diff options
Diffstat (limited to 'site/datasets/final')
| -rw-r--r-- | site/datasets/final/adience.csv | 102 | ||||
| -rw-r--r-- | site/datasets/final/aflw.csv | 212 | ||||
| -rw-r--r-- | site/datasets/final/casia_webface.csv | 312 | ||||
| -rw-r--r-- | site/datasets/final/cofw.csv | 233 | ||||
| -rw-r--r-- | site/datasets/final/feret.csv | 639 | ||||
| -rw-r--r-- | site/datasets/final/ijb_c.csv | 141 | ||||
| -rw-r--r-- | site/datasets/final/images_of_groups.csv | 103 | ||||
| -rw-r--r-- | site/datasets/final/imdb_wiki.csv | 130 | ||||
| -rw-r--r-- | site/datasets/final/morph.csv | 286 | ||||
| -rw-r--r-- | site/datasets/final/morph_nc.csv | 286 | ||||
| -rw-r--r-- | site/datasets/final/msceleb.csv | 113 | ||||
| -rw-r--r-- | site/datasets/final/pipa.csv | 37 | ||||
| -rw-r--r-- | site/datasets/final/umd_faces.csv | 34 | ||||
| -rw-r--r-- | site/datasets/final/voc.csv | 401 | ||||
| -rw-r--r-- | site/datasets/final/yfcc_100m.csv | 69 | ||||
| -rw-r--r-- | site/datasets/final/youtube_poses.csv | 20 |
16 files changed, 3118 insertions, 0 deletions
diff --git a/site/datasets/final/adience.csv b/site/datasets/final/adience.csv new file mode 100644 index 00000000..9c9f2b76 --- /dev/null +++ b/site/datasets/final/adience.csv @@ -0,0 +1,102 @@ +index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,Adience,adience,0.0,0.0,,,1be498d4bbc30c3bfd0029114c784bc2114d67c0,main,http://www.openu.ac.il/home/hassner/Adience/EidingerEnbarHassner_tifs.pdf,Age and Gender Estimation of Unfiltered Faces,2014 +1,Adience,adience,28.5456282,77.2731505,"IIIT Delhi, India",edu,f726738954e7055bb3615fa7e8f59f136d3e0bdc,citation,https://arxiv.org/pdf/1803.07385.pdf,Are you eligible? Predicting adulthood from face images via class specific mean autoencoder,2018 +2,Adience,adience,37.43131385,-122.16936535,Stanford University,edu,16d6737b50f969247339a6860da2109a8664198a,citation,https://pdfs.semanticscholar.org/16d6/737b50f969247339a6860da2109a8664198a.pdf,Convolutional Neural Networks for Age and Gender Classification,2016 +3,Adience,adience,40.00229045,116.32098908,Tsinghua University,edu,2149d49c84a83848d6051867290d9c8bfcef0edb,citation,https://doi.org/10.1109/TIFS.2017.2746062,Label-Sensitive Deep Metric Learning for Facial Age Estimation,2018 +4,Adience,adience,51.5217668,-0.13019072,University of London,edu,31ea88f29e7f01a9801648d808f90862e066f9ea,citation,https://arxiv.org/pdf/1605.06391.pdf,Deep Multi-task Representation Learning: A Tensor Factorisation Approach,2016 +5,Adience,adience,40.0044795,116.370238,Chinese Academy of Sciences,edu,d492dbfaa42b4f8b8a74786d7343b3be6a3e9a1d,citation,https://pdfs.semanticscholar.org/d492/dbfaa42b4f8b8a74786d7343b3be6a3e9a1d.pdf,Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation,0 +6,Adience,adience,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,d492dbfaa42b4f8b8a74786d7343b3be6a3e9a1d,citation,https://pdfs.semanticscholar.org/d492/dbfaa42b4f8b8a74786d7343b3be6a3e9a1d.pdf,Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation,0 +7,Adience,adience,34.0224149,-118.28634407,University of Southern California,edu,29f298dd5f806c99951cb434834bc8dcc765df18,citation,https://doi.org/10.1109/ICPR.2016.7899837,Computationally efficient template-based face recognition,2016 +8,Adience,adience,45.5039761,-73.5749687,McGill University,edu,ed9d11e995baeec17c5d2847ec1a8d5449254525,citation,https://pdfs.semanticscholar.org/ed9d/11e995baeec17c5d2847ec1a8d5449254525.pdf,Efficient Gender Classification Using a Deep LDA-Pruned Net,2017 +9,Adience,adience,12.9551259,77.5741985,Bangalore Institute of Technology,edu,10126b467391e153d36f1a496ef5618097775ad1,citation,https://pdfs.semanticscholar.org/1012/6b467391e153d36f1a496ef5618097775ad1.pdf,An Active Age Estimation of Facial image using Anthropometric Model and Fast ICA,2017 +10,Adience,adience,42.36782045,-71.12666653,Harvard University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 +11,Adience,adience,40.9153196,-73.1270626,Stony Brook University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 +12,Adience,adience,38.8760446,115.4973873,North China Electric Power University,edu,56f86bef26209c85f2ef66ec23b6803d12ca6cd6,citation,http://arxiv.org/abs/1710.00307,Pyramidal RoR for image classification,2017 +13,Adience,adience,40.00229045,116.32098908,Tsinghua University,edu,51f626540860ad75b68206025a45466a6d087aa6,citation,https://doi.org/10.1109/ICIP.2017.8296595,Cluster convolutional neural networks for facial age estimation,2017 +14,Adience,adience,45.5039761,-73.5749687,McGill University,edu,407bb798ab153bf6156ba2956f8cf93256b6910a,citation,http://pdfs.semanticscholar.org/407b/b798ab153bf6156ba2956f8cf93256b6910a.pdf,Fisher Pruning of Deep Nets for Facial Trait Classification,2018 +15,Adience,adience,39.2899685,-76.62196103,University of Maryland,edu,81fc86e86980a32c47410f0ba7b17665048141ec,citation,http://pdfs.semanticscholar.org/81fc/86e86980a32c47410f0ba7b17665048141ec.pdf,Segment-based Methods for Facial Attribute Detection from Partial Faces,2018 +16,Adience,adience,22.304572,114.17976285,Hong Kong Polytechnic University,edu,dc2f16f967eac710cb9b7553093e9c977e5b761d,citation,https://doi.org/10.1109/ICPR.2016.7900141,Learning a lightweight deep convolutional network for joint age and gender recognition,2016 +17,Adience,adience,23.09461185,113.28788994,Sun Yat-Sen University,edu,dc2f16f967eac710cb9b7553093e9c977e5b761d,citation,https://doi.org/10.1109/ICPR.2016.7900141,Learning a lightweight deep convolutional network for joint age and gender recognition,2016 +18,Adience,adience,39.65404635,-79.96475355,West Virginia University,edu,7a65fc9e78eff3ab6062707deaadde024d2fad40,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Zhu_A_Study_on_ICCV_2015_paper.pdf,A Study on Apparent Age Estimation,2015 +19,Adience,adience,42.357757,-83.06286711,Wayne State University,edu,28d99dc2d673d62118658f8375b414e5192eac6f,citation,http://www.cs.wayne.edu/~mdong/cvpr17.pdf,Using Ranking-CNN for Age Estimation,2017 +20,Adience,adience,25.0410728,121.6147562,Institute of Information Science,edu,0951f42abbf649bb564a21d4ff5dddf9a5ea54d9,citation,https://arxiv.org/pdf/1806.02023.pdf,Joint Estimation of Age and Gender from Unconstrained Face Images Using Lightweight Multi-Task CNN for Mobile Applications,2018 +21,Adience,adience,34.0224149,-118.28634407,University of Southern California,edu,eb6ee56e085ebf473da990d032a4249437a3e462,citation,http://www-scf.usc.edu/~chuntinh/doc/Age_Gender_Classification_APSIPA_2017.pdf,Age/gender classification with whole-component convolutional neural networks (WC-CNN),2017 +22,Adience,adience,32.77824165,34.99565673,Open University of Israel,edu,0a34fe39e9938ae8c813a81ae6d2d3a325600e5c,citation,https://arxiv.org/pdf/1708.07517.pdf,FacePoseNet: Making a Case for Landmark-Free Face Alignment,2017 +23,Adience,adience,40.51865195,-74.44099801,State University of New Jersey,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791154,A cascaded convolutional neural network for age estimation of unconstrained faces,2016 +24,Adience,adience,39.2899685,-76.62196103,University of Maryland,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791154,A cascaded convolutional neural network for age estimation of unconstrained faces,2016 +25,Adience,adience,32.8536333,-117.2035286,Kyung Hee University,edu,9d4692e243e25eb465a0480376beb60a5d2f0f13,citation,https://doi.org/10.1109/ICCE.2016.7430617,Positional Ternary Pattern (PTP): An edge based image descriptor for human age recognition,2016 +26,Adience,adience,1.340216,103.965089,Singapore University of Technology and Design,edu,00823e6c0b6f1cf22897b8d0b2596743723ec51c,citation,https://arxiv.org/pdf/1708.07689.pdf,Understanding and Comparing Deep Neural Networks for Age and Gender Classification,2017 +27,Adience,adience,45.47567215,9.23336232,Università degli Studi di Milano,edu,a713a01971e73d0c3118d0409dc7699a24f521d6,citation,https://doi.org/10.1109/SSCI.2017.8285381,Age estimation based on face images and pre-trained convolutional neural networks,2017 +28,Adience,adience,37.2830003,127.04548469,Ajou University,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018 +29,Adience,adience,37.403917,127.159786,Korea Electronics Technology Institute,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018 +30,Adience,adience,37.26728,126.9841151,Seoul National University,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018 +31,Adience,adience,49.2767454,-122.91777375,Simon Fraser University,edu,975978ee6a32383d6f4f026b944099e7739e5890,citation,https://pdfs.semanticscholar.org/9759/78ee6a32383d6f4f026b944099e7739e5890.pdf,Privacy-Preserving Age Estimation for Content Rating,2018 +32,Adience,adience,49.8091536,-97.13304179,University of Manitoba,edu,975978ee6a32383d6f4f026b944099e7739e5890,citation,https://pdfs.semanticscholar.org/9759/78ee6a32383d6f4f026b944099e7739e5890.pdf,Privacy-Preserving Age Estimation for Content Rating,2018 +33,Adience,adience,33.7774349,-84.3973208,"College of Computing, Georgia Tech",edu,58df849378fbcfb6b1a8ebddfbe4caa450226b9d,citation,https://doi.org/10.1109/ICIP.2017.8296770,Head pose estimation using learned discretization,2017 +34,Adience,adience,39.95472495,-75.15346905,Temple University,edu,58df849378fbcfb6b1a8ebddfbe4caa450226b9d,citation,https://doi.org/10.1109/ICIP.2017.8296770,Head pose estimation using learned discretization,2017 +35,Adience,adience,36.1017956,-79.501733,Elon University,edu,58df849378fbcfb6b1a8ebddfbe4caa450226b9d,citation,https://doi.org/10.1109/ICIP.2017.8296770,Head pose estimation using learned discretization,2017 +36,Adience,adience,23.7289899,90.3982682,Institute of Information Technology,edu,2e58ec57d71b2b2a3e71086234dd7037559cc17e,citation,https://pdfs.semanticscholar.org/2e58/ec57d71b2b2a3e71086234dd7037559cc17e.pdf,A Gender Recognition System from Facial Image,2018 +37,Adience,adience,23.7316957,90.3965275,University of Dhaka,edu,2e58ec57d71b2b2a3e71086234dd7037559cc17e,citation,https://pdfs.semanticscholar.org/2e58/ec57d71b2b2a3e71086234dd7037559cc17e.pdf,A Gender Recognition System from Facial Image,2018 +38,Adience,adience,37.98782705,23.73179733,National Technical University of Athens,edu,bd572e9cbec095bcf5700cb7cd73d1cdc2fe02f4,citation,http://pdfs.semanticscholar.org/bd57/2e9cbec095bcf5700cb7cd73d1cdc2fe02f4.pdf,Deep Learning for Computer Vision: A Brief Review,2018 +39,Adience,adience,47.00646895,-120.5367304,Central Washington University,edu,56c2fb2438f32529aec604e6fc3b06a595ddbfcc,citation,http://pdfs.semanticscholar.org/56c2/fb2438f32529aec604e6fc3b06a595ddbfcc.pdf,Comparison of Recent Machine Learning Techniques for Gender Recognition from Facial Images,2016 +40,Adience,adience,32.77824165,34.99565673,Open University of Israel,edu,c75e6ce54caf17b2780b4b53f8d29086b391e839,citation,https://arxiv.org/pdf/1802.00542.pdf,"ExpNet: Landmark-Free, Deep, 3D Facial Expressions",2018 +41,Adience,adience,31.83907195,117.26420748,University of Science and Technology of China,edu,47cd161546c59ab1e05f8841b82e985f72e5ddcb,citation,https://doi.org/10.1109/ICIP.2017.8296552,Gender classification in live videos,2017 +42,Adience,adience,25.0410728,121.6147562,Institute of Information Science,edu,1862f2df2e278505c9ca970f9c5a25ea3aeb9686,citation,https://pdfs.semanticscholar.org/1862/f2df2e278505c9ca970f9c5a25ea3aeb9686.pdf,Merging Deep Neural Networks for Mobile Devices,0 +43,Adience,adience,45.42580475,-75.68740118,University of Ottawa,edu,16820ccfb626dcdc893cc7735784aed9f63cbb70,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W12/papers/Azarmehr_Real-Time_Embedded_Age_2015_CVPR_paper.pdf,Real-time embedded age and gender classification in unconstrained video,2015 +44,Adience,adience,37.26728,126.9841151,Seoul National University,edu,282503fa0285240ef42b5b4c74ae0590fe169211,citation,http://pdfs.semanticscholar.org/2825/03fa0285240ef42b5b4c74ae0590fe169211.pdf,Feeding Hand-Crafted Features for Enhancing the Performance of Convolutional Neural Networks,2018 +45,Adience,adience,32.8536333,-117.2035286,Kyung Hee University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +46,Adience,adience,24.7246403,46.62335012,King Saud University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +47,Adience,adience,23.7289899,90.3982682,Institute of Information Technology,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +48,Adience,adience,53.21967825,6.56251482,University of Groningen,edu,361c9ba853c7d69058ddc0f32cdbe94fbc2166d5,citation,http://pdfs.semanticscholar.org/361c/9ba853c7d69058ddc0f32cdbe94fbc2166d5.pdf,Deep Reinforcement Learning of Video Games,2017 +49,Adience,adience,41.1664858,-73.1920564,University of Bridgeport,edu,ac9a331327cceda4e23f9873f387c9fd161fad76,citation,http://pdfs.semanticscholar.org/ac9a/331327cceda4e23f9873f387c9fd161fad76.pdf,Deep Convolutional Neural Network for Age Estimation based on VGG-Face Model,2017 +50,Adience,adience,53.21967825,6.56251482,University of Groningen,edu,4ff4c27e47b0aa80d6383427642bb8ee9d01c0ac,citation,https://doi.org/10.1109/SSCI.2015.37,Deep Convolutional Neural Networks and Support Vector Machines for Gender Recognition,2015 +51,Adience,adience,40.4319722,-86.92389368,Purdue University,edu,6193c833ad25ac27abbde1a31c1cabe56ce1515b,citation,https://pdfs.semanticscholar.org/5f25/7ca18a92c3595db3bda3224927ec494003a5.pdf,Trojaning Attack on Neural Networks,2018 +52,Adience,adience,40.4319722,-86.92389368,Purdue University,edu,b18858ad6ec88d8b443dffd3e944e653178bc28b,citation,http://pdfs.semanticscholar.org/b188/58ad6ec88d8b443dffd3e944e653178bc28b.pdf,Trojaning Attack on Neural Networks,2017 +53,Adience,adience,40.9153196,-73.1270626,Stony Brook University,edu,25bf288b2d896f3c9dab7e7c3e9f9302e7d6806b,citation,http://pdfs.semanticscholar.org/25bf/288b2d896f3c9dab7e7c3e9f9302e7d6806b.pdf,Neural Networks with Smooth Adaptive Activation Functions for Regression,2016 +54,Adience,adience,40.9153196,-73.1270626,Stony Brook University,edu,1190cba0cae3c8bb81bf80d6a0a83ae8c41240bc,citation,https://pdfs.semanticscholar.org/1190/cba0cae3c8bb81bf80d6a0a83ae8c41240bc.pdf,Squared Earth Mover ’ s Distance Loss for Training Deep Neural Networks on Ordered-Classes,2017 +55,Adience,adience,40.9153196,-73.1270626,Stony Brook University,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,http://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017 +56,Adience,adience,40.90826665,-73.11520891,Stony Brook University