From a7f1d363eaef9149e54e5846072e710713cca7a7 Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Wed, 31 Oct 2018 01:32:04 +0100 Subject: initial commit --- ...Unconstrained Videos with Matched Background Similarity.csv | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv (limited to 'datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv') diff --git a/datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv b/datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv new file mode 100644 index 00000000..0a064c73 --- /dev/null +++ b/datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv @@ -0,0 +1,10 @@ +Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC|http://scholar.google.com/https://www.sciencedirect.com/science/article/pii/S037026931200857X|2012|7814|57|1650605866782918575|None|http://scholar.google.com/scholar?cites=1650605866782918575&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=1650605866782918575&hl=en&as_sdt=0,5|None|A search for the Standard Model Higgs boson in proton–proton collisions with the ATLAS detector at the LHC is presented. The datasets used correspond to integrated luminosities of approximately 4.8 fb− 1 collected at s= 7 TeV in 2011 and 5.8 fb− 1 at s= 8 TeV in 2012 … +The relation between load and penetration in the axisymmetric Boussinesq problem for a punch of arbitrary profile|http://scholar.google.com/https://www.sciencedirect.com/science/article/pii/0020722565900194|1965|3816|3|17409802386521537480|None|http://scholar.google.com/scholar?cites=17409802386521537480&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=17409802386521537480&hl=en&as_sdt=0,5|None|A solution of the axisymmetric Boussinesq problem is derived from which are deduced simple formulae for the depth of penetration of the tip of a punch of arbitrary profile and for the total load which must be applied to the punch to achieve this penetration. Simple … +Evolution of the Altaid tectonic collage and Palaeozoic crustal growth in Eurasia|http://scholar.google.com/https://www.nature.com/articles/364299a0|1993|2702|10|10041893458681953783|None|http://scholar.google.com/scholar?cites=10041893458681953783&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=10041893458681953783&hl=en&as_sdt=0,5|None|A new tectonic model, postulating the growth of giant subduction-accretion complexes along a single magmatic arc now found contorted between Siberia and Baltica, shows that Asia grew by 5.3 million square kilometres during the Palaeozoic era. Half of this growth may … +A" silent" polymorphism in the MDR1 gene changes substrate specificity|http://science.sciencemag.org/content/315/5811/525.short|2007|2225|16|13672882627203076963|None|http://scholar.google.com/scholar?cites=13672882627203076963&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=13672882627203076963&hl=en&as_sdt=0,5|None|Synonymous single-nucleotide polymorphisms (SNPs) do not produce altered coding sequences, and therefore they are not expected to change the function of the protein in which they occur. We report that a synonymous SNP in the Multidrug Resistance 1 (MDR1) … +An unusual supernova in the error box of the γ-ray burst of 25 April 1998|http://scholar.google.com/https://www.nature.com/articles/doi:10.1038%2F27150|1998|1939|18|6579723347909169058|None|http://scholar.google.com/scholar?cites=6579723347909169058&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6579723347909169058&hl=en&as_sdt=0,5|None|The discovery of afterglows associated with γ-ray bursts at X-ray 1, optical 2 and radio 3 wavelengths and the measurement of the redshifts of some of these events 4, 5 has established that γ-ray bursts lie at extreme distances, making them the most powerful photon … +Jet energy measurement with the ATLAS detector in proton-proton collisions at|http://scholar.google.com/https://link.springer.com/article/10.1140/epjc/s10052-013-2304-2|2013|1671|105|5651138599710101302|None|http://scholar.google.com/scholar?cites=5651138599710101302&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=5651138599710101302&hl=en&as_sdt=0,5|None|The jet energy scale and its systematic uncertainty are determined for jets measured with the ATLAS detector at the LHC in proton-proton collision data at a centre-of-mass energy of\(\sqrt {s}= 7\\mathrm {TeV}\) corresponding to an integrated luminosity of 38 pb− 1. Jets … +Assessment of mitral annulus velocity by Doppler tissue imaging in the evaluation of left ventricular diastolic function|http://www.onlinejacc.org/content/30/2/474.abstract|1997|1902|11|4226712902543427028|None|http://scholar.google.com/scholar?cites=4226712902543427028&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=4226712902543427028&hl=en&as_sdt=0,5|None|Objectives. This study assessed the clinical utility of mitral annulus velocity in the evaluation of left ventricular diastolic function. Background. Mitral inflow velocity recorded by Doppler echocardiography has been widely used to evaluate left ventricular diastolic function but is … +Topics in algebra|None|2006|2269|10|212858366598806886|None|http://scholar.google.com/scholar?cites=212858366598806886&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=212858366598806886&hl=en&as_sdt=0,5|None|None +Basic concepts in the theory of seepage of homogeneous liquids in fissured rocks [strata]|http://scholar.google.com/https://pdfs.semanticscholar.org/38a2/e7b4ae9da080578a83e3a8f9135e21359042.pdf|1960|2088|7|15437573464014520045|http://scholar.google.com/https://pdfs.semanticscholar.org/38a2/e7b4ae9da080578a83e3a8f9135e21359042.pdf|http://scholar.google.com/scholar?cites=15437573464014520045&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=15437573464014520045&hl=en&as_sdt=0,5|None|The modern theory of seepage [infiltration 1 is based on the concept of a porous medium consisting of impermeable grains separated by pore spaces. Comparison of the results of theoretical and laboratory investigations of non-steady-state flow of liquids with data for … +Improved luminosity determination in pp collisions at using the ATLAS detector at the LHC|http://scholar.google.com/https://link.springer.com/10.1140/epjc/s10052-013-2518-3|2013|1614|60|11477579481550698253|None|http://scholar.google.com/scholar?cites=11477579481550698253&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=11477579481550698253&hl=en&as_sdt=0,5|None|The