diff options
50 files changed, 239 insertions, 30 deletions
diff --git a/client/index.js b/client/index.js index 1a80e74f..c72fd02c 100644 --- a/client/index.js +++ b/client/index.js @@ -139,14 +139,27 @@ function buildWaypoints() { } function main() { - const paras = document.querySelectorAll('section p') + // const paras = document.querySelectorAll('section p') // if (paras.length) { // paras[0].classList.add('first_paragraph') // } - toArray(document.querySelectorAll('header .links a')).forEach(tag => { - if (window.location.href.match(tag.href)) { + let active = '' + const { href } = window.location + if (href.match('news')) { + active = 'news' + } else if (href.match('about')) { + active = 'about' + } else if (href.match('datasets')) { + active = 'datasets' + } else { + active = href + } + toArray(document.querySelectorAll('header .links a')).some(tag => { + if (tag.href.match(active)) { tag.classList.add('active') + return true } + return false }) runApplets() buildWaypoints() diff --git a/megapixels/commands/datasets/citations_to_csv.py b/megapixels/commands/datasets/citations_to_csv.py index f3277d7e..2aa33af2 100644 --- a/megapixels/commands/datasets/citations_to_csv.py +++ b/megapixels/commands/datasets/citations_to_csv.py @@ -6,10 +6,13 @@ from app.models.citations import Paper log = Logger.getLogger() +fp_in_default = '../site/datasets/verified/' +fp_out_default = '../site/datasets/verified/' + @click.command() -@click.option('-i', '--input', 'opt_fp_in', required=True, +@click.option('-i', '--input', 'opt_fp_in', default=fp_in_default, help='Input citation data file or folder') -@click.option('-o', '--output', 'opt_dir_out', +@click.option('-o', '--output', 'opt_dir_out', default=fp_out_default, help='Output directory') @click.pass_context def cli(ctx, opt_fp_in, opt_dir_out): diff --git a/site/content/pages/about/attribution.md b/site/content/pages/about/attribution.md index bf190478..593ee642 100644 --- a/site/content/pages/about/attribution.md +++ b/site/content/pages/about/attribution.md @@ -16,6 +16,7 @@ authors: Adam Harvey <section class="about-menu"> <ul> <li><a href="/about/">About</a></li> +<li><a href="/about/updates/">Updates</a></li> <li><a href="/about/press/">Press</a></li> <li><a class="current" href="/about/attribution/">Attribution</a></li> <li><a href="/about/legal/">Legal / Privacy</a></li> diff --git a/site/content/pages/about/index.md b/site/content/pages/about/index.md index 36e28d22..24194325 100644 --- a/site/content/pages/about/index.md +++ b/site/content/pages/about/index.md @@ -16,6 +16,7 @@ authors: Adam Harvey <section class="about-menu"> <ul> <li><a class="current" href="/about/">About</a></li> +<li><a href="/about/updates/">Updates</a></li> <li><a href="/about/press/">Press</a></li> <li><a href="/about/attribution/">Attribution</a></li> <li><a href="/about/legal/">Legal / Privacy</a></li> diff --git a/site/content/pages/about/legal.md b/site/content/pages/about/legal.md index e88fbb17..1a443eec 100644 --- a/site/content/pages/about/legal.md +++ b/site/content/pages/about/legal.md @@ -16,6 +16,7 @@ authors: Adam Harvey <section class="about-menu"> <ul> <li><a href="/about/">About</a></li> +<li><a href="/about/updates/">Updates</a></li> <li><a href="/about/press/">Press</a></li> <li><a href="/about/attribution/">Attribution</a></li> <li><a class="current" href="/about/legal/">Legal / Privacy</a></li> diff --git a/site/content/pages/about/press.md b/site/content/pages/about/press.md index 91480e93..e2400640 100644 --- a/site/content/pages/about/press.md +++ b/site/content/pages/about/press.md @@ -16,6 +16,7 @@ authors: Adam Harvey <section class="about-menu"> <ul> <li><a href="/about/">About</a></li> +<li><a href="/about/updates/">Updates</a></li> <li><a class="current" href="/about/press/">Press</a></li> <li><a href="/about/attribution/">Attribution</a></li> <li><a href="/about/legal/">Legal / Privacy</a></li> diff --git a/site/content/pages/about/updates.md b/site/content/pages/about/updates.md index 3cac2143..cfcbe684 100644 --- a/site/content/pages/about/updates.md +++ b/site/content/pages/about/updates.md @@ -23,16 +23,16 @@ authors: Adam Harvey </ul> </section> -Since publishing this project, several of datasets have disappeared. Below is a chronical of recents events related to the datasets on this site. +Since publishing MegaPixels, several of the datasets mentioned have disappeared and one surveillance workshop was cancelled. Below is a list of notable responses and reactions. June 2019 -- June 2: The Duke MTMC main webpage was deactivated and the entire dataset seems to be no longer available from Duke -- June 2: The has been https://reid-mct.github.io/2019/ -- June 1: The Brainwash face/head dataset has been taken down by its author after posting it about it +- June 2: The Duke MTMC dataset website (<http://vision.cs.duke.edu/DukeMTMC/>) has been taken down +- June 2: A person tracking workshop at CVPR has been cancelled due to the Duke MTMC dataset takedown <https://reid-mct.github.io/2019/> +- June 1: The Brainwash face/head dataset has been taken down by its author <https://exhibits.stanford.edu/data/catalog/sx925dc9385>. "This data was removed from access at the request of the depositor." +- June 1: the [UCCS dataset page](/dataset/uccs) page with a response from the author to clarify that he did not provide any face data to government agencies. Funding was for technology transfer. May 2019 -- May 31: Semantic Scholar appears to be censoring citations used in this project. Two of the citations linking the Brainwash dataset to a military research in China have been intentionally disabled. -- May 28: The Microsoft Celeb (MS Celeb) face dataset website is now 404 and all the download links are deactivated. It appears that Microsoft Reserach has shuttered access to their MS Celeb dataset. Yet it remains available, as of June 2, on [Imperial College London's website](https://ibug.doc.ic.ac.uk/resources/lightweight-face-recognition-challenge-workshop/) --
\ No newline at end of file +- May 31: Semantic Scholar appears to be censoring citations used in this project. Two of the citations linking the Brainwash dataset to research from the National University of Defense Technology (NUDT) in China have disabled. [NUDT citation 1](https://www.semanticscholar.org/paper/A-Replacement-Algorithm-of-Non-Maximum-Suppression-Zhao-Wang/591a4bfa6380c9fcd5f3ae690e3ac5c09b7bf37b), [NUDT citation 2](https://www.semanticscholar.org/paper/Localized-region-context-and-object-feature-fusion-Li-Dou/b02d31c640b0a31fb18c4f170d841d8e21ffb66c), and the [original paper](https://www.semanticscholar.org/paper/End-to-End-People-Detection-in-Crowded-Scenes-Stewart-Andriluka/1bd1645a629f1b612960ab9bba276afd4cf7c666) show that the NUDT citation has been censored (see references) +- May 28: The Microsoft Celeb (MS Celeb) face dataset website is now 404 and all the download links were deactivated. It appears that Microsoft Reserach has shuttered access to their MS Celeb dataset. Yet it remains available, as of June 2, on [Imperial College London's website](https://ibug.doc.ic.ac.uk/resources/lightweight-face-recognition-challenge-workshop/) and on <https://msropendata.com/datasets/98fdfc70-85ee-5288-a69f-d859bbe9c737> diff --git a/site/datasets/verified/adience.csv b/site/datasets/verified/adience.csv index f6e229b6..f46d4483 100644 --- a/site/datasets/verified/adience.csv +++ b/site/datasets/verified/adience.csv @@ -138,3 +138,29 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,t 136,Malaysia,Adience,adience,3.12267405,101.65356103,"University of Malaya, Kuala Lumpur",edu,d4d1ac1cfb2ca703c4db8cc9a1c7c7531fa940f9,citation,,"Gender estimation based on supervised HOG, Action Units and unsupervised CNN feature extraction",2017 137,United Kingdom,Adience,adience,51.5247272,-0.03931035,Queen Mary University of London,edu,d7fd3dedb6b260702ed5e4b9175127815286e8da,citation,,Knowledge sharing: From atomic to parametrised context and shallow to deep models,2017 138,Taiwan,Adience,adience,25.0421852,121.6145477,"Academia