Hospital,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,http://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017 +57,Adience,adience,45.5039761,-73.5749687,McGill University,edu,13719bbb4bb8bbe0cbcdad009243a926d93be433,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w6/papers/Tian_Deep_LDA-Pruned_Nets_CVPR_2017_paper.pdf,Deep LDA-Pruned Nets for Efficient Facial Gender Classification,2017 +58,Adience,adience,41.10427915,29.02231159,Istanbul Technical University,edu,fd53be2e0a9f33080a9db4b5a5e416e24ae8e198,citation,https://arxiv.org/pdf/1606.02909.pdf,Apparent Age Estimation Using Ensemble of Deep Learning Models,2016 +59,Adience,adience,47.3804685,8.5430355,"Disney Research, Zurich",edu,017e94ad51c9be864b98c9b75582753ce6ee134f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7892240,Rapid one-shot acquisition of dynamic VR avatars,2017 +60,Adience,adience,34.1579742,-118.2894729,"Disney Research, UK",company,017e94ad51c9be864b98c9b75582753ce6ee134f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7892240,Rapid one-shot acquisition of dynamic VR avatars,2017 +61,Adience,adience,34.1619174,-118.2883702,Walt Disney Imagineering,company,017e94ad51c9be864b98c9b75582753ce6ee134f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7892240,Rapid one-shot acquisition of dynamic VR avatars,2017 +62,Adience,adience,49.2593879,-122.9151893,"AltumView Systems Inc., Burnaby, BC, Canada",company,b44f03b5fa8c6275238c2d13345652e6ff7e6ea9,citation,https://doi.org/10.1109/GlobalSIP.2017.8309138,Lapped convolutional neural networks for embedded systems,2017 +63,Adience,adience,37.2830003,127.04548469,Ajou University,edu,24286ef164f0e12c3e9590ec7f636871ba253026,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369721,Age and gender classification using wide convolutional neural network and Gabor filter,2018 +64,Adience,adience,37.26728,126.9841151,Seoul National University,edu,24286ef164f0e12c3e9590ec7f636871ba253026,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369721,Age and gender classification using wide convolutional neural network and Gabor filter,2018 +65,Adience,adience,47.6543238,-122.30800894,University of Washington,edu,96e0cfcd81cdeb8282e29ef9ec9962b125f379b0,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.527,The MegaFace Benchmark: 1 Million Faces for Recognition at Scale,2016 +66,Adience,adience,65.0592157,25.46632601,University of Oulu,edu,1fe121925668743762ce9f6e157081e087171f4c,citation,https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W02/papers/Ylioinas_Unsupervised_Learning_of_2015_CVPR_paper.pdf,Unsupervised learning of overcomplete face descriptors,2015 +67,Adience,adience,23.0886214,-82.4481944,"Advanced Technologies Application Center, Havana, Cuba",edu,c5eba789aeb41904aa1b03fad1dc7cea5d0cd3b6,citation,https://doi.org/10.1109/BTAS.2017.8272773,Age and gender classification using local appearance descriptors from facial components,2017 +68,Adience,adience,40.7240176,8.5578947,University of Sassari,edu,c5eba789aeb41904aa1b03fad1dc7cea5d0cd3b6,citation,https://doi.org/10.1109/BTAS.2017.8272773,Age and gender classification using local appearance descriptors from facial components,2017 +69,Adience,adience,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,cb27b45329d61f5f95ed213798d4b2a615e76be2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8329236,Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion,2018 +70,Adience,adience,37.2520226,127.0555019,"Samsung SAIT, Korea",company,cb27b45329d61f5f95ed213798d4b2a615e76be2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8329236,Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion,2018 +71,Adience,adience,22.42031295,114.20788644,Chinese University of Hong Kong,edu,d80a3d1f3a438e02a6685e66ee908446766fefa9,citation,https://arxiv.org/pdf/1708.09687.pdf,Quantifying Facial Age by Posterior of Age Comparisons,2017 +72,Adience,adience,42.357757,-83.06286711,Wayne State University,edu,4f1249369127cc2e2894f6b2f1052d399794919a,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8239663,Deep Age Estimation: From Classification to Ranking,2018 +73,Adience,adience,35.2742655,137.01327841,Chubu University,edu,5fb5d9389e2a2a4302c81bcfc068a4c8d4efe70c,citation,http://pdfs.semanticscholar.org/5fb5/d9389e2a2a4302c81bcfc068a4c8d4efe70c.pdf,Multiple Facial Attributes Estimation Based on Weighted Heterogeneous Learning,2016 +74,Adience,adience,1.3484104,103.68297965,Nanyang Technological University,edu,d0471d5907d6557cf081edf4c7c2296c3c221a38,citation,https://pdfs.semanticscholar.org/d047/1d5907d6557cf081edf4c7c2296c3c221a38.pdf,A Constrained Deep Neural Network for Ordinal Regression,0 +75,Adience,adience,41.3868913,2.16352385,University of Barcelona,edu,500fbe18afd44312738cab91b4689c12b4e0eeee,citation,http://www.maia.ub.es/~sergio/linked/ijcnn_age_and_cultural_2015.pdf,ChaLearn looking at people 2015 new competitions: Age estimation and cultural event recognition,2015 +76,Adience,adience,45.4312742,12.3265377,University of Venezia,edu,500fbe18afd44312738cab91b4689c12b4e0eeee,citation,http://www.maia.ub.es/~sergio/linked/ijcnn_age_and_cultural_2015.pdf,ChaLearn looking at people 2015 new competitions: Age estimation and cultural event recognition,2015 +77,Adience,adience,40.47913175,-74.43168868,Rutgers University,edu,31f1e711fcf82c855f27396f181bf5e565a2f58d,citation,http://doi.ieeecomputersociety.org/10.1109/ICCVW.2015.54,Unconstrained Age Estimation with Deep Convolutional Neural Networks,2015 +78,Adience,adience,39.2899685,-76.62196103,University of Maryland,edu,31f1e711fcf82c855f27396f181bf5e565a2f58d,citation,http://doi.ieeecomputersociety.org/10.1109/ICCVW.2015.54,Unconstrained Age Estimation with Deep Convolutional Neural Networks,2015 +79,Adience,adience,32.77824165,34.99565673,Open University of Israel,edu,62e913431bcef5983955e9ca160b91bb19d9de42,citation,http://pdfs.semanticscholar.org/62e9/13431bcef5983955e9ca160b91bb19d9de42.pdf,Facial Landmark Detection with Tweaked Convolutional Neural Networks,2015 +80,Adience,adience,34.67567405,33.04577648,Cyprus University of Technology,edu,9f3c9e41f46df9c94d714b1f080dafad6b4de1de,citation,https://doi.org/10.1109/ICT.2017.7998260,On the detection of images containing child-pornographic material,2017 +81,Adience,adience,51.49887085,-0.17560797,Imperial College London,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +82,Adience,adience,51.59029705,-0.22963221,Middlesex University,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +83,Adience,adience,37.5600406,126.9369248,Yonsei University,edu,fde41dc4ec6ac6474194b99e05b43dd6a6c4f06f,citation,https://arxiv.org/pdf/1809.01990.pdf,Multi-Expert Gender Classification on Age Group by Integrating Deep Neural Networks,2018 +84,Adience,adience,23.143197,113.34009651,South China Normal University,edu,dc6ad30c7a4bc79bb06b4725b16e202d3d7d8935,citation,http://doi.org/10.1007/s11042-017-4646-5,Age classification with deep learning face representation,2017 +85,Adience,adience,23.0502042,113.39880323,South China University of Technology,edu,dc6ad30c7a4bc79bb06b4725b16e202d3d7d8935,citation,http://doi.org/10.1007/s11042-017-4646-5,Age classification with deep learning face representation,2017 +86,Adience,adience,34.2152538,117.1398541,China University of Mining and Technology,edu,bc6a7390135bf127b93b90a21b1fdebbfb56ad30,citation,https://doi.org/10.1109/TIFS.2017.2766039,Bimodal Vein Data Mining via Cross-Selected-Domain Knowledge Transfer,2018 +87,Adience,adience,31.2284923,121.40211389,East China Normal University,edu,bc6a7390135bf127b93b90a21b1fdebbfb56ad30,citation,https://doi.org/10.1109/TIFS.2017.2766039,Bimodal Vein Data Mining via Cross-Selected-Domain Knowledge Transfer,2018 +88,Adience,adience,25.0410728,121.6147562,Institute of Information Science,edu,337dd4aaca2c5f9b5d2de8e0e2401b5a8feb9958,citation,https://arxiv.org/pdf/1810.11160.pdf,Data-specific Adaptive Threshold for Face Recognition and Authentication,2018 +89,Adience,adience,28.3656193,75.5834953,"Central Electronics Research Institute, Pilani, India",edu,1aeef2ab062c27e0dbba481047e818d4c471ca57,citation,https://doi.org/10.1109/ICACCI.2015.7275860,Analyzing impact of image scaling algorithms on viola-jones face detection framework,2015 +90,Adience,adience,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,2911e7f0fb6803851b0eddf8067a6fc06e8eadd6,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Jung_Joint_Fine-Tuning_in_ICCV_2015_paper.pdf,Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition,2015 +91,Adience,adience,23.7317915,90.3805625,Dhaka University,edu,026e4ee480475e63ae68570d73388f8dfd4b4cde,citation,http://pdfs.semanticscholar.org/026e/4ee480475e63ae68570d73388f8dfd4b4cde.pdf,Evaluating gender portrayal in Bangladeshi TV,2017 +92,Adience,adience,40.0505672,-75.37109326,Eastern University,edu,026e4ee480475e63ae68570d73388f8dfd4b4cde,citation,http://pdfs.semanticscholar.org/026e/4ee480475e63ae68570d73388f8dfd4b4cde.pdf,Evaluating gender portrayal in Bangladeshi TV,2017 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Barcelona,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0 +97,Adience,adience,31.76909325,117.17795091,Anhui University,edu,b910590a0eb191d03e1aedb3d55c905129e92e6b,citation,http://doi.acm.org/10.1145/2808492.2808570,Robust gender classification on unconstrained face images,2015 +98,Adience,adience,40.0044795,116.370238,Chinese Academy of Sciences,edu,b910590a0eb191d03e1aedb3d55c905129e92e6b,citation,http://doi.acm.org/10.1145/2808492.2808570,Robust gender classification on unconstrained face images,2015 +99,Adience,adience,43.7743911,-79.50481085,York University,edu,ffe4bb47ec15f768e1744bdf530d5796ba56cfc1,citation,https://arxiv.org/pdf/1706.04277.pdf,AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces,2017 +100,Adience,adience,27.18794105,31.17009498,Assiut University,edu,ffe4bb47ec15f768e1744bdf530d5796ba56cfc1,citation,https://arxiv.org/pdf/1706.04277.pdf,AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces,2017 diff --git a/site/datasets/final/aflw.csv b/site/datasets/final/aflw.csv new file mode 100644 index 00000000..29cfe134 --- /dev/null +++ b/site/datasets/final/aflw.csv @@ -0,0 +1,212 @@ +index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,AFLW,aflw,0.0,0.0,,,a74251efa970b92925b89eeef50a5e37d9281ad0,main,http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf,"Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization",2011 +1,AFLW,aflw,42.2942142,-83.71003894,University of Michigan,edu,860588fafcc80c823e66429fadd7e816721da42a,citation,https://arxiv.org/pdf/1804.04412.pdf,Unsupervised Discovery of Object Landmarks as Structural Representations,2018 +2,AFLW,aflw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,433a6d6d2a3ed8a6502982dccc992f91d665b9b3,citation,http://pdfs.semanticscholar.org/433a/6d6d2a3ed8a6502982dccc992f91d665b9b3.pdf,Transferring Landmark Annotations for Cross-Dataset Face Alignment,2014 +3,AFLW,aflw,40.00229045,116.32098908,Tsinghua University,edu,433a6d6d2a3ed8a6502982dccc992f91d665b9b3,citation,http://pdfs.semanticscholar.org/433a/6d6d2a3ed8a6502982dccc992f91d665b9b3.pdf,Transferring Landmark Annotations for Cross-Dataset Face Alignment,2014 +4,AFLW,aflw,-27.47715625,153.02841004,Queensland University of Technology,edu,6342a4c54835c1e14159495373ab18b4233d2d9b,citation,http://pdfs.semanticscholar.org/6342/a4c54835c1e14159495373ab18b4233d2d9b.pdf,Towards Pose-robust Face Recognition on Video,2014 +5,AFLW,aflw,39.993008,116.329882,SenseTime,company,38183fe28add21693729ddeaf3c8a90a2d5caea3,citation,http://arxiv.org/abs/1706.09876,Scale-Aware Face Detection,2017 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University,edu,32ecbbd76fdce249f9109594eee2d52a1cafdfc7,citation,http://pdfs.semanticscholar.org/32ec/bbd76fdce249f9109594eee2d52a1cafdfc7.pdf,Object Specific Deep Learning Feature and Its Application to Face Detection,2016 +10,AFLW,aflw,52.9387428,-1.20029569,University of Nottingham,edu,32ecbbd76fdce249f9109594eee2d52a1cafdfc7,citation,http://pdfs.semanticscholar.org/32ec/bbd76fdce249f9109594eee2d52a1cafdfc7.pdf,Object Specific Deep Learning Feature and Its Application to Face Detection,2016 +11,AFLW,aflw,40.0044795,116.370238,Chinese Academy of Sciences,edu,4e6c17966efae956133bf8f22edeffc24a0470c1,citation,http://pdfs.semanticscholar.org/4e6c/17966efae956133bf8f22edeffc24a0470c1.pdf,Face Classification: A Specialized Benchmark Study,2016 +12,AFLW,aflw,22.15263985,113.56803206,Macau University of Science and Technology,edu,4e6c17966efae956133bf8f22edeffc24a0470c1,citation,http://pdfs.semanticscholar.org/4e6c/17966efae956133bf8f22edeffc24a0470c1.pdf,Face Classification: A Specialized 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York,edu,c146aa6d56233ce700032f1cb179700778557601,citation,https://arxiv.org/pdf/1708.07199.pdf,3D Morphable Models as Spatial Transformer Networks,2017 +21,AFLW,aflw,51.24303255,-0.59001382,University of Surrey,edu,438e7999c937b94f0f6384dbeaa3febff6d283b6,citation,https://arxiv.org/pdf/1705.02402v2.pdf,"Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild",2017 +22,AFLW,aflw,31.4854255,120.2739581,Jiangnan University,edu,438e7999c937b94f0f6384dbeaa3febff6d283b6,citation,https://arxiv.org/pdf/1705.02402v2.pdf,"Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild",2017 +23,AFLW,aflw,37.4102193,-122.05965487,Carnegie Mellon University,edu,b1fdd4ae17d82612cefd4e78b690847b071379d3,citation,https://pdfs.semanticscholar.org/4fc5/416b6c7173d3462e5be796bda3ad8d5645a1.pdf,Supervised Descent Method,2015 +24,AFLW,aflw,40.0044795,116.370238,Chinese Academy of Sciences,edu,a5f35880477ae82902c620245e258cf854c09be9,citation,http://doi.org/10.1016/j.imavis.2013.12.004,Face detection by structural models,2014 +25,AFLW,aflw,51.24303255,-0.59001382,University of Surrey,edu,96c6f50ce8e1b9e8215b8791dabd78b2bbd5f28d,citation,https://arxiv.org/pdf/1611.05396.pdf,Dynamic Attention-Controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-Set Sample Weighting,2017 +26,AFLW,aflw,31.4854255,120.2739581,Jiangnan University,edu,96c6f50ce8e1b9e8215b8791dabd78b2bbd5f28d,citation,https://arxiv.org/pdf/1611.05396.pdf,Dynamic Attention-Controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-Set Sample Weighting,2017 +27,AFLW,aflw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,d3b0839324d0091e70ce34f44c979b9366547327,citation,https://arxiv.org/pdf/1804.10743.pdf,Precise Box Score: Extract More Information from Datasets to Improve