af734dab1854c2e8dfe499fe0e353226e45.pdf,Hot Anchors: A Heuristic Anchors Sampling Method in RCNN-Based Object Detection,2018 +137,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,ffe0f43206169deef3a2bf64cec90fe35bb1a8e5,citation,http://pdfs.semanticscholar.org/ffe0/f43206169deef3a2bf64cec90fe35bb1a8e5.pdf,"Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans +",2015 +138,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,ffe0f43206169deef3a2bf64cec90fe35bb1a8e5,citation,http://pdfs.semanticscholar.org/ffe0/f43206169deef3a2bf64cec90fe35bb1a8e5.pdf,"Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans +",2015 +139,VOC,voc,45.77445695,126.67684917,Harbin Engineering University,edu,479eb6579194d4d944671dfe5e90b122ca4b58fd,citation,https://pdfs.semanticscholar.org/479e/b6579194d4d944671dfe5e90b122ca4b58fd.pdf,Structural inference embedded adversarial networks for scene parsing,2018 +140,VOC,voc,34.2469152,108.91061982,Northwestern Polytechnical University,edu,479eb6579194d4d944671dfe5e90b122ca4b58fd,citation,https://pdfs.semanticscholar.org/479e/b6579194d4d944671dfe5e90b122ca4b58fd.pdf,Structural inference embedded adversarial networks for scene parsing,2018 +141,VOC,voc,1.29500195,103.84909214,Singapore Management University,edu,d289ce63055c10937e5715e940a4bb9d0af7a8c5,citation,http://dl.acm.org/citation.cfm?id=3081360,DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications,2017 +142,VOC,voc,60.18558755,24.8242733,Aalto University,edu,061bba574c7c2ef0ba9de91afc4fcab70feddd4f,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2017.272,Paying Attention to Descriptions Generated by Image Captioning Models,2017 +143,VOC,voc,28.59899755,-81.19712501,University of Central Florida,edu,061bba574c7c2ef0ba9de91afc4fcab70feddd4f,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2017.272,Paying Attention to Descriptions Generated by Image Captioning Models,2017 +144,VOC,voc,34.7275714,135.2371,Kobe University,edu,ee2217f9d22d6a18aaf97f05768035c38305d1fa,citation,https://doi.org/10.1109/APSIPA.2015.7415501,Detection of facial parts via deformable part model using part annotation,2015 +145,VOC,voc,50.7791703,6.06728733,RWTH Aachen University,edu,18219d85bb14f851fc4714df19cc7f38dff8ddc3,citation,http://pdfs.semanticscholar.org/1821/9d85bb14f851fc4714df19cc7f38dff8ddc3.pdf,Online Adaptation of Convolutional Neural Networks for the 2017 DAVIS Challenge on Video Object Segmentation,2017 +146,VOC,voc,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,da44881db32c132eb9cdef524618e3c8ed340b47,citation,https://arxiv.org/pdf/1802.00383.pdf,Annotation-Free and One-Shot Learning for Instance Segmentation of Homogeneous Object Clusters,2018 +147,VOC,voc,50.7338124,7.1022465,University of Bonn,edu,cc94b423c298003f0f164e63e63177d443291a77,citation,https://arxiv.org/pdf/1805.03994.pdf,Multi-View Semantic Labeling of 3D Point Clouds for Automated Plant Phenotyping,2018 +148,VOC,voc,39.9922379,116.30393816,Peking University,edu,83a811fd947415df2413d15386dbc558f07595cb,citation,https://arxiv.org/pdf/1709.08295.pdf,Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN,2017 +149,VOC,voc,-33.8809651,151.20107299,University of Technology Sydney,edu,3a5f5aca6138abcf22ede1af5572e01eb0f761d1,citation,https://pdfs.semanticscholar.org/3a5f/5aca6138abcf22ede1af5572e01eb0f761d1.pdf,Optimizing Multivariate Performance Measures from Multi-View Data,2016 +150,VOC,voc,34.2469152,108.91061982,Northwestern Polytechnical University,edu,ce300b006f42c1b64ca0e53d1cf28d11a98ece8f,citation,https://pdfs.semanticscholar.org/ce30/0b006f42c1b64ca0e53d1cf28d11a98ece8f.pdf,Learning Multi-Instance Enriched Image Representations via Non-Greedy Ratio Maximization of the l 1-Norm Distances,0 +151,VOC,voc,34.0224149,-118.28634407,University of Southern California,edu,71b038958df0b7855fc7b8b8e7dcde8537a7c1ad,citation,http://pdfs.semanticscholar.org/71b0/38958df0b7855fc7b8b8e7dcde8537a7c1ad.pdf,Kernel Methods for Unsupervised Domain Adaptation by Boqing Gong,2015 +152,VOC,voc,34.2469152,108.91061982,Northwestern Polytechnical University,edu,af7cab9b4a2a2a565a3efe0a226c517f47289077,citation,https://arxiv.org/pdf/1803.10910.pdf,Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective,2018 +153,VOC,voc,-35.2776999,149.118527,Australian National University,edu,af7cab9b4a2a2a565a3efe0a226c517f47289077,citation,https://arxiv.org/pdf/1803.10910.pdf,Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective,2018 +154,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,3a6ebdfb6375093885e846153a48139ef1ecfae6,citation,http://arxiv.org/abs/1411.7466,The treasure beneath convolutional layers: Cross-convolutional-layer pooling for image classification,2015 +155,VOC,voc,51.24303255,-0.59001382,University of Surrey,edu,a7e9d230bc44dfbe56757f3025d5b4caa49032f3,citation,http://pdfs.semanticscholar.org/a7e9/d230bc44dfbe56757f3025d5b4caa49032f3.pdf,Unity in Diversity: Discovering Topics from Words - Information Theoretic Co-clustering for Visual Categorization,2012 +156,VOC,voc,37.5557271,127.0436642,Hanyang University,edu,50137d663802224e683951c48970496b38b02141,citation,http://pdfs.semanticscholar.org/5013/7d663802224e683951c48970496b38b02141.pdf,DETRAC: A New Benchmark and Protocol for Multi-Object Tracking,2015 +157,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,07de8371ad4901356145722aa29abaeafd0986b9,citation,http://pdfs.semanticscholar.org/07de/8371ad4901356145722aa29abaeafd0986b9.pdf,Towards Usable Multimedia Event Detection,2017 +158,VOC,voc,41.21002475,-73.80407056,IBM Thomas J. Watson Research Center,company,af386bb1b5e8c9f65b3ae836198a93aa860d6331,citation,https://arxiv.org/pdf/1805.04574.pdf,Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation,2018 +159,VOC,voc,17.4454957,78.34854698,International Institute of Information Technology,edu,d6b1b0e60e1764982ef95d4ade8fcaa10bfb156a,citation,http://pdfs.semanticscholar.org/d6b1/b0e60e1764982ef95d4ade8fcaa10bfb156a.pdf,A Sketch-based Approach for Multimedia Retrieval,2016 +160,VOC,voc,51.49887085,-0.17560797,Imperial College London,edu,37b3637dab65b91a5c91bb6a583e69c448823cc1,citation,https://arxiv.org/pdf/1705.05994.pdf,Learning a Hierarchical Latent-Variable Model of 3D Shapes,2018 +161,VOC,voc,39.9574,-75.19026706,Drexel University,edu,83d16fb8f53156c9e2b28d75abb6532af515440f,citation,http://pdfs.semanticscholar.org/83d1/6fb8f53156c9e2b28d75abb6532af515440f.pdf,Large-scale Document Labeling using Supervised Sequence Embedding,2012 +162,VOC,voc,45.51181205,-122.68492999,Portland State University,edu,05e45f61dc7577c50114a382abc6e952ae24cdac,citation,https://pdfs.semanticscholar.org/05e4/5f61dc7577c50114a382abc6e952ae24cdac.pdf,"Object Detection and Recognition in Natural Settings by George William Dittmar A thesis submitted in partial fulfilment of the requirements of the degree Master of Science in Computer Science Thesis Committee: Melanie Mitchell, Chair",2012 +163,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,192235f5a9e4c9d6a28ec0d333e36f294b32f764,citation,http://www.andrew.cmu.edu/user/sjayasur/iccv.pdf,Reconfiguring the Imaging Pipeline for Computer Vision,2017 +164,VOC,voc,42.4505507,-76.4783513,Cornell University,edu,192235f5a9e4c9d6a28ec0d333e36f294b32f764,citation,http://www.andrew.cmu.edu/user/sjayasur/iccv.pdf,Reconfiguring the Imaging Pipeline for Computer Vision,2017 +165,VOC,voc,50.0764296,14.41802312,Czech Technical University,edu,bd4f2e7a196c0d6033a49390ee8836f4f551b7c8,citation,http://rrc.cvc.uab.es/files/Robust-Reading-Competition-Karatzas.pdf,ICDAR 2015 competition on Robust Reading,2015 +166,VOC,voc,33.59914655,130.22359848,Kyushu University,edu,bd4f2e7a196c0d6033a49390ee8836f4f551b7c8,citation,http://rrc.cvc.uab.es/files/Robust-Reading-Competition-Karatzas.pdf,ICDAR 2015 competition on Robust Reading,2015 +167,VOC,voc,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,3d5575e9ba02128d94c20330f4525fc816411ec2,citation,https://arxiv.org/pdf/1612.02646.pdf,Learning Video Object Segmentation from Static Images,2017 +168,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,78f62042bfb3bb49ba10e142d118a9bb058b2a19,citation,http://pdfs.semanticscholar.org/78f6/2042bfb3bb49ba10e142d118a9bb058b2a19.pdf,WebSeg: Learning Semantic Segmentation from Web Searches,2018 +169,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,0c7aac75ccd17d696cff2e1ce95db0493f5c18a2,citation,https://arxiv.org/pdf/1809.01123.pdf,VideoMatch: Matching Based Video Object Segmentation,2018 +170,VOC,voc,3.12267405,101.65356103,University of Malaya,edu,6c78add400f749c897dc3eb93996eda1c796e91c,citation,https://arxiv.org/pdf/1410.3752.pdf,Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding,2014 +171,VOC,voc,51.49887085,-0.17560797,Imperial College London,edu,6c78add400f749c897dc3eb93996eda1c796e91c,citation,https://arxiv.org/pdf/1410.3752.pdf,Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding,2014 +172,VOC,voc,39.9922379,116.30393816,Peking University,edu,6c78add400f749c897dc3eb93996eda1c796e91c,citation,https://arxiv.org/pdf/1410.3752.pdf,Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding,2014 +173,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,b61c0b11b1c25958d202b4f7ca772e1d95ee1037,citation,http://pdfs.semanticscholar.org/b61c/0b11b1c25958d202b4f7ca772e1d95ee1037.pdf,Bridging Category-level and Instance-level Semantic Image Segmentation,2016 +174,VOC,voc,34.0224149,-118.28634407,University of Southern California,edu,79894ddf290d3c7a768d634eceb7888564b5cf19,citation,https://arxiv.org/pdf/1708.01676.pdf,Query-Guided Regression Network with Context Policy for Phrase Grounding,2017 +175,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,fec2a5a06a3aab5efe923a78d208ec747d5e4894,citation,https://arxiv.org/pdf/1805.12018.pdf,Generalizing to Unseen Domains via Adversarial Data Augmentation,2018 +176,VOC,voc,31.30104395,121.50045497,Fudan University,edu,5ac63895a7d3371a739d066bb1631fc178d8276a,citation,http://doi.acm.org/10.1145/3123266.3123379,Learning Semantic Feature Map for Visual Content Recognition,2017 +177,VOC,voc,39.2899685,-76.62196103,University of Maryland,edu,5ac63895a7d3371a739d066bb1631fc178d8276a,citation,http://doi.acm.org/10.1145/3123266.3123379,Learning Semantic Feature Map for Visual Content Recognition,2017 +178,VOC,voc,-34.40505545,150.87834655,University of Wollongong,edu,4e559f23bcf502c752f2938ad7f0182047b8d1e4,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Wang_A_Fast_Approximate_2013_CVPR_paper.pdf,A Fast Approximate AIB Algorithm for Distributional Word Clustering,2013 +179,VOC,voc,-35.2776999,149.118527,Australian National University,edu,7536b6a9f3cb4ae810e2ef6d0219134b4e546dd0,citation,http://pdfs.semanticscholar.org/7536/b6a9f3cb4ae810e2ef6d0219134b4e546dd0.pdf,Semi-Automatic Image Labelling Using Depth Information,2015 +180,VOC,voc,42.7298459,-73.67950216,Rensselaer Polytechnic Institute,edu,11b89011298e193d9e6a1d99302221c1d8645bda,citation,http://openaccess.thecvf.com/content_iccv_2015/papers/Gao_Structured_Feature_Selection_ICCV_2015_paper.pdf,Structured Feature Selection,2015 +181,VOC,voc,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,de3245c795bc50ebdb5d929c8da664341238264a,citation,https://arxiv.org/pdf/1705.08590.pdf,Generative Model With Coordinate Metric Learning for Object Recognition Based on 3D Models,2018 +182,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,cc2eaa182f33defbb33d69e9547630aab7ed9c9c,citation,http://pdfs.semanticscholar.org/ce2e/e807a63bbdffa530c80915b04d11a7f29a21.pdf,Surpassing Humans and Computers with JELLYBEAN: Crowd-Vision-Hybrid Counting Algorithms,2015 +183,VOC,voc,40.00471095,-83.02859368,Ohio State University,edu,cc2eaa182f33defbb33d69e9547630aab7ed9c9c,citation,http://pdfs.semanticscholar.org/ce2e/e807a63bbdffa530c80915b04d11a7f29a21.pdf,Surpassing Humans and Computers with JELLYBEAN: Crowd-Vision-Hybrid Counting Algorithms,2015 +184,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,cc2eaa182f33defbb33d69e9547630aab7ed9c9c,citation,http://pdfs.semanticscholar.org/ce2e/e807a63bbdffa530c80915b04d11a7f29a21.pdf,Surpassing Humans and Computers with JELLYBEAN: Crowd-Vision-Hybrid Counting Algorithms,2015 +185,VOC,voc,32.0565957,118.77408833,Nanjing University,edu,9c71e6f4e27b3a6f0f872ec683b0f6dfe0966c05,citation,http://pdfs.semanticscholar.org/9c71/e6f4e27b3a6f0f872ec683b0f6dfe0966c05.pdf,"Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey",2017 +186,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,b88b83d2ffd30bf3bc3be3fb7492fd88f633b2fe,citation,http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/4989a827.pdf,Subcategory-Aware Object Classification,2013 +187,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,b6a3802075d460093977f8566c451f950edf7a47,citation,https://pdfs.semanticscholar.org/0999/e5baf505eed0df8e2661c29354f3757b3399.pdf,Facilitating and Exploring Planar Homogeneous Texture for Indoor Scene Understanding,2016 +188,VOC,voc,51.7555205,-1.2261597,Oxford Brookes University,edu,cd6cab9357f333ad9966abc76f830c190a1b7911,citation,https://pdfs.semanticscholar.org/cd6c/ab9357f333ad9966abc76f830c190a1b7911.pdf,"Recognition, reorganisation, reconstruction and reinteraction for scene understanding",2014 +189,VOC,voc,47.3764534,8.54770931,ETH Zürich,edu,0fe8b5503681128da84a8454a4cc94470adc09ea,citation,http://pdfs.semanticscholar.org/b96a/0ccae1d15cffe3b479b2c56d9132b05cd846.pdf,Sparsity Potentials for Detecting Objects with the Hough Transform,2012 +190,VOC,voc,35.7036227,51.35125097,Sharif University of Technology,edu,0fe8b5503681128da84a8454a4cc94470adc09ea,citation,http://pdfs.semanticscholar.org/b96a/0ccae1d15cffe3b479b2c56d9132b05cd846.pdf,Sparsity Potentials for Detecting Objects with the Hough Transform,2012 +191,VOC,voc,47.6423318,-122.1369302,Microsoft,company,9bbc952adb3e3c6091d45d800e806d3373a52bac,citation,https://pdfs.semanticscholar.org/9bbc/952adb3e3c6091d45d800e806d3373a52bac.pdf,Learning Visual Classifiers using Human-centric Annotations,2015 +192,VOC,voc,35.6572957,139.54255868,University of Electro-Communications,edu,6e209d7d33c0be8afae863f4e4e9c3e86826711f,citation,http://img.cs.uec.ac.jp/pub/conf16/161204shimok_1_ppt.pdf,Weakly-supervised segmentation by combining CNN feature maps and object saliency maps,2016 +193,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,46d85e1dc7057bef62647bd9241601e9896a1b02,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/2A_040_ext.pdf,Improving object proposals with multi-thresholding straddling expansion,2015 +194,VOC,voc,35.2742655,137.01327841,Chubu University,edu,67e3fac91c699c085d47774990572d8ccdc36f15,citation,http://pdfs.semanticscholar.org/67e3/fac91c699c085d47774990572d8ccdc36f15.pdf,Multiple Skip Connections and Dilated Convolutions for Semantic Segmentation,2017 +195,VOC,voc,34.0224149,-118.28634407,University of Southern California,edu,a4f29217d2120ed1490aea7e1c5b78c3b76e972f,citation,https://arxiv.org/pdf/1610.06907.pdf,Enhanced object detection via fusion with prior beliefs from image classification,2017 +196,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,f2d07a77711a8d74bbfa48a0436dae18a698b05a,citation,http://pdfs.semanticscholar.org/f2d0/7a77711a8d74bbfa48a0436dae18a698b05a.pdf,Composite Statistical Learning and Inference for Semantic Segmentation,2014 +197,VOC,voc,40.2075951,-8.42566148,University of Coimbra,edu,f2d07a77711a8d74bbfa48a0436dae18a698b05a,citation,http://pdfs.semanticscholar.org/f2d0/7a77711a8d74bbfa48a0436dae18a698b05a.pdf,Composite Statistical Learning and Inference for Semantic Segmentation,2014 +198,VOC,voc,55.7039571,13.1902011,Lund University,edu,f2d07a77711a8d74bbfa48a0436dae18a698b05a,citation,http://pdfs.semanticscholar.org/f2d0/7a77711a8d74bbfa48a0436dae18a698b05a.pdf,Composite