Sinica, Taipei, Taiwan",edu,aa6f7c3daed31d331ef626758e990cbc04632852,citation,,Merging Deep Neural Networks for Mobile Devices,2018 +139,China,Adience,adience,22.4162632,114.2109318,Chinese University of Hong Kong,edu,aaa2b45153051e23d5a35ccf9af8ecabc0fe24cd,citation,https://pdfs.semanticscholar.org/aaa2/b45153051e23d5a35ccf9af8ecabc0fe24cd.pdf,1 How Good can Human Predict Facial Age ?,2017 +140,China,Adience,adience,39.993008,116.329882,SenseTime,company,aaa2b45153051e23d5a35ccf9af8ecabc0fe24cd,citation,https://pdfs.semanticscholar.org/aaa2/b45153051e23d5a35ccf9af8ecabc0fe24cd.pdf,1 How Good can Human Predict Facial Age ?,2017 +141,China,Adience,adience,39.993008,116.329882,SenseTime,company,8fee9b8c44626c4ac6b96ef183394bc4f36dc95f,citation,https://arxiv.org/pdf/1708.09687.pdf,Quantifying Facial Age by Posterior of Age Comparisons,2017 +142,China,Adience,adience,22.4162632,114.2109318,Chinese University of Hong Kong,edu,8fee9b8c44626c4ac6b96ef183394bc4f36dc95f,citation,https://arxiv.org/pdf/1708.09687.pdf,Quantifying Facial Age by Posterior of Age Comparisons,2017 +143,United States,Adience,adience,40.9153196,-73.1270626,Stony Brook University,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,https://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017 +144,United States,Adience,adience,35.93006535,-84.31240032,Oak Ridge National Laboratory,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,https://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017 +145,United States,Adience,adience,40.90826665,-73.11520891,Stony Brook University Hospital,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,https://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017 +146,India,Adience,adience,28.5456282,77.2731505,"IIIT Delhi, India",edu,c43d3ad956118ea1d26d39903097e2db86eae822,citation,https://arxiv.org/pdf/1904.01219.pdf,Deep Learning for Face Recognition: Pride or Prejudiced?,2019 +147,Ireland,Adience,adience,53.27639715,-9.05829961,National University of Ireland Galway,edu,e08038b14165536c52ffe950d90d0f43be9c8f15,citation,https://arxiv.org/pdf/1703.08383.pdf,Smart Augmentation Learning an Optimal Data Augmentation Strategy,2017 +148,Taiwan,Adience,adience,25.0410728,121.6147562,Institute of Information Science,edu,39539b7fcf1c637b04de84b23dc9c85a8b2f9f40,citation,https://arxiv.org/pdf/1805.04980.pdf,Unifying and Merging Well-trained Deep Neural Networks for Inference Stage,2018 +149,Taiwan,Adience,adience,25.021321,121.5360683,MOST Joint Research Center for AI Technology and All Vista Healthcare,company,39539b7fcf1c637b04de84b23dc9c85a8b2f9f40,citation,https://arxiv.org/pdf/1805.04980.pdf,Unifying and Merging Well-trained Deep Neural Networks for Inference Stage,2018 +150,India,Adience,adience,28.5449756,77.1926284,"IIT Delhi, India",edu,0dc61f199539cd15f847b688740be49b39e3d520,citation,https://pdfs.semanticscholar.org/0dc6/1f199539cd15f847b688740be49b39e3d520.pdf,Age Group Determination from Face Using Texture Classification based on Probabilistic Non-Extensive Entropy,2017 +151,Spain,Adience,adience,41.5008957,2.111553,Autonomous University of Barcelona,edu,d7f6eaa5caa0d187cd1fe51d5bc27343921e7539,citation,https://arxiv.org/pdf/1807.07320.pdf,Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery,2018 +152,Singapore,Adience,adience,1.2962018,103.77689944,National University of Singapore,edu,3b50a85ba29f0f7eb49fb275be86e6c2b4f8fa4b,citation,https://pdfs.semanticscholar.org/3b50/a85ba29f0f7eb49fb275be86e6c2b4f8fa4b.pdf,Image ordinal classification with deep multi-view learning,2018 +153,China,Adience,adience,28.874513,105.431827,"Sichuan Police College, Luzhou, China",gov,3b50a85ba29f0f7eb49fb275be86e6c2b4f8fa4b,citation,https://pdfs.semanticscholar.org/3b50/a85ba29f0f7eb49fb275be86e6c2b4f8fa4b.pdf,Image ordinal classification with deep multi-view learning,2018 +154,China,Adience,adience,30.788537,103.888902,"UESTC, Chengdu, China",edu,3b50a85ba29f0f7eb49fb275be86e6c2b4f8fa4b,citation,https://pdfs.semanticscholar.org/3b50/a85ba29f0f7eb49fb275be86e6c2b4f8fa4b.pdf,Image ordinal classification with deep multi-view learning,2018 +155,United States,Adience,adience,32.8536333,-117.2035286,Kyung Hee University,edu,73b83ef7ee5f929be51a91096b57c098008f384e,citation,https://pdfs.semanticscholar.org/73b8/3ef7ee5f929be51a91096b57c098008f384e.pdf,Mining Wrinkle-Patterns with Local EdgePrototypic Pattern (LEPP) Descriptor for the Recognition of Human Age-groups,2018 +156,India,Adience,adience,10.9365094,76.9562405,"Sri Krishna College of Engineering and Technology, Coimbatore, India",edu,ece46e3a126953f639149fc233bddcd44d8afad1,citation,https://pdfs.semanticscholar.org/ece4/6e3a126953f639149fc233bddcd44d8afad1.pdf,Semantic-Based Facial Image-Retrieval System with Aid of Adaptive Particle Swarm Optimization and Squared Euclidian Distance,2015 +157,Canada,Adience,adience,45.5010087,-73.6157778,University of Montreal,edu,3540625bc996601a9d04c4027169b7fcad1b9eae,citation,https://pdfs.semanticscholar.org/3540/625bc996601a9d04c4027169b7fcad1b9eae.pdf,TECHNIQUES IN ORDINAL CLASSIFICATION AND IMAGE-TO-IMAGE TRANSLATION,2018 +158,Canada,Adience,adience,45.5307147,-73.6135931,"Institute for Learning, Algorithms Montreal, Canada",edu,e8d0eb3c3bf64b38ec04e982745147428459e2d2,citation,https://arxiv.org/pdf/1705.05278.pdf,Unimodal Probability Distributions for Deep Ordinal Classification,2017 +159,China,Adience,adience,45.7413921,126.62552755,Harbin Institute of Technology,edu,09e353946fb6adf1621f33041853c58aecfd183b,citation,,Deep convolutional neural networks-based age and gender classification with facial images,2017 +160,China,Adience,adience,26.085573,119.372442,Fujian University of Technology,edu,09e353946fb6adf1621f33041853c58aecfd183b,citation,,Deep convolutional neural networks-based age and gender classification with facial images,2017 +161,China,Adience,adience,25.28164,110.337304,Guilin University of Electronic Technology,edu,09e353946fb6adf1621f33041853c58aecfd183b,citation,,Deep convolutional neural networks-based age and gender classification with facial images,2017 +162,Israel,Adience,adience,32.77824165,34.99565673,Open University of Israel,edu,1be498d4bbc30c3bfd0029114c784bc2114d67c0,citation,,Age and Gender Estimation of Unfiltered Faces,2014 +163,South Korea,Adience,adience,37.5509442,126.9410023,Sogang University,edu,0deea943ac4dc1be822c02f97d0c6c97e201ba8d,citation,,Age category estimation using matching convolutional neural network,2018 +164,Taiwan,Adience,adience,25.0411727,121.6146518,"Insititute of Information Science, Academia Sinica, Taipei, Taiwan",edu,3d0444be5be1d19d93e91519e48e314b3035e4cf,citation,,Joint Estimation of Age and Gender from Unconstrained Face Images Using Lightweight Multi-Task CNN for Mobile Applications,2018 diff --git a/site/datasets/verified/duke_mtmc.csv b/site/datasets/verified/duke_mtmc.csv index b85d9458..5ede8ed5 100644 --- a/site/datasets/verified/duke_mtmc.csv +++ b/site/datasets/verified/duke_mtmc.csv @@ -223,3 +223,79 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,t 221,China,Duke MTMC,duke_mtmc,31.2284923,121.40211389,East China Normal University,edu,0353fe24ecd237f4d9ae4dbc277a6a67a69ce8ed,citation,https://pdfs.semanticscholar.org/0353/fe24ecd237f4d9ae4dbc277a6a67a69ce8ed.pdf,Discriminative Feature Representation for Person Re-identification by Batch-contrastive Loss,2018 222,China,Duke MTMC,duke_mtmc,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,fd2bc4833c19a60d3646368952dcf35dbda007f3,citation,,Improving Person Re-Identification by Adaptive Hard Sample Mining,2018 223,China,Duke MTMC,duke_mtmc,30.60903415,114.3514284,Wuhan University of Technology,edu,fd2bc4833c19a60d3646368952dcf35dbda007f3,citation,,Improving Person Re-Identification by Adaptive Hard Sample Mining,2018 +224,China,Duke MTMC,duke_mtmc,30.19331415,120.11930822,Zhejiang University,edu,b350b567b13ab2b7ba94159767a41917fc38a2cb,citation,https://arxiv.org/pdf/1903.07071.pdf,Bag of Tricks and A Strong Baseline for Deep Person Re-identification,2019 +225,China,Duke MTMC,duke_mtmc,32.035225,118.855317,PLA