the Performance of Face Detection,2018 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Kong,edu,8a3c5507237957d013a0fe0f082cab7f757af6ee,citation,http://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf,Facial Landmark Detection by Deep Multi-task Learning,2014 +33,AFLW,aflw,47.05821,15.46019568,Graz University of Technology,edu,5c8672c0d2f28fd5d2d2c4b9818fcff43fb01a48,citation,http://pdfs.semanticscholar.org/5c86/72c0d2f28fd5d2d2c4b9818fcff43fb01a48.pdf,Robust Face Detection by Simple Means,2012 +34,AFLW,aflw,30.642769,104.06751175,"Sichuan University, Chengdu",edu,5cbe1445d683d605b31377881ac8540e1d17adf0,citation,https://arxiv.org/pdf/1509.06161.pdf,On 3D face reconstruction via cascaded regression in shape space,2017 +35,AFLW,aflw,51.24303255,-0.59001382,University of Surrey,edu,3c6cac7ecf546556d7c6050f7b693a99cc8a57b3,citation,https://pdfs.semanticscholar.org/3c6c/ac7ecf546556d7c6050f7b693a99cc8a57b3.pdf,Robust facial landmark detection in the wild,2016 +36,AFLW,aflw,22.53521465,113.9315911,Shenzhen University,edu,287de191c49a3caa38ad7594093045dfba1eb420,citation,https://doi.org/10.23919/MVA.2017.7986829,Object specific deep feature and its application to face detection,2017 +37,AFLW,aflw,52.9387428,-1.20029569,University of Nottingham,edu,287de191c49a3caa38ad7594093045dfba1eb420,citation,https://doi.org/10.23919/MVA.2017.7986829,Object specific deep feature and its application to face detection,2017 +38,AFLW,aflw,40.0044795,116.370238,Chinese Academy of Sciences,edu,2f04ba0f74df046b0080ca78e56898bd4847898b,citation,http://arxiv.org/abs/1407.4023,Aggregate channel features for multi-view face detection,2014 +39,AFLW,aflw,33.6431901,-117.84016494,"University of California, Irvine",edu,65126e0b1161fc8212643b8ff39c1d71d262fbc1,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Ghiasi_Occlusion_Coherence_Localizing_2014_CVPR_paper.pdf,Occlusion Coherence: Localizing Occluded Faces with a Hierarchical Deformable Part Model,2014 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Technology,edu,04661729f0ff6afe4b4d6223f18d0da1d479accf,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.419,From Facial Parts Responses to Face Detection: A Deep Learning Approach,2015 +47,AFLW,aflw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,04661729f0ff6afe4b4d6223f18d0da1d479accf,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.419,From Facial Parts Responses to Face Detection: A Deep Learning Approach,2015 +48,AFLW,aflw,17.4454957,78.34854698,International Institute of Information Technology,edu,185263189a30986e31566394680d6d16b0089772,citation,https://pdfs.semanticscholar.org/1852/63189a30986e31566394680d6d16b0089772.pdf,Efficient Annotation of Objects for Video Analysis,2018 +49,AFLW,aflw,35.77184965,-78.67408695,North Carolina State University,edu,9bd35145c48ce172b80da80130ba310811a44051,citation,https://arxiv.org/pdf/1606.00850.pdf,Face Detection with End-to-End Integration of a ConvNet and a 3D Model,2016 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Detection: a Challenge Dataset and Baseline Results,2018 +58,AFLW,aflw,40.47913175,-74.43168868,Rutgers University,edu,377f2b65e6a9300448bdccf678cde59449ecd337,citation,https://arxiv.org/pdf/1804.10275.pdf,Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results,2018 +59,AFLW,aflw,39.2899685,-76.62196103,University of Maryland,edu,93420d9212dd15b3ef37f566e4d57e76bb2fab2f,citation,https://arxiv.org/pdf/1611.00851.pdf,An All-In-One Convolutional Neural Network for Face Analysis,2017 +60,AFLW,aflw,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,8ee5b1c9fb0bded3578113c738060290403ed472,citation,https://infoscience.epfl.ch/record/200452/files/wacv2014-RGE.pdf,Extending explicit shape regression with mixed feature channels and pose priors,2014 +61,AFLW,aflw,34.0224149,-118.28634407,University of Southern California,edu,43e99b76ca8e31765d4571d609679a689afdc99e,citation,http://arxiv.org/abs/1709.00536,Learning Dense Facial Correspondences in Unconstrained Images,2017 +62,AFLW,aflw,38.88140235,121.52281098,Dalian University of Technology,edu,f074e86e003d5b7a3b6e1780d9c323598d93f3bc,citation,http://pdfs.semanticscholar.org/f074/e86e003d5b7a3b6e1780d9c323598d93f3bc.pdf,Characteristic Number: Theory and Its Application to Shape Analysis,2014 +63,AFLW,aflw,38.99203005,-76.9461029,University of Maryland College Park,edu,1389ba6c3ff34cdf452ede130c738f37dca7e8cb,citation,http://pdfs.semanticscholar.org/1389/ba6c3ff34cdf452ede130c738f37dca7e8cb.pdf,A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection,2017 +64,AFLW,aflw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,85674b1b6007634f362cbe9b921912b697c0a32c,citation,http://pdfs.semanticscholar.org/8567/4b1b6007634f362cbe9b921912b697c0a32c.pdf,Optimizing Facial Landmark Detection by Facial Attribute Learning,2014 +65,AFLW,aflw,51.7534538,-1.25400997,University of Oxford,edu,8d9ffe9f7bf1ff3ecc320afe50a92a867a12aeb7,citation,https://arxiv.org/pdf/1809.02169.pdf,Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings,2018 +66,AFLW,aflw,38.99203005,-76.9461029,University of Maryland College Park,edu,f7824758800a7b1a386db5bd35f84c81454d017a,citation,https://arxiv.org/pdf/1702.05085.pdf,KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors,2017 +67,AFLW,aflw,17.4454957,78.34854698,International Institute of Information Technology,edu,156cd2a0e2c378e4c3649a1d046cd080d3338bca,citation,http://pdfs.semanticscholar.org/156c/d2a0e2c378e4c3649a1d046cd080d3338bca.pdf,Exemplar based approaches on Face Fiducial Detection and Frontalization,2017 +68,AFLW,aflw,39.7275037,39.47127034,Firat University,edu,5cfbeae360398de9e20e4165485837bd42b93217,citation,http://pdfs.semanticscholar.org/5cfb/eae360398de9e20e4165485837bd42b93217.pdf,Comparison Of Hog (Histogram of Oriented Gradients) and Haar Cascade Algorithms with a Convolutional Neural Network Based Face Detection Approaches,2017 +69,AFLW,aflw,29.5084174,106.57858552,Chongqing University,edu,a065080353d18809b2597246bb0b48316234c29a,citation,http://pdfs.semanticscholar.org/a065/080353d18809b2597246bb0b48316234c29a.pdf,FHEDN: A based on context modeling Feature Hierarchy Encoder-Decoder Network for face detection,2017 +70,AFLW,aflw,52.22165395,21.00735776,Warsaw University of Technology,edu,f27b8b8f2059248f77258cf8595e9434cf0b0228,citation,https://arxiv.org/pdf/1706.01789.pdf,Deep Alignment Network: A Convolutional Neural Network for Robust Face Alignment,2017 +71,AFLW,aflw,53.46600455,-2.23300881,University of Manchester,edu,68c1090f912b69b76437644dd16922909dd40d60,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6987312,Robust and Accurate Shape Model Matching Using Random Forest Regression-Voting,2012 +72,AFLW,aflw,32.77824165,34.99565673,Open University of Israel,edu,62e913431bcef5983955e9ca160b91bb19d9de42,citation,http://pdfs.semanticscholar.org/62e9/13431bcef5983955e9ca160b91bb19d9de42.pdf,Facial Landmark Detection with Tweaked Convolutional Neural Networks,2015 +73,AFLW,aflw,50.0764296,14.41802312,Czech Technical University,edu,f4ba07d2ae6c9673502daf50ee751a5e9262848f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7284810,Real-time multi-view facial landmark detector learned by the structured output SVM,2015 +74,AFLW,aflw,35.6924853,139.7582533,"National Institute of Informatics, Japan",edu,f4ba07d2ae6c9673502daf50ee751a5e9262848f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7284810,Real-time multi-view facial landmark detector learned by the structured output SVM,2015 +75,AFLW,aflw,40.00229045,116.32098908,Tsinghua University,edu,204f1cf56794bb23f9516b5f225a6ae00d3d30b8,citation,https://doi.org/10.1109/JSYST.2015.2418680,An AdaBoost-Based Face Detection System Using Parallel Configurable Architecture With Optimized Computation,2017 +76,AFLW,aflw,30.44235995,-84.29747867,Florida State University,edu,1ed6c7e02b4b3ef76f74dd04b2b6050faa6e2177,citation,http://pdfs.semanticscholar.org/6433/c412149382418ccd8aa966aa92973af41671.pdf,Face Detection with a 3D Model,2014 +77,AFLW,aflw,39.00041165,-77.10327775,National Institutes of Health,edu,1ed6c7e02b4b3ef76f74dd04b2b6050faa6e2177,citation,http://pdfs.semanticscholar.org/6433/c412149382418ccd8aa966aa92973af41671.pdf,Face Detection with a 3D Model,2014 +78,AFLW,aflw,42.718568,-84.47791571,Michigan State University,edu,37ce1d3a6415d6fc1760964e2a04174c24208173,citation,http://www.cse.msu.edu/~liuxm/publication/Jourabloo_Liu_ICCV2015.pdf,Pose-Invariant 3D Face Alignment,2015 +79,AFLW,aflw,42.718568,-84.47791571,Michigan State University,edu,ec8ec2dfd73cf3667f33595fef84c95c42125945,citation,https://arxiv.org/pdf/1707.06286.pdf,Pose-Invariant Face Alignment with a Single CNN,2017 +80,AFLW,aflw,43.07982815,-89.43066425,University of Wisconsin Madison,edu,2e091b311ac48c18aaedbb5117e94213f1dbb529,citation,http://pdfs.semanticscholar.org/b1a1/a049f1d78f6e3d072236237c467292ccd537.pdf,Collaborative Facial Landmark Localization for Transferring Annotations Across Datasets,2014 +81,AFLW,aflw,42.718568,-84.47791571,Michigan State University,edu,b53485dbdd2dc5e4f3c7cff26bd8707964bb0503,citation,http://doi.org/10.1007/s11263-017-1012-z,Pose-Invariant Face Alignment via CNN-Based Dense 3D Model Fitting,2017 +82,AFLW,aflw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,abdd17e411a7bfe043f280abd4e560a04ab6e992,citation,https://arxiv.org/pdf/1803.00839.pdf,Pose-Robust Face Recognition via Deep Residual Equivariant Mapping,2018 +83,AFLW,aflw,42.718568,-84.47791571,Michigan State University,edu,085ceda1c65caf11762b3452f87660703f914782,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Jourabloo_Large-Pose_Face_Alignment_CVPR_2016_paper.pdf,Large-Pose Face Alignment via CNN-Based Dense 3D Model 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Nottingham,edu,721e5ba3383b05a78ef1dfe85bf38efa7e2d611d,citation,http://pdfs.semanticscholar.org/74f1/9d0986c9d39aabb359abaa2a87a248a48deb.pdf,"BULAT, TZIMIROPOULOS: CONVOLUTIONAL AGGREGATION OF LOCAL EVIDENCE 1 Convolutional aggregation of local evidence for large pose face alignment",2016 +92,AFLW,aflw,47.5612651,7.5752961,University of Basel,edu,0c20fd90d867fe1be2459223a3cb1a69fa3d44bf,citation,http://pdfs.semanticscholar.org/0c20/fd90d867fe1be2459223a3cb1a69fa3d44bf.pdf,A Monte Carlo Strategy to Integrate Detection and Model-Based Face Analysis,2013 +93,AFLW,aflw,39.9041999,116.4073963,"Beijing FaceAll Co., Beijing, China",edu,c7cd490e43ee4ff81e8f86f790063695369c2830,citation,https://doi.org/10.1109/VCIP.2016.7805472,Use fast R-CNN and cascade structure for face detection,2016 +94,AFLW,aflw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,c7cd490e43ee4ff81e8f86f790063695369c2830,citation,https://doi.org/10.1109/VCIP.2016.7805472,Use fast R-CNN and 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University,edu,cc70fb1ab585378c79a2ab94776723e597afe379,citation,https://doi.org/10.1109/ICIP.2017.8297067,Detect face in the wild using CNN cascade with feature aggregation at multi-resolution,2017 +165,AFLW,aflw,51.49887085,-0.17560797,Imperial College London,edu,59d8fa6fd91cdb72cd0fa74c04016d79ef5a752b,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Zafeiriou_The_Menpo_Facial_CVPR_2017_paper.pdf,The Menpo Facial Landmark Localisation Challenge: A Step Towards the Solution,2017 +166,AFLW,aflw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,2f61d91033a06dd904ff9d1765d57e5b4d7f57a6,citation,https://doi.org/10.1109/ICIP.2016.7532953,FCFD: Teach the machine to accomplish face detection step by step,2016 +167,AFLW,aflw,40.47913175,-74.43168868,Rutgers 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Face Alignment,2018 +171,AFLW,aflw,46.0658836,11.1159894,University of Trento,edu,f201baf618574108bcee50e9a8b65f5174d832ee,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8031057,Viewpoint-Consistent 3D Face Alignment,2018 +172,AFLW,aflw,13.65450525,100.49423171,Robotics Institute,edu,f201baf618574108bcee50e9a8b65f5174d832ee,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8031057,Viewpoint-Consistent 3D Face Alignment,2018 +173,AFLW,aflw,40.0044795,116.370238,Chinese Academy of Sciences,edu,4c6233765b5f83333f6c675d3389bbbf503805e3,citation,https://perceptual.mpi-inf.mpg.de/files/2015/03/Yan_Vis13.pdf,Real-time high performance deformable model for face detection in the wild,2013 +174,AFLW,aflw,40.51865195,-74.44099801,State University of New Jersey,edu,02820c1491b10a1ff486fed32c269e4077c36551,citation,https://arxiv.org/pdf/1610.07930v1.pdf,Active user authentication for smartphones: A challenge data set and benchmark results,2016 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Webface,casia_webface,32.1119889,34.80459702,Tel Aviv University,edu,7859667ed6c05a467dfc8a322ecd0f5e2337db56,citation,http://pdfs.semanticscholar.org/7859/667ed6c05a467dfc8a322ecd0f5e2337db56.pdf,Web-Scale Transfer Learning for Unconstrained 1:N Face Identification,2015 +40,CASIA Webface,casia_webface,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,b8084d5e193633462e56f897f3d81b2832b72dff,citation,http://pdfs.semanticscholar.org/b808/4d5e193633462e56f897f3d81b2832b72dff.pdf,DeepID3: Face Recognition with Very Deep Neural Networks,2015 +41,CASIA Webface,casia_webface,22.42031295,114.20788644,Chinese University of Hong Kong,edu,b8084d5e193633462e56f897f3d81b2832b72dff,citation,http://pdfs.semanticscholar.org/b808/4d5e193633462e56f897f3d81b2832b72dff.pdf,DeepID3: Face Recognition with Very Deep Neural Networks,2015 +42,CASIA Webface,casia_webface,30.19331415,120.11930822,Zhejiang 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Telecommunications,edu,3ac3a714042d3ebc159546c26321a1f8f4f5f80c,citation,http://dl.acm.org/citation.cfm?id=3025149,Clustering lightened deep representation for large scale face identification,2017 +98,CASIA Webface,casia_webface,37.26728,126.9841151,Seoul National University,edu,282503fa0285240ef42b5b4c74ae0590fe169211,citation,http://pdfs.semanticscholar.org/2825/03fa0285240ef42b5b4c74ae0590fe169211.pdf,Feeding Hand-Crafted Features for Enhancing the Performance of Convolutional Neural Networks,2018 +99,CASIA Webface,casia_webface,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,29db046dd1f8100b279c3f5f5c5ef19bdbf5af9a,citation,https://arxiv.org/pdf/1706.04717.pdf,Recent Progress of Face Image Synthesis,2017 +100,CASIA Webface,casia_webface,39.2899685,-76.62196103,University of Maryland,edu,4f7b92bd678772552b3c3edfc9a7c5c4a8c60a8e,citation,https://pdfs.semanticscholar.org/4f7b/92bd678772552b3c3edfc9a7c5c4a8c60a8e.pdf,Deep