luminosity calibration for the ATLAS detector at the LHC during pp collisions at\(\sqrt {s}= 7~\mathrm {TeV}\) in 2010 and 2011 is presented. Evaluation of the luminosity scale is performed using several luminosity-sensitive detectors, and comparisons are made of the … -- cgit v1.2.3-70-g09d2 From a92337ed2270af9b10806c746dcb4e9fa959ffbb Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Wed, 31 Oct 2018 01:53:14 +0100 Subject: codingg --- .../entries/Coding Facial Expressions with Gabor Wavelets.csv | 1 + ...Unconstrained Videos with Matched Background Similarity.csv | 10 ---------- ...: Visual Semantic Role Labeling for Image Understanding.csv | 1 + .../The CMU Pose, Illumination, and Expression Database.csv | 10 ---------- fetch-entries.py | 5 +++-- 5 files changed, 5 insertions(+), 22 deletions(-) create mode 100644 datasets/scholar/entries/Coding Facial Expressions with Gabor Wavelets.csv delete mode 100644 datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv create mode 100644 datasets/scholar/entries/Situation Recognition: Visual Semantic Role Labeling for Image Understanding.csv delete mode 100644 datasets/scholar/entries/The CMU Pose, Illumination, and Expression Database.csv (limited to 'datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv') diff --git a/datasets/scholar/entries/Coding Facial Expressions with Gabor Wavelets.csv b/datasets/scholar/entries/Coding Facial Expressions with Gabor Wavelets.csv new file mode 100644 index 00000000..36b9e0cf --- /dev/null +++ b/datasets/scholar/entries/Coding Facial Expressions with Gabor Wavelets.csv @@ -0,0 +1 @@ +Coding facial expressions with gabor wavelets|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/670949/|1998|1623|14|1158641084116311050|None|http://scholar.google.com/scholar?cites=1158641084116311050&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=1158641084116311050&hl=en&as_sdt=0,5|None|A method for extracting information about facial expressions from images is presented. Facial expression images are coded using a multi-orientation multi-resolution set of Gabor filters which are topographically ordered and aligned approximately with the face. The similarity space derived from this representation is compared with one derived from semantic ratings of the images by human observers. The results show that it is possible to construct a facial expression classifier with Gabor coding of the facial images as the input … diff --git a/datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv b/datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv deleted file mode 100644 index 0a064c73..00000000 --- a/datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv +++ /dev/null @@ -1,10 +0,0 @@ -Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC|http://scholar.google.com/https://www.sciencedirect.com/science/article/pii/S037026931200857X|2012|7814|57|1650605866782918575|None|http://scholar.google.com/scholar?cites=1650605866782918575&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=1650605866782918575&hl=en&as_sdt=0,5|None|A search for the Standard Model Higgs boson in proton–proton collisions with the ATLAS detector at the LHC is presented. The datasets used correspond to integrated luminosities of approximately 4.8 fb− 1 collected at s= 7 TeV in 2011 and 5.8 fb− 1 at s= 8 TeV in 2012 … -The relation between load and penetration in the axisymmetric Boussinesq problem for a punch of arbitrary profile|http://scholar.google.com/https://www.sciencedirect.com/science/article/pii/0020722565900194|1965|3816|3|17409802386521537480|None|http://scholar.google.com/scholar?cites=17409802386521537480&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=17409802386521537480&hl=en&as_sdt=0,5|None|A solution of the axisymmetric Boussinesq problem is derived from which are deduced simple formulae for the depth of penetration of the tip of a punch of arbitrary profile and for the total load which must be applied to the punch to achieve this penetration. Simple … -Evolution of the Altaid tectonic collage and Palaeozoic crustal growth in Eurasia|http://scholar.google.com/https://www.nature.com/articles/364299a0|1993|2702|10|10041893458681953783|None|http://scholar.google.com/scholar?cites=10041893458681953783&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=10041893458681953783&hl=en&as_sdt=0,5|None|A new tectonic model, postulating the growth of giant subduction-accretion complexes along a single magmatic arc now found contorted between Siberia and Baltica, shows that Asia grew by 5.3 million square kilometres during the Palaeozoic era. Half of this growth may … -A" silent" polymorphism in the MDR1 gene changes substrate specificity|http://science.sciencemag.org/content/315/5811/525.short|2007|2225|16|13672882627203076963|None|http://scholar.google.com/scholar?cites=13672882627203076963&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=13672882627203076963&hl=en&as_sdt=0,5|None|Synonymous single-nucleotide polymorphisms (SNPs) do not produce altered coding sequences, and therefore they are not expected to change the function of the protein in which they occur. We report that a synonymous SNP in the Multidrug Resistance 1 (MDR1) … -An unusual supernova in the error box of the γ-ray burst of 25 April 1998|http://scholar.google.com/https://www.nature.com/articles/doi:10.1038%2F27150|1998|1939|18|6579723347909169058|None|http://scholar.google.com/scholar?cites=6579723347909169058&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6579723347909169058&hl=en&as_sdt=0,5|None|The discovery of afterglows associated with γ-ray bursts at X-ray 1, optical 2 and radio 3 wavelengths and the measurement of the redshifts of some of these events 4, 5 has established that γ-ray bursts lie at extreme distances, making them the most powerful photon … -Jet energy measurement with the ATLAS detector in proton-proton collisions at|http://scholar.google.com/https://link.springer.com/article/10.1140/epjc/s10052-013-2304-2|2013|1671|105|5651138599710101302|None|http://scholar.google.com/scholar?cites=5651138599710101302&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=5651138599710101302&hl=en&as_sdt=0,5|None|The jet energy scale and its systematic uncertainty are determined for jets measured with the ATLAS detector at the LHC in proton-proton collision data at