Statistical Learning and Inference for Semantic Segmentation,2014 +199,VOC,voc,61.44964205,23.85877462,Tampere University of Technology,edu,ff11cb09e409996020a2dc3a8afc3b535e6b2482,citation,https://arxiv.org/pdf/1807.03142.pdf,Faster Bounding Box Annotation for Object Detection in Indoor Scenes,2018 +200,VOC,voc,35.84658875,127.1350133,Chonbuk National University,edu,e103fa24d7fa297cd206b22b3bf670bfda6c65c4,citation,https://pdfs.semanticscholar.org/e103/fa24d7fa297cd206b22b3bf670bfda6c65c4.pdf,Object Detection in Very High-Resolution Aerial Images Using One-Stage Densely Connected Feature Pyramid Network,2018 +201,VOC,voc,41.8268682,-71.40123146,Brown University,edu,9a781a01b5a9c210dd2d27db8b73b7d62bc64837,citation,http://pdfs.semanticscholar.org/9a78/1a01b5a9c210dd2d27db8b73b7d62bc64837.pdf,An Attempt to Build Object Detection Models by Reusing Parts,2013 +202,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,ac559888f996923c06b1cf90db6b57b12e582289,citation,http://pdfs.semanticscholar.org/ac55/9888f996923c06b1cf90db6b57b12e582289.pdf,Benchmarking neuromorphic vision: lessons learnt from computer vision,2015 +203,VOC,voc,47.3764534,8.54770931,ETH Zürich,edu,ac559888f996923c06b1cf90db6b57b12e582289,citation,http://pdfs.semanticscholar.org/ac55/9888f996923c06b1cf90db6b57b12e582289.pdf,Benchmarking neuromorphic vision: lessons learnt from computer vision,2015 +204,VOC,voc,39.2899685,-76.62196103,University of Maryland,edu,ac559888f996923c06b1cf90db6b57b12e582289,citation,http://pdfs.semanticscholar.org/ac55/9888f996923c06b1cf90db6b57b12e582289.pdf,Benchmarking neuromorphic vision: lessons learnt from computer vision,2015 +205,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,2a4fc35acaf09517e9c63821cadd428a84832416,citation,http://www.vision.ee.ethz.ch/en/publications/papers/proceedings/eth_biwi_00905.pdf,Learning object class detectors from weakly annotated video,2012 +206,VOC,voc,22.053565,113.39913285,Jilin University,edu,cd4850de71e4e858be5f5e6ef7f48d5bf7decea6,citation,http://pdfs.semanticscholar.org/cd48/50de71e4e858be5f5e6ef7f48d5bf7decea6.pdf,Distribution Entropy Boosted VLAD for Image Retrieval,2016 +207,VOC,voc,40.4319722,-86.92389368,Purdue University,edu,34b925a111ba29f73f5c0d1b363f357958d563c1,citation,https://www.microsoft.com/en-us/research/wp-content/uploads/2015/03/Shoaib_DATE_2015.pdf,SAPPHIRE: An always-on context-aware computer vision system for portable devices,2015 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for One-Shot Semantic Segmentation,2018 +212,VOC,voc,42.3583961,-71.09567788,MIT,edu,26aa0aff1ea1baf848a521363cc455044690e090,citation,http://pdfs.semanticscholar.org/26aa/0aff1ea1baf848a521363cc455044690e090.pdf,A 2D + 3D Rich Data Approach to Scene Understanding,2013 +213,VOC,voc,46.0658836,11.1159894,University of Trento,edu,3548cb9ee54bd4c8b3421f1edd393da9038da293,citation,http://www.huppelen.nl/publications/2012cvprUnseenEventCompositionality.pdf,(Unseen) event recognition via semantic compositionality,2012 +214,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,25ee08db14dca641d085584909b551042618b8bf,citation,http://pdfs.semanticscholar.org/25ee/08db14dca641d085584909b551042618b8bf.pdf,Learning to Segment Instances in Videos with Spatial Propagation Network,2017 +215,VOC,voc,37.36566745,-120.42158888,"University of California, Merced",edu,25ee08db14dca641d085584909b551042618b8bf,citation,http://pdfs.semanticscholar.org/25ee/08db14dca641d085584909b551042618b8bf.pdf,Learning to Segment Instances in Videos with Spatial Propagation Network,2017 +216,VOC,voc,48.9095338,9.1831892,University of Stuttgart,edu,d0f81c31e11af1783644704321903a3d2bd83fd6,citation,https://pdfs.semanticscholar.org/d0f8/1c31e11af1783644704321903a3d2bd83fd6.pdf,3D Façade Labeling over Complex Scenarios: A Case Study Using Convolutional Neural Network and Structure-From-Motion,2018 +217,VOC,voc,50.7369302,-3.53647672,University of Exeter,edu,d0f81c31e11af1783644704321903a3d2bd83fd6,citation,https://pdfs.semanticscholar.org/d0f8/1c31e11af1783644704321903a3d2bd83fd6.pdf,3D Façade Labeling over Complex Scenarios: A Case Study Using Convolutional Neural Network and Structure-From-Motion,2018 +218,VOC,voc,38.99203005,-76.9461029,University of Maryland College Park,edu,a996f22a2d0c685f7e4972df9f45e99efc3cbb76,citation,https://arxiv.org/pdf/1708.00079.pdf,Towards the Success Rate of One: Real-Time Unconstrained Salient Object Detection,2018 +219,VOC,voc,47.05821,15.46019568,Graz University of Technology,edu,4da5f0c1d07725a06c6b4a2646e31ea3a5f14435,citation,http://pdfs.semanticscholar.org/4da5/f0c1d07725a06c6b4a2646e31ea3a5f14435.pdf,End-to-End Training of Hybrid CNN-CRF Models for Semantic Segmentation using Structured Learning,2017 +220,VOC,voc,52.3553655,4.9501644,University of Amsterdam,edu,26c58e24687ccbe9737e41837aab74e4a499d259,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Li_Codemaps_-_Segment_2013_ICCV_paper.pdf,"Codemaps - Segment, Classify and Search Objects Locally",2013 +221,VOC,voc,37.4219999,-122.0840575,Google,company,299b65d5d3914dad9aae2f936165dcebcf78db88,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.203,Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation,2015 +222,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,cb5dcd048b0eaa78a887a014be26a8a7b1325d36,citation,https://arxiv.org/pdf/1709.04093.pdf,Joint Learning of Set Cardinality and State Distribution,2018 +223,VOC,voc,34.2469152,108.91061982,Northwestern Polytechnical University,edu,63660c50e2669a5115c2379e622549d8ed79be00,citation,http://porikli.com/mysite/pdfs/porikli%202017%20-%20Deep%20salient%20object%20detection%20by%20integrating%20multi-level%20cues.pdf,Deep Salient Object Detection by Integrating Multi-level Cues,2017 +224,VOC,voc,-35.2776999,149.118527,Australian National University,edu,63660c50e2669a5115c2379e622549d8ed79be00,citation,http://porikli.com/mysite/pdfs/porikli%202017%20-%20Deep%20salient%20object%20detection%20by%20integrating%20multi-level%20cues.pdf,Deep Salient Object Detection by Integrating Multi-level Cues,2017 +225,VOC,voc,48.14955455,11.56775314,Technical University Munich,edu,472541ccd941b9b4c52e1f088cc1152de9b3430f,citation,https://arxiv.org/pdf/1612.00197.pdf,Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses,2017 +226,VOC,voc,47.3764534,8.54770931,ETH Zürich,edu,9184b0c04013bfdfd82f4f271b5f017396c2f085,citation,https://pdfs.semanticscholar.org/9184/b0c04013bfdfd82f4f271b5f017396c2f085.pdf,Semantic Segmentation for Line Drawing Vectorization Using Neural Networks,2018 +227,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,57488aa24092fa7118aa5374c90b282a32473cf9,citation,https://arxiv.org/pdf/1807.01257.pdf,A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities,2018 +228,VOC,voc,39.9492344,-75.19198985,University of Pennsylvania,edu,57488aa24092fa7118aa5374c90b282a32473cf9,citation,https://arxiv.org/pdf/1807.01257.pdf,A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities,2018 +229,VOC,voc,32.0565957,118.77408833,Nanjing University,edu,7771807cd05f78a4591f2d0b094ddd3e0bd5339a,citation,https://arxiv.org/pdf/1707.06399.pdf,Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors,2017 +230,VOC,voc,50.7944026,-1.0971748,Cambridge University,edu,4558338873556d01fd290de6ddc55721c633a1ad,citation,http://pdfs.semanticscholar.org/4558/338873556d01fd290de6ddc55721c633a1ad.pdf,Training Constrained Deconvolutional Networks for Road Scene Semantic Segmentation,2016 +231,VOC,voc,42.3583961,-71.09567788,MIT,edu,85957b49896246bb416c0a182e52b355a8fa40b4,citation,https://arxiv.org/pdf/1806.03510.pdf,Feature Pyramid Network for Multi-Class Land Segmentation,2018 +232,VOC,voc,17.4454957,78.34854698,International Institute of Information