Density Clustering of Unconstrained Faces,0 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Violent Settings,2018 +109,CASIA Webface,casia_webface,46.0501558,14.46907327,University of Ljubljana,edu,69adbfa7b0b886caac15ebe53b89adce390598a3,citation,https://arxiv.org/pdf/1805.10938.pdf,Face hallucination using cascaded super-resolution and identity priors,2018 +110,CASIA Webface,casia_webface,41.70456775,-86.23822026,University of Notre Dame,edu,69adbfa7b0b886caac15ebe53b89adce390598a3,citation,https://arxiv.org/pdf/1805.10938.pdf,Face hallucination using cascaded super-resolution and identity priors,2018 +111,CASIA Webface,casia_webface,42.3383668,-71.08793524,Northeastern University,edu,feea73095b1be0cbae1ad7af8ba2c4fb6f316d35,citation,http://dl.acm.org/citation.cfm?id=3126693,Deep Face Recognition with Center Invariant Loss,2017 +112,CASIA Webface,casia_webface,35.9990522,-78.9290629,Duke University,edu,3f0c51989c516a7c5dee7dec4d7fb474ae6c28d9,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.720,Not Afraid of the Dark: NIR-VIS Face Recognition via Cross-Spectral Hallucination and Low-Rank Embedding,2017 +113,CASIA Webface,casia_webface,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,91a4ebf1ca0314a74c436729700ef09bddaa6222,citation,https://arxiv.org/pdf/1808.01338.pdf,Detailed Human Avatars from Monocular Video,2018 +114,CASIA Webface,casia_webface,47.5612651,7.5752961,University of Basel,edu,0081e2188c8f34fcea3e23c49fb3e17883b33551,citation,http://pdfs.semanticscholar.org/0081/e2188c8f34fcea3e23c49fb3e17883b33551.pdf,Training Deep Face Recognition Systems with Synthetic Data,2018 +115,CASIA Webface,casia_webface,24.4399419,118.09301781,Xiamen University,edu,57ba4b6de23a6fc9d45ff052ed2563e5de00b968,citation,https://doi.org/10.1109/ICIP.2017.8296993,An efficient deep neural networks training framework for robust face recognition,2017 +116,CASIA Webface,casia_webface,40.0044795,116.370238,Chinese Academy of Sciences,edu,43fe03ec1acb6ea9d05d2b22eeddb2631bd30437,citation,https://doi.org/10.1109/ICIP.2017.8296394,Weakly supervised multiscale-inception learning for web-scale face recognition,2017 +117,CASIA Webface,casia_webface,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,0be418e63d111e3b94813875f75909e4dc27d13a,citation,https://doi.org/10.1109/ICB.2016.7550057,Fine-grained LFW database,2016 +118,CASIA Webface,casia_webface,40.51865195,-74.44099801,State University of New Jersey,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791154,A cascaded convolutional neural network for age estimation of unconstrained faces,2016 +119,CASIA Webface,casia_webface,39.2899685,-76.62196103,University of Maryland,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791154,A cascaded convolutional neural network for age estimation of unconstrained faces,2016 +120,CASIA Webface,casia_webface,42.718568,-84.47791571,Michigan State University,edu,450c6a57f19f5aa45626bb08d7d5d6acdb863b4b,citation,https://arxiv.org/pdf/1805.00611.pdf,Towards Interpretable Face Recognition,2018 +121,CASIA Webface,casia_webface,30.2931534,120.1620458,Zhejiang University of Technology,edu,cb9921d5fc4ffa50be537332e111f03d74622442,citation,https://doi.org/10.1007/978-3-319-46654-5_79,Face Occlusion Detection Using Cascaded Convolutional Neural Network,2016 +122,CASIA Webface,casia_webface,29.6328784,-82.3490133,University of Florida,edu,291de30ceecb5dcf0644c35e2b5935d341ea148b,citation,https://arxiv.org/pdf/1810.00024.pdf,Explainable Black-Box Attacks Against Model-based Authentication,2018 +123,CASIA Webface,casia_webface,42.3383668,-71.08793524,Northeastern University,edu,3f540faf85e1f8de6ce04fb37e556700b67e4ad3,citation,http://pdfs.semanticscholar.org/3f54/0faf85e1f8de6ce04fb37e556700b67e4ad3.pdf,Face Verification with Multi-Task and Multi-Scale Feature Fusion,2017 +124,CASIA Webface,casia_webface,29.7207902,-95.34406271,University of Houston,edu,8334da483f1986aea87b62028672836cb3dc6205,citation,https://arxiv.org/pdf/1805.06306.pdf,Fully Associative Patch-Based 1-to-N Matcher for Face Recognition,2018 +125,CASIA Webface,casia_webface,39.2899685,-76.62196103,University of Maryland,edu,0019925779bff96448f0c75492717e4473f88377,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w3/papers/Reale_Deep_Heterogeneous_Face_CVPR_2017_paper.pdf,Deep Heterogeneous Face Recognition Networks Based on Cross-Modal Distillation and an Equitable Distance Metric,2017 +126,CASIA Webface,casia_webface,45.7413921,126.62552755,Harbin Institute of Technology,edu,05455f5e3c3989be4991cb74b73cdfd0d6522622,citation,https://arxiv.org/pdf/1804.04829.pdf,Learning Warped Guidance for Blind Face Restoration,2018 +127,CASIA Webface,casia_webface,23.09461185,113.28788994,Sun Yat-Sen University,edu,05455f5e3c3989be4991cb74b73cdfd0d6522622,citation,https://arxiv.org/pdf/1804.04829.pdf,Learning Warped Guidance for Blind Face Restoration,2018 +128,CASIA Webface,casia_webface,38.0333742,-84.5017758,University of Kentucky,edu,05455f5e3c3989be4991cb74b73cdfd0d6522622,citation,https://arxiv.org/pdf/1804.04829.pdf,Learning Warped Guidance for Blind Face Restoration,2018 +129,CASIA Webface,casia_webface,51.49887085,-0.17560797,Imperial College London,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +130,CASIA Webface,casia_webface,51.59029705,-0.22963221,Middlesex University,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +131,CASIA Webface,casia_webface,25.01682835,121.53846924,National Taiwan University,edu,81884e1de00e59f24bc20254584d73a1a1806933,citation,https://arxiv.org/pdf/1811.02328.pdf,Super-Identity Convolutional Neural Network for Face Hallucination,2018 +132,CASIA Webface,casia_webface,39.993008,116.329882,SenseTime,company,81884e1de00e59f24bc20254584d73a1a1806933,citation,https://arxiv.org/pdf/1811.02328.pdf,Super-Identity Convolutional Neural Network for Face Hallucination,2018 +133,CASIA Webface,casia_webface,30.284151,-97.73195598,University of Texas at Austin,edu,81884e1de00e59f24bc20254584d73a1a1806933,citation,https://arxiv.org/pdf/1811.02328.pdf,Super-Identity Convolutional Neural Network for Face Hallucination,2018 +134,CASIA Webface,casia_webface,47.6543238,-122.30800894,University of Washington,edu,405526dfc79de98f5bf3c97bf4aa9a287700f15d,citation,http://pdfs.semanticscholar.org/8a6c/57fcd99a77982ec754e0b97fd67519ccb60c.pdf,MegaFace: A Million Faces for Recognition at Scale,2015 +135,CASIA Webface,casia_webface,32.77824165,34.99565673,Open University of Israel,edu,582edc19f2b1ab2ac6883426f147196c8306685a,citation,http://pdfs.semanticscholar.org/be6c/db7b181e73f546d43cf2ab6bc7181d7d619b.pdf,Do We Really Need to Collect Millions of Faces for Effective Face Recognition?,2016 +136,CASIA Webface,casia_webface,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,076d3fc800d882445c11b9af466c3af7d2afc64f,citation,http://slsp.kaist.ac.kr/paperdata/Face_attribute_classification.pdf,Face attribute classification using attribute-aware correlation map and gated convolutional neural networks,2015 +137,CASIA Webface,casia_webface,31.83907195,117.26420748,University of Science and Technology of China,edu,e1256ff535bf4c024dd62faeb2418d48674ddfa2,citation,https://arxiv.org/pdf/1803.11182.pdf,Towards Open-Set Identity Preserving Face Synthesis,2018 +138,CASIA Webface,casia_webface,36.383765,127.36694,"Electronics and Telecommunications Research Institute, Daejeon, Korea",edu,77c5437107f8138d48cb7e10b2b286fa51473678,citation,https://doi.org/10.1109/URAI.2016.7734005,A pseudo ensemble convolutional neural networks,2016 +139,CASIA Webface,casia_webface,36.3851395,127.3683413,"University of Science and Technology, Korea",edu,77c5437107f8138d48cb7e10b2b286fa51473678,citation,https://doi.org/10.1109/URAI.2016.7734005,A pseudo ensemble convolutional neural networks,2016 +140,CASIA Webface,casia_webface,40.00229045,116.32098908,Tsinghua University,edu,93eb3963bc20e28af26c53ef3bce1e76b15e3209,citation,https://doi.org/10.1109/ICIP.2017.8296992,Occlusion robust face recognition based on mask learning,2017 +141,CASIA Webface,casia_webface,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,16b9d258547f1eccdb32111c9f45e2e4bbee79af,citation,https://arxiv.org/pdf/1704.06369.pdf,NormFace: L2 Hypersphere Embedding for Face Verification,2017 +142,CASIA Webface,casia_webface,39.94976005,116.33629046,Beijing Jiaotong University,edu,7e2cfbfd43045fbd6aabd9a45090a5716fc4e179,citation,https://arxiv.org/pdf/1808.00435.pdf,Global Norm-Aware Pooling for Pose-Robust Face Recognition at Low False Positive Rate,2018 +143,CASIA Webface,casia_webface,40.0044795,116.370238,Chinese Academy of Sciences,edu,99facca6fc50cc30f13b7b6dd49ace24bc94f702,citation,https://arxiv.org/pdf/1609.03892.pdf,VIPLFaceNet: an open source deep face recognition SDK,2016 +144,CASIA Webface,casia_webface,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,99facca6fc50cc30f13b7b6dd49ace24bc94f702,citation,https://arxiv.org/pdf/1609.03892.pdf,VIPLFaceNet: an open source deep face recognition SDK,2016 +145,CASIA Webface,casia_webface,25.01682835,121.53846924,National Taiwan University,edu,b50edfea790f86373407a964b4255bf8e436d377,citation,http://doi.acm.org/10.1145/3136755.3143008,Group emotion recognition with individual facial emotion CNNs and global image based CNNs,2017 +146,CASIA Webface,casia_webface,1.2962018,103.77689944,National University of Singapore,edu,c17c7b201cfd0bcd75441afeaa734544c6ca3416,citation,https://doi.org/10.1109/TCSVT.2016.2587389,Layerwise Class-Aware Convolutional Neural Network,2017 +147,CASIA Webface,casia_webface,32.0575279,118.78682252,Southeast University,edu,c17c7b201cfd0bcd75441afeaa734544c6ca3416,citation,https://doi.org/10.1109/TCSVT.2016.2587389,Layerwise Class-Aware Convolutional Neural Network,2017 +148,CASIA Webface,casia_webface,22.3386304,114.2620337,Hong Kong University of Science and Technology,edu,585260468d023ffc95f0e539c3fa87254c28510b,citation,http://pdfs.semanticscholar.org/5852/60468d023ffc95f0e539c3fa87254c28510b.pdf,Cardea: Context-Aware Visual Privacy Protection from Pervasive Cameras,2016 +149,CASIA Webface,casia_webface,39.65404635,-79.96475355,West Virginia University,edu,3b9b200e76a35178da940279d566bbb7dfebb787,citation,http://pdfs.semanticscholar.org/3b9b/200e76a35178da940279d566bbb7dfebb787.pdf,Learning Channel Inter-dependencies at Multiple Scales on Dense Networks for Face Recognition,2017 +150,CASIA Webface,casia_webface,39.65404635,-79.96475355,West Virginia University,edu,8bfada57140aa1aa22a575e960c2a71140083293,citation,http://pdfs.semanticscholar.org/8bfa/da57140aa1aa22a575e960c2a71140083293.pdf,Can we match Ultraviolet Face Images against their Visible Counterparts?,2015 +151,CASIA Webface,casia_webface,40.0044795,116.370238,Chinese Academy of Sciences,edu,72a7eb68f0955564e1ceafa75aeeb6b5bbb14e7e,citation,https://pdfs.semanticscholar.org/72a7/eb68f0955564e1ceafa75aeeb6b5bbb14e7e.pdf,Face Recognition with Contrastive Convolution,2018 +152,CASIA Webface,casia_webface,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,72a7eb68f0955564e1ceafa75aeeb6b5bbb14e7e,citation,https://pdfs.semanticscholar.org/72a7/eb68f0955564e1ceafa75aeeb6b5bbb14e7e.pdf,Face Recognition with Contrastive Convolution,2018 +153,CASIA Webface,casia_webface,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,44b827df6c433ca49bcf44f9f3ebfdc0774ee952,citation,https://doi.org/10.1109/LSP.2017.2726105,Deep Correlation Feature Learning for Face Verification in the Wild,2017 +154,CASIA Webface,casia_webface,22.42031295,114.20788644,Chinese University of Hong Kong,edu,58d76380d194248b3bb291b8c7c5137a0a376897,citation,https://pdfs.semanticscholar.org/58d7/6380d194248b3bb291b8c7c5137a0a376897.pdf,FaceID-GAN : Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis,2018 +155,CASIA Webface,casia_webface,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,58d76380d194248b3bb291b8c7c5137a0a376897,citation,https://pdfs.semanticscholar.org/58d7/6380d194248b3bb291b8c7c5137a0a376897.pdf,FaceID-GAN : Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis,2018 +156,CASIA Webface,casia_webface,39.2899685,-76.62196103,University of Maryland,edu,b6f758be954d34817d4ebaa22b30c63a4b8ddb35,citation,http://arxiv.org/abs/1703.04835,A Proximity-Aware Hierarchical Clustering of Faces,2017 +157,CASIA Webface,casia_webface,28.2290209,112.99483204,"National University of Defense Technology, China",edu,511a8cdf2127ef8aa07cbdf9660fe9e0e2dfbde7,citation,https://pdfs.semanticscholar.org/511a/8cdf2127ef8aa07cbdf9660fe9e0e2dfbde7.pdf,A Community Detection Approach to Cleaning Extremely Large Face Database,2018 +158,CASIA Webface,casia_webface,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,cdef0eaff4a3c168290d238999fc066ebc3a93e8,citation,https://arxiv.org/pdf/1707.07391.pdf,Contrastive-center loss for deep neural networks,2017 +159,CASIA Webface,casia_webface,30.2810654,120.02139087,"Alibaba Group, Hangzhou, China",edu,1e62ca5845a6f0492574a5da049e9b43dbeadb1b,citation,https://doi.org/10.1109/LSP.2016.2637400,Cross-Modality Face Recognition via Heterogeneous Joint Bayesian,2017 +160,CASIA Webface,casia_webface,40.0044795,116.370238,Chinese Academy of Sciences,edu,1e62ca5845a6f0492574a5da049e9b43dbeadb1b,citation,https://doi.org/10.1109/LSP.2016.2637400,Cross-Modality Face Recognition via Heterogeneous Joint Bayesian,2017 +161,CASIA Webface,casia_webface,47.6543238,-122.30800894,University of Washington,edu,96e0cfcd81cdeb8282e29ef9ec9962b125f379b0,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.527,The MegaFace Benchmark: 1 Million Faces for Recognition at Scale,2016 +162,CASIA Webface,casia_webface,40.9153196,-73.1270626,Stony Brook University,edu,40883844c1ceab95cb92498a92bfdf45beaa288e,citation,https://arxiv.org/pdf/1709.02848.pdf,Improving Heterogeneous Face Recognition with Conditional Adversarial Networks,2017 +163,CASIA Webface,casia_webface,51.7534538,-1.25400997,University of Oxford,edu,8ec82da82416bb8da8cdf2140c740e1574eaf84f,citation,http://pdfs.semanticscholar.org/8ec8/2da82416bb8da8cdf2140c740e1574eaf84f.pdf,Lip Reading in Profile,2017 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Webface,casia_webface,37.3936717,-122.0807262,Facebook,company,628a3f027b7646f398c68a680add48c7969ab1d9,citation,https://pdfs.semanticscholar.org/628a/3f027b7646f398c68a680add48c7969ab1d9.pdf,Plan for Final Year Project : HKU-Face : A Large Scale Dataset for Deep Face Recognition,2017 +168,CASIA