Army Engineering University,mil,c8ac121e9c4eb9964be9c5713f22a95c1c3b57e9,citation,https://arxiv.org/pdf/1901.05798.pdf,Ensemble Feature for Person Re-Identification,2019 +226,China,Duke MTMC,duke_mtmc,22.4162632,114.2109318,Chinese University of Hong Kong,edu,0c769c19d894e0dbd6eb314781dc1db3c626df57,citation,https://arxiv.org/pdf/1604.01850.pdf,Joint Detection and Identification Feature Learning for Person Search,2017 +227,China,Duke MTMC,duke_mtmc,39.993008,116.329882,SenseTime,company,0c769c19d894e0dbd6eb314781dc1db3c626df57,citation,https://arxiv.org/pdf/1604.01850.pdf,Joint Detection and Identification Feature Learning for Person Search,2017 +228,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,0c769c19d894e0dbd6eb314781dc1db3c626df57,citation,https://arxiv.org/pdf/1604.01850.pdf,Joint Detection and Identification Feature Learning for Person Search,2017 +229,United States,Duke MTMC,duke_mtmc,22.5447154,113.9357164,Tencent,company,57c144f668d11ef7e2c89fdfcf67341a4733dd64,citation,https://pdfs.semanticscholar.org/57c1/44f668d11ef7e2c89fdfcf67341a4733dd64.pdf,Unlabeled images Auxiliary reference person images Backbone ResNet ‐ 50 Reference learning,2019 +230,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,57c144f668d11ef7e2c89fdfcf67341a4733dd64,citation,https://pdfs.semanticscholar.org/57c1/44f668d11ef7e2c89fdfcf67341a4733dd64.pdf,Unlabeled images Auxiliary reference person images Backbone ResNet ‐ 50 Reference learning,2019 +231,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,57c144f668d11ef7e2c89fdfcf67341a4733dd64,citation,https://pdfs.semanticscholar.org/57c1/44f668d11ef7e2c89fdfcf67341a4733dd64.pdf,Unlabeled images Auxiliary reference person images Backbone ResNet ‐ 50 Reference learning,2019 +232,China,Duke MTMC,duke_mtmc,31.83907195,117.26420748,University of Science and Technology of China,edu,59a4cec1afb2804eeff1774c4eb315701443af76,citation,https://arxiv.org/pdf/1904.02998.pdf,Relation-Aware Global Attention,2019 +233,United States,Duke MTMC,duke_mtmc,42.3614256,-71.0812092,Microsoft Research Asia,company,59a4cec1afb2804eeff1774c4eb315701443af76,citation,https://arxiv.org/pdf/1904.02998.pdf,Relation-Aware Global Attention,2019 +234,China,Duke MTMC,duke_mtmc,32.0565957,118.77408833,Nanjing University,edu,9a433055551c1f5c670f2a69a57f6aad3a5810d9,citation,https://arxiv.org/pdf/1904.03425.pdf,A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification,2019 +235,Australia,Duke MTMC,duke_mtmc,-34.40505545,150.87834655,University of Wollongong,edu,9a433055551c1f5c670f2a69a57f6aad3a5810d9,citation,https://arxiv.org/pdf/1904.03425.pdf,A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification,2019 +236,Australia,Duke MTMC,duke_mtmc,-33.88890695,151.18943366,University of Sydney,edu,9a433055551c1f5c670f2a69a57f6aad3a5810d9,citation,https://arxiv.org/pdf/1904.03425.pdf,A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification,2019 +237,China,Duke MTMC,duke_mtmc,24.4399419,118.09301781,Xiamen University,edu,b9cc54c5f94371cfc8e79179c20ec559a1a43cbb,citation,https://arxiv.org/pdf/1904.01990.pdf,Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification,2019 +238,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,b9cc54c5f94371cfc8e79179c20ec559a1a43cbb,citation,https://arxiv.org/pdf/1904.01990.pdf,Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification,2019 +239,Australia,Duke MTMC,duke_mtmc,-35.2776999,149.118527,Australian National University,edu,b9cc54c5f94371cfc8e79179c20ec559a1a43cbb,citation,https://arxiv.org/pdf/1904.01990.pdf,Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification,2019 +240,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,123478b496a3fa39a9043ccaa660e81c473a14e9,citation,https://pdfs.semanticscholar.org/1234/78b496a3fa39a9043ccaa660e81c473a14e9.pdf,A Bottom-Up Clustering Approach to Unsupervised Person Re-identification,2019 +241,United States,Duke MTMC,duke_mtmc,29.888411,-97.938351,Texas State University,edu,123478b496a3fa39a9043ccaa660e81c473a14e9,citation,https://pdfs.semanticscholar.org/1234/78b496a3fa39a9043ccaa660e81c473a14e9.pdf,A Bottom-Up Clustering Approach to Unsupervised Person Re-identification,2019 +242,United States,Duke MTMC,duke_mtmc,42.3383668,-71.08793524,Northeastern University,edu,78fde57462fb68530a49f913c89343da5727580d,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w17/papers/Gou_DukeMTMC4ReID_A_Large-Scale_CVPR_2017_paper.pdf,DukeMTMC4ReID: A Large-Scale Multi-camera Person Re-identification Dataset,2017 +243,United States,Duke MTMC,duke_mtmc,42.7298459,-73.67950216,Rensselaer Polytechnic Institute,edu,78fde57462fb68530a49f913c89343da5727580d,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w17/papers/Gou_DukeMTMC4ReID_A_Large-Scale_CVPR_2017_paper.pdf,DukeMTMC4ReID: A Large-Scale Multi-camera Person Re-identification Dataset,2017 +244,United States,Duke MTMC,duke_mtmc,38.5336349,-121.79077264,"University of California, Davis",edu,79c959833ff49f860e20b6654dbf4d6acdee0230,citation,https://arxiv.org/pdf/1811.02545.pdf,Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond,2018 +245,China,Duke MTMC,duke_mtmc,30.19331415,120.11930822,Zhejiang University,edu,79c959833ff49f860e20b6654dbf4d6acdee0230,citation,https://arxiv.org/pdf/1811.02545.pdf,Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond,2018 +246,China,Duke MTMC,duke_mtmc,30.2931534,120.1620458,Zhejiang University of Technology,edu,8fbb73bc6fb74e119b5fdf02482fa90afb7e443e,citation,https://pdfs.semanticscholar.org/8fbb/73bc6fb74e119b5fdf02482fa90afb7e443e.pdf,Parts Semantic Segmentation Aware Representation Learning for Person Re-Identification,2019 +247,China,Duke MTMC,duke_mtmc,39.061004,117.142023,Tianjin University of Technology,edu,8fbb73bc6fb74e119b5fdf02482fa90afb7e443e,citation,https://pdfs.semanticscholar.org/8fbb/73bc6fb74e119b5fdf02482fa90afb7e443e.pdf,Parts Semantic Segmentation Aware Representation Learning for Person Re-Identification,2019 +248,China,Duke MTMC,duke_mtmc,27.712328,112.006373,Hunan University of Humanities,edu,2ff0f94f1a05fb4e6cb906f8b5aa59d50c9754be,citation,https://arxiv.org/pdf/1807.11042.pdf,Towards Good Practices on Building Effective CNN Baseline Model for Person Re-identification,2018 +249,Singapore,Duke MTMC,duke_mtmc,1.2988926,103.7873107,"A*STAR, Singapore",edu,2ff0f94f1a05fb4e6cb906f8b5aa59d50c9754be,citation,https://arxiv.org/pdf/1807.11042.pdf,Towards Good Practices on Building Effective CNN Baseline Model for Person Re-identification,2018 +250,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,5f12ca6b863b5bc28f58443ba2b70a102af965bd,citation,https://arxiv.org/pdf/1903.09776.pdf,Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification,2019 +251,Italy,Duke MTMC,duke_mtmc,46.0658836,11.1159894,University of Trento,edu,4c903009e7b963f1cd4f02482ea4b242d71e8058,citation,https://arxiv.org/pdf/1904.01308.pdf,Camera Adversarial Transfer for Unsupervised Person Re-Identification,2019 +252,United States,Duke MTMC,duke_mtmc,47.6543238,-122.30800894,University of Washington,edu,17829aec0f06dc8f45f417e667e3d92eeba923dc,citation,https://arxiv.org/pdf/1903.09254.pdf,CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification,2019 +253,China,Duke MTMC,duke_mtmc,40.00229045,116.32098908,Tsinghua University,edu,4f83ef534c164bd7fbd1e71fe6a3d09a30326b26,citation,https://arxiv.org/pdf/1810.10221.pdf,Cross-Resolution Person Re-identification with Deep Antithetical Learning,2018 +254,United States,Duke MTMC,duke_mtmc,28.59899755,-81.19712501,University of Central Florida,edu,427aee2aaf7d2d67738b046aea2782f9b8892c68,citation,https://arxiv.org/pdf/1904.11397.pdf,Deep Constrained Dominant Sets for Person Re-identification,2019 +255,China,Duke MTMC,duke_mtmc,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,07dead6b98379faac1cf0b2cb34a5db842ab9de9,citation,https://arxiv.org/pdf/1711.10658.pdf,Deep-Person: Learning Discriminative Deep Features for Person Re-Identification,2017 +256,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,19a0f34440c25323544b90d9d822a212bfed0eb5,citation,https://arxiv.org/pdf/1901.10100.pdf,Discovering