a centre-of-mass energy of\(\sqrt {s}= 7\\mathrm {TeV}\) corresponding to an integrated luminosity of 38 pb− 1. Jets … -Assessment of mitral annulus velocity by Doppler tissue imaging in the evaluation of left ventricular diastolic function|http://www.onlinejacc.org/content/30/2/474.abstract|1997|1902|11|4226712902543427028|None|http://scholar.google.com/scholar?cites=4226712902543427028&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=4226712902543427028&hl=en&as_sdt=0,5|None|Objectives. This study assessed the clinical utility of mitral annulus velocity in the evaluation of left ventricular diastolic function. Background. Mitral inflow velocity recorded by Doppler echocardiography has been widely used to evaluate left ventricular diastolic function but is … -Topics in algebra|None|2006|2269|10|212858366598806886|None|http://scholar.google.com/scholar?cites=212858366598806886&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=212858366598806886&hl=en&as_sdt=0,5|None|None -Basic concepts in the theory of seepage of homogeneous liquids in fissured rocks [strata]|http://scholar.google.com/https://pdfs.semanticscholar.org/38a2/e7b4ae9da080578a83e3a8f9135e21359042.pdf|1960|2088|7|15437573464014520045|http://scholar.google.com/https://pdfs.semanticscholar.org/38a2/e7b4ae9da080578a83e3a8f9135e21359042.pdf|http://scholar.google.com/scholar?cites=15437573464014520045&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=15437573464014520045&hl=en&as_sdt=0,5|None|The modern theory of seepage [infiltration 1 is based on the concept of a porous medium consisting of impermeable grains separated by pore spaces. Comparison of the results of theoretical and laboratory investigations of non-steady-state flow of liquids with data for … -Improved luminosity determination in pp collisions at using the ATLAS detector at the LHC|http://scholar.google.com/https://link.springer.com/10.1140/epjc/s10052-013-2518-3|2013|1614|60|11477579481550698253|None|http://scholar.google.com/scholar?cites=11477579481550698253&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=11477579481550698253&hl=en&as_sdt=0,5|None|The luminosity calibration for the ATLAS detector at the LHC during pp collisions at\(\sqrt {s}= 7~\mathrm {TeV}\) in 2010 and 2011 is presented. Evaluation of the luminosity scale is performed using several luminosity-sensitive detectors, and comparisons are made of the … diff --git a/datasets/scholar/entries/Situation Recognition: Visual Semantic Role Labeling for Image Understanding.csv b/datasets/scholar/entries/Situation Recognition: Visual Semantic Role Labeling for Image Understanding.csv new file mode 100644 index 00000000..503356df --- /dev/null +++ b/datasets/scholar/entries/Situation Recognition: Visual Semantic Role Labeling for Image Understanding.csv @@ -0,0 +1 @@ +Situation recognition: Visual semantic role labeling for image understanding|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Yatskar_Situation_Recognition_Visual_CVPR_2016_paper.html|2016|53|9|14769542088507071062|None|http://scholar.google.com/scholar?cites=14769542088507071062&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=14769542088507071062&hl=en&as_sdt=0,5|None|This paper introduces situation recognition, the problem of producing a concise summary of the situation an image depicts including:(1) the main activity (eg, clipping),(2) the participating actors, objects, substances, and locations (eg, man, shears, sheep, wool, and field) and most importantly (3) the roles these participants play in the activity (eg, the man is clipping, the shears are his tool, the wool is being clipped from the sheep, and the clipping is in a field). We use FrameNet, a verb and role lexicon developed by linguists, to define a … diff --git a/datasets/scholar/entries/The CMU Pose, Illumination, and Expression Database.csv b/datasets/scholar/entries/The CMU Pose, Illumination, and Expression Database.csv deleted file mode 100644 index 0e864941..00000000 --- a/datasets/scholar/entries/The CMU Pose, Illumination, and Expression Database.csv +++ /dev/null @@ -1,10 +0,0 @@ -Illumination for computer generated pictures|http://scholar.google.com/https://dl.acm.org/citation.cfm?id=360839|1975|4208|20|1255931990633678422|None|http://scholar.google.com/scholar?cites=1255931990633678422&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=1255931990633678422&hl=en&as_sdt=0,5|None|The quality of computer generated images of three-dimensional scenes depends on the shading technique used to paint the objects on the cathode-ray tube screen. The shading algorithm itself depends in part on the method for modeling the object, which also … -The CMU pose, illumination, and expression (PIE) database|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/1004130/|2002|2928|43|4991067299609636976|None|http://scholar.google.com/scholar?cites=4991067299609636976&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=4991067299609636976&hl=en&as_sdt=0,5|None|Between October 2000 and December 2000, we collected a database of over 40,000 facial images of 68 people. Using the CMU (Carnegie Mellon University) 3D Room, we imaged each person across 13 different poses, under 43 different illumination conditions, and with … -From few to many: Illumination cone models for face recognition under variable lighting and pose|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/927464/|2001|4267|10|3224668761286229244|None|http://scholar.google.com/scholar?cites=3224668761286229244&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=3224668761286229244&hl=en&as_sdt=0,5|None|We present a generative appearance-based method for recognizing human faces under variation in lighting and viewpoint. Our method exploits the fact that the set of images of an object in fixed pose, but under all possible illumination conditions, is a convex cone in the … -An improved illumination model for shaded display|http://scholar.google.com/https://dl.acm.org/citation.cfm?id=1198743|2005|2743|33|15908121760235438227|None|http://scholar.google.com/scholar?cites=15908121760235438227&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=15908121760235438227&hl=en&as_sdt=0,5|None|To accurately render a two-dimensional image of a three-dimensional scene, global illumination information that affects the intensity of each pixel of the image must