Webface,casia_webface,38.88140235,121.52281098,Dalian University of Technology,edu,052f994898c79529955917f3dfc5181586282cf8,citation,https://arxiv.org/pdf/1708.02191.pdf,Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos,2017 +169,CASIA Webface,casia_webface,37.4102193,-122.05965487,Carnegie Mellon University,edu,d35534f3f59631951011539da2fe83f2844ca245,citation,https://arxiv.org/pdf/1705.07904.pdf,Semantically Decomposing the Latent Spaces of Generative Adversarial Networks,2017 +170,CASIA Webface,casia_webface,37.43131385,-122.16936535,Stanford University,edu,d35534f3f59631951011539da2fe83f2844ca245,citation,https://arxiv.org/pdf/1705.07904.pdf,Semantically Decomposing the Latent Spaces of Generative Adversarial Networks,2017 +171,CASIA Webface,casia_webface,32.87935255,-117.23110049,"University of California, San Diego",edu,d35534f3f59631951011539da2fe83f2844ca245,citation,https://arxiv.org/pdf/1705.07904.pdf,Semantically Decomposing the Latent Spaces of Generative Adversarial Networks,2017 +172,CASIA Webface,casia_webface,23.0502042,113.39880323,South China University of Technology,edu,6880013eb0b91a2b334e0be0dced0a1a79943469,citation,https://arxiv.org/pdf/1810.11809.pdf,Discrimination-aware Channel Pruning for Deep Neural Networks,2018 +173,CASIA Webface,casia_webface,32.7283683,-97.11201835,University of Texas at Arlington,edu,6880013eb0b91a2b334e0be0dced0a1a79943469,citation,https://arxiv.org/pdf/1810.11809.pdf,Discrimination-aware Channel Pruning for Deep Neural Networks,2018 +174,CASIA Webface,casia_webface,39.65404635,-79.96475355,West Virginia University,edu,9fb93b7c2bae866608f26c4254e5bd69cc5031d6,citation,https://arxiv.org/pdf/1809.08999.pdf,Fast Geometrically-Perturbed Adversarial Faces,2018 +175,CASIA Webface,casia_webface,32.1638824,34.8115862,FDNA Israel,company,92de9a54515f4ac8cc8e4e6b0dfab20e5e6bb09d,citation,https://doi.org/10.1109/ICIP.2016.7533062,Quality scores for deep regression systems,2016 +176,CASIA Webface,casia_webface,39.2899685,-76.62196103,University of Maryland,edu,2d748f8ee023a5b1fbd50294d176981ded4ad4ee,citation,http://pdfs.semanticscholar.org/2d74/8f8ee023a5b1fbd50294d176981ded4ad4ee.pdf,Triplet Similarity Embedding for Face Verification,2016 +177,CASIA Webface,casia_webface,40.51865195,-74.44099801,State University of New Jersey,edu,5495e224ac7b45b9edc5cfeabbb754d8a40a879b,citation,http://pdfs.semanticscholar.org/5495/e224ac7b45b9edc5cfeabbb754d8a40a879b.pdf,Feature Reconstruction Disentangling for Pose-invariant Face Recognition Supplementary Material,2017 +178,CASIA Webface,casia_webface,32.87935255,-117.23110049,"University of California, San Diego",edu,5495e224ac7b45b9edc5cfeabbb754d8a40a879b,citation,http://pdfs.semanticscholar.org/5495/e224ac7b45b9edc5cfeabbb754d8a40a879b.pdf,Feature Reconstruction Disentangling for Pose-invariant Face Recognition Supplementary Material,2017 +179,CASIA Webface,casia_webface,42.718568,-84.47791571,Michigan State University,edu,cd55fb30737625e86454a2861302b96833ed549d,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139094,Annotating Unconstrained Face Imagery: A scalable approach,2015 +180,CASIA Webface,casia_webface,38.95187,-77.363259,"Noblis, Falls Church, VA, U.S.A.",company,cd55fb30737625e86454a2861302b96833ed549d,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139094,Annotating Unconstrained Face Imagery: A scalable approach,2015 +181,CASIA Webface,casia_webface,22.2081469,114.25964115,University of Hong 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Oxford,edu,a3d0ebb50d49116289fb176d28ea98a92badada6,citation,https://pdfs.semanticscholar.org/a3d0/ebb50d49116289fb176d28ea98a92badada6.pdf,Unsupervised Learning of Object Landmarks through Conditional Image Generation,2018 +50,COFW,cofw,55.94951105,-3.19534913,University of Edinburgh,edu,a3d0ebb50d49116289fb176d28ea98a92badada6,citation,https://pdfs.semanticscholar.org/a3d0/ebb50d49116289fb176d28ea98a92badada6.pdf,Unsupervised Learning of Object Landmarks through Conditional Image Generation,2018 +51,COFW,cofw,30.642769,104.06751175,"Sichuan University, Chengdu",edu,a0aa32bb7f406693217fba6dcd4aeb6c4d5a479b,citation,https://pdfs.semanticscholar.org/a0aa/32bb7f406693217fba6dcd4aeb6c4d5a479b.pdf,Cascaded Regressor based 3D Face Reconstruction from a Single Arbitrary View Image,2015 +52,COFW,cofw,25.01353105,121.54173736,National Taiwan University of Science and Technology,edu,deb89950939ae9847f0a1a4bb198e6dbfed62778,citation,https://doi.org/10.1109/LSP.2016.2543019,Accurate Facial Landmark Extraction,2016 +53,COFW,cofw,3.12267405,101.65356103,University of Malaya,edu,deb89950939ae9847f0a1a4bb198e6dbfed62778,citation,https://doi.org/10.1109/LSP.2016.2543019,Accurate Facial Landmark Extraction,2016 +54,COFW,cofw,37.4102193,-122.05965487,Carnegie Mellon University,edu,78598e7005f7c96d64cc47ff47e6f13ae52245b8,citation,https://arxiv.org/pdf/1708.00370.pdf,Hand2Face: Automatic synthesis and recognition of hand over face occlusions,2017 +55,COFW,cofw,28.59899755,-81.19712501,University of Central Florida,edu,78598e7005f7c96d64cc47ff47e6f13ae52245b8,citation,https://arxiv.org/pdf/1708.00370.pdf,Hand2Face: Automatic synthesis and recognition of hand over face occlusions,2017 +56,COFW,cofw,52.17638955,0.14308882,University of Cambridge,edu,9901f473aeea177a55e58bac8fd4f1b086e575a4,citation,https://arxiv.org/pdf/1509.04954.pdf,Human and sheep facial landmarks localisation by triplet interpolated features,2016 +57,COFW,cofw,40.00229045,116.32098908,Tsinghua University,edu,e4fa062bff299a0bcef9f6b2e593c85be116c9f1,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7407641,Cascaded Elastically Progressive Model for Accurate Face Alignment,2017 +58,COFW,cofw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,5c820e47981d21c9dddde8d2f8020146e600368f,citation,http://pdfs.semanticscholar.org/5c82/0e47981d21c9dddde8d2f8020146e600368f.pdf,Extended Supervised Descent Method for Robust Face Alignment,2014 +59,COFW,cofw,51.49887085,-0.17560797,Imperial College London,edu,29c340c83b3bbef9c43b0c50b4d571d5ed037cbd,citation,https://pdfs.semanticscholar.org/29c3/40c83b3bbef9c43b0c50b4d571d5ed037cbd.pdf,Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment,2018 +60,COFW,cofw,30.19331415,120.11930822,Zhejiang University,edu,5213549200bccec57232fc3ff788ddf1043af7b3,citation,http://doi.acm.org/10.1145/2601097.2601204,Displaced dynamic expression regression for real-time facial tracking and 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Technology,edu,2f73203fd71b755a9601d00fc202bbbd0a595110,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8394868,Micro-expression Analysis by Fusing Deep Convolutional Neural Network and Optical Flow,2018 +194,COFW,cofw,33.620813,133.719755,Kochi University of Technology,edu,2f73203fd71b755a9601d00fc202bbbd0a595110,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8394868,Micro-expression Analysis by Fusing Deep Convolutional Neural Network and Optical Flow,2018 +195,COFW,cofw,32.0565957,118.77408833,Nanjing University,edu,5b0bf1063b694e4b1575bb428edb4f3451d9bf04,citation,http://doi.ieeecomputersociety.org/10.1109/ICCVW.2015.131,Facial Shape Tracking via Spatio-Temporal Cascade Shape Regression,2015 +196,COFW,cofw,31.83907195,117.26420748,University of Science and Technology of China,edu,dd715a98dab34437ad05758b20cc640c2cdc5715,citation,https://doi.org/10.1007/s41095-017-0082-8,Joint head pose and facial landmark regression from depth images,2017 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Long-Range Vision for an Offroad Robot,2008 +40,FERET,feret,40.72925325,-73.99625394,New York University,edu,4b8d80f91d271f61b26db5ad627e24e59955c56a,citation,http://pdfs.semanticscholar.org/4b8d/80f91d271f61b26db5ad627e24e59955c56a.pdf,Learning Long-Range Vision for an Offroad Robot,2008 +41,FERET,feret,51.24303255,-0.59001382,University of Surrey,edu,7af15295224c3ad69d56f17ff635763dd008a8a4,citation,http://pdfs.semanticscholar.org/7af1/5295224c3ad69d56f17ff635763dd008a8a4.pdf,Learning Support Vectors for Face Authentication: Sensitivity to Mis-Registrations,2007 +42,FERET,feret,50.0764296,14.41802312,Czech Technical University,edu,7af15295224c3ad69d56f17ff635763dd008a8a4,citation,http://pdfs.semanticscholar.org/7af1/5295224c3ad69d56f17ff635763dd008a8a4.pdf,Learning Support Vectors for Face Authentication: Sensitivity to Mis-Registrations,2007 +43,FERET,feret,40.00229045,116.32098908,Tsinghua 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California,edu,21358489b5ce0e94ff37792a8a5eea198e7272f3,citation,http://pdfs.semanticscholar.org/c0cc/2073cad539d979fc6f860177b531b45fafc1.pdf,Face Inpainting with Local Linear Representations,2004 +51,FERET,feret,61.44964205,23.85877462,Tampere University of Technology,edu,dc4e4b9c507e8be2d832faf64e5a2e8887115265,citation,https://pdfs.semanticscholar.org/dc4e/4b9c507e8be2d832faf64e5a2e8887115265.pdf,Face Retrieval Based on Robust Local Features and Statistical-Structural Learning Approach,2008 +52,FERET,feret,37.3219575,127.1250723,Dankook University,edu,891d435fd1a070bb66225abfd62b2e2c5350e87c,citation,https://pdfs.semanticscholar.org/891d/435fd1a070bb66225abfd62b2e2c5350e87c.pdf,Selective Feature Generation Method for Classification of Low-dimensional Data,2018 +53,FERET,feret,32.8536333,-117.2035286,Kyung Hee University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for 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Kong,edu,55498d89f9eb0c9df9760f5e0e47a15ae7e92f25,citation,http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/264.pdf,Learning-based face hallucination in DCT domain,2008 +61,FERET,feret,40.0044795,116.370238,Chinese Academy of Sciences,edu,f12813073a7f894f82fe2b166893424edba7dc79,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2008.4587375,Unified Principal Component Analysis with generalized Covariance Matrix for face recognition,2008 +62,FERET,feret,45.7413921,126.62552755,Harbin Institute of Technology,edu,f12813073a7f894f82fe2b166893424edba7dc79,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2008.4587375,Unified Principal Component Analysis with generalized Covariance Matrix for face recognition,2008 +63,FERET,feret,39.87549675,32.78553506,Middle East Technical University,edu,946c2036c940e77260ade031ba413ec9f2435985,citation,http://pdfs.semanticscholar.org/946c/2036c940e77260ade031ba413ec9f2435985.pdf,PCA for Gender Estimation: Which Eigenvectors Contribute?,2002 +64,FERET,feret,36.1017956,-79.501733,Elon University,edu,a129c30b176820bf7f4756b4b4efc92d2a83f190,citation,https://pdfs.semanticscholar.org/a129/c30b176820bf7f4756b4b4efc92d2a83f190.pdf,Older adults' associative memory is modified by manner of presentation at encoding and retrieval.,2018 +65,FERET,feret,13.01119095,74.79498825,"National Institute of Technology, Karnataka",edu,e1fac9e9427499d3758213daf1c781b9a42a3420,citation,https://pdfs.semanticscholar.org/7c90/60a809bd28ef61421588f48e33f6eae6ddfd.pdf,Face Image Retrieval Based on Probe Sketch Using SIFT Feature Descriptors,2012 +66,FERET,feret,35.9542493,-83.9307395,University of Tennessee,edu,7735f63e5790006cb3d989c8c19910e40200abfc,citation,http://pdfs.semanticscholar.org/7735/f63e5790006cb3d989c8c19910e40200abfc.pdf,Multispectral Imaging For Face Recognition Over Varying Illumination,2008 +67,FERET,feret,32.0565957,118.77408833,Nanjing 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Maryland,edu,355af3c3adbb17d25f0d2a4193e3daadffc0d4e8,citation,http://pdfs.semanticscholar.org/355a/f3c3adbb17d25f0d2a4193e3daadffc0d4e8.pdf,Pattern recognition: Historical perspective and future directions,2000 +75,FERET,feret,38.83133325,-77.30798839,George Mason University,edu,355af3c3adbb17d25f0d2a4193e3daadffc0d4e8,citation,http://pdfs.semanticscholar.org/355a/f3c3adbb17d25f0d2a4193e3daadffc0d4e8.pdf,Pattern recognition: Historical perspective and future directions,2000 +76,FERET,feret,40.8927159,29.37863323,Sabanci University,edu,1e6d1e811da743df02481bca1a7bdaa73b809913,citation,http://research.sabanciuniv.edu/608/1/3011800001159.pdf,Multimodal person recognition for human-vehicle interaction,2006 +77,FERET,feret,50.7338124,7.1022465,University of Bonn,edu,f4aafb50c93c5ad3e5c4696ed24b063a1932915a,citation,http://pdfs.semanticscholar.org/f4aa/fb50c93c5ad3e5c4696ed24b063a1932915a.pdf,What would you look like in Springfield? Linear Transformations between High-Dimensional Spaces,2011 +78,FERET,feret,45.7413921,126.62552755,Harbin Institute of Technology,edu,10156890bc53cb6be97bd144a68fde693bf13612,citation,http://pdfs.semanticscholar.org/1015/6890bc53cb6be97bd144a68fde693bf13612.pdf,Face Recognition Using Sparse Representation-Based Classification on K-Nearest Subspace,2013 +79,FERET,feret,45.42580475,-75.68740118,University of Ottawa,edu,16820ccfb626dcdc893cc7735784aed9f63cbb70,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W12/papers/Azarmehr_Real-Time_Embedded_Age_2015_CVPR_paper.pdf,Real-time embedded age and gender classification in unconstrained video,2015 +80,FERET,feret,42.3504253,-71.10056114,Boston University,edu,966b76acfa75253679b1a82ecc5a68e523f5c0c9,citation,http://pdfs.semanticscholar.org/f204/2494d5666e436f5e96ff5e0cd3b5f5e5485b.pdf,Preference suppression caused by misattribution of task-irrelevant subliminal motion.,2012 +81,FERET,feret,40.8419836,-73.94368971,Columbia University,edu,0c7f27d23a162d4f3896325d147f412c40160b52,citation,http://pdfs.semanticscholar.org/0c7f/27d23a162d4f3896325d147f412c40160b52.pdf,Models and Algorithms for Vision through the Atmosphere,2003 +82,FERET,feret,40.47913175,-74.43168868,Rutgers University,edu,6069b4bc1a21341b77b49f01341c238c770d52e0,citation,http://pdfs.semanticscholar.org/b02b/50ed995fe526208b1577b9d7ef6262bf3ecf.pdf,Comparing Kernel-based Learning Methods for Face Recognition,2003 +83,FERET,feret,51.49887085,-0.17560797,Imperial College London,edu,af31ef1e81c1132f186d7aebb141d7f59a815010,citation,http://cas.ee.ic.ac.uk/people/ccb98/papers/LiuGlobalSIP13.pdf,Domain-specific progressive sampling of face images,2013 +84,FERET,feret,51.5073219,-0.1276474,"London, United Kingdom",edu,af31ef1e81c1132f186d7aebb141d7f59a815010,citation,http://cas.ee.ic.ac.uk/people/ccb98/papers/LiuGlobalSIP13.pdf,Domain-specific progressive sampling of face images,2013 +85,FERET,feret,39.1254938,-77.22293475,National Institute of Standards and Technology,edu,07f31bef7a7035792e3791473b3c58d03928abbf,citation,https://doi.org/10.1016/j.imavis.2016.08.004,Lessons from collecting a million biometric