Technology,edu,f5eb411217f729ad7ae84bfd4aeb3dedb850206a,citation,https://pdfs.semanticscholar.org/f5eb/411217f729ad7ae84bfd4aeb3dedb850206a.pdf,Tackling Low Resolution for Better Scene Understanding,2018 +233,VOC,voc,53.8338371,10.7035939,Institute of Systems and Robotics,edu,7fb8d9c36c23f274f2dd84945dd32ec2cc143de1,citation,http://pdfs.semanticscholar.org/8e44/ba779d7cdc23d597c2c6e4420129834e7e21.pdf,Semantic Segmentation with Second-Order Pooling,2012 +234,VOC,voc,50.7338124,7.1022465,University of Bonn,edu,7fb8d9c36c23f274f2dd84945dd32ec2cc143de1,citation,http://pdfs.semanticscholar.org/8e44/ba779d7cdc23d597c2c6e4420129834e7e21.pdf,Semantic Segmentation with Second-Order Pooling,2012 +235,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,b5e3beb791cc17cdaf131d5cca6ceb796226d832,citation,http://pdfs.semanticscholar.org/b5e3/beb791cc17cdaf131d5cca6ceb796226d832.pdf,Novel Dataset for Fine-Grained Image Categorization: Stanford Dogs,2012 +236,VOC,voc,39.94976005,116.33629046,Beijing Jiaotong University,edu,b5968e7bb23f5f03213178c22fd2e47af3afa04c,citation,https://arxiv.org/pdf/1705.07206.pdf,Multiple-Human Parsing in the Wild,2017 +237,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,b5968e7bb23f5f03213178c22fd2e47af3afa04c,citation,https://arxiv.org/pdf/1705.07206.pdf,Multiple-Human Parsing in the Wild,2017 +238,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,532c089b43983935e1001c5e35aa35440263beaf,citation,https://arxiv.org/pdf/1804.03166.pdf,G-Distillation: Reducing Overconfident Errors on Novel Samples,2018 +239,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,35fc0b28d0d674b28dd625d170bc641a36b17318,citation,http://pdfs.semanticscholar.org/35fc/0b28d0d674b28dd625d170bc641a36b17318.pdf,CSI: Composite Statistical Inference Techniques for Semantic Segmentation,2013 +240,VOC,voc,55.7039571,13.1902011,Lund University,edu,35fc0b28d0d674b28dd625d170bc641a36b17318,citation,http://pdfs.semanticscholar.org/35fc/0b28d0d674b28dd625d170bc641a36b17318.pdf,CSI: Composite Statistical Inference Techniques for Semantic Segmentation,2013 +241,VOC,voc,58.38131405,26.72078081,University of Tartu,edu,e4cb27d2a3e1153cb517d97d61de48ff0483c988,citation,https://pdfs.semanticscholar.org/e4cb/27d2a3e1153cb517d97d61de48ff0483c988.pdf,Viktoria Plemakova Vehicle Detection Based on Convolutional Neural Networks,2018 +242,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,3d0660e18c17db305b9764bb86b21a429241309e,citation,https://arxiv.org/pdf/1604.03505.pdf,Counting Everyday Objects in Everyday Scenes,2017 +243,VOC,voc,37.2381023,127.1903431,Myongji University,edu,a67da2dd79c01e8cc4029ecc5a05b97967403862,citation,https://pdfs.semanticscholar.org/a67d/a2dd79c01e8cc4029ecc5a05b97967403862.pdf,On Selecting Helpful Unlabeled Data for Improving Semi-Supervised Support Vector Machines,2014 +244,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,4ab69672e1116427d685bf7c1edb5b1fd0573b5e,citation,http://bigml.cs.tsinghua.edu.cn/~lingxi/PDFs/Xie_ACMMM12_EdgeGPP.pdf,Spatial pooling of heterogeneous features for image applications,2012 +245,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,989c7cdafa9b90ab2ea0a9d8fa60634cc698f174,citation,http://pdfs.semanticscholar.org/989c/7cdafa9b90ab2ea0a9d8fa60634cc698f174.pdf,YoloFlow Real - time Object Tracking in Video CS 229 Course Project,2016 +246,VOC,voc,3.12267405,101.65356103,University of Malaya,edu,85af6c005df806b57b306a732dcb98e096d15bfb,citation,https://arxiv.org/pdf/1805.11227.pdf,Getting to Know Low-light Images with The Exclusively Dark Dataset,2018 +247,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,cdb293381ff396d6e9c0f5e9578d411e759347fd,citation,https://pdfs.semanticscholar.org/022e/eae0edc09deb228da26d5390874f781ace0f.pdf,3 DR 2 N 2 : A Unified Approach for Single and Multiview 3 D Object Reconstruction,2016 +248,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,0e67717484684d90ae9d4e1bb9cdceb74b194910,citation,http://pdfs.semanticscholar.org/0e67/717484684d90ae9d4e1bb9cdceb74b194910.pdf,Mining Pixels: Weakly Supervised Semantic Segmentation Using Image Labels,2016 +249,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,5b4b84ce3518c8a14f57f5f95a1d07fb60e58223,citation,https://pdfs.semanticscholar.org/9f92/05a60ddf1135929e0747db34363b3a8c6bc8.pdf,Diagnosing Error in Object Detectors,2012 +250,VOC,voc,42.718568,-84.47791571,Michigan State University,edu,47203943c86e4d9355ffd99cd3d75f37211fd805,citation,http://pdfs.semanticscholar.org/be18/9c7066c4d99d617d137c975139c594ad09af.pdf,Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning,2012 +251,VOC,voc,42.8298248,-73.87719385,GE Global Research Center,edu,47203943c86e4d9355ffd99cd3d75f37211fd805,citation,http://pdfs.semanticscholar.org/be18/9c7066c4d99d617d137c975139c594ad09af.pdf,Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning,2012 +252,VOC,voc,39.95472495,-75.15346905,Temple University,edu,45ff38add61df32a027048624f58952a67a7c5f5,citation,http://pdfs.semanticscholar.org/45ff/38add61df32a027048624f58952a67a7c5f5.pdf,Deep Context Convolutional Neural Networks for Semantic Segmentation,2017 +253,VOC,voc,43.08250655,-77.67121663,Rochester Institute of Technology,edu,0a789733ccb300d0dd9df6174faaa7e8c64e0409,citation,http://pdfs.semanticscholar.org/0a78/9733ccb300d0dd9df6174faaa7e8c64e0409.pdf,High-Resolution Multispectral Dataset for Semantic Segmentation,2017 +254,VOC,voc,47.05821,15.46019568,Graz University of Technology,edu,9d3a6e459e0cecda20a8afd69d182877ff0224cf,citation,http://pdfs.semanticscholar.org/9d3a/6e459e0cecda20a8afd69d182877ff0224cf.pdf,A Framework for Articulated Hand Pose Estimation and Evaluation,2015 +255,VOC,voc,52.3553655,4.9501644,University of Amsterdam,edu,943a1e218b917172199e524944006aa349f58968,citation,https://arxiv.org/pdf/1807.11857.pdf,Joint Learning of Intrinsic Images and Semantic Segmentation,2018 +256,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,5f68e2131d9275d56092e9fca05bcfc65abea0d8,citation,http://doi.acm.org/10.1145/2806416.2806469,Cross-Modal Similarity Learning: A Low Rank Bilinear Formulation,2015 +257,VOC,voc,40.9153196,-73.1270626,Stony Brook University,edu,f989a20fbcc2d576c0c4514a0e5085c741580778,citation,https://arxiv.org/pdf/1612.03236.pdf,Co-localization with Category-Consistent Features and Geodesic Distance Propagation,2017 +258,VOC,voc,42.36782045,-71.12666653,Harvard University,edu,f989a20fbcc2d576c0c4514a0e5085c741580778,citation,https://arxiv.org/pdf/1612.03236.pdf,Co-localization with Category-Consistent Features and Geodesic Distance Propagation,2017 +259,VOC,voc,24.7925484,120.9951183,National Tsing Hua University,edu,cf94200a476dc15d6da95db809349db4cfd8e92c,citation,https://arxiv.org/pdf/1807.11436.pdf,Leveraging Motion Priors in Videos for Improving Human Segmentation,2018 +260,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,25dba68e4db0ce361032126b91f734f9252cae7c,citation,https://arxiv.org/pdf/1611.08998.pdf,DeepSetNet: Predicting Sets with Deep Neural Networks,2017 +261,VOC,voc,59.34986645,18.07063213,"KTH Royal Institute of Technology, Stockholm",edu,883767948f535ea2bf8a0c03047ca9064e1b078f,citation,https://pdfs.semanticscholar.org/8837/67948f535ea2bf8a0c03047ca9064e1b078f.pdf,A Combination of Object Recognition and Localisation for an Autonomous Racecar,0 +262,VOC,voc,23.09461185,113.28788994,Sun Yat-Sen University,edu,18095a530b532a70f3b615fef2f59e6fdacb2d84,citation,https://arxiv.org/pdf/1604.02271v3.pdf,Deep Structured Scene Parsing by Learning with Image Descriptions,2016 +263,VOC,voc,45.7413921,126.62552755,Harbin Institute of Technology,edu,18095a530b532a70f3b615fef2f59e6fdacb2d84,citation,https://arxiv.org/pdf/1604.02271v3.pdf,Deep Structured Scene Parsing by Learning with Image Descriptions,2016 +264,VOC,voc,-27.47715625,153.02841004,Queensland University of