be known at the time the intensity is calculated. In a simplified form, this information is stored in a tree of" … -Nonlinear structured-illumination microscopy: wide-field fluorescence imaging with theoretically unlimited resolution|http://www.pnas.org/content/102/37/13081.short|2005|1790|16|2480431343055084609|None|http://scholar.google.com/scholar?cites=2480431343055084609&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=2480431343055084609&hl=en&as_sdt=0,5|None|Contrary to the well known diffraction limit, the fluorescence microscope is in principle capable of unlimited resolution. The necessary elements are spatially structured illumination light and a nonlinear dependence of the fluorescence emission rate on the illumination  … -Optical sectioning deep inside live embryos by selective plane illumination microscopy|http://science.sciencemag.org/content/305/5686/1007.short|2004|1472|21|5764347062910566949|None|http://scholar.google.com/scholar?cites=5764347062910566949&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=5764347062910566949&hl=en&as_sdt=0,5|None|Large, living biological specimens present challenges to existing optical imaging techniques because of their absorptive and scattering properties. We developed selective plane illumination microscopy (SPIM) to generate multidimensional images of samples up to a few … -Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy|http://scholar.google.com/https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1365-2818.2000.00710.x|2000|2168|20|3581528673297602471|None|http://scholar.google.com/scholar?cites=3581528673297602471&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=3581528673297602471&hl=en&as_sdt=0,5|None|Lateral resolution that exceeds the classical diffraction limit by a factor of two is achieved by using spatially structured illumination in a wide‐field fluorescence microscope. The sample is illuminated with a series of excitation light patterns, which cause normally inaccessible … -Radiosity and global illumination|http://www.springerlink.com/index/W178736M32651326.pdf|1995|610|6|1673174372572809854|http://www.springerlink.com/index/W178736M32651326.pdf|http://scholar.google.com/scholar?cites=1673174372572809854&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=1673174372572809854&hl=en&as_sdt=0,5|None|Francois Sillion and Claude Puech have provided a superb book on the simulation of global illumination in computer graphics. Specifically, the authors deal with the radiosity method and Monte Carlo techniques. The authors place a far greater emphasis on the radiosity … -Evaluation as illumination: A new approach to the study of innovatory programs|None|1972|1314|0|533529432640875086|None|http://scholar.google.com/scholar?cites=533529432640875086&as_sdt=2005&sciodt=0,5&hl=en|None|None|None -Efficient region tracking with parametric models of geometry and illumination|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/722606/|1998|1498|18|15626375701713969545|None|http://scholar.google.com/scholar?cites=15626375701713969545&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=15626375701713969545&hl=en&as_sdt=0,5|None|As an object moves through the field of view of a camera, the images of the object may change dramatically. This is not simply due to the translation of the object across the image plane; complications arise due to the fact that the object undergoes changes in pose relative … diff --git a/fetch-entries.py b/fetch-entries.py index 4c9915b6..37cd37f9 100644 --- a/fetch-entries.py +++ b/fetch-entries.py @@ -4,7 +4,7 @@ import csv import subprocess import time import random - +import re import click @click.command() @@ -19,9 +19,10 @@ def fetch_entries(index): print(entries_fn) if not os.path.exists(entries_fn): with open(entries_fn, 'w') as f: + t = re.sub(r'\W+', '', title) subprocess.call([ './vendor/scholar.py', - '-t', '-s', title, '--csv', + '-t', '-A', t, '--csv', ], stdout=f) time.sleep(random.randint(20, 32)) -- cgit v1.2.3-70-g09d2 From 93b3392d9346226c328ea2a878ff968d0303f826 Mon Sep 17 00:00:00 2001 From: "jules@lens" Date: Wed, 31 Oct 2018 02:14:14 +0100 Subject: data --- ...Wild Challenge: The first facial landmark localization Challenge.csv | 1 + .../scholar/entries/Age and Gender Estimation of Unfiltered Faces.csv | 0 .../DEX: Deep EXpectation of apparent age from a single image.csv | 1 + ...al and apparent age from a single image without facial landmarks.csv | 1 + .../Distance Estimation of an Unknown Person from a Portrait .csv | 1 + ...ition in Unconstrained Videos with Matched Background Similarity.csv | 2 ++ ...detection, pose estimation and landmark localization in the wild.csv | 1 + ...cial Parts Responses to Face Detection: A Deep Learning Approach.csv | 1 + ...one Models for Face Recognition under Variable Lighting and Pose.csv | 0 ...ase for Studying Face Recognition in Unconstrained Environments..csv | 1 + ... Age-Gap Face Verification by Feature Injection in Deep Networks.csv | 1 + datasets/scholar/entries/Learning Face Representation from Scratch.csv | 0 .../Localizing Parts of Faces Using a Consensus of Exemplars.csv | 1 + ...H: A Longitudinal Image Database of Normal Adult Age-Progression.csv | 1 + ...leb-1M: A Dataset and Benchmark for Large-Scale Face Recognition.csv | 1 + .../Presentation and validation of the Radboud Faces Database.csv | 1 + .../entries/Spoofing Faces Using Makeup: An Investigative Study.csv | 1 + .../entries/The CMU Pose, Illumination, and Expression Database.csv | 0 .../entries/The Do's and Don'ts for CNN-based Face Verification.csv | 1 + ...ression dataset for action unit and emotion-specified expression.csv | 1 + ...The MegaFace Benchmark: 1 Million Faces for Recognition at Scale.csv | 1 + .../UMDFaces: An Annotated Face Dataset for Training Deep Networks.csv | 1 + datasets/scholar/entries/Who's in the Picture .csv | 0 23 files changed, 19 insertions(+) create mode 100644 datasets/scholar/entries/300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge.csv create mode 100644 datasets/scholar/entries/Age and Gender Estimation of Unfiltered Faces.csv create mode 100644 datasets/scholar/entries/DEX: Deep EXpectation of apparent age from a single image.csv create mode 100644 datasets/scholar/entries/Deep expectation of real and apparent age from a single image without facial landmarks.csv