samples,2015 +86,FERET,feret,41.70456775,-86.23822026,University of Notre Dame,edu,07f31bef7a7035792e3791473b3c58d03928abbf,citation,https://doi.org/10.1016/j.imavis.2016.08.004,Lessons from collecting a million biometric samples,2015 +87,FERET,feret,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,d275714c323dd4e400e8003fa8c33070f8ea03d1,citation,https://pdfs.semanticscholar.org/d275/714c323dd4e400e8003fa8c33070f8ea03d1.pdf,"White Fear, Dehumanization and Low Empathy: a Lethal Combination for Shooting Biases by Yara Mekawi",2014 +88,FERET,feret,40.0044795,116.370238,Chinese Academy of Sciences,edu,1a5a79b4937b89420049bc279a7b7f765d143881,citation,http://pdfs.semanticscholar.org/1a5a/79b4937b89420049bc279a7b7f765d143881.pdf,Are Rich People Perceived as More Trustworthy? Perceived Socioeconomic Status Modulates Judgments of Trustworthiness and Trust Behavior Based on Facial Appearance,2018 +89,FERET,feret,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,1a5a79b4937b89420049bc279a7b7f765d143881,citation,http://pdfs.semanticscholar.org/1a5a/79b4937b89420049bc279a7b7f765d143881.pdf,Are Rich People Perceived as More Trustworthy? Perceived Socioeconomic Status Modulates Judgments of Trustworthiness and Trust Behavior Based on Facial Appearance,2018 +90,FERET,feret,37.548215,-77.45306424,Virginia Commonwealth University,edu,1a5a79b4937b89420049bc279a7b7f765d143881,citation,http://pdfs.semanticscholar.org/1a5a/79b4937b89420049bc279a7b7f765d143881.pdf,Are Rich People Perceived as More Trustworthy? Perceived Socioeconomic Status Modulates Judgments of Trustworthiness and Trust Behavior Based on Facial Appearance,2018 +91,FERET,feret,39.1254938,-77.22293475,National Institute of Standards and Technology,edu,88ee6d0b8342852a5bd55864dc7a1c8452c10bbf,citation,http://pdfs.semanticscholar.org/88ee/6d0b8342852a5bd55864dc7a1c8452c10bbf.pdf,Support Vector Machines Applied to Face Recognition,1998 +92,FERET,feret,32.0565957,118.77408833,Nanjing University,edu,59f83e94a7f52cbb728d434426f6fe85f756259c,citation,https://pdfs.semanticscholar.org/59f8/3e94a7f52cbb728d434426f6fe85f756259c.pdf,An Improved Illumination Normalization Approach based on Wavelet Tranform for Face Recognition from Single Training Image Per Person,2010 +93,FERET,feret,40.0044795,116.370238,Chinese Academy of Sciences,edu,5d1c4e93e32ee686234c5aae7f38025523993c8c,citation,http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/4989d539.pdf,Towards Pose Robust Face Recognition,2013 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marks",2009 +105,FERET,feret,-34.40505545,150.87834655,University of Wollongong,edu,a3bc6020cd57ebe3a82a0b232f969bcc4e372e53,citation,http://pdfs.semanticscholar.org/a3bc/6020cd57ebe3a82a0b232f969bcc4e372e53.pdf,A Hybrid Feature Extraction Technique for Face Recognition,2014 +106,FERET,feret,40.0044795,116.370238,Chinese Academy of Sciences,edu,13d591220f9fdb22d81c2438a008c80843b61fd4,citation,https://pdfs.semanticscholar.org/13d5/91220f9fdb22d81c2438a008c80843b61fd4.pdf,Boosting Multi-gabor Subspaces for Face Recognition,2006 +107,FERET,feret,22.42031295,114.20788644,Chinese University of Hong Kong,edu,13d591220f9fdb22d81c2438a008c80843b61fd4,citation,https://pdfs.semanticscholar.org/13d5/91220f9fdb22d81c2438a008c80843b61fd4.pdf,Boosting Multi-gabor Subspaces for Face Recognition,2006 +108,FERET,feret,-27.49741805,153.01316956,University of Queensland,edu,621e8882c41cdaf03a2c4a986a6404f0272ba511,citation,https://doi.org/10.1109/IJCNN.2012.6252611,On robust biometric identity verification via sparse encoding of faces: Holistic vs local approaches,2012 +109,FERET,feret,52.2380139,6.8566761,University of Twente,edu,8780f14d04671d4f2ed50307d16062d72cc51863,citation,http://pdfs.semanticscholar.org/8780/f14d04671d4f2ed50307d16062d72cc51863.pdf,Likelihood Ratio-Based Detection of Facial Features,2000 +110,FERET,feret,43.66333345,-79.39769975,University of Toronto,edu,7a52eb0886892c04c6c80b78795d880a70796cb6,citation,http://www.cs.toronto.edu/~jepson/papers/ChennubhotlaJepsonICPR2004.pdf,Perceptual distance normalization for appearance detection,2004 +111,FERET,feret,65.0592157,25.46632601,University of Oulu,edu,1fe121925668743762ce9f6e157081e087171f4c,citation,https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W02/papers/Ylioinas_Unsupervised_Learning_of_2015_CVPR_paper.pdf,Unsupervised learning of overcomplete face descriptors,2015 +112,FERET,feret,29.5084174,106.57858552,Chongqing University,edu,f3cb97791ded4a5c3bed717f820215a1c9648226,citation,http://pdfs.semanticscholar.org/f3cb/97791ded4a5c3bed717f820215a1c9648226.pdf,Multi-scale Block Weber Local Descriptor for Face Recognition,2015 +113,FERET,feret,38.83133325,-77.30798839,George Mason University,edu,d28d697b578867500632b35b1b19d3d76698f4a9,citation,http://pdfs.semanticscholar.org/d28d/697b578867500632b35b1b19d3d76698f4a9.pdf,Face Recognition Using Shape and Texture,1999 +114,FERET,feret,58.38131405,26.72078081,University of Tartu,edu,5a5ae31263517355d15b7b09d74cb03e40093046,citation,http://pdfs.semanticscholar.org/5a5a/e31263517355d15b7b09d74cb03e40093046.pdf,Super Resolution and Face Recognition Based People Activity Monitoring Enhancement Using Surveillance Camera,2016 +115,FERET,feret,32.0565957,118.77408833,Nanjing University,edu,82524c49ea20390c711e0606e50570ac2183c281,citation,http://pdfs.semanticscholar.org/8252/4c49ea20390c711e0606e50570ac2183c281.pdf,(2D)PCA: 2-Directional 2-Dimensional PCA for 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University,edu,cd0503a31a9f9040736ccfb24086dc934508cfc7,citation,http://pdfs.semanticscholar.org/cd05/03a31a9f9040736ccfb24086dc934508cfc7.pdf,Maximizing Resource Utilization In Video Streaming Systems,2015 +120,FERET,feret,47.5612651,7.5752961,University of Basel,edu,183c10b7d9ff26576e13a6639de0f7af206ed058,citation,http://gravis.cs.unibas.ch/publications/CVPR05_Blanz.pdf,Face recognition based on frontal views generated from non-frontal images,2005 +121,FERET,feret,1.3461952,103.6815499,"Nanyang Technological University, Singapore",edu,96d34c1a749e74af0050004162d9dc5132098a79,citation,https://doi.org/10.1109/TNN.2005.844909,High-speed face recognition based on discrete cosine transform and RBF neural networks,2005 +122,FERET,feret,41.10427915,29.02231159,Istanbul Technical University,edu,559645d2447004355c83737a19c9a811b45780f1,citation,https://doi.org/10.1109/ICB.2015.7139114,Combining view-based pose normalization and feature transform for cross-pose face recognition,2015 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University,edu,63a584487beb7382cad8ed70020f108ded5bf076,citation,https://pdfs.semanticscholar.org/2bb3/4f45b1f0ae2b602a6f25f1966cd0f84e3f5f.pdf,Face Detection and Modeling for Recognition,2002 +127,FERET,feret,22.5611537,88.41310194,Jadavpur University,edu,5e6c23d2e2f92a90bd35bdbc937b2d7d95ee2d55,citation,http://pdfs.semanticscholar.org/5e6c/23d2e2f92a90bd35bdbc937b2d7d95ee2d55.pdf,Fusion of Wavelet Coefficients from Visual and Thermal Face Images for Human Face Recognition - A Comparative Study,2006 +128,FERET,feret,40.0044795,116.370238,Chinese Academy of Sciences,edu,c03e01717b2d93f04cce9b5fd2dcfd1143bcc180,citation,http://pdfs.semanticscholar.org/c03e/01717b2d93f04cce9b5fd2dcfd1143bcc180.pdf,Locality-Constrained Active Appearance Model,2012 +129,FERET,feret,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,c03e01717b2d93f04cce9b5fd2dcfd1143bcc180,citation,http://pdfs.semanticscholar.org/c03e/01717b2d93f04cce9b5fd2dcfd1143bcc180.pdf,Locality-Constrained Active 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University,edu,52909a123ba3b088a5a93d930dcd029ec2f1f24f,citation,http://pdfs.semanticscholar.org/5d05/a0deec42a061541bbd399bc9e40d4ad3374a.pdf,A Gabor-Block-Based Kernel Discriminative Common Vector Approach Using Cosine Kernels for Human Face Recognition,2012 +135,FERET,feret,35.14479945,33.90492318,Eastern Mediterranean University,edu,b374391ab793a1bb2ecde4df51be9d97c2cbf79a,citation,https://pdfs.semanticscholar.org/b374/391ab793a1bb2ecde4df51be9d97c2cbf79a.pdf,Improved PCA based Face Recognition using Feature based Classifier Ensemble,2015 +136,FERET,feret,42.3573046,-71.0582415,"Affectiva, Inc.",company,d10cfcf206b0991e3bc20ac28df1f61c63516f30,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6553776,Smile or smirk? Automatic detection of spontaneous asymmetric smiles to understand viewer experience,2013 +137,FERET,feret,32.8536333,-117.2035286,Kyung Hee University,edu,bf0836e5c10add0b13005990ba019a9c4b744b06,citation,https://doi.org/10.1109/TCE.2009.5373791,An enhanced independent component-based human facial expression recognition from video,2009 +138,FERET,feret,42.718568,-84.47791571,Michigan State University,edu,051f03bc25ec633592aa2ff5db1d416b705eac6c,citation,http://www.cse.msu.edu/biometrics/Publications/Face/LiaoJain_PartialFR_AlignmentFreeApproach_ICJB11.pdf,Partial face recognition: An alignment free approach,2011 +139,FERET,feret,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,aba31184783150c723805831cde0f22fe257b835,citation,http://pdfs.semanticscholar.org/aba3/1184783150c723805831cde0f22fe257b835.pdf,Contribution of Non-scrambled Chroma Information in Privacy-Protected Face Images to Privacy Leakage,2011 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Rome,edu,757e4cb981e807d83539d9982ad325331cb59b16,citation,http://pdfs.semanticscholar.org/757e/4cb981e807d83539d9982ad325331cb59b16.pdf,Demographics versus Biometric Automatic Interoperability,2013 +144,FERET,feret,49.2622421,-123.2450052,University of Perugia,edu,67c08e2b8b918a61dcbd0d4c63a74b89b833d259,citation,http://pdfs.semanticscholar.org/67c0/8e2b8b918a61dcbd0d4c63a74b89b833d259.pdf,Multi-class texture analysis in colorectal cancer histology,2016 +145,FERET,feret,38.83133325,-77.30798839,George Mason University,edu,ac942c4870e55fe1d9822d62edcdb685d41cd2bf,citation,http://pdfs.semanticscholar.org/ac94/2c4870e55fe1d9822d62edcdb685d41cd2bf.pdf,Pose Discriminiation and Eye Detection Using Support Vector Machines (SVM),1998 +146,FERET,feret,44.97308605,-93.23708813,University of Minnesota,edu,ac942c4870e55fe1d9822d62edcdb685d41cd2bf,citation,http://pdfs.semanticscholar.org/ac94/2c4870e55fe1d9822d62edcdb685d41cd2bf.pdf,Pose Discriminiation and Eye Detection Using Support Vector Machines (SVM),1998 +147,FERET,feret,51.4584837,-2.6097752,University of Bristol,edu,a632ebe6f1e7d9b2b652b0186abef8db218037f3,citation,http://pdfs.semanticscholar.org/a632/ebe6f1e7d9b2b652b0186abef8db218037f3.pdf,Subliminally and Supraliminally Acquired Long-Term Memories Jointly Bias Delayed Decisions,2017 +148,FERET,feret,32.8536333,-117.2035286,Kyung Hee University,edu,027f769aed0cfcb3169ef60f182ce1decc0e99eb,citation,http://www.ijicic.org/10-12018-1.pdf,Local Directional Pattern (LDP) for face recognition,2010 +149,FERET,feret,32.0565957,118.77408833,Nanjing University,edu,edd6ed94207ab614c71ac0591d304a708d708e7b,citation,http://doi.org/10.1016/j.neucom.2012.02.001,Reconstructive discriminant analysis: A feature extraction method induced from linear regression classification,2012 +150,FERET,feret,42.718568,-84.47791571,Michigan State University,edu,5dbf772b98cb944befa9cf01ec5d15da713a338b,citation,http://pdfs.semanticscholar.org/9d82/44d5a32ecc314860c1d673d687df28f77d84.pdf,Face 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Telecommunications,edu,9729930ab0f9cbcd07f1105bc69c540330cda50a,citation,https://doi.org/10.1109/ACCESS.2017.2749331,Compressing Fisher Vector for Robust Face Recognition,2017 +158,FERET,feret,31.32235655,121.38400941,Shanghai University,edu,459eb3cfd9b52a0d416571e4bc4e75f979f4b901,citation,https://doi.org/10.1109/ROBIO.2015.7418998,Vision development of humanoid head robot SHFR-III,2015 +159,FERET,feret,34.8452999,48.5596212,Islamic Azad University,edu,14b2dff604f148c4e5b54aa25fbecbf7f9071205,citation,http://www.iranprc.org/pdf/paper/2011-06.pdf,A new preselection method for face recognition in JPEG domain based on face segmentation,2011 +160,FERET,feret,47.5612651,7.5752961,University of Basel,edu,ff47698be7313005d0ea0fe0cc72c13f2f4b092a,citation,http://pdfs.semanticscholar.org/ff47/698be7313005d0ea0fe0cc72c13f2f4b092a.pdf,Caring or daring? Exploring the impact of facial masculinity/femininity and gender category information on first impressions,2017 +161,FERET,feret,35.84658875,127.1350133,Chonbuk National University,edu,0c6a18b0cee01038eb1f9373c369835b236373ae,citation,https://doi.org/10.1007/s11042-017-4359-9,Learning warps based similarity for pose-unconstrained face recognition,2017 +162,FERET,feret,40.72925325,-73.99625394,New York University,edu,54e6343f4368d9e5468c3e83b6eeb3a58a3c7555,citation,http://pdfs.semanticscholar.org/54e6/343f4368d9e5468c3e83b6eeb3a58a3c7555.pdf,Reconstructing Perceived and Retrieved Faces from Activity Patterns in Lateral Parietal Cortex.,2016 +163,FERET,feret,38.8833413,-77.1045977,DARPA,mil,00d6e5a1b347463f6aeb08a10cd912273c9d1347,citation,http://pdfs.semanticscholar.org/00d6/e5a1b347463f6aeb08a10cd912273c9d1347.pdf,Face Recognition Vendor Test 2002 : Evaluation Report,2003 +164,FERET,feret,39.1254938,-77.22293475,National Institute of Standards and Technology,edu,00d6e5a1b347463f6aeb08a10cd912273c9d1347,citation,http://pdfs.semanticscholar.org/00d6/e5a1b347463f6aeb08a10cd912273c9d1347.pdf,Face Recognition Vendor Test 2002 : Evaluation Report,2003 +165,FERET,feret,41.5007811,2.11143663,Universitat Autònoma de Barcelona,edu,dc964b9c7242a985eb255b2410a9c45981c2f4d0,citation,http://doi.org/10.1007/s10851-018-0837-6,Feature Extraction by Using Dual-Generalized Discriminative Common Vectors,2018 +166,FERET,feret,40.00229045,116.32098908,Tsinghua University,edu,8023864256a1a4a26e130a7165f3d70875c27467,citation,http://pdfs.semanticscholar.org/8023/864256a1a4a26e130a7165f3d70875c27467.pdf,LUT-Based Adaboost for Gender Classification,2003 +167,FERET,feret,47.05821,15.46019568,Graz University of Technology,edu,2a77e3221d0512aa5674cf6f9041c1ce81fc07f0,citation,http://pdfs.semanticscholar.org/65de/08bab21921fba39e97f0bc3585f62cb2bd5d.pdf,An Automatic Hybrid Segmentation Approach for Aligned Face Portrait Images,2009 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Basel,edu,916498961a51f56a592c3551b0acc25978571fa7,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2011.6126275,Optimal landmark detection using shape models and branch and bound,2011 +226,FERET,feret,35.9023226,14.4834189,University of