Technology,edu,9397e7acd062245d37350f5c05faf56e9cfae0d6,citation,http://pdfs.semanticscholar.org/9397/e7acd062245d37350f5c05faf56e9cfae0d6.pdf,DeepFruits: A Fruit Detection System Using Deep Neural Networks,2016 +265,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,03a24d15533dae78de78fd9d5f6c9050fb97f186,citation,https://doi.org/10.1109/SSCI.2016.7850112,Pedestrian detection aided by scale-discriminative network,2016 +266,VOC,voc,-33.88890695,151.18943366,University of Sydney,edu,17d4fd92352baf6f0039ec64d43ca572c8252384,citation,https://arxiv.org/pdf/1806.07049.pdf,MoE-SPNet: A mixture-of-experts scene parsing network,2018 +267,VOC,voc,47.05821,15.46019568,Graz University of Technology,edu,30a29f6c407749e97bc7c2db5674a62773af9d27,citation,http://pdfs.semanticscholar.org/30a2/9f6c407749e97bc7c2db5674a62773af9d27.pdf,Tracking and Visual Quality Inspection in Harsh Environments (print-version),2012 +268,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,280d632ef3234c5ab06018c6eaccead75bc173b3,citation,http://pdfs.semanticscholar.org/6b1a/c8e438041ac02cc8fab5762ca069c386f473.pdf,Efficient Image and Video Co-localization with Frank-Wolfe Algorithm,2014 +269,VOC,voc,31.83907195,117.26420748,University of Science and Technology of China,edu,0f945f796a9343b51a3dc69941c0fa1a98c0f448,citation,http://pdfs.semanticscholar.org/a7ef/979ce52b9e4bcbd6ee5524dfd4e92baf6292.pdf,Local Hypersphere Coding Based on Edges between Visual Words,2012 +270,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,0db6a58927a671c01089c53248b0e1c36bdc3231,citation,http://openaccess.thecvf.com/content_cvpr_2016/papers/Pham_Efficient_Point_Process_CVPR_2016_paper.pdf,Efficient Point Process Inference for Large-Scale Object Detection,2016 +271,VOC,voc,42.2942142,-83.71003894,University of Michigan,edu,14d0afea52c4e9b7a488f6398e4a92bd4f4b93c7,citation,https://arxiv.org/pdf/1804.07667.pdf,Rethinking the Faster R-CNN Architecture for Temporal Action Localization,2018 +272,VOC,voc,42.2942142,-83.71003894,University of Michigan,edu,8da1b0834688edb311a803532e33939e9ecf8292,citation,https://arxiv.org/pdf/1808.01244.pdf,CornerNet: Detecting Objects as Paired Keypoints,2018 +273,VOC,voc,39.2899685,-76.62196103,University of Maryland,edu,f42d3225afd9e463ddb7a355f64b54af8bd14227,citation,https://arxiv.org/pdf/1804.10343.pdf,Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation,2018 +274,VOC,voc,31.83907195,117.26420748,University of Science and Technology of China,edu,a1dd88f44d045b360569a9a8721f728afbd951c3,citation,https://pdfs.semanticscholar.org/a1dd/88f44d045b360569a9a8721f728afbd951c3.pdf,Relief Impression Image Detection : Unsupervised Extracting Objects Directly From Feature Arrangements of Deep CNN,2016 +275,VOC,voc,34.0687788,-118.4450094,"University of California, Los Angeles",edu,fc027fccb19512a439fc17181c34ee1c3aad51b5,citation,https://arxiv.org/pdf/1708.03383.pdf,Joint Multi-person Pose Estimation and Semantic Part Segmentation,2017 +276,VOC,voc,39.329053,-76.619425,Johns Hopkins University,edu,377f2b65e6a9300448bdccf678cde59449ecd337,citation,https://arxiv.org/pdf/1804.10275.pdf,Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results,2018 +277,VOC,voc,40.47913175,-74.43168868,Rutgers University,edu,377f2b65e6a9300448bdccf678cde59449ecd337,citation,https://arxiv.org/pdf/1804.10275.pdf,Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results,2018 +278,VOC,voc,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,451eed7fd8ae281d1cc76ca8cdecbaf47816e55a,citation,http://pdfs.semanticscholar.org/451e/ed7fd8ae281d1cc76ca8cdecbaf47816e55a.pdf,Close Yet Distinctive Domain Adaptation,2017 +279,VOC,voc,35.9990522,-78.9290629,Duke University,edu,992b93ab9d016640551a8cebcaf4757288154f32,citation,http://pdfs.semanticscholar.org/e38c/f96363aaf1f17c487c484ad27d3175ca4b31.pdf,Nested Pictorial Structures,2012 +280,VOC,voc,43.08250655,-77.67121663,Rochester Institute of Technology,edu,7489990ea3d6ab4c1c86c9ed9f049399961dfaef,citation,https://people.rit.edu/ndcsma/pubs/WNYISPW_Nov_2014_Chew.pdf,Normalized cutswith soft must-link constraints for image segmentation and clustering,2014 +281,VOC,voc,59.34986645,18.07063213,"KTH Royal Institute of Technology, Stockholm",edu,41199678ad9370ff8ca7e9e3c2617b62a297fac3,citation,http://pdfs.semanticscholar.org/4119/9678ad9370ff8ca7e9e3c2617b62a297fac3.pdf,Multitask Deep Learning models for real-time deployment in embedded systems,2017 +282,VOC,voc,39.7487516,30.47653071,Eskisehir Osmangazi University,edu,7fb74f5abab4830e3cdaf477230e5571d9e3ca57,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Cevikalp_Polyhedral_Conic_Classifiers_CVPR_2017_paper.pdf,Polyhedral Conic Classifiers for Visual Object Detection and Classification,2017 +283,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,10793d1475607929fedc6d9a677911ad16843e58,citation,http://openaccess.thecvf.com/content_cvpr_2016/papers/Li_Unsupervised_Learning_of_CVPR_2016_paper.pdf,Unsupervised Learning of Edges,2016 +284,VOC,voc,31.30104395,121.50045497,Fudan University,edu,c94fd258a8f1e8f4033a7fe491f1372dcf7d3cd6,citation,https://arxiv.org/pdf/1807.04897.pdf,TS ^2 2 C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection,2018 +285,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,c94fd258a8f1e8f4033a7fe491f1372dcf7d3cd6,citation,https://arxiv.org/pdf/1807.04897.pdf,TS ^2 2 C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection,2018 +286,VOC,voc,52.5180641,13.3250425,TU Berlin,edu,2581a12189eb1a0b5b27a7fd1c2cbe44c88fcc20,citation,http://arxiv.org/pdf/1512.00172v1.pdf,Analyzing Classifiers: Fisher Vectors and Deep Neural Networks,2016 +287,VOC,voc,32.0565957,118.77408833,Nanjing University,edu,96416b1b44fb05302c6e9a8ab1b74d9204995e73,citation,http://pdfs.semanticscholar.org/9641/6b1b44fb05302c6e9a8ab1b74d9204995e73.pdf,Learning Effective Binary Visual Representations with Deep Networks,2018 +288,VOC,voc,42.3619407,-71.0904378,MIT CSAIL,edu,aa2ddae22760249729ac2c2c4e24c8b665bcd40e,citation,https://pdfs.semanticscholar.org/8c47/635ae7f1641c2bdd45026ad7dbff70c24398.pdf,Interpretable Basis Decomposition for Visual Explanation,2018 +289,VOC,voc,42.2942142,-83.71003894,University of Michigan,edu,60542b1a857024c79db8b5b03db6e79f74ec8f9f,citation,https://arxiv.org/pdf/1702.05448.pdf,Learning to Detect Human-Object Interactions,2018 +290,VOC,voc,36.3693473,120.673818,Shandong University,edu,bd8a85acaa45d4068fca584e8d9e3bd3bb4eea4d,citation,http://pdfs.semanticscholar.org/bd8a/85acaa45d4068fca584e8d9e3bd3bb4eea4d.pdf,Toward Scene Recognition by Discovering Semantic Structures and Parts,2015 +291,VOC,voc,49.2767454,-122.91777375,Simon Fraser University,edu,bd8a85acaa45d4068fca584e8d9e3bd3bb4eea4d,citation,http://pdfs.semanticscholar.org/bd8a/85acaa45d4068fca584e8d9e3bd3bb4eea4d.pdf,Toward Scene Recognition by Discovering Semantic Structures and Parts,2015 +292,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,456abee9c8d31f004b2f0a3b47222043e20f5042,citation,https://arxiv.org/pdf/1603.09188.pdf,Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings,2016 +293,VOC,voc,31.83907195,117.26420748,University of Science and Technology of China,edu,7c2f6424b0bb2c28f282fbc0b4e98bf85d5584eb,citation,http://pdfs.semanticscholar.org/a5ae/7d662ed086bc5b0c9a2c1dc54fcb23635000.pdf,Relief R-CNN: Utilizing Convolutional Feature Interrelationship for Fast Object Detection Deployment,2016 +294,VOC,voc,22.53521465,113.9315911,Shenzhen University,edu,7c2f6424b0bb2c28f282fbc0b4e98bf85d5584eb,citation,http://pdfs.semanticscholar.org/a5ae/7d662ed086bc5b0c9a2c1dc54fcb23635000.pdf,Relief R-CNN: Utilizing Convolutional Feature Interrelationship for Fast Object Detection Deployment,2016 +295,VOC,voc,37.5557271,127.0436642,Hanyang University,edu,59e9934720baf3c5df3a0e1e988202856e1f83ce,citation,https://arxiv.org/pdf/1511.04136.pdf,UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking,2015 +296,VOC,voc,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,d58c44bd9b464d9ac1db1344445c31364925f75a,citation,https://pdfs.semanticscholar.org/d58c/44bd9b464d9ac1db1344445c31364925f75a.pdf,TBN: Convolutional Neural Network with Ternary Inputs and Binary Weights,2018 +297,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,81ba5202424906f64b77f68afca063658139fbb2,citation,https://arxiv.org/pdf/1611.09078.pdf,Social Scene Understanding: End-to-End Multi-person Action Localization and Collective Activity Recognition,2017 +298,VOC,voc,46.109237,7.08453549,IDIAP Research Institute,edu,81ba5202424906f64b77f68afca063658139fbb2,citation,https://arxiv.org/pdf/1611.09078.pdf,Social Scene Understanding: End-to-End Multi-person Action Localization and Collective Activity Recognition,2017 +299,VOC,voc,50.7338124,7.1022465,University of Bonn,edu,0b6f64c78c44dc043e2972fa7bfe2a5753768609,citation,https://doi.org/10.1109/ICPR.2016.7900008,A future for learning semantic models of man-made environments,2016 +300,VOC,voc,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,016eb7b32d1fdec0899151fb03799378bf59bbe5,citation,http://pdfs.semanticscholar.org/016e/b7b32d1fdec0899151fb03799378bf59bbe5.pdf,Point Linking Network for Object Detection,2017 +301,VOC,voc,33.9928298,-81.02685168,University of South Carolina,edu,cd9d654c6a4250e0cf8bcfddc2afab9e70ee6cae,citation,http://pdfs.semanticscholar.org/cd9d/654c6a4250e0cf8bcfddc2afab9e70ee6cae.pdf,Object Detection with Mask-based Feature Encoding,2018 +302,VOC,voc,36.20304395,117.05842113,Tianjin University,edu,cd9d654c6a4250e0cf8bcfddc2afab9e70ee6cae,citation,http://pdfs.semanticscholar.org/cd9d/654c6a4250e0cf8bcfddc2afab9e70ee6cae.pdf,Object Detection with Mask-based Feature Encoding,2018 +303,VOC,voc,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,28737575297a20d431dd2b777a79a8be2c9c2bbd,citation,http://pdfs.semanticscholar.org/2873/7575297a20d431dd2b777a79a8be2c9c2bbd.pdf,Object Ranking on Deformable Part Models with Bagged LambdaMART,2014 +304,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,46702e0127e16a4d6a1feda3ffc5f0f123957e87,citation,https://arxiv.org/pdf/1809.06131.pdf,Revisit Multinomial Logistic Regression in Deep Learning: Data Dependent Model Initialization for Image Recognition,2018 +305,VOC,voc,34.0687788,-118.4450094,"University of California, Los Angeles",edu,d2b2cb1d5cc1aa30cf5be7bcb0494198934caabb,citation,http://pdfs.semanticscholar.org/d2b2/cb1d5cc1aa30cf5be7bcb0494198934caabb.pdf,A Restricted Visual Turing Test for Deep Scene and Event Understanding,2015 +306,VOC,voc,37.8687126,-122.25586815,"University of California, Berkeley",edu,446fbff6a2a7c9989b0a0465f960e236d9a5e886,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Pathak_Context_Encoders_Feature_CVPR_2016_paper.pdf,Context Encoders: Feature Learning by Inpainting,2016 +307,VOC,voc,51.49887085,-0.17560797,Imperial College London,edu,291e5377df2eec4835b5c6889896941831a11c69,citation,http://pdfs.semanticscholar.org/291e/5377df2eec4835b5c6889896941831a11c69.pdf,Recovering 6D Object Pose: Multi-modal Analyses on Challenges,2017 +308,VOC,voc,40.9153196,-73.1270626,Stony Brook University,edu,b69fbf046faf685655b5fa52fef07fb77e75eff4,citation,http://pdfs.semanticscholar.org/b69f/bf046faf685655b5fa52fef07fb77e75eff4.pdf,Modeling guidance and recognition in categorical search: bridging human and computer object detection.,2013 +309,VOC,voc,39.7487516,30.47653071,Eskisehir Osmangazi University,edu,13bda03fc8984d5943ed8d02e49a779d27c84114,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6248047,Efficient object detection using cascades of nearest convex model classifiers,2012 +310,VOC,voc,50.7338124,7.1022465,University of Bonn,edu,87a66ccc68374ffb704ee6fb9fa7df369718095c,citation,http://pdfs.semanticscholar.org/ea90/16fb585ba6449d3d6f98bf85fa0bcd1f4621.pdf,Multi-person Pose Estimation with Local Joint-to-Person Associations,2016 +311,VOC,voc,39.9922379,116.30393816,Peking University,edu,4960ab1cef23e5ccd60173725ea280f462164a0e,citation,https://pdfs.semanticscholar.org/4960/ab1cef23e5ccd60173725ea280f462164a0e.pdf,Video Object Segmentation by Learning Location-Sensitive Embeddings,2018 +312,VOC,voc,39.977217,116.337632,Microsoft Research Asia,company,4960ab1cef23e5ccd60173725ea280f462164a0e,citation,https://pdfs.semanticscholar.org/4960/ab1cef23e5ccd60173725ea280f462164a0e.pdf,Video Object Segmentation by Learning Location-Sensitive Embeddings,2018 +313,VOC,voc,35.9990522,-78.9290629,Duke University,edu,8856fbf333b2aba7b9f1f746e16a2b7f083ee5b8,citation,http://pdfs.semanticscholar.org/8856/fbf333b2aba7b9f1f746e16a2b7f083ee5b8.pdf,Analyzing animal behavior via classifying each video frame using convolutional neural networks,2015 +314,VOC,voc,34.1235825,108.83546,Xidian University,edu,f9f01af981f8d25f0c96ea06d88be62dabb79256,citation,https://pdfs.semanticscholar.org/f9f0/1af981f8d25f0c96ea06d88be62dabb79256.pdf,Terahertz Image Detection with the Improved Faster Region-Based Convolutional Neural Network,2018 +315,VOC,voc,37.5600406,126.9369248,Yonsei University,edu,09066d7d0bb6273bf996c8538d7b34c38ea6a500,citation,https://arxiv.org/pdf/1809.01845.pdf,"Yes, IoU loss is submodular - as a function of the mispredictions",2018 +316,VOC,voc,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,4aeebd1c9b4b936ed2e4d988d8d28e27f129e6f1,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Chiu_See_the_Difference_ICCV_2015_paper.pdf,See the Difference: Direct Pre-Image Reconstruction and Pose Estimation by Differentiating HOG,2015 +317,VOC,voc,34.0687788,-118.4450094,"University of California, Los Angeles",edu,232ff2dab49cb5a1dae1012fd7ba53382909ec18,citation,http://pdfs.semanticscholar.org/232f/f2dab49cb5a1dae1012fd7ba53382909ec18.pdf,Semantic Video Segmentation from Occlusion Relations within a Convex Optimization Framework,2013 +318,VOC,voc,50.13053055,8.69234224,University of Frankfurt,edu,465c34c3334f29de28f973b7702a235509649429,citation,http://pdfs.semanticscholar.org/465c/34c3334f29de28f973b7702a235509649429.pdf,Stereopsis via deep learning,2013 +319,VOC,voc,47.6543238,-122.30800894,University of Washington,edu,caa2ded6d8d5de97c824d29b0c7a18d220c596c8,citation,https://arxiv.org/pdf/1709.02554.pdf,Learning to Segment Breast Biopsy Whole Slide Images,2018 +320,VOC,voc,44.48116865,-73.2002179,University of Vermont,edu,caa2ded6d8d5de97c824d29b0c7a18d220c596c8,citation,https://arxiv.org/pdf/1709.02554.pdf,Learning to Segment Breast Biopsy Whole Slide Images,2018 +321,VOC,voc,42.2942142,-83.71003894,University of Michigan,edu,289d833a35c2156b7e332e67d1cb099fd0683025,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Chao_HICO_A_Benchmark_ICCV_2015_paper.pdf,HICO: A Benchmark for Recognizing Human-Object Interactions in Images,2015 +322,VOC,voc,37.8687126,-122.25586815,"University of California, Berkeley",edu,0fbdd4b8eb9e4c4cfbe5b76ab29ab8b0219fbdc0,citation,https://people.eecs.berkeley.edu/~pathak/papers/iccv15.pdf,Constrained Convolutional Neural Networks for Weakly Supervised Segmentation,2015 +323,VOC,voc,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,f94f79168c1cfaebb8eab5151e01d56478ab0b73,citation,http://pdfs.semanticscholar.org/f94f/79168c1cfaebb8eab5151e01d56478ab0b73.pdf,Optimizing Region Selection for Weakly Supervised Object Detection,2017 +324,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,6bb51f431f348b2b3e1db859827e80f97a576c30,citation,http://pdfs.semanticscholar.org/6bb5/1f431f348b2b3e1db859827e80f97a576c30.pdf,Irregular Convolutional Neural Networks,2017 +325,VOC,voc,22.42031295,114.20788644,Chinese University of Hong Kong,edu,b78e611c32dc0daf762cfa93044558cdb545d857,citation,http://pdfs.semanticscholar.org/b78e/611c32dc0daf762cfa93044558cdb545d857.pdf,Temporal Action Detection with Structured Segment Networks Supplementary