create mode 100644 datasets/scholar/entries/Distance Estimation of an Unknown Person from a Portrait .csv create mode 100644 datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv create mode 100644 datasets/scholar/entries/Face detection, pose estimation and landmark localization in the wild.csv create mode 100644 datasets/scholar/entries/From Facial Parts Responses to Face Detection: A Deep Learning Approach.csv create mode 100644 datasets/scholar/entries/From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose.csv create mode 100644 datasets/scholar/entries/Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments..csv create mode 100644 datasets/scholar/entries/Large Age-Gap Face Verification by Feature Injection in Deep Networks.csv create mode 100644 datasets/scholar/entries/Learning Face Representation from Scratch.csv create mode 100644 datasets/scholar/entries/Localizing Parts of Faces Using a Consensus of Exemplars.csv create mode 100644 datasets/scholar/entries/MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.csv create mode 100644 datasets/scholar/entries/MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition.csv create mode 100644 datasets/scholar/entries/Presentation and validation of the Radboud Faces Database.csv create mode 100644 datasets/scholar/entries/Spoofing Faces Using Makeup: An Investigative Study.csv create mode 100644 datasets/scholar/entries/The CMU Pose, Illumination, and Expression Database.csv create mode 100644 datasets/scholar/entries/The Do's and Don'ts for CNN-based Face Verification.csv create mode 100644 datasets/scholar/entries/The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression.csv create mode 100644 datasets/scholar/entries/The MegaFace Benchmark: 1 Million Faces for Recognition at Scale.csv create mode 100644 datasets/scholar/entries/UMDFaces: An Annotated Face Dataset for Training Deep Networks.csv create mode 100644 datasets/scholar/entries/Who's in the Picture .csv (limited to 'datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv') diff --git a/datasets/scholar/entries/300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge.csv b/datasets/scholar/entries/300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge.csv new file mode 100644 index 00000000..38f502f9 --- /dev/null +++ b/datasets/scholar/entries/300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge.csv @@ -0,0 +1 @@ +300 faces in-the-wild challenge: The first facial landmark localization challenge|http://openaccess.thecvf.com/content_iccv_workshops_2013/W11/papers/Sagonas_300_Faces_in-the-Wild_2013_ICCV_paper.pdf|2013|396|15|7861246476672124064|http://openaccess.thecvf.com/content_iccv_workshops_2013/W11/papers/Sagonas_300_Faces_in-the-Wild_2013_ICCV_paper.pdf|http://scholar.google.com/scholar?cites=7861246476672124064&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=7861246476672124064&hl=en&as_sdt=0,5|None|Automatic facial point detection plays arguably the most important role in face analysis. Several methods have been proposed which reported their results on databases of both constrained and unconstrained conditions. Most of these databases provide annotations with different mark-ups and in some cases the are problems related to the accuracy of the fiducial points. The aforementioned issues as well as the lack of a evaluation protocol makes it difficult to compare performance between different systems. In this paper, we present the … diff --git a/datasets/scholar/entries/Age and Gender Estimation of Unfiltered Faces.csv b/datasets/scholar/entries/Age and Gender Estimation of Unfiltered Faces.csv new file mode 100644 index 00000000..e69de29b diff --git a/datasets/scholar/entries/DEX: Deep EXpectation of apparent age from a single image.csv b/datasets/scholar/entries/DEX: Deep EXpectation of apparent age from a single image.csv new file mode 100644 index 00000000..b3728548 --- /dev/null +++ b/datasets/scholar/entries/DEX: Deep EXpectation of apparent age from a single image.csv @@ -0,0 +1 @@ +Dex: Deep expectation of apparent age from a single image|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/html/Rothe_DEX_Deep_EXpectation_ICCV_2015_paper.html|2015|155|15|12384435539194835187|None|http://scholar.google.com/scholar?cites=12384435539194835187&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=12384435539194835187&hl=en&as_sdt=0,5|None|In this paper we tackle the estimation of apparent age in still face images with deep learning. Our convolutional neural networks (CNNs) use the VGG-16 architecture and are pretrained on ImageNet for image classification. In addition, due to the limited number of apparent age annotated images, we explore the benefit of finetuning over crawled Internet face images with available age. We crawled 0.5 million images of celebrities from IMDB and Wikipedia that we make public. This is the largest public dataset for age prediction to date. We pose the … diff --git a/datasets/scholar/entries/Deep expectation of real and apparent age from a single image without facial landmarks.csv b/datasets/scholar/entries/Deep expectation of real and apparent age from a single image without facial landmarks.csv new file mode 100644 index 00000000..ed47fdef --- /dev/null +++ b/datasets/scholar/entries/Deep expectation of real and apparent age from a single image without facial landmarks.csv @@ -0,0 +1 @@ +Deep expectation of real and apparent age from a single image without facial landmarks|http://scholar.google.com/https://link.springer.com/article/10.1007/s11263-016-0940-3|2018|135|7|11164967779616636427|None|http://scholar.google.com/scholar?cites=11164967779616636427&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=11164967779616636427&hl=en&as_sdt=0,5|None|In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a recent endeavor. We tackle both tasks with our convolutional neural networks (CNNs) of VGG-16 architecture which are pre-trained on ImageNet for … diff --git a/datasets/scholar/entries/Distance Estimation of an Unknown Person from a Portrait .csv b/datasets/scholar/entries/Distance Estimation of an Unknown Person from a Portrait .csv new file mode 100644 index 00000000..5cd26552 --- /dev/null +++ b/datasets/scholar/entries/Distance Estimation of an Unknown Person from a Portrait .csv @@ -0,0 +1 @@ +Distance estimation of an unknown person