Underlying Person Structure Pattern with Relative Local Distance for Person Re-identification,2019 +257,China,Duke MTMC,duke_mtmc,22.053565,113.39913285,Jilin University,edu,05f9d47bcc438ffcd4efcc5d77792a7b1984342a,citation,https://arxiv.org/pdf/1811.11510.pdf,Identity Preserving Generative Adversarial Network for Cross-Domain Person Re-identification,2018 +258,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,424cce55355f2fa4b3c020d56967e1f7b82b1de9,citation,https://pdfs.semanticscholar.org/424c/ce55355f2fa4b3c020d56967e1f7b82b1de9.pdf,M 2 M-GAN : Many-to-Many Generative Adversarial Transfer Learning for Person Re-Identification,2018 +259,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,8824638e8077f62283d292804006ce94c92764bf,citation,https://arxiv.org/pdf/1811.03768.pdf,M2M-GAN: Many-to-Many Generative Adversarial Transfer Learning for Person Re-Identification,2018 +260,China,Duke MTMC,duke_mtmc,31.28473925,121.49694909,Tongji University,edu,74e38dfeb5abc7ddf077abc01de90f4d0a49c142,citation,https://arxiv.org/pdf/1812.05319.pdf,Omni-directional Feature Learning for Person Re-identification,2018 +261,United States,Duke MTMC,duke_mtmc,40.1019523,-88.2271615,UIUC,edu,040c0612e0f006fa93f140ccb97b9738efcf74a5,citation,https://arxiv.org/pdf/1811.10144.pdf,One Shot Domain Adaptation for Person Re-Identification,2018 +262,Spain,Duke MTMC,duke_mtmc,41.5007811,2.11143663,Universitat Autònoma de Barcelona,edu,388b03244e7cdf28c750d7f6d4b4eb64219c3e7a,citation,https://arxiv.org/pdf/1812.02937.pdf,Optimizing Speed/Accuracy Trade-Off for Person Re-identification via Knowledge Distillation,2018 +263,China,Duke MTMC,duke_mtmc,22.53521465,113.9315911,Shenzhen University,edu,1e3cb57830fde3bb588acbe2784b01e922f031b0,citation,https://arxiv.org/pdf/1904.00355.pdf,Pedestrian re-identification based on Tree branch network with local and global learning,2019 +264,United States,Duke MTMC,duke_mtmc,43.0008093,-78.7889697,University at Buffalo,edu,1ba61a4fedc217f7bd052d1b2904567c9985dc44,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w6/papers/Narayan_Person_Re-Identification_for_CVPR_2017_paper.pdf,Person Re-identification for Improved Multi-person Multi-camera Tracking by Continuous Entity Association,2017 +265,United States,Duke MTMC,duke_mtmc,28.59899755,-81.19712501,University of Central Florida,edu,a1e97c4043d5cc9896dc60ae7ca135782d89e5fc,citation,https://arxiv.org/pdf/1612.02155.pdf,"Re-identification of Humans in Crowds using Personal, Social and Environmental Constraints",2016 +266,United States,Duke MTMC,duke_mtmc,42.7298459,-73.67950216,Rensselaer Polytechnic Institute,edu,24d6d3adf2176516ef0de2e943ce2084e27c4f94,citation,https://arxiv.org/pdf/1811.07487.pdf,Re-Identification with Consistent Attentive Siamese Networks,2018 +267,United States,Duke MTMC,duke_mtmc,42.7298459,-73.67950216,Rensselaer Polytechnic Institute,edu,afc01c33b7dd9de9e5c84c063aaccc4e0c839e74,citation,https://arxiv.org/pdf/1811.07487.pdf,Re-Identification with Consistent Attentive Siamese Networks,2018 +268,China,Duke MTMC,duke_mtmc,30.19331415,120.11930822,Zhejiang University,edu,74bfaacd4e86a1304d2b5e7340591cffb38d84dd,citation,https://arxiv.org/pdf/1807.00537.pdf,SphereReID: Deep Hypersphere Manifold Embedding for Person Re-Identification,2019 +269,United States,Duke MTMC,duke_mtmc,35.9990522,-78.9290629,Duke University,edu,0c0e26737fbc27d2dc7aab58783b155b009a06cf,citation,https://arxiv.org/pdf/1803.05872.pdf,Virtual CNN Branching: Efficient Feature Ensemble for Person Re-Identification,2018 +270,China,Duke MTMC,duke_mtmc,40.00229045,116.32098908,Tsinghua University,edu,753d2a35c9edf5dfcac4ef3a6adc993b657b01f0,citation,https://arxiv.org/pdf/1711.09349.pdf,Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline),2017 +271,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,753d2a35c9edf5dfcac4ef3a6adc993b657b01f0,citation,https://arxiv.org/pdf/1711.09349.pdf,Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline),2017 +272,United States,Duke MTMC,duke_mtmc,29.58333105,-98.61944505,University of Texas at San Antonio,edu,753d2a35c9edf5dfcac4ef3a6adc993b657b01f0,citation,https://arxiv.org/pdf/1711.09349.pdf,Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline),2017 +273,China,Duke MTMC,duke_mtmc,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,26ac3ee756d4a24ec31de918f54098012e17fd25,citation,https://arxiv.org/pdf/1711.10658.pdf,Deep-Person: Learning Discriminative Deep Features for Person Re-Identification,2017 +274,China,Duke MTMC,duke_mtmc,40.0044795,116.370238,Chinese Academy of Sciences,edu,3c89455d9a91560eb08e59237dbc4f9fac16ff09,citation,https://arxiv.org/pdf/1904.04975.pdf,Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification,2019 +275,Australia,Duke MTMC,duke_mtmc,-35.2776999,149.118527,Australian National University,edu,48b4b0bbbfee08604b68bb0246b295e357444ed1,citation,https://arxiv.org/pdf/1904.07223.pdf,Joint Discriminative and Generative Learning for Person Re-identification,2019 +276,United States,Duke MTMC,duke_mtmc,37.3706254,-121.9671894,NVIDIA,company,48b4b0bbbfee08604b68bb0246b295e357444ed1,citation,https://arxiv.org/pdf/1904.07223.pdf,Joint Discriminative and Generative Learning for Person Re-identification,2019 +277,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,48b4b0bbbfee08604b68bb0246b295e357444ed1,citation,https://arxiv.org/pdf/1904.07223.pdf,Joint Discriminative and Generative Learning for Person Re-identification,2019 +278,China,Duke MTMC,duke_mtmc,35.86166,104.195397,"Megvii Inc. (Face++), China",company,10c20cf47d61063032dce4af73a4b8e350bf1128,citation,https://arxiv.org/pdf/1712.09531.pdf,"Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project",2017 +279,France,Duke MTMC,duke_mtmc,45.7833244,4.8781984,University of Lyon,edu,19650d66be1bf350fe784467da3ff7074c94c940,citation,https://pdfs.semanticscholar.org/1965/0d66be1bf350fe784467da3ff7074c94c940.pdf,Person re-identification in images with deep learning,2018 +280,Singapore,Duke MTMC,duke_mtmc,1.3392609,103.8916077,Panasonic Singapore,company,70ce1a17f257320fc718d61964b21e7aeabd8cd5,citation,https://arxiv.org/pdf/1803.10630.pdf,Person re-identification with fusion of hand-crafted and deep pose-based body region features,2018 +281,China,Duke MTMC,duke_mtmc,31.30104395,121.50045497,Fudan University,edu,66e4f5e354240a022789353798ce92e4ab68e109,citation,https://arxiv.org/pdf/1712.02225.pdf,Pose-Normalized Image Generation for Person Re-identification,2018 +282,Japan,Duke MTMC,duke_mtmc,34.7321121,135.7328585,"Nara Institute of Science and Technology, Japan",edu,66e4f5e354240a022789353798ce92e4ab68e109,citation,https://arxiv.org/pdf/1712.02225.pdf,Pose-Normalized Image Generation for Person Re-identification,2018 +283,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,66e4f5e354240a022789353798ce92e4ab68e109,citation,https://arxiv.org/pdf/1712.02225.pdf,Pose-Normalized Image Generation for Person Re-identification,2018 +284,China,Duke MTMC,duke_mtmc,28.2290209,112.99483204,"National University of Defense Technology, China",mil,e799c5c7e169f471950eb76dbb329c2d031347ae,citation,https://arxiv.org/pdf/1809.03137.pdf,Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers,2018 +285,United Kingdom,Duke MTMC,duke_mtmc,54.6141723,-5.9002151,Queen's University Belfast,edu,05c4eace439fcc011aaa70c8c00c7386a0cf479e,citation,https://pdfs.semanticscholar.org/05c4/eace439fcc011aaa70c8c00c7386a0cf479e.pdf,Video Person Re-Identification for Wide Area Tracking based on Recurrent Neural Networks,2017 +286,China,Duke MTMC,duke_mtmc,39.979203,116.33287,"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China",edu,f12e2888e6db23433166db72ff77c448cb6845e8,citation,,GLAD: Global–Local-Alignment Descriptor for Scalable Person Re-Identification,2018 +287,China,Duke MTMC,duke_mtmc,39.9922379,116.30393816,Peking University,edu,f12e2888e6db23433166db72ff77c448cb6845e8,citation,,GLAD: Global–Local-Alignment Descriptor for Scalable Person Re-Identification,2018 +288,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,a34f8768b10d928aa4f4105afb971819c26a2219,citation,,Multi-Pseudo