Materials,2017 +326,VOC,voc,48.14955455,11.56775314,Technical University Munich,edu,bc12715a1ddf1a540dab06bf3ac4f3a32a26b135,citation,http://pdfs.semanticscholar.org/bc12/715a1ddf1a540dab06bf3ac4f3a32a26b135.pdf,Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking,2017 +327,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,bc12715a1ddf1a540dab06bf3ac4f3a32a26b135,citation,http://pdfs.semanticscholar.org/bc12/715a1ddf1a540dab06bf3ac4f3a32a26b135.pdf,Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking,2017 +328,VOC,voc,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,4d1757aacbc49c74a5d4e53259c92ab0e47544da,citation,https://arxiv.org/pdf/1805.04310.pdf,Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer,2018 +329,VOC,voc,36.1112058,140.1055176,University of Tsukuba,edu,d392098688a999c70589c995bd4427c212eff69d,citation,http://pdfs.semanticscholar.org/d392/098688a999c70589c995bd4427c212eff69d.pdf,Object Repositioning Based on the Perspective in a Single Image,2014 +330,VOC,voc,22.42031295,114.20788644,Chinese University of Hong Kong,edu,1c1f21bf136fe2eec412e5f70fd918c27c5ccb0a,citation,http://pdfs.semanticscholar.org/1c1f/21bf136fe2eec412e5f70fd918c27c5ccb0a.pdf,Object Detection and Viewpoint Estimation with Auto-masking Neural Network,2014 +331,VOC,voc,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,1c1f21bf136fe2eec412e5f70fd918c27c5ccb0a,citation,http://pdfs.semanticscholar.org/1c1f/21bf136fe2eec412e5f70fd918c27c5ccb0a.pdf,Object Detection and Viewpoint Estimation with Auto-masking Neural Network,2014 +332,VOC,voc,51.49887085,-0.17560797,Imperial College London,edu,72e9acdd64e71fc2084acaf177aafaa2e075bd8c,citation,http://pdfs.semanticscholar.org/72e9/acdd64e71fc2084acaf177aafaa2e075bd8c.pdf,The 2017 Hands in the Million Challenge on 3D Hand Pose Estimation,2017 +333,VOC,voc,51.49887085,-0.17560797,Imperial College London,edu,0209389b8369aaa2a08830ac3b2036d4901ba1f1,citation,https://arxiv.org/pdf/1612.01202v2.pdf,DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild,2017 +334,VOC,voc,51.5231607,-0.1282037,University College London,edu,0209389b8369aaa2a08830ac3b2036d4901ba1f1,citation,https://arxiv.org/pdf/1612.01202v2.pdf,DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild,2017 +335,VOC,voc,50.7338124,7.1022465,University of Bonn,edu,07b8a9a225b738c4074a50cf80ee5fe516878421,citation,https://arxiv.org/pdf/1807.09169.pdf,Convolutional Simplex Projection Network for Weakly Supervised Semantic Segmentation,2018 +336,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,1bd1645a629f1b612960ab9bba276afd4cf7c666,citation,http://arxiv.org/pdf/1506.04878.pdf,End-to-End People Detection in Crowded Scenes,2016 +337,VOC,voc,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,1bd1645a629f1b612960ab9bba276afd4cf7c666,citation,http://arxiv.org/pdf/1506.04878.pdf,End-to-End People Detection in Crowded Scenes,2016 +338,VOC,voc,43.7776426,11.259765,University of Florence,edu,1bbe0371ca22c2fdb6e0d098049bbf6430324bdb,citation,http://doi.acm.org/10.1145/2906152,"Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval",2016 +339,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,1bbe0371ca22c2fdb6e0d098049bbf6430324bdb,citation,http://doi.acm.org/10.1145/2906152,"Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval",2016 +340,VOC,voc,34.7275714,135.2371,Kobe University,edu,9954f7ee5288724184f9420e39cca9165efa6822,citation,http://www.me.cs.scitec.kobe-u.ac.jp/~takigu/pdf/2015/Th5_4.pdf,Estimation of object functions using deformable part model,2015 +341,VOC,voc,48.14955455,11.56775314,Technical University Munich,edu,e212b2bc41645fe467a73d004067fcf1ca77d87f,citation,http://pdfs.semanticscholar.org/e212/b2bc41645fe467a73d004067fcf1ca77d87f.pdf,Deep Active Contours,2016 +342,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,51c4ecf4539f56c4b1035b890f743b3a91dd758b,citation,http://arxiv.org/abs/1504.06434,Situational object boundary detection,2015 +343,VOC,voc,37.8687126,-122.25586815,"University of California, Berkeley",edu,007e86cb55f0ba0415a7764a1e9f9566c1e8784b,citation,http://pdfs.semanticscholar.org/2677/3023b17ba560bad6a679930710a9049abca5.pdf,Adversarial Feature Learning,2016 +344,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,54d97ea9a5f92761dddd148fb0e602c2293e7c16,citation,https://pdfs.semanticscholar.org/54d9/7ea9a5f92761dddd148fb0e602c2293e7c16.pdf,Associating Inter-image Salient Instances for Weakly Supervised Semantic Segmentation,2018 +345,VOC,voc,51.4879961,-3.17969747,Cardiff University,edu,54d97ea9a5f92761dddd148fb0e602c2293e7c16,citation,https://pdfs.semanticscholar.org/54d9/7ea9a5f92761dddd148fb0e602c2293e7c16.pdf,Associating Inter-image Salient Instances for Weakly Supervised Semantic Segmentation,2018 +346,VOC,voc,51.5231607,-0.1282037,University College London,edu,0e923b74fd41f73f57e22f66397feeea67e834f0,citation,http://pdfs.semanticscholar.org/0e92/3b74fd41f73f57e22f66397feeea67e834f0.pdf,Invariant encoding schemes for visual recognition,2012 +347,VOC,voc,34.0224149,-118.28634407,University of Southern California,edu,93cba94ff0ff96f865ce24ea01e9c006369d75ff,citation,https://arxiv.org/pdf/1803.03879.pdf,Knowledge Aided Consistency for Weakly Supervised Phrase Grounding,2018 +348,VOC,voc,35.704514,51.40972058,Amirkabir University of Technology,edu,24fc311970e097efc317c0f98d2df37b828bfbad,citation,https://arxiv.org/pdf/1709.08019v2.pdf,Semi-supervised hierarchical semantic object parsing,2017 +349,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,5c4d4fd37e8c80ae95c00973531f34a6d810ea3a,citation,https://arxiv.org/pdf/1603.09439.pdf,The Open World of Micro-Videos,2016 +350,VOC,voc,37.26728,126.9841151,Seoul National University,edu,71b973c87965e4086e75fd2379dd1bd8e3f8231e,citation,https://arxiv.org/pdf/1606.02393.pdf,Progressive Attention Networks for Visual Attribute Prediction,2018 +351,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,20c02e98602f6adf1cebaba075d45cef50de089f,citation,https://arxiv.org/pdf/1808.07507.pdf,Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition,2018 +352,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,20c02e98602f6adf1cebaba075d45cef50de089f,citation,https://arxiv.org/pdf/1808.07507.pdf,Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition,2018 +353,VOC,voc,47.6543238,-122.30800894,University of Washington,edu,c17ed26650a67e80151f5312fa15b5c423acc797,citation,http://pdfs.semanticscholar.org/c17e/d26650a67e80151f5312fa15b5c423acc797.pdf,Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and Camera Self-Calibration,2017 +354,VOC,voc,36.05238585,140.11852361,Institute of Industrial Science,edu,c17ed26650a67e80151f5312fa15b5c423acc797,citation,http://pdfs.semanticscholar.org/c17e/d26650a67e80151f5312fa15b5c423acc797.pdf,Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and Camera Self-Calibration,2017 +355,VOC,voc,35.9020448,139.93622009,University of Tokyo,edu,c17ed26650a67e80151f5312fa15b5c423acc797,citation,http://pdfs.semanticscholar.org/c17e/d26650a67e80151f5312fa15b5c423acc797.pdf,Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and Camera Self-Calibration,2017 +356,VOC,voc,47.6423318,-122.1369302,Microsoft,company,c17ed26650a67e80151f5312fa15b5c423acc797,citation,http://pdfs.semanticscholar.org/c17e/d26650a67e80151f5312fa15b5c423acc797.pdf,Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and Camera Self-Calibration,2017 +357,VOC,voc,31.21051105,29.91314562,Alexandria University,edu,0ce08f1cc6684495d12c2da157a056c7b88ffcd9,citation,http://pdfs.semanticscholar.org/0ce0/8f1cc6684495d12c2da157a056c7b88ffcd9.pdf,Multi-Modality Feature Transform: An Interactive Image Segmentation Approach,2015 +358,VOC,voc,1.3484104,103.68297965,Nanyang Technological University,edu,567078a51ea63b70396dca5dabb50a10a736d991,citation,https://pdfs.semanticscholar.org/1b5a/3bdb174df1ff36c1c101739d6daaec07760d.pdf,Conditional