from a portrait|http://scholar.google.com/https://link.springer.com/chapter/10.1007/978-3-319-10590-1_21|2014|7|9|11199246855168438175|None|http://scholar.google.com/scholar?cites=11199246855168438175&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=11199246855168438175&hl=en&as_sdt=0,5|None|We propose the first automated method for estimating distance from frontal pictures of unknown faces. Camera calibration is not necessary, nor is the reconstruction of a 3D representation of the shape of the head. Our method is based on estimating automatically the position of face and head landmarks in the image, and then using a regressor to estimate distance from such measurements. We collected and annotated a dataset of frontal portraits of 53 individuals spanning a number of attributes (sex, age, race, hair), each … diff --git a/datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv b/datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv new file mode 100644 index 00000000..2f1e41af --- /dev/null +++ b/datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv @@ -0,0 +1,2 @@ +Face recognition in unconstrained videos with matched background similarity|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/5995566/|2011|657|10|5401801956686441353|None|http://scholar.google.com/scholar?cites=5401801956686441353&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=5401801956686441353&hl=en&as_sdt=0,5|None|Recognizing faces in unconstrained videos is a task of mounting importance. While obviously related to face recognition in still images, it has its own unique characteristics and algorithmic requirements. Over the years several methods have been suggested for this problem, and a few benchmark data sets have been assembled to facilitate its study. However, there is a sizable gap between the actual application needs and the current state of the art. In this paper we make the following contributions.(a) We present a comprehensive … +Face Recognition in Unconstrained Videos with Matched Background Similarity|http://www.cs.tau.ac.il/thesis/thesis/Maoz.Itay-MSc.Thesis.pdf|2012|0|0|None|http://www.cs.tau.ac.il/thesis/thesis/Maoz.Itay-MSc.Thesis.pdf|None|None|None|Recognizing faces in unconstrained videos is a task of mounting importance. While obviously related to face recognition in still images, it has its own unique characteristics and algorithmic requirements. Over the years several methods have been suggested for this problem, and a few benchmark data sets have been assembled to facilitate its study. However, there is a sizable gap between the actual application needs and the current state of the art. In this work we make the following contributions:(a) We present a comprehensive … diff --git a/datasets/scholar/entries/Face detection, pose estimation and landmark localization in the wild.csv b/datasets/scholar/entries/Face detection, pose estimation and landmark localization in the wild.csv new file mode 100644 index 00000000..43da8a92 --- /dev/null +++ b/datasets/scholar/entries/Face detection, pose estimation and landmark localization in the wild.csv @@ -0,0 +1 @@ +Face detection, pose estimation, and landmark localization in the wild|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/6248014/|2012|1693|7|4876235110904982186|None|http://scholar.google.com/scholar?cites=4876235110904982186&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=4876235110904982186&hl=en&as_sdt=0,5|None|We present a unified model for face detection, pose estimation, and landmark estimation in real-world, cluttered images. Our model is based on a mixtures of trees with a shared pool of parts; we model every facial landmark as a part and use global mixtures to capture topological changes due to viewpoint. We show that tree-structured models are surprisingly effective at capturing global elastic deformation, while being easy to optimize unlike dense graph structures. We present extensive results on standard face benchmarks, as well as a … diff --git a/datasets/scholar/entries/From Facial Parts Responses to Face Detection: A Deep Learning Approach.csv b/datasets/scholar/entries/From Facial Parts Responses to Face Detection: A Deep Learning Approach.csv new file mode 100644 index 00000000..e22f032b --- /dev/null +++ b/datasets/scholar/entries/From Facial Parts Responses to Face Detection: A Deep Learning Approach.csv @@ -0,0 +1 @@ +From facial parts responses to face detection: A deep learning approach|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_iccv_2015/html/Yang_From_Facial_Parts_ICCV_2015_paper.html|2015|213|12|1818335115841631894|None|http://scholar.google.com/scholar?cites=1818335115841631894&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=1818335115841631894&hl=en&as_sdt=0,5|None|In this paper, we propose a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically, our method achieves a high recall rate of 90.99% on the challenging FDDB benchmark, outperforming the state-of-the-art method by a large margin of 2.91%. Importantly, we consider finding faces from a new perspective through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is carefully formulated considering challenging … diff --git a/datasets/scholar/entries/From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose.csv b/datasets/scholar/entries/From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose.csv new file mode 100644 index 00000000..e69de29b diff --git a/datasets/scholar/entries/Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments..csv b/datasets/scholar/entries/Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments..csv new file mode 100644 index 00000000..23c90284 --- /dev/null +++ b/datasets/scholar/entries/Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments..csv @@ -0,0 +1 @@ +Labeled faces in the wild: A database forstudying face recognition in unconstrained environments|http://scholar.google.com/https://hal.inria.fr/inria-00321923/|2008|3014|37|6713997626354918066|None|http://scholar.google.com/scholar?cites=6713997626354918066&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6713997626354918066&hl=en&as_sdt=0,5|None|Most face databases have been created under controlled conditions to facilitate the study of specific parameters on the face recognition problem. These parameters include such variables as position, pose, lighting, background, camera