Regularized Label for Generated Data in Person Re-Identification,2018 +289,China,Duke MTMC,duke_mtmc,40.0044795,116.370238,Chinese Academy of Sciences,edu,a34f8768b10d928aa4f4105afb971819c26a2219,citation,,Multi-Pseudo Regularized Label for Generated Data in Person Re-Identification,2018 +290,China,Duke MTMC,duke_mtmc,31.0252201,121.4337784,Shanghai Jiaotong University,edu,f8c4959ca67846d0c08f371ee884bb8a0845af1e,citation,,Enhancing Model Performance of Person Re-Indentification on Unknown Target Domain,2018 +291,China,Duke MTMC,duke_mtmc,31.83907195,117.26420748,University of Science and Technology of China,edu,f81f69570113e5171203ac121d1ec1d8b91df4a4,citation,,Local Convolutional Neural Networks for Person Re-Identification,2018 +292,China,Duke MTMC,duke_mtmc,34.1235825,108.83546,Xidian University,edu,03df42c643872aa664a7d6a8f5dbb12cbc3d09f3,citation,,An End-to-End Noise-Weakened Person Re-Identification and Tracking With Adaptive Partial Information,2019 +293,China,Duke MTMC,duke_mtmc,39.0607286,117.1256421,Tianjin Normal University,edu,59161bd01e739ad69a93f88303fa2b6e21f6ea98,citation,,Discrimination-Aware Integration for Person Re-Identification in Camera Networks,2019 +294,China,Duke MTMC,duke_mtmc,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,960cdda2dcd299ecdf64e867a7538e24ee4e2a99,citation,,Learning deep embedding with mini-cluster loss for person re-identification,2019 +295,China,Duke MTMC,duke_mtmc,22.8376,108.289839,Guangxi University,edu,aaca2ebcd26ed668788f364dd7af8b4615492b66,citation,,Omnidirectional Feature Learning for Person Re-Identification,2019 +296,China,Duke MTMC,duke_mtmc,31.28473925,121.49694909,Tongji University,edu,aaca2ebcd26ed668788f364dd7af8b4615492b66,citation,,Omnidirectional Feature Learning for Person Re-Identification,2019 +297,China,Duke MTMC,duke_mtmc,34.2469152,108.91061982,Northwestern Polytechnical University,edu,11cb49d8f19f0491e1930d9471988a3c07b70bb4,citation,,Person Re-Identification With Triplet Focal Loss,2018 +298,China,Duke MTMC,duke_mtmc,34.250803,108.983693,Xi’an Jiaotong University,edu,11cb49d8f19f0491e1930d9471988a3c07b70bb4,citation,,Person Re-Identification With Triplet Focal Loss,2018 +299,United States,Duke MTMC,duke_mtmc,42.0551164,-87.67581113,Northwestern University,edu,665b263ce030bcb3356fcd6e45b219c9184d09e1,citation,,Random linear interpolation data augmentation for person re-identification,2018 diff --git a/site/datasets/verified/imdb_face.csv b/site/datasets/verified/imdb_face.csv index 57609d4b..2154db39 100644 --- a/site/datasets/verified/imdb_face.csv +++ b/site/datasets/verified/imdb_face.csv @@ -1,2 +1,7 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year 0,,IMDb Face,imdb_face,0.0,0.0,,,,main,,The Devil of Face Recognition is in the Noise,2018 +1,China,IMDb Face,imdb_face,39.993008,116.329882,SenseTime,company,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +2,China,IMDb Face,imdb_face,28.2290209,112.99483204,"National University of Defense Technology, China",mil,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +3,China,IMDb Face,imdb_face,22.4162632,114.2109318,Chinese University of Hong Kong,edu,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +4,China,IMDb Face,imdb_face,39.9808333,116.34101249,Beihang University,edu,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +5,China,IMDb Face,imdb_face,40.00229045,116.32098908,Tsinghua University,edu,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 diff --git a/site/datasets/verified/megaface.csv b/site/datasets/verified/megaface.csv index 4c38af0b..77af9bc8 100644 --- a/site/datasets/verified/megaface.csv +++ b/site/datasets/verified/megaface.csv @@ -64,3 +64,8 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,t 62,United States,MegaFace,megaface,47.6543238,-122.30800894,University of Washington,edu,28d4e027c7e90b51b7d8908fce68128d1964668a,citation,,Level Playing Field for Million Scale Face Recognition,2017 63,China,MegaFace,megaface,31.30104395,121.50045497,Fudan University,edu,c5e37630d0672e4d44f7dee83ac2c1528be41c2e,citation,,Multi-task Deep Neural Network for Joint Face Recognition and Facial Attribute Prediction,2017 64,United States,MegaFace,megaface,39.65404635,-79.96475355,West Virginia University,edu,b1b7603a70860cbe5ff7b963976b5e6f780c88fc,citation,,A Deep Face Identification Network Enhanced by Facial Attributes Prediction,2018 +65,China,MegaFace,megaface,39.993008,116.329882,SenseTime,company,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +66,China,MegaFace,megaface,28.2290209,112.99483204,"National University of Defense Technology, China",mil,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +67,China,MegaFace,megaface,22.4162632,114.2109318,Chinese University of Hong Kong,edu,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +68,China,MegaFace,megaface,39.9808333,116.34101249,Beihang University,edu,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +69,China,MegaFace,megaface,40.00229045,116.32098908,Tsinghua University,edu,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 diff --git a/site/datasets/verified/morph_nc.csv b/site/datasets/verified/morph_nc.csv index a14720dd..21a91e8c 100644 --- a/site/datasets/verified/morph_nc.csv +++ b/site/datasets/verified/morph_nc.csv @@ -1,2 +1,2 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year -0,,MORPH Non-Commercial,morph_nc,0.0,0.0,,,,main,,MORPH: a longitudinal image database of normal adult age-progression,2006 +0,,MORPH-II,morph_nc,0.0,0.0,,,,main,,MORPH: a longitudinal image database of normal adult age-progression,2006 diff --git a/site/datasets/verified/msceleb.csv b/site/datasets/verified/msceleb.csv index be5b063c..5cf48ab3 100644 --- a/site/datasets/verified/msceleb.csv +++ b/site/datasets/verified/msceleb.csv @@ -93,3 +93,32 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,t 91,China,MsCeleb,msceleb,39.98177,116.330086,National Laboratory of Pattern Recognition,edu,c7c8d150ece08b12e3abdb6224000c07a6ce7d47,citation,https://arxiv.org/pdf/1611.05271.pdf,DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification,2018 92,South Korea,MsCeleb,msceleb,36.0138857,129.3231836,POSTECH,edu,e6b45d5a86092bbfdcd6c3c54cda3d6c3ac6b227,citation,https://arxiv.org/pdf/1808.04976.pdf,Pairwise Relational Networks for Face Recognition,2018 93,China,MsCeleb,msceleb,30.318764,120.363977,China Jiliang University,edu,406c5aeca71011fd8f8bd233744a81b53ccf635a,citation,,Scalable softmax loss for face verification,2017 +94,India,MsCeleb,msceleb,28.5456282,77.2731505,"IIIT Delhi, India",edu,c43d3ad956118ea1d26d39903097e2db86eae822,citation,https://arxiv.org/pdf/1904.01219.pdf,Deep Learning for Face Recognition: Pride or Prejudiced?,2019 +95,China,MsCeleb,msceleb,39.993008,116.329882,SenseTime,company,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +96,China,MsCeleb,msceleb,28.2290209,112.99483204,"National University of Defense Technology, China",mil,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +97,China,MsCeleb,msceleb,22.4162632,114.2109318,Chinese University of Hong Kong,edu,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +98,China,MsCeleb,msceleb,39.9808333,116.34101249,Beihang University,edu,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +99,China,MsCeleb,msceleb,40.00229045,116.32098908,Tsinghua University,edu,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +100,China,MsCeleb,msceleb,22.4162632,114.2109318,Chinese University of Hong Kong,edu,2401cd5606c6bc5390acc352d00c1685f0c8af60,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018 +101,China,MsCeleb,msceleb,39.993008,116.329882,SenseTime,company,2401cd5606c6bc5390acc352d00c1685f0c8af60,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018 +102,Singapore,MsCeleb,msceleb,1.3484104,103.68297965,Nanyang Technological University,edu,2401cd5606c6bc5390acc352d00c1685f0c8af60,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018 +103,United States,MsCeleb,msceleb,33.776033,-84.39884086,Georgia Institute of Technology,edu,5b7a5b8ea99ea79e0a0ae53b45bc9b2b1aa99952,citation,https://arxiv.org/pdf/1805.09298.pdf,Learning