Generative Adversarial Network for Structured Domain Adaptation,2018 +359,VOC,voc,43.0008093,-78.7889697,University at Buffalo,edu,567078a51ea63b70396dca5dabb50a10a736d991,citation,https://pdfs.semanticscholar.org/1b5a/3bdb174df1ff36c1c101739d6daaec07760d.pdf,Conditional Generative Adversarial Network for Structured Domain Adaptation,2018 +360,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,6e4e5ef25f657de8fb383c8dfeb8e229eea28bb9,citation,https://arxiv.org/pdf/1707.01691.pdf,RON: Reverse Connection with Objectness Prior Networks for Object Detection,2017 +361,VOC,voc,50.0764296,14.41802312,Czech Technical University,edu,cf528f9fe6588b71efa94c219979ce111fc9c1c9,citation,http://pdfs.semanticscholar.org/cf52/8f9fe6588b71efa94c219979ce111fc9c1c9.pdf,On Evaluation of 6D Object Pose Estimation,2016 +362,VOC,voc,22.2081469,114.25964115,University of Hong Kong,edu,3b67645cd512898806aaf1df1811035f2d957f6b,citation,https://arxiv.org/pdf/1705.04043.pdf,SCNet: Learning Semantic Correspondence,2017 +363,VOC,voc,26.513188,80.23651945,Indian Institute of Technology Kanpur,edu,ef2e36daf429899bb48d80ce6804731c3f99bb85,citation,http://pdfs.semanticscholar.org/f7bd/b4df0fb5b3ff9fa0ebfe7c2a9ddc34c09a5c.pdf,"Debnath, Banerjee, Namboodiri: Adapting Ransac-svm to Detect Outliers for Robust Classification",2015 +364,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,79a3a07661b8c6a36070fd767344e15c847a30ef,citation,http://pdfs.semanticscholar.org/79a3/a07661b8c6a36070fd767344e15c847a30ef.pdf,Contextual Pooling in Image Classification,2012 +365,VOC,voc,13.0222347,77.56718325,Indian Institute of Science Bangalore,edu,5aa7f33cdc00787284b609aa63f5eb5c0a3212f6,citation,http://pdfs.semanticscholar.org/5aa7/f33cdc00787284b609aa63f5eb5c0a3212f6.pdf,Multiplicative mixing of object identity and image attributes in single inferior temporal neurons,2018 +366,VOC,voc,51.5247272,-0.03931035,Queen Mary University of London,edu,38f88655debf4bf32978a7b39fbd56aea6ee5752,citation,https://arxiv.org/pdf/1712.03162.pdf,Class Rectification Hard Mining for Imbalanced Deep Learning,2017 +367,VOC,voc,36.1244756,-97.05004383,Oklahoma State University,edu,7b3b2912c1d7a70839bc71a150e33f8634d0fff3,citation,https://pdfs.semanticscholar.org/7b3b/2912c1d7a70839bc71a150e33f8634d0fff3.pdf,Convolutional Neural Network-Based Embarrassing Situation Detection under Camera for Social Robot in Smart Homes,2018 +368,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,acdc333f7b32d987e65ce15f21db64e850ca9471,citation,https://pdfs.semanticscholar.org/acdc/333f7b32d987e65ce15f21db64e850ca9471.pdf,Direct Loss Minimization for Training Deep Neural Nets,2015 +369,VOC,voc,43.66333345,-79.39769975,University of Toronto,edu,acdc333f7b32d987e65ce15f21db64e850ca9471,citation,https://pdfs.semanticscholar.org/acdc/333f7b32d987e65ce15f21db64e850ca9471.pdf,Direct Loss Minimization for Training Deep Neural Nets,2015 +370,VOC,voc,28.2290209,112.99483204,"National University of Defense Technology, China",edu,da4137396f26bf3e76d04eeed0c94e11b7824aa6,citation,https://arxiv.org/pdf/1711.06828.pdf,Transferable Semi-Supervised Semantic Segmentation,2018 +371,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,da4137396f26bf3e76d04eeed0c94e11b7824aa6,citation,https://arxiv.org/pdf/1711.06828.pdf,Transferable Semi-Supervised Semantic Segmentation,2018 +372,VOC,voc,40.11571585,-88.22750772,Beckman Institute,edu,da4137396f26bf3e76d04eeed0c94e11b7824aa6,citation,https://arxiv.org/pdf/1711.06828.pdf,Transferable Semi-Supervised Semantic Segmentation,2018 +373,VOC,voc,40.9153196,-73.1270626,Stony Brook University,edu,5240941af3b263609acaa168f96e1decdb0b3fe4,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W06/papers/Ge_Action_Classification_in_2015_CVPR_paper.pdf,Action classification in still images using human eye movements,2015 +374,VOC,voc,43.66333345,-79.39769975,University of Toronto,edu,126250d6077a6a68ae06277352eb42c4fa4c8b10,citation,http://pdfs.semanticscholar.org/1262/50d6077a6a68ae06277352eb42c4fa4c8b10.pdf,Learning Patch-based Structural Element Models with Hierarchical Palettes Abstract Learning Patch-based Structural Element Models with Hierarchical Palettes,2012 +375,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,0cbbbfac2fe925479c6b34712e056f840a10fa4d,citation,https://pdfs.semanticscholar.org/0cbb/bfac2fe925479c6b34712e056f840a10fa4d.pdf,Quality Evaluation Methods for Crowdsourced Image Segmentation,2018 +376,VOC,voc,37.3936717,-122.0807262,Facebook,company,0cbbbfac2fe925479c6b34712e056f840a10fa4d,citation,https://pdfs.semanticscholar.org/0cbb/bfac2fe925479c6b34712e056f840a10fa4d.pdf,Quality Evaluation Methods for Crowdsourced Image Segmentation,2018 +377,VOC,voc,42.718568,-84.47791571,Michigan State University,edu,28df3f11894ce0c48dd8aee65a6ec76d9009cbbd,citation,https://arxiv.org/pdf/1809.08318.pdf,Recurrent Flow-Guided Semantic Forecasting,2018 +378,VOC,voc,42.30791465,-83.07176915,University of Windsor,edu,535ed3850e79ccd51922601546ef0fc48c5fb468,citation,http://arxiv.org/abs/1705.04301,A feature embedding strategy for high-level CNN representations from multiple convnets,2017 +379,VOC,voc,30.19331415,120.11930822,Zhejiang University,edu,535ed3850e79ccd51922601546ef0fc48c5fb468,citation,http://arxiv.org/abs/1705.04301,A feature embedding strategy for high-level CNN representations from multiple convnets,2017 +380,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,247ca98c5a46616044cf6ae32b0d5b4140a7a161,citation,http://pdfs.semanticscholar.org/247c/a98c5a46616044cf6ae32b0d5b4140a7a161.pdf,High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks,2016 +381,VOC,voc,28.2290209,112.99483204,"National University of Defense Technology, China",edu,5f771fed91c8e4b666489ba2384d0705bcf75030,citation,https://arxiv.org/pdf/1804.03287.pdf,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018 +382,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,5f771fed91c8e4b666489ba2384d0705bcf75030,citation,https://arxiv.org/pdf/1804.03287.pdf,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018 +383,VOC,voc,51.6091578,-3.97934429,Swansea 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\ No newline at end of file diff --git a/webpack.geocode.dev.js b/webpack.geocode.dev.js new file mode 100644 index 00000000..7ebd43ac --- /dev/null +++ b/webpack.geocode.dev.js @@ -0,0 +1,59 @@ +require('dotenv').config() + +// const HtmlWebpackPlugin = require('html-webpack-plugin') +// const CleanWebpackPlugin = require('clean-webpack-plugin') +const webpack = require('webpack') +const path = require('path') + +module.exports = { + entry: { + main: './scraper/client/index.js' + }, + output: { + path: path.resolve(__dirname, 'scraper/reports'), + filename: 'geocode-app.js' + }, + devtool: 'inline-source-map', + plugins: [ + // new CleanWebpackPlugin(['dist']), + new webpack.DefinePlugin({ + 'process.env.NODE_ENV': '"development"', + 'process.env.S3_HOST': '"' + process.env.S3_HOST + '"', + 'process.env.API_HOST': '""', + }), + // new HtmlWebpackPlugin({ + // title: 'VFrame Metadata', + // meta: { + // viewport: 'width=device-width,initial-scale=1.0' + // } + // }), + // new webpack.HotModuleReplacementPlugin() + ], + module: { + rules: [ + { + test: /\.css$/, + use: ['style-loader', 'css-loader'] + }, + { + test: /\.js$/, + // include: path.resolve(__dirname, 'client'), + exclude: /(node_modules|bower_components|build)/, + use: { + loader: 'babel-loader', + options: { + presets: ['env'], + plugins: [ + require('babel-plugin-transform-runtime'), + require('babel-plugin-transform-es2015-arrow-functions'), + require('babel-plugin-transform-object-rest-spread'), + require('babel-plugin-transform-class-properties'), + require('babel-plugin-transform-react-jsx'), + require('react-hot-loader/babel') + ] + } + } + } + ] + } +}; |