quality, and gender. While there are many applications for face recognition technology in which one can control the parameters of image acquisition, there are also many applications in which the practitioner has little or no control over such parameters. This database, Labeled Faces in the Wild, is … diff --git a/datasets/scholar/entries/Large Age-Gap Face Verification by Feature Injection in Deep Networks.csv b/datasets/scholar/entries/Large Age-Gap Face Verification by Feature Injection in Deep Networks.csv new file mode 100644 index 00000000..9cd388eb --- /dev/null +++ b/datasets/scholar/entries/Large Age-Gap Face Verification by Feature Injection in Deep Networks.csv @@ -0,0 +1 @@ +Large age-gap face verification by feature injection in deep networks|http://scholar.google.com/https://www.sciencedirect.com/science/article/pii/S0167865517300727|2017|12|8|6980699793307007950|None|http://scholar.google.com/scholar?cites=6980699793307007950&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6980699793307007950&hl=en&as_sdt=0,5|None|This paper introduces a new method for face verification across large age gaps and also a dataset containing variations of age in the wild, the Large Age-Gap (LAG) dataset, with images ranging from child/young to adult/old. The proposed method exploits a deep convolutional neural network (DCNN) pre-trained for the face recognition task on a large dataset and then fine-tuned for the large age-gap face verification task. Fine-tuning is performed in a Siamese architecture using a contrastive loss function. A feature injection … diff --git a/datasets/scholar/entries/Learning Face Representation from Scratch.csv b/datasets/scholar/entries/Learning Face Representation from Scratch.csv new file mode 100644 index 00000000..e69de29b diff --git a/datasets/scholar/entries/Localizing Parts of Faces Using a Consensus of Exemplars.csv b/datasets/scholar/entries/Localizing Parts of Faces Using a Consensus of Exemplars.csv new file mode 100644 index 00000000..0fa7a800 --- /dev/null +++ b/datasets/scholar/entries/Localizing Parts of Faces Using a Consensus of Exemplars.csv @@ -0,0 +1 @@ +Localizing parts of faces using a consensus of exemplars|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/6412675/|2013|740|13|8801930631236620204|None|http://scholar.google.com/scholar?cites=8801930631236620204&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=8801930631236620204&hl=en&as_sdt=0,5|None|We present a novel approach to localizing parts in images of human faces. The approach combines the output of local detectors with a nonparametric set of global models for the part locations based on over 1,000 hand-labeled exemplar images. By assuming that the global models generate the part locations as hidden variables, we derive a Bayesian objective function. This function is optimized using a consensus of models for these hidden variables. The resulting localizer handles a much wider range of expression, pose, lighting, and … diff --git a/datasets/scholar/entries/MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.csv b/datasets/scholar/entries/MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.csv new file mode 100644 index 00000000..a41ffc41 --- /dev/null +++ b/datasets/scholar/entries/MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.csv @@ -0,0 +1 @@ +Morph: A longitudinal image database of normal adult age-progression|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/1613043/|2006|673|6|4438087728034206462|None|http://scholar.google.com/scholar?cites=4438087728034206462&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=4438087728034206462&hl=en&as_sdt=0,5|None|This paper details MORPH a longitudinal face database developed for researchers investigating all facets of adult age-progression, eg face modeling, photo-realistic animation, face recognition, etc. This database contributes to several active research areas, most notably face recognition, by providing: the largest set of publicly available longitudinal images; longitudinal spans from a few months to over twenty years; and, the inclusion of key physical parameters that affect aging appearance. The direct contribution of this data corpus … diff --git a/datasets/scholar/entries/MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition.csv b/datasets/scholar/entries/MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition.csv new file mode 100644 index 00000000..3af655d0 --- /dev/null +++ b/datasets/scholar/entries/MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition.csv @@ -0,0 +1 @@ +Ms-celeb-1m: A dataset and benchmark for large-scale face recognition|http://scholar.google.com/https://link.springer.com/chapter/10.1007/978-3-319-46487-9_6|2016|189|6|7096719334274798105|None|http://scholar.google.com/scholar?cites=7096719334274798105&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=7096719334274798105&hl=en&as_sdt=0,5|None|In this paper, we design a benchmark task and provide the associated datasets for recognizing face images and link them to corresponding entity keys in a knowledge base. More specifically, we propose a benchmark task to recognize one million celebrities from their face images, by using all the possibly collected face images of this individual on the web as training data. The rich information provided by the knowledge base helps to conduct disambiguation and improve the recognition accuracy, and contributes to various real-world … diff --git a/datasets/scholar/entries/Presentation and validation of the Radboud Faces Database.csv b/datasets/scholar/entries/Presentation and validation of the Radboud Faces Database.csv new file mode 100644 index 00000000..89746fe9 --- /dev/null +++ b/datasets/scholar/entries/Presentation and validation of the Radboud Faces Database.csv @@ -0,0 +1 @@ +Presentation and validation of the Radboud Faces Database|http://scholar.google.com/https://www.tandfonline.com/doi/abs/10.1080/02699930903485076|2010|1052|10|9283473996856444608|None|http://scholar.google.com/scholar?cites=9283473996856444608&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=9283473996856444608&hl=en&as_sdt=0,5|None|Many research fields concerned with the processing of information contained in human faces would benefit from face stimulus sets in which specific facial characteristics are systematically