towards Minimum Hyperspherical Energy,2018 +104,United States,MsCeleb,msceleb,37.3706254,-121.9671894,NVIDIA,company,5b7a5b8ea99ea79e0a0ae53b45bc9b2b1aa99952,citation,https://arxiv.org/pdf/1805.09298.pdf,Learning towards Minimum Hyperspherical Energy,2018 +105,China,MsCeleb,msceleb,23.0502042,113.39880323,South China University of Technology,edu,5b7a5b8ea99ea79e0a0ae53b45bc9b2b1aa99952,citation,https://arxiv.org/pdf/1805.09298.pdf,Learning towards Minimum Hyperspherical Energy,2018 +106,Singapore,MsCeleb,msceleb,1.3484104,103.68297965,Nanyang Technological University,edu,9e31e77f9543ab42474ba4e9330676e18c242e72,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018 +107,China,MsCeleb,msceleb,39.993008,116.329882,SenseTime,company,9e31e77f9543ab42474ba4e9330676e18c242e72,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018 +108,United States,MsCeleb,msceleb,32.87935255,-117.23110049,"University of California, San Diego",edu,9e31e77f9543ab42474ba4e9330676e18c242e72,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018 +109,China,MsCeleb,msceleb,22.4162632,114.2109318,Chinese University of Hong Kong,edu,53840c83f7b6ae78d4310c5b84ab3fde1a33bc4f,citation,https://arxiv.org/pdf/1801.01687.pdf,Accelerated Training for Massive Classification via Dynamic Class Selection,2018 +110,China,MsCeleb,msceleb,39.993008,116.329882,SenseTime,company,53840c83f7b6ae78d4310c5b84ab3fde1a33bc4f,citation,https://arxiv.org/pdf/1801.01687.pdf,Accelerated Training for Massive Classification via Dynamic Class Selection,2018 +111,United States,MsCeleb,msceleb,45.57022705,-122.63709346,Concordia University,edu,db374308655256da1479c272582d7c7139c97173,citation,https://arxiv.org/pdf/1811.11080.pdf,MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices,2018 +112,United States,MsCeleb,msceleb,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,db374308655256da1479c272582d7c7139c97173,citation,https://arxiv.org/pdf/1811.11080.pdf,MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices,2018 +113,United States,MsCeleb,msceleb,36.0678324,-94.1736551,University of Arkansas,edu,db374308655256da1479c272582d7c7139c97173,citation,https://arxiv.org/pdf/1811.11080.pdf,MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices,2018 +114,United States,MsCeleb,msceleb,47.6423318,-122.1369302,Microsoft,company,291265db88023e92bb8c8e6390438e5da148e8f5,citation,https://arxiv.org/pdf/1607.08221.pdf,MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition,2016 +115,China,MsCeleb,msceleb,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,3c5ba48d25fbe24691ed060fa8f2099cc9eba14f,citation,https://arxiv.org/pdf/1812.00194.pdf,Racial Faces in-the-Wild: Reducing Racial Bias by Deep Unsupervised Domain Adaptation,2018 +116,China,MsCeleb,msceleb,22.4162632,114.2109318,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 +117,Spain,MsCeleb,msceleb,40.4167754,-3.7037902,"Computer Vision Group (www.vision4uav.com), Centro de Automática y Robótica (CAR) UPM-CSIC, Universidad Politécnica de Madrid, José Gutiérrez Abascal 2, 28006, Spain",edu,726f76f11e904d7fcb12736c276a0b00eb5cde49,citation,https://arxiv.org/pdf/1901.05903.pdf,A Performance Comparison of Loss Functions for Deep Face Recognition,2019 +118,India,MsCeleb,msceleb,13.5568171,80.0261283,"Indian Institute of Information Technology, Sri City, India",edu,726f76f11e904d7fcb12736c276a0b00eb5cde49,citation,https://arxiv.org/pdf/1901.05903.pdf,A Performance Comparison of Loss Functions for Deep Face Recognition,2019 +119,China,MsCeleb,msceleb,39.98177,116.330086,National Laboratory of Pattern Recognition,edu,3a27d164e931c422d16481916a2fa6401b74bcef,citation,https://arxiv.org/pdf/1709.03654.pdf,Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification,2018 +120,China,MsCeleb,msceleb,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,3a27d164e931c422d16481916a2fa6401b74bcef,citation,https://arxiv.org/pdf/1709.03654.pdf,Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification,2018 +121,China,MsCeleb,msceleb,40.0044795,116.370238,Chinese Academy of Sciences,edu,4cdb6144d56098b819076a8572a664a2c2d27f72,citation,https://arxiv.org/pdf/1806.01196.pdf,Face Synthesis for Eyeglass-Robust Face Recognition,2018 +122,China,MsCeleb,msceleb,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,4cdb6144d56098b819076a8572a664a2c2d27f72,citation,https://arxiv.org/pdf/1806.01196.pdf,Face Synthesis for Eyeglass-Robust Face Recognition,2018 diff --git a/site/datasets/verified/pipa.csv b/site/datasets/verified/pipa.csv index 1124eebc..6a5b1ef2 100644 --- a/site/datasets/verified/pipa.csv +++ b/site/datasets/verified/pipa.csv @@ -45,3 +45,10 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,t 43,Belgium,PIPA,pipa,50.8784802,4.4348624,"Toyota Motor Europe (TME), Brussels 1140, Belgium",edu,503906ca940fa3b01e39d05879c9b6a36524aaf5,citation,,Natural and Effective Obfuscation by Head Inpainting,2018 44,Singapore,PIPA,pipa,1.2966426,103.7763939,National University of Singapore & Qihoo 360 AI Institute,edu,af4759f5e636b5d9049010d5f0e2b0df2a69cd72,citation,,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018 45,Singapore,PIPA,pipa,1.2962018,103.77689944,National University of Singapore,edu,af4759f5e636b5d9049010d5f0e2b0df2a69cd72,citation,,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018 +46,China,PIPA,pipa,23.09461185,113.28788994,Sun Yat-Sen University,edu,2b7e18ecfa27cee95dbf8653b18d6d3cdbe80926,citation,https://arxiv.org/pdf/1807.00504.pdf,Deep Reasoning with Knowledge Graph for Social Relationship Understanding,2018 +47,China,PIPA,pipa,39.993008,116.329882,SenseTime,company,2b7e18ecfa27cee95dbf8653b18d6d3cdbe80926,citation,https://arxiv.org/pdf/1807.00504.pdf,Deep Reasoning with Knowledge Graph for Social Relationship Understanding,2018 +48,United States,PIPA,pipa,42.4505507,-76.4783513,Cornell University,edu,c8a4b38913153611652038a29c8f88ef1ddaa5a7,citation,https://arxiv.org/pdf/1805.04049.pdf,Exploiting Unintended Feature Leakage in Collaborative Learning,2018 +49,United Kingdom,PIPA,pipa,51.5247272,-0.03931035,Queen Mary University of London,edu,ccf8cc3b1ead41cbf4fddd77648a2e9538fb747a,citation,https://arxiv.org/pdf/1811.08965.pdf,Low-Resolution Face Recognition,2018 +50,China,PIPA,pipa,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,4e7942a35fedc6bcb8a973608809f798a8d182bf,citation,https://arxiv.org/pdf/1901.03067.pdf,Multi-Granularity Reasoning for Social Relation Recognition from Images,2019 +51,China,PIPA,pipa,22.4162632,114.2109318,Chinese University of Hong Kong,edu,1db5d63aaaa739d36d3dcb7fd17b2f0775ade681,citation,https://arxiv.org/pdf/1710.00870.pdf,Rethinking Feature Discrimination and Polymerization for Large-scale Recognition,2017 +52,Australia,PIPA,pipa,-34.9189226,138.60423668,University of Adelaide,edu,3d24b386d003bee176a942c26336dbe8f427aadd,citation,https://arxiv.org/pdf/1611.09967.pdf,Sequential Person Recognition in Photo Albums with a Recurrent Network,2017 diff --git a/site/datasets/verified/uccs.csv b/site/datasets/verified/uccs.csv index 1cbefd32..0ecea1af 100644 --- a/site/datasets/verified/uccs.csv +++ b/site/datasets/verified/uccs.csv @@ -5,3 +5,6 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,t 3,United States,UCCS,uccs,41.70456775,-86.23822026,University of Notre Dame,edu,841855205818d3a6d6f85ec17a22515f4f062882,citation,https://arxiv.org/pdf/1805.11529.pdf,Low Resolution Face Recognition in the Wild,2018 4,China,UCCS,uccs,39.9808333,116.34101249,Beihang University,edu,c50e498ede6f5216cffd0645e747ce67fae2096a,citation,https://arxiv.org/pdf/1811.09998.pdf,Low-Resolution Face Recognition in the Wild via Selective Knowledge Distillation,2018 5,China,UCCS,uccs,39.97426,116.21589,"Institute of Information Engineering, CAS, Beijing, China",edu,c50e498ede6f5216cffd0645e747ce67fae2096a,citation,https://arxiv.org/pdf/1811.09998.pdf,Low-Resolution Face Recognition in the Wild via Selective Knowledge Distillation,2018 +6,United Kingdom,UCCS,uccs,51.5247272,-0.03931035,Queen Mary University of