varied while other important picture characteristics are kept constant. Specifically, a face database in which displayed expressions, gaze direction, and head orientation are parametrically varied in a complete factorial design would be highly useful in many research domains. Furthermore, these stimuli should be standardised in several … diff --git a/datasets/scholar/entries/Spoofing Faces Using Makeup: An Investigative Study.csv b/datasets/scholar/entries/Spoofing Faces Using Makeup: An Investigative Study.csv new file mode 100644 index 00000000..6fa46797 --- /dev/null +++ b/datasets/scholar/entries/Spoofing Faces Using Makeup: An Investigative Study.csv @@ -0,0 +1 @@ +Spoofing faces using makeup: An investigative study|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/7947686/|2017|6|8|1291042674502639294|None|http://scholar.google.com/scholar?cites=1291042674502639294&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=1291042674502639294&hl=en&as_sdt=0,5|None|Makeup can be used to alter the facial appearance of a person. Previous studies have established the potential of using makeup to obfuscate the identity of an individual with respect to an automated face matcher. In this work, we analyze the potential of using makeup for spoofing an identity, where an individual attempts to impersonate another person's facial appearance. In this regard, we first assemble a set of face images downloaded from the internet where individuals use facial cosmetics to impersonate … diff --git a/datasets/scholar/entries/The CMU Pose, Illumination, and Expression Database.csv b/datasets/scholar/entries/The CMU Pose, Illumination, and Expression Database.csv new file mode 100644 index 00000000..e69de29b diff --git a/datasets/scholar/entries/The Do's and Don'ts for CNN-based Face Verification.csv b/datasets/scholar/entries/The Do's and Don'ts for CNN-based Face Verification.csv new file mode 100644 index 00000000..d494c943 --- /dev/null +++ b/datasets/scholar/entries/The Do's and Don'ts for CNN-based Face Verification.csv @@ -0,0 +1 @@ +The Do's and Don'ts for CNN-based Face Verification.|http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w37/Bansal_The_Dos_and_ICCV_2017_paper.pdf|2017|21|7|16583671830808674747|http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w37/Bansal_The_Dos_and_ICCV_2017_paper.pdf|http://scholar.google.com/scholar?cites=16583671830808674747&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=16583671830808674747&hl=en&as_sdt=0,5|None|While the research community appears to have developed a consensus on the methods of acquiring annotated data, design and training of CNNs, many questions still remain to be answered. In this paper, we explore the following questions that are critical to face recognition research:(i) Can we train on still images and expect the systems to work on videos?(ii) Are deeper datasets better than wider datasets?(iii) Does adding label noise lead to improvement in performance of deep networks?(iv) Is alignment needed for face … diff --git a/datasets/scholar/entries/The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression.csv b/datasets/scholar/entries/The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression.csv new file mode 100644 index 00000000..399b667f --- /dev/null +++ b/datasets/scholar/entries/The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression.csv @@ -0,0 +1 @@ +The extended cohn-kanade dataset (ck+) a complete expression dataset for action unit and emotion-speified expression|None|2010|3|0|13817454793240235261|None|http://scholar.google.com/scholar?cites=13817454793240235261&as_sdt=2005&sciodt=0,5&hl=en|None|None|None diff --git a/datasets/scholar/entries/The MegaFace Benchmark: 1 Million Faces for Recognition at Scale.csv b/datasets/scholar/entries/The MegaFace Benchmark: 1 Million Faces for Recognition at Scale.csv new file mode 100644 index 00000000..68dc8389 --- /dev/null +++ b/datasets/scholar/entries/The MegaFace Benchmark: 1 Million Faces for Recognition at Scale.csv @@ -0,0 +1 @@ +The megaface benchmark: 1 million faces for recognition at scale|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Kemelmacher-Shlizerman_The_MegaFace_Benchmark_CVPR_2016_paper.html|2016|159|11|6051410257476935491|None|http://scholar.google.com/scholar?cites=6051410257476935491&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6051410257476935491&hl=en&as_sdt=0,5|None|Recent face recognition experiments on a major benchmark LFW show stunning performance--a number of algorithms achieve near to perfect score, surpassing human recognition rates. In this paper, we advocate evaluations at the million scale (LFW includes only 13K photos of 5K people). To this end, we have assembled the MegaFace dataset and created the first MegaFace challenge. Our dataset includes One Million photos that capture more than 690K different individuals. The challenge evaluates performance of algorithms … diff --git a/datasets/scholar/entries/UMDFaces: An Annotated Face Dataset for Training Deep Networks.csv b/datasets/scholar/entries/UMDFaces: An Annotated Face Dataset for Training Deep Networks.csv new file mode 100644 index 00000000..adf44ba7 --- /dev/null +++ b/datasets/scholar/entries/UMDFaces: An Annotated Face Dataset for Training Deep Networks.csv @@ -0,0 +1 @@ +Umdfaces: An annotated face dataset for training deep networks|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/8272731/|2017|35|5|15417824747310072694|None|http://scholar.google.com/scholar?cites=15417824747310072694&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=15417824747310072694&hl=en&as_sdt=0,5|None|Recent progress in face detection (including keypoint detection), and recognition is mainly being driven by (i) deeper convolutional neural network architectures, and (ii) larger datasets. However, most of the large datasets are maintained by private companies and are not publicly available. The academic computer vision community needs larger and more varied datasets to make further progress. In this paper, we introduce a new face dataset, called UMDFaces, which has 367,888 annotated faces of 8,277 subjects. We also introduce … diff --git a/datasets/scholar/entries/Who's in the Picture .csv b/datasets/scholar/entries/Who's in the Picture .csv new file mode 100644 index 00000000..e69de29b -- cgit v1.2.3-70-g09d2