London,edu,2306b2a8fba28539306052764a77a0d0f5d1236a,citation,https://arxiv.org/pdf/1804.09691.pdf,Surveillance Face Recognition Challenge,2018 +7,United Kingdom,UCCS,uccs,55.378051,-3.435973,"Vision Semantics Ltd, UK",edu,2306b2a8fba28539306052764a77a0d0f5d1236a,citation,https://arxiv.org/pdf/1804.09691.pdf,Surveillance Face Recognition Challenge,2018 +8,United States,UCCS,uccs,41.70456775,-86.23822026,University of Notre Dame,edu,e94c2c9be4abd121d3d601bfff27edf35f3514ad,citation,https://arxiv.org/pdf/1805.11529.pdf,On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques,2019 diff --git a/site/includes/dashboard.html b/site/includes/dashboard.html index 0ba870b6..d5e5693d 100644 --- a/site/includes/dashboard.html +++ b/site/includes/dashboard.html @@ -19,7 +19,7 @@ <section> - <h3>Biometric Trade Routes</h3> + <h3>Information Supply chain</h3> <p> To help understand how {{ metadata.meta.dataset.name_display }} has been used around the world by commercial, military, and academic organizations; existing publicly available research citing {{ metadata.meta.dataset.name_full }} was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. diff --git a/site/public/about/assets/LICENSE/index.html b/site/public/about/assets/LICENSE/index.html index e73a8d77..074114c4 100644 --- a/site/public/about/assets/LICENSE/index.html +++ b/site/public/about/assets/LICENSE/index.html @@ -49,6 +49,7 @@ <div class='links'> <a href="/datasets/">Datasets</a> <a href="/about/">About</a> + <a href="/about/updates/">Updates</a> </div> </header> <div class="content content-"> diff --git a/site/public/about/attribution/index.html b/site/public/about/attribution/index.html index 3afb30b2..cbd9cd9e 100644 --- a/site/public/about/attribution/index.html +++ b/site/public/about/attribution/index.html @@ -49,6 +49,7 @@ <div class='links'> <a href="/datasets/">Datasets</a> <a href="/about/">About</a> + <a href="/about/updates/">Updates</a> </div> </header> <div class="content content-about"> @@ -57,6 +58,7 @@ <section class="about-menu"> <ul> <li><a href="/about/">About</a></li> +<li><a href="/about/updates/">Updates</a></li> <li><a href="/about/press/">Press</a></li> <li><a class="current" href="/about/attribution/">Attribution</a></li> <li><a href="/about/legal/">Legal / Privacy</a></li> diff --git a/site/public/about/index.html b/site/public/about/index.html index 2c008504..6c06f6aa 100644 --- a/site/public/about/index.html +++ b/site/public/about/index.html @@ -49,6 +49,7 @@ <div class='links'> <a href="/datasets/">Datasets</a> <a href="/about/">About</a> + <a href="/about/updates/">Updates</a> </div> </header> <div class="content content-about"> @@ -57,6 +58,7 @@ <section class="about-menu"> <ul> <li><a class="current" href="/about/">About</a></li> +<li><a href="/about/updates/">Updates</a></li> <li><a href="/about/press/">Press</a></li> <li><a href="/about/attribution/">Attribution</a></li> <li><a href="/about/legal/">Legal / Privacy</a></li> diff --git a/site/public/about/legal/index.html b/site/public/about/legal/index.html index 28300043..4b652ff8 100644 --- a/site/public/about/legal/index.html +++ b/site/public/about/legal/index.html @@ -49,6 +49,7 @@ <div class='links'> <a href="/datasets/">Datasets</a> <a href="/about/">About</a> + <a href="/about/updates/">Updates</a> </div> </header> <div class="content content-about"> @@ -57,6 +58,7 @@ <section class="about-menu"> <ul> <li><a href="/about/">About</a></li> +<li><a href="/about/updates/">Updates</a></li> <li><a href="/about/press/">Press</a></li> <li><a href="/about/attribution/">Attribution</a></li> <li><a class="current" href="/about/legal/">Legal / Privacy</a></li> diff --git a/site/public/about/press/index.html b/site/public/about/press/index.html index 9dce4deb..678ac456 100644 --- a/site/public/about/press/index.html +++ b/site/public/about/press/index.html @@ -49,6 +49,7 @@ <div class='links'> <a href="/datasets/">Datasets</a> <a href="/about/">About</a> + <a href="/about/updates/">Updates</a> </div> </header> <div class="content content-about"> @@ -57,6 +58,7 @@ <section class="about-menu"> <ul> <li><a href="/about/">About</a></li> +<li><a href="/about/updates/">Updates</a></li> <li><a class="current" href="/about/press/">Press</a></li> <li><a href="/about/attribution/">Attribution</a></li> <li><a href="/about/legal/">Legal / Privacy</a></li> diff --git a/site/public/about/updates/index.html b/site/public/about/updates/index.html index 6796e579..d6375fcf 100644 --- a/site/public/about/updates/index.html +++ b/site/public/about/updates/index.html @@ -49,6 +49,7 @@ <div class='links'> <a href="/datasets/">Datasets</a> <a href="/about/">About</a> + <a href="/about/updates/">Updates</a> </div> </header> <div class="content content-about"> @@ -62,18 +63,18 @@ <li><a href="/about/attribution/">Attribution</a></li> <li><a href="/about/legal/">Legal / Privacy</a></li> </ul> -</section><p>Since publishing this project, several of datasets have disappeared. Below is a chronical of recents events related to the datasets on this site.</p> +</section><p>Since publishing MegaPixels, several of the datasets mentioned have disappeared and one surveillance workshop was cancelled. Below is a list of notable responses and reactions.</p> <p>June 2019</p> <ul> -<li>June 2: The Duke MTMC main webpage was deactivated and the entire dataset seems to be no longer available from Duke</li> -<li>June 2: The has been <a href="https://reid-mct.github.io/2019/">https://reid-mct.github.io/2019/</a></li> -<li>June 1: The Brainwash face/head dataset has been taken down by its author after posting it about it</li> +<li>June 2: The Duke MTMC dataset website (<a href="http://vision.cs.duke.edu/DukeMTMC/">http://vision.cs.duke.edu/DukeMTMC/</a>) has been taken down</li> +<li>June 2: A person tracking workshop at CVPR has been cancelled due to the Duke MTMC dataset takedown <a href="https://reid-mct.github.io/2019/">https://reid-mct.github.io/2019/</a></li> +<li>June 1: The Brainwash face/head dataset has been taken down by its author <a href="https://exhibits.stanford.edu/data/catalog/sx925dc9385">https://exhibits.stanford.edu/data/catalog/sx925dc9385</a>. "This data was removed from access at the request of the depositor."</li> +<li>June 1: the <a href="/dataset/uccs">UCCS dataset page</a> page with a response from the author to clarify that he did not provide any face data to government agencies. Funding was for technology transfer.</li> </ul> <p>May 2019</p> <ul> -<li>May 31: Semantic Scholar appears to be censoring citations used in this project. Two of the citations linking the Brainwash dataset to a military research in China have been intentionally disabled.</li> -<li>May 28: The Microsoft Celeb (MS Celeb) face dataset website is now 404 and all the download links are deactivated. It appears that Microsoft Reserach has shuttered access to their MS Celeb dataset. Yet it remains available, as of June 2, on <a href="https://ibug.doc.ic.ac.uk/resources/lightweight-face-recognition-challenge-workshop/">Imperial College London's website</a></li> -<li></li> +<li>May 31: Semantic Scholar appears to be censoring citations used in this project. Two of the citations linking the Brainwash dataset to research from the National University of Defense Technology (NUDT) in China have disabled. <a href="https://www.semanticscholar.org/paper/A-Replacement-Algorithm-of-Non-Maximum-Suppression-Zhao-Wang/591a4bfa6380c9fcd5f3ae690e3ac5c09b7bf37b">NUDT citation 1</a>, <a href="https://www.semanticscholar.org/paper/Localized-region-context-and-object-feature-fusion-Li-Dou/b02d31c640b0a31fb18c4f170d841d8e21ffb66c">NUDT citation 2</a>, and the <a href="https://www.semanticscholar.org/paper/End-to-End-People-Detection-in-Crowded-Scenes-Stewart-Andriluka/1bd1645a629f1b612960ab9bba276afd4cf7c666">original paper</a> show that the NUDT citation has been censored (see references)</li> +<li>May 28: The Microsoft Celeb (MS Celeb) face dataset website is now 404 and all the download links were deactivated. It appears that Microsoft Reserach has shuttered access to their MS Celeb dataset. Yet it remains available, as of June 2, on <a href="https://ibug.doc.ic.ac.uk/resources/lightweight-face-recognition-challenge-workshop/">Imperial College London's website</a> and on <a href="https://msropendata.com/datasets/98fdfc70-85ee-5288-a69f-d859bbe9c737">https://msropendata.com/datasets/98fdfc70-85ee-5288-a69f-d859bbe9c737</a></li> </ul> </section> diff --git a/site/public/datasets/brainwash/index.html b/site/public/datasets/brainwash/index.html index 898a77e3..a64d61fd 100644 --- a/site/public/datasets/brainwash/index.html +++ b/site/public/datasets/brainwash/index.html @@ -49,6 +49,7 @@ <div class='links'> <a href="/datasets/">Datasets</a> <a href="/about/">About</a> + <a href="/about/updates/">Updates</a> </div> </header> <div class="content content-dataset"> @@ -103,7 +104,7 @@ <section> - <h3>Biometric Trade Routes</h3> + <h3>Information Supply chain</h3> <p> To help understand how Brainwash Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Brainwash Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. diff --git a/site/public/datasets/duke_mtmc/index.html b/site/public/datasets/duke_mtmc/index.html index 3655e1c9..1b159f1d 100644 --- a/site/public/datasets/duke_mtmc/index.html +++ b/site/public/datasets/duke_mtmc/index.html @@ -49,6 +49,7 @@ <div class='links'> <a href="/datasets/">Datasets</a> <a href="/about/">About</a> + <a href="/about/updates/">Updates</a> </div> </header> <div class="content content-dataset"> @@ -266,7 +267,7 @@ <section> - <h3>Biometric Trade Routes</h3> + <h3>Information Supply chain</h3> <p> To help understand how Duke MTMC Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Duke Multi-Target, Multi-Camera Tracking Project was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. diff --git a/site/public/datasets/hrt_transgender/index.html b/site/public/datasets/hrt_transgender/index.html index 3cc64826..0abab1f7 100644 --- a/site/public/datasets/hrt_transgender/index.html +++ b/site/public/datasets/hrt_transgender/index.html @@ -49,6 +49,7 @@ <div class='links'> <a href="/datasets/">Datasets</a> <a href="/about/">About</a> + <a href="/about/updates/">Updates</a> </div> </header> <div class="content content-dataset"> diff --git a/site/public/datasets/ijb_c/index.html b/site/public/datasets/ijb_c/index.html index f1457bbd..ec0f716e 100644 --- a/site/public/datasets/ijb_c/index.html +++ b/site/public/datasets/ijb_c/index.html @@ -49,6 +49,7 @@ <div class='links'> <a href="/datasets/">Datasets</a> <a href="/about/">About</a> + <a href="/about/updates/">Updates</a> </div> </header> <div class="content content-dataset"> @@ -63,7 +64,7 @@ <div>21,294 </div> </div><div class='meta'> <div class='gray'>Videos</div> - <div>11,779 </div> + <div>11,799 </div> </div><div class='meta'> <div class='gray'>Identities</div> <div>3,531 </div> @@ -155,7 +156,7 @@ <section> - <h3>Biometric Trade Routes</h3> + <h3>Information Supply chain</h3> <p> To help understand how IJB-C has been used around the world by commercial, military, and academic organizations; existing publicly available research citing IARPA Janus Benchmark C was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. diff --git a/site/public/datasets/index.html b/site/public/datasets/index.html index 8a92beca..697f666d 100644 --- a/site/public/datasets/index.html +++ b/site/public/datasets/index.html @@ -49,6 +49,7 @@ <div class='links'> <a href="/datasets/">Datasets</a> <a href="/about/">About</a> + <a href="/about/updates/">Updates</a> </div> </header> <div class="content content-"> @@ -102,7 +103,7 @@ <div class='year visible'><span>2016</span></div> <div class='purpose'><span>Face recognition</span></div> - <div class='images'><span>10,000,000 images</span></div> + <div class='images'><span>8,200,000 images</span></div> </div> </div> diff --git a/site/public/datasets/msceleb/assets/notes/index.html b/site/public/datasets/msceleb/assets/notes/index.html index 670b2df1..18aa4b4d 100644 --- a/site/public/datasets/msceleb/assets/notes/index.html +++ b/site/public/datasets/msceleb/assets/notes/index.html @@ -49,6 +49,7 @@ <div class='links'> <a href="/datasets/">Datasets</a> <a href="/about/">About</a> + <a href="/about/updates/">Updates</a> </div> </header> <div class="content content-"> diff --git a/site/public/datasets/msceleb/index.html b/site/public/datasets/msceleb/index.html index 5cc9d4ba..d4d3608a 100644 --- a/site/public/datasets/msceleb/index.html +++ b/site/public/datasets/msceleb/index.html @@ -49,6 +49,7 @@ <div class='links'> <a href="/datasets/">Datasets</a> <a href="/about/">About</a> + <a href="/about/updates/">Updates</a> </div> </header> <div class="content content-dataset"> @@ -60,7 +61,7 @@ <div>2016</div> </div><div class='meta'> <div class='gray'>Images</div> - <div>10,000,000 </div> + <div>8,200,000 </div> </div><div class='meta'> <div class='gray'>Identities</div> <div>100,000 </div> @@ -238,7 +239,7 @@ <section> - <h3>Biometric Trade Routes</h3> + <h3>Information Supply chain</h3> <p> To help understand how Microsoft Celeb has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Microsoft Celebrity Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. diff --git a/site/public/datasets/oxford_town_centre/index.html b/site/public/datasets/oxford_town_centre/index.html index fd554809..a43119a5 100644 --- a/site/public/datasets/oxford_town_centre/index.html +++ b/site/public/datasets/oxford_town_centre/index.html @@ -49,6 +49,7 @@ <div class='links'> <a href="/datasets/">Datasets</a> <a href="/about/">About</a> + <a href="/about/updates/">Updates</a> </div> </header> <div class="content content-dataset"> @@ -99,7 +100,7 @@ <section> - <h3>Biometric Trade Routes</h3> + <h3>Information Supply chain</h3> <p> To help understand how TownCentre has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Oxford Town Centre was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. diff --git a/site/public/datasets/uccs/assets/notes/index.html b/site/public/datasets/uccs/assets/notes/index.html index 3f7843cf..55d9015a 100644 --- a/site/public/datasets/uccs/assets/notes/index.html +++ b/site/public/datasets/uccs/assets/notes/index.html @@ -49,6 +49,7 @@ <div class='links'> <a href="/datasets/">Datasets</a> <a href="/about/">About</a> + <a href="/about/updates/">Updates</a> </div> </header> <div class="content content-"> diff --git a/site/public/datasets/uccs/index.html b/site/public/datasets/uccs/index.html index 1674ae64..1838c4a5 100644 --- a/site/public/datasets/uccs/index.html +++ b/site/public/datasets/uccs/index.html @@ -49,6 +49,7 @@ <div class='links'> <a href="/datasets/">Datasets</a> <a href="/about/">About</a> + <a href="/about/updates/">Updates</a> </div> </header> <div class="content content-dataset"> @@ -105,7 +106,7 @@ Their setup made it impossible for students to know they were being photographed <section> - <h3>Biometric Trade Routes</h3> + <h3>Information Supply chain</h3> <p> To help understand how UCCS has been used around the world by commercial, military, and academic organizations; existing publicly available research citing UnConstrained College Students Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. diff --git a/site/public/index.html b/site/public/index.html index feb50455..368ade3d 100644 --- a/site/public/index.html +++ b/site/public/index.html @@ -49,6 +49,7 @@ <div class='links'> <a href="/datasets/" class='aboutLink'>DATASETS</a> <a href="/about/" class='aboutLink'>ABOUT</a> + <a href="/about/updates" class='updateLink'>UPDATES</a> </div> </header> <div class="splash"> diff --git a/site/public/info/index.html b/site/public/info/index.html index 2084cfdb..617a9ce4 100644 --- a/site/public/info/index.html +++ b/site/public/info/index.html @@ -49,6 +49,7 @@ <div class='links'> <a href="/datasets/">Datasets</a> <a href="/about/">About</a> + <a href="/about/updates/">Updates</a> </div> </header> <div class="content content-"> diff --git a/site/public/research/00_introduction/index.html b/site/public/research/00_introduction/index.html index 92e51673..9615a9a1 100644 --- 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