Malta,edu,4efd58102ff46b7435c9ec6d4fc3dd21d93b15b4,citation,https://doi.org/10.1109/TIFS.2017.2788002,"Matching Software-Generated Sketches to Face Photographs With a Very Deep CNN, Morphed Faces, and Transfer Learning",2018 +227,FERET,feret,24.7246403,46.62335012,King Saud University,edu,e26a7e343fe109e2b52d1eeea5b02dae836f3502,citation,https://doi.org/10.1109/ACCESS.2017.2676238,Facial Expression Recognition Utilizing Local Direction-Based Robust Features and Deep Belief Network,2017 +228,FERET,feret,59.93891665,10.72170765,University of Oslo,edu,e26a7e343fe109e2b52d1eeea5b02dae836f3502,citation,https://doi.org/10.1109/ACCESS.2017.2676238,Facial Expression Recognition Utilizing Local Direction-Based Robust Features and Deep Belief 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Oxford,edu,313d5eba97fe064bdc1f00b7587a4b3543ef712a,citation,https://pdfs.semanticscholar.org/cb7f/93467b0ec1afd43d995e511f5d7bf052a5af.pdf,Compact Deep Aggregation for Set Retrieval,2018 +4,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,5865b6d83ba6dbbf9167f1481e9339c2ef1d1f6b,citation,https://doi.org/10.1109/ICPR.2016.7900278,Regularized metric adaptation for unconstrained face verification,2016 +5,IJB-A,ijb_c,37.4102193,-122.05965487,Carnegie Mellon University,edu,48a9241edda07252c1aadca09875fabcfee32871,citation,https://arxiv.org/pdf/1611.08657v5.pdf,Convolutional Experts Constrained Local Model for Facial Landmark Detection,2017 +6,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,86204fc037936754813b91898377e8831396551a,citation,https://arxiv.org/pdf/1709.01442.pdf,Dense Face Alignment,2017 +7,IJB-A,ijb_c,22.57423855,88.4337303,"Institute of Engineering and Management, Kolkata, India",edu,b2cb335ded99b10f37002d09753bd5a6ea522ef1,citation,https://doi.org/10.1109/ISBA.2017.7947679,Analysis of adaptability of deep features for verifying blurred and cross-resolution images,2017 +8,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,b2cb335ded99b10f37002d09753bd5a6ea522ef1,citation,https://doi.org/10.1109/ISBA.2017.7947679,Analysis of adaptability of deep features for verifying blurred and cross-resolution images,2017 +9,IJB-A,ijb_c,45.7835966,4.7678948,École Centrale de Lyon,edu,486840f4f524e97f692a7f6b42cd19019ee71533,citation,https://arxiv.org/pdf/1703.08388v2.pdf,DeepVisage: Making Face Recognition Simple Yet With Powerful Generalization Skills,2017 +10,IJB-A,ijb_c,48.832493,2.267474,Safran Identity and Security,company,486840f4f524e97f692a7f6b42cd19019ee71533,citation,https://arxiv.org/pdf/1703.08388v2.pdf,DeepVisage: Making Face Recognition Simple Yet With Powerful Generalization Skills,2017 +11,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,2d748f8ee023a5b1fbd50294d176981ded4ad4ee,citation,http://pdfs.semanticscholar.org/2d74/8f8ee023a5b1fbd50294d176981ded4ad4ee.pdf,Triplet Similarity Embedding for Face Verification,2016 +12,IJB-A,ijb_c,38.99203005,-76.9461029,University of Maryland College Park,edu,f7824758800a7b1a386db5bd35f84c81454d017a,citation,https://arxiv.org/pdf/1702.05085.pdf,KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors,2017 +13,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,02467703b6e087799e04e321bea3a4c354c5487d,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.27,Grouper: Optimizing Crowdsourced Face Annotations,2016 +14,IJB-A,ijb_c,39.329053,-76.619425,Johns Hopkins University,edu,377f2b65e6a9300448bdccf678cde59449ecd337,citation,https://arxiv.org/pdf/1804.10275.pdf,Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results,2018 +15,IJB-A,ijb_c,40.47913175,-74.43168868,Rutgers University,edu,377f2b65e6a9300448bdccf678cde59449ecd337,citation,https://arxiv.org/pdf/1804.10275.pdf,Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results,2018 +16,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,cd55fb30737625e86454a2861302b96833ed549d,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139094,Annotating Unconstrained Face Imagery: A scalable approach,2015 +17,IJB-A,ijb_c,38.95187,-77.363259,"Noblis, Falls Church, VA, U.S.A.",company,cd55fb30737625e86454a2861302b96833ed549d,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139094,Annotating Unconstrained Face Imagery: A scalable approach,2015 +18,IJB-A,ijb_c,46.0501558,14.46907327,University of Ljubljana,edu,5226296884b3e151ce317a37f94827dbda0b9d16,citation,https://doi.org/10.1109/IWBF.2016.7449690,Deep pair-wise similarity learning for face recognition,2016 +19,IJB-A,ijb_c,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,80be8624771104ff4838dcba9629bacfe6b3ea09,citation,http://www.ifp.illinois.edu/~moulin/Papers/ECCV14-jiwen.pdf,Simultaneous Feature and Dictionary Learning for Image Set Based Face Recognition,2014 +20,IJB-A,ijb_c,1.3484104,103.68297965,Nanyang Technological University,edu,80be8624771104ff4838dcba9629bacfe6b3ea09,citation,http://www.ifp.illinois.edu/~moulin/Papers/ECCV14-jiwen.pdf,Simultaneous Feature and Dictionary Learning for Image Set Based Face Recognition,2014 +21,IJB-A,ijb_c,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,80be8624771104ff4838dcba9629bacfe6b3ea09,citation,http://www.ifp.illinois.edu/~moulin/Papers/ECCV14-jiwen.pdf,Simultaneous Feature and Dictionary Learning for Image Set Based Face Recognition,2014 +22,IJB-A,ijb_c,22.304572,114.17976285,Hong Kong Polytechnic University,edu,50b58becaf67e92a6d9633e0eea7d352157377c3,citation,https://pdfs.semanticscholar.org/50b5/8becaf67e92a6d9633e0eea7d352157377c3.pdf,Dependency-Aware Attention Control for Unconstrained Face Recognition with Image Sets,2018 +23,IJB-A,ijb_c,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,cd6aaa37fffd0b5c2320f386be322b8adaa1cc68,citation,https://arxiv.org/pdf/1804.06655.pdf,Deep Face Recognition: A Survey,2018 +24,IJB-A,ijb_c,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,ac2881bdf7b57dc1672a17b221d68a438d79fce8,citation,https://arxiv.org/pdf/1806.08472.pdf,Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization,2018 +25,IJB-A,ijb_c,40.0044795,116.370238,Chinese Academy of Sciences,edu,72a7eb68f0955564e1ceafa75aeeb6b5bbb14e7e,citation,https://pdfs.semanticscholar.org/72a7/eb68f0955564e1ceafa75aeeb6b5bbb14e7e.pdf,Face Recognition with Contrastive Convolution,2018 +26,IJB-A,ijb_c,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,72a7eb68f0955564e1ceafa75aeeb6b5bbb14e7e,citation,https://pdfs.semanticscholar.org/72a7/eb68f0955564e1ceafa75aeeb6b5bbb14e7e.pdf,Face Recognition with Contrastive Convolution,2018 +27,IJB-A,ijb_c,42.3889785,-72.5286987,University of Massachusetts,edu,368e99f669ea5fd395b3193cd75b301a76150f9d,citation,https://arxiv.org/pdf/1506.01342.pdf,One-to-many face recognition with bilinear CNNs,2016 +28,IJB-A,ijb_c,32.77824165,34.99565673,Open University of Israel,edu,1e6ed6ca8209340573a5e907a6e2e546a3bf2d28,citation,http://arxiv.org/pdf/1607.01450v1.pdf,Pooling Faces: Template Based Face Recognition with Pooled Face Images,2016 +29,IJB-A,ijb_c,38.88140235,121.52281098,Dalian University of Technology,edu,052f994898c79529955917f3dfc5181586282cf8,citation,https://arxiv.org/pdf/1708.02191.pdf,Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos,2017 +30,IJB-A,ijb_c,32.9820799,-96.7566278,University of Texas at Dallas,edu,4e8168fbaa615009d1618a9d6552bfad809309e9,citation,http://pdfs.semanticscholar.org/4e81/68fbaa615009d1618a9d6552bfad809309e9.pdf,Deep Convolutional Neural Network Features and the Original Image,2016 +31,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,4e8168fbaa615009d1618a9d6552bfad809309e9,citation,http://pdfs.semanticscholar.org/4e81/68fbaa615009d1618a9d6552bfad809309e9.pdf,Deep Convolutional Neural Network Features and the Original Image,2016 +32,IJB-A,ijb_c,29.7207902,-95.34406271,University of Houston,edu,3cb2841302af1fb9656f144abc79d4f3d0b27380,citation,https://pdfs.semanticscholar.org/3cb2/841302af1fb9656f144abc79d4f3d0b27380.pdf,When 3 D-Aided 2 D Face Recognition Meets Deep Learning : An extended UR 2 D for Pose-Invariant Face Recognition,2017 +33,IJB-A,ijb_c,24.4469025,54.3942563,Khalifa University,edu,0c1d85a197a1f5b7376652a485523e616a406273,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.169,Joint Registration and Representation Learning for Unconstrained Face Identification,2017 +34,IJB-A,ijb_c,-35.23656905,149.08446994,University of Canberra,edu,0c1d85a197a1f5b7376652a485523e616a406273,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.169,Joint Registration and Representation Learning for Unconstrained Face Identification,2017 +35,IJB-A,ijb_c,32.77824165,34.99565673,Open University of Israel,edu,c75e6ce54caf17b2780b4b53f8d29086b391e839,citation,https://arxiv.org/pdf/1802.00542.pdf,"ExpNet: Landmark-Free, Deep, 3D Facial Expressions",2018 +36,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,450c6a57f19f5aa45626bb08d7d5d6acdb863b4b,citation,https://arxiv.org/pdf/1805.00611.pdf,Towards Interpretable Face Recognition,2018 +37,IJB-A,ijb_c,51.7534538,-1.25400997,University of Oxford,edu,30180f66d5b4b7c0367e4b43e2b55367b72d6d2a,citation,http://www.robots.ox.ac.uk/~vgg/publications/2017/Crosswhite17/crosswhite17.pdf,Template Adaptation for Face Verification and Identification,2017 +38,IJB-A,ijb_c,29.7207902,-95.34406271,University of Houston,edu,8334da483f1986aea87b62028672836cb3dc6205,citation,https://arxiv.org/pdf/1805.06306.pdf,Fully Associative Patch-Based 1-to-N Matcher for Face Recognition,2018 +39,IJB-A,ijb_c,-33.8809651,151.20107299,University of Technology Sydney,edu,3b64efa817fd609d525c7244a0e00f98feacc8b4,citation,http://doi.acm.org/10.1145/2845089,A Comprehensive Survey on Pose-Invariant Face Recognition,2016 +40,IJB-A,ijb_c,40.9153196,-73.1270626,Stony Brook University,edu,6fbb179a4ad39790f4558dd32316b9f2818cd106,citation,http://pdfs.semanticscholar.org/6fbb/179a4ad39790f4558dd32316b9f2818cd106.pdf,Input Aggregated Network for Face Video Representation,2016 +41,IJB-A,ijb_c,38.8920756,-104.79716389,"University of Colorado, Colorado Springs",edu,d4f1eb008eb80595bcfdac368e23ae9754e1e745,citation,https://arxiv.org/pdf/1708.02337.pdf,Unconstrained Face Detection and Open-Set Face Recognition Challenge,2017 +42,IJB-A,ijb_c,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018 +43,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018 +44,IJB-A,ijb_c,34.0224149,-118.28634407,University of Southern California,edu,d28d32af7ef9889ef9cb877345a90ea85e70f7f1,citation,http://doi.ieeecomputersociety.org/10.1109/FG.2017.84,Local-Global Landmark Confidences for Face Recognition,2017 +45,IJB-A,ijb_c,37.4102193,-122.05965487,Carnegie Mellon University,edu,d28d32af7ef9889ef9cb877345a90ea85e70f7f1,citation,http://doi.ieeecomputersociety.org/10.1109/FG.2017.84,Local-Global Landmark Confidences for Face Recognition,2017 +46,IJB-A,ijb_c,51.5247272,-0.03931035,Queen Mary University of London,edu,a29566375836f37173ccaffa47dea25eb1240187,citation,https://arxiv.org/pdf/1809.09409.pdf,Vehicle Re-Identification in Context,2018 +47,IJB-A,ijb_c,34.0224149,-118.28634407,University of Southern California,edu,29f298dd5f806c99951cb434834bc8dcc765df18,citation,https://doi.org/10.1109/ICPR.2016.7899837,Computationally efficient template-based face recognition,2016 +48,IJB-A,ijb_c,51.49887085,-0.17560797,Imperial College London,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +49,IJB-A,ijb_c,51.59029705,-0.22963221,Middlesex University,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +50,IJB-A,ijb_c,50.8142701,8.771435,Philipps-Universität Marburg,edu,5981c309bd0ffd849c51b1d8a2ccc481a8ec2f5c,citation,https://doi.org/10.1109/ICT.2017.7998256,SmartFace: Efficient face detection on smartphones for wireless on-demand emergency networks,2017 +51,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,a2b4a6c6b32900a066d0257ae6d4526db872afe2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8272466,Learning Face Image Quality From Human Assessments,2018 +52,IJB-A,ijb_c,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,3dfb822e16328e0f98a47209d7ecd242e4211f82,citation,https://arxiv.org/pdf/1708.08197.pdf,Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments,2017 +53,IJB-A,ijb_c,47.6423318,-122.1369302,Microsoft,company,291265db88023e92bb8c8e6390438e5da148e8f5,citation,http://pdfs.semanticscholar.org/4603/cb8e05258bb0572ae912ad20903b8f99f4b1.pdf,MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition,2016 +54,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,d29eec5e047560627c16803029d2eb8a4e61da75,citation,http://pdfs.semanticscholar.org/d29e/ec5e047560627c16803029d2eb8a4e61da75.pdf,Feature Transfer Learning for Deep Face Recognition with Long-Tail Data,2018 +55,IJB-A,ijb_c,36.20304395,117.05842113,Tianjin University,edu,5180df9d5eb26283fb737f491623395304d57497,citation,https://arxiv.org/pdf/1804.10899.pdf,Scalable Angular Discriminative Deep Metric Learning for Face Recognition,2018 +56,IJB-A,ijb_c,22.42031295,114.20788644,Chinese University of Hong Kong,edu,abdd17e411a7bfe043f280abd4e560a04ab6e992,citation,https://arxiv.org/pdf/1803.00839.pdf,Pose-Robust Face Recognition via Deep Residual Equivariant Mapping,2018 +57,IJB-A,ijb_c,28.5456282,77.2731505,"IIIT Delhi, India",edu,3cf1f89d73ca4b25399c237ed3e664a55cd273a2,citation,https://arxiv.org/pdf/1710.02914.pdf,Face Sketch Matching via Coupled Deep Transform Learning,2017 +58,IJB-A,ijb_c,-27.49741805,153.01316956,University of Queensland,edu,f27fd2a1bc229c773238f1912db94991b8bf389a,citation,https://doi.org/10.1109/IVCNZ.2016.7804414,How do you develop a face detector for the unconstrained environment?,2016 +59,IJB-A,ijb_c,39.86742125,32.73519072,Hacettepe University,edu,9865fe20df8fe11717d92b5ea63469f59cf1635a,citation,https://arxiv.org/pdf/1805.07566.pdf,Wildest Faces: Face Detection and Recognition in Violent Settings,2018 +60,IJB-A,ijb_c,39.87549675,32.78553506,Middle East Technical University,edu,9865fe20df8fe11717d92b5ea63469f59cf1635a,citation,https://arxiv.org/pdf/1805.07566.pdf,Wildest Faces: Face Detection and Recognition in Violent Settings,2018 +61,IJB-A,ijb_c,28.2290209,112.99483204,"National University of Defense Technology, China",edu,c1cc2a2a1ab66f6c9c6fabe28be45d1440a57c3d,citation,https://pdfs.semanticscholar.org/aae7/a5182e59f44b7bb49f61999181ce011f800b.pdf,Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis,2017 +62,IJB-A,ijb_c,1.2962018,103.77689944,National University of Singapore,edu,c1cc2a2a1ab66f6c9c6fabe28be45d1440a57c3d,citation,https://pdfs.semanticscholar.org/aae7/a5182e59f44b7bb49f61999181ce011f800b.pdf,Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis,2017 +63,IJB-A,ijb_c,17.4454957,78.34854698,International Institute of Information Technology,edu,f5eb411217f729ad7ae84bfd4aeb3dedb850206a,citation,https://pdfs.semanticscholar.org/f5eb/411217f729ad7ae84bfd4aeb3dedb850206a.pdf,Tackling Low Resolution for Better Scene Understanding,2018 +64,IJB-A,ijb_c,40.51865195,-74.44099801,State University of New Jersey,edu,96e731e82b817c95d4ce48b9e6b08d2394937cf8,citation,http://arxiv.org/pdf/1508.01722v2.pdf,Unconstrained face verification using deep CNN features,2016 +65,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,96e731e82b817c95d4ce48b9e6b08d2394937cf8,citation,http://arxiv.org/pdf/1508.01722v2.pdf,Unconstrained face verification using deep CNN features,2016 +66,IJB-A,ijb_c,32.77824165,34.99565673,Open University of Israel,edu,870433ba89d8cab1656e57ac78f1c26f4998edfb,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.163,Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network,2017 +67,IJB-A,ijb_c,55.6801502,12.572327,University of Copenhagen,edu,3dfd94d3fad7e17f52a8ae815eb9cc5471172bc0,citation,http://pdfs.semanticscholar.org/3dfd/94d3fad7e17f52a8ae815eb9cc5471172bc0.pdf,Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions,2018 +68,IJB-A,ijb_c,35.9023226,14.4834189,University of Malta,edu,3dfd94d3fad7e17f52a8ae815eb9cc5471172bc0,citation,http://pdfs.semanticscholar.org/3dfd/94d3fad7e17f52a8ae815eb9cc5471172bc0.pdf,Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions,2018 +69,IJB-A,ijb_c,34.0224149,-118.28634407,University of Southern California,edu,6341274aca0c2977c3e1575378f4f2126aa9b050,citation,http://arxiv.org/pdf/1609.03536v1.pdf,A multi-scale cascade fully convolutional network face detector,2016 +70,IJB-A,ijb_c,41.70456775,-86.23822026,University of Notre Dame,edu,17479e015a2dcf15d40190e06419a135b66da4e0,citation,https://arxiv.org/pdf/1610.08119.pdf,Predicting First Impressions With Deep Learning,2017 +71,IJB-A,ijb_c,37.4102193,-122.05965487,Carnegie Mellon University,edu,a0b1990dd2b4cd87e4fd60912cc1552c34792770,citation,https://pdfs.semanticscholar.org/a0b1/990dd2b4cd87e4fd60912cc1552c34792770.pdf,Deep Constrained Local Models for Facial Landmark Detection,2016 +72,IJB-A,ijb_c,30.642769,104.06751175,"Sichuan University, Chengdu",edu,772474b5b0c90629f4d9c223fd9c1ef45e1b1e66,citation,https://doi.org/10.1109/BTAS.2017.8272716,Multi-dim: A multi-dimensional face database towards the application of 3D technology in real-world scenarios,2017 +73,IJB-A,ijb_c,38.8920756,-104.79716389,"University of Colorado, Colorado Springs",edu,4b3f425274b0c2297d136f8833a31866db2f2aec,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2017.85,Toward Open-Set Face Recognition,2017 +74,IJB-A,ijb_c,56.46255985,84.95565495,Tomsk Polytechnic University,edu,17ded725602b4329b1c494bfa41527482bf83a6f,citation,http://pdfs.semanticscholar.org/cb10/434a5d68ffbe9ed0498771192564ecae8894.pdf,Compact Convolutional Neural Network Cascade for Face Detection,2015 +75,IJB-A,ijb_c,37.3351908,-121.88126008,San Jose State University,edu,14b016c7a87d142f4b9a0e6dc470dcfc073af517,citation,http://ws680.nist.gov/publication/get_pdf.cfm?pub_id=918912,Modest proposals for improving biometric recognition papers,2015 +76,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,93420d9212dd15b3ef37f566e4d57e76bb2fab2f,citation,https://arxiv.org/pdf/1611.00851.pdf,An All-In-One Convolutional Neural Network for Face Analysis,2017 +77,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,def2983576001bac7d6461d78451159800938112,citation,https://arxiv.org/pdf/1705.07426.pdf,The Do’s and Don’ts for CNN-Based Face Verification,2017 +78,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,4b605e6a9362485bfe69950432fa1f896e7d19bf,citation,http://biometrics.cse.msu.edu/Publications/Face/BlantonAllenMillerKalkaJain_CVPRWB2016_HID.pdf,A Comparison of Human and Automated Face Verification Accuracy on Unconstrained Image Sets,2016 +79,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,8d3e95c31c93548b8c71dbeee2e9f7180067a888,citation,https://doi.org/10.1109/ICPR.2016.7899841,Template regularized sparse coding for face verification,2016 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Morphable Model,2018 +88,IJB-A,ijb_c,37.3936717,-122.0807262,Facebook,company,628a3f027b7646f398c68a680add48c7969ab1d9,citation,https://pdfs.semanticscholar.org/628a/3f027b7646f398c68a680add48c7969ab1d9.pdf,Plan for Final Year Project : HKU-Face : A Large Scale Dataset for Deep Face Recognition,2017 +89,IJB-A,ijb_c,40.2773077,-7.5095801,University of Beira Interior,edu,61262450d4d814865a4f9a84299c24daa493f66e,citation,http://doi.org/10.1007/s10462-016-9474-x,Biometric recognition in surveillance scenarios: a survey,2016 +90,IJB-A,ijb_c,-31.95040445,115.79790037,University of Western Australia,edu,626913b8fcbbaee8932997d6c4a78fe1ce646127,citation,https://arxiv.org/pdf/1711.05942.pdf,Learning from Millions of 3D Scans for Large-scale 3D Face Recognition,2017 +91,IJB-A,ijb_c,35.9023226,14.4834189,University of Malta,edu,4efd58102ff46b7435c9ec6d4fc3dd21d93b15b4,citation,https://doi.org/10.1109/TIFS.2017.2788002,"Matching Software-Generated Sketches to Face Photographs With a Very Deep CNN, Morphed Faces, and Transfer Learning",2018 +92,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,b6f758be954d34817d4ebaa22b30c63a4b8ddb35,citation,http://arxiv.org/abs/1703.04835,A Proximity-Aware Hierarchical Clustering of Faces,2017 +93,IJB-A,ijb_c,32.77824165,34.99565673,Open University of Israel,edu,0a34fe39e9938ae8c813a81ae6d2d3a325600e5c,citation,https://arxiv.org/pdf/1708.07517.pdf,FacePoseNet: Making a Case for Landmark-Free Face Alignment,2017 +94,IJB-A,ijb_c,40.2773077,-7.5095801,University of Beira Interior,edu,84ae55603bffda40c225fe93029d39f04793e01f,citation,https://doi.org/10.1109/ICB.2016.7550066,ICB-RW 2016: International challenge on biometric recognition in the wild,2016 +95,IJB-A,ijb_c,41.70456775,-86.23822026,University of Notre Dame,edu,73ea06787925157df519a15ee01cc3dc1982a7e0,citation,https://arxiv.org/pdf/1811.01474.pdf,Fast Face Image Synthesis with Minimal Training,2018 +96,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,c6382de52636705be5898017f2f8ed7c70d7ae96,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139089,Unconstrained face detection: State of the art baseline and challenges,2015 +97,IJB-A,ijb_c,38.95187,-77.363259,"Noblis, Falls Church, VA, U.S.A.",company,c6382de52636705be5898017f2f8ed7c70d7ae96,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139089,Unconstrained face detection: State of the art baseline and challenges,2015 +98,IJB-A,ijb_c,40.47913175,-74.43168868,Rutgers University,edu,eee06d68497be8bf3a8aba4fde42a13aa090b301,citation,https://arxiv.org/pdf/1806.11191.pdf,CR-GAN: Learning Complete Representations for Multi-view Generation,2018 +99,IJB-A,ijb_c,35.3103441,-80.73261617,University of North Carolina at Charlotte,edu,eee06d68497be8bf3a8aba4fde42a13aa090b301,citation,https://arxiv.org/pdf/1806.11191.pdf,CR-GAN: Learning Complete Representations for Multi-view Generation,2018 +100,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,a3201e955d6607d383332f3a12a7befa08c5a18c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7900276,VLAD encoded Deep Convolutional features for unconstrained face verification,2016 +101,IJB-A,ijb_c,40.47913175,-74.43168868,Rutgers University,edu,a3201e955d6607d383332f3a12a7befa08c5a18c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7900276,VLAD encoded Deep Convolutional features for unconstrained face verification,2016 +102,IJB-A,ijb_c,22.42031295,114.20788644,Chinese University of Hong Kong,edu,52d7eb0fbc3522434c13cc247549f74bb9609c5d,citation,https://arxiv.org/pdf/1511.06523.pdf,WIDER FACE: A Face Detection Benchmark,2016 +103,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,19458454308a9f56b7de76bf7d8ff8eaa52b0173,citation,https://pdfs.semanticscholar.org/1945/8454308a9f56b7de76bf7d8ff8eaa52b0173.pdf,Deep Features for Recognizing Disguised Faces in the Wild,0 +104,IJB-A,ijb_c,43.7776426,11.259765,University of Florence,edu,746c0205fdf191a737df7af000eaec9409ede73f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8423119,Investigating Nuisances in DCNN-Based Face Recognition,2018 +105,IJB-A,ijb_c,47.5612651,7.5752961,University of Basel,edu,0081e2188c8f34fcea3e23c49fb3e17883b33551,citation,http://pdfs.semanticscholar.org/0081/e2188c8f34fcea3e23c49fb3e17883b33551.pdf,Training Deep Face Recognition Systems with Synthetic Data,2018 +106,IJB-A,ijb_c,37.4102193,-122.05965487,Carnegie Mellon University,edu,2b869d5551b10f13bf6fcdb8d13f0aa4d1f59fc4,citation,https://arxiv.org/pdf/1803.00130.pdf,Ring loss: Convex Feature Normalization for Face Recognition,2018 +107,IJB-A,ijb_c,28.2290209,112.99483204,"National University of Defense Technology, China",edu,5f771fed91c8e4b666489ba2384d0705bcf75030,citation,https://arxiv.org/pdf/1804.03287.pdf,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018 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Technology,edu,58d76380d194248b3bb291b8c7c5137a0a376897,citation,https://pdfs.semanticscholar.org/58d7/6380d194248b3bb291b8c7c5137a0a376897.pdf,FaceID-GAN : Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis,2018 +112,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,7fb5006b6522436ece5bedf509e79bdb7b79c9a7,citation,https://pdfs.semanticscholar.org/7fb5/006b6522436ece5bedf509e79bdb7b79c9a7.pdf,Multi-Task Convolutional Neural Network for Face Recognition,2017 +113,IJB-A,ijb_c,-27.49741805,153.01316956,University of Queensland,edu,28646c6220848db46c6944967298d89a6559c700,citation,https://pdfs.semanticscholar.org/2864/6c6220848db46c6944967298d89a6559c700.pdf,It takes two to tango : Cascading off-the-shelf face detectors,2018 +114,IJB-A,ijb_c,51.7534538,-1.25400997,University of Oxford,edu,5812d8239d691e99d4108396f8c26ec0619767a6,citation,https://arxiv.org/pdf/1810.09951.pdf,GhostVLAD for set-based face recognition,2018 +115,IJB-A,ijb_c,25.01353105,121.54173736,National Taiwan University of Science and Technology,edu,e4c3587392d477b7594086c6f28a00a826abf004,citation,https://doi.org/10.1109/ICIP.2017.8296998,Face recognition by facial attribute assisted network,2017 +116,IJB-A,ijb_c,1.3484104,103.68297965,Nanyang Technological University,edu,47190d213caef85e8b9dd0d271dbadc29ed0a953,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018 +117,IJB-A,ijb_c,32.87935255,-117.23110049,"University of California, San Diego",edu,47190d213caef85e8b9dd0d271dbadc29ed0a953,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018 +118,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,ce6d60b69eb95477596535227958109e07c61e1e,citation,http://www.rci.rutgers.edu/~vmp93/Conference_pub/BTAS_2015_FVFF_JunCheng_Chen.pdf,Unconstrained face verification using fisher vectors computed from frontalized faces,2015 +119,IJB-A,ijb_c,29.7207902,-95.34406271,University of Houston,edu,38d8ff137ff753f04689e6b76119a44588e143f3,citation,http://pdfs.semanticscholar.org/38d8/ff137ff753f04689e6b76119a44588e143f3.pdf,When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition,2017 +120,IJB-A,ijb_c,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,9627f28ea5f4c389350572b15968386d7ce3fe49,citation,https://arxiv.org/pdf/1802.07447.pdf,Load Balanced GANs for Multi-view Face Image Synthesis,2018 +121,IJB-A,ijb_c,34.0224149,-118.28634407,University of Southern California,edu,4e7ed13e541b8ed868480375785005d33530e06d,citation,http://doi.ieeecomputersociety.org/10.1109/WACV.2016.7477555,Face recognition using deep multi-pose representations,2016 +122,IJB-A,ijb_c,32.77824165,34.99565673,Open University of Israel,edu,582edc19f2b1ab2ac6883426f147196c8306685a,citation,http://pdfs.semanticscholar.org/be6c/db7b181e73f546d43cf2ab6bc7181d7d619b.pdf,Do We Really Need to Collect Millions of Faces for Effective Face Recognition?,2016 +123,IJB-A,ijb_c,37.4102193,-122.05965487,Carnegie Mellon University,edu,87e6cb090aecfc6f03a3b00650a5c5f475dfebe1,citation,https://pdfs.semanticscholar.org/87e6/cb090aecfc6f03a3b00650a5c5f475dfebe1.pdf,Holistically Constrained Local Model: Going Beyond Frontal Poses for Facial Landmark Detection,2016 +124,IJB-A,ijb_c,34.0224149,-118.28634407,University of Southern California,edu,87e6cb090aecfc6f03a3b00650a5c5f475dfebe1,citation,https://pdfs.semanticscholar.org/87e6/cb090aecfc6f03a3b00650a5c5f475dfebe1.pdf,Holistically Constrained Local Model: Going Beyond Frontal Poses for Facial Landmark Detection,2016 +125,IJB-A,ijb_c,39.65404635,-79.96475355,West Virginia University,edu,3b9b200e76a35178da940279d566bbb7dfebb787,citation,http://pdfs.semanticscholar.org/3b9b/200e76a35178da940279d566bbb7dfebb787.pdf,Learning Channel Inter-dependencies at Multiple Scales on Dense Networks for Face Recognition,2017 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Israel,edu,62e913431bcef5983955e9ca160b91bb19d9de42,citation,http://pdfs.semanticscholar.org/62e9/13431bcef5983955e9ca160b91bb19d9de42.pdf,Facial Landmark Detection with Tweaked Convolutional Neural Networks,2015 +130,IJB-A,ijb_c,29.5084174,106.57858552,Chongqing University,edu,acd4280453b995cb071c33f7c9db5760432f4279,citation,https://doi.org/10.1007/s00138-018-0907-1,Deep transformation learning for face recognition in the unconstrained scene,2018 +131,IJB-A,ijb_c,38.99203005,-76.9461029,University of Maryland College Park,edu,ceeb67bf53ffab1395c36f1141b516f893bada27,citation,http://pdfs.semanticscholar.org/ceeb/67bf53ffab1395c36f1141b516f893bada27.pdf,Face Alignment by Local Deep Descriptor Regression,2016 +132,IJB-A,ijb_c,40.47913175,-74.43168868,Rutgers University,edu,ceeb67bf53ffab1395c36f1141b516f893bada27,citation,http://pdfs.semanticscholar.org/ceeb/67bf53ffab1395c36f1141b516f893bada27.pdf,Face Alignment by Local Deep Descriptor Regression,2016 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Maryland,edu,4f7b92bd678772552b3c3edfc9a7c5c4a8c60a8e,citation,https://pdfs.semanticscholar.org/4f7b/92bd678772552b3c3edfc9a7c5c4a8c60a8e.pdf,Deep Density Clustering of Unconstrained Faces,0 +138,IJB-A,ijb_c,1.2962018,103.77689944,National University of Singapore,edu,fca9ebaa30d69ccec8bb577c31693c936c869e72,citation,https://arxiv.org/pdf/1809.00338.pdf,Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition,2018 +139,IJB-A,ijb_c,40.0044795,116.370238,Chinese Academy of Sciences,edu,fca9ebaa30d69ccec8bb577c31693c936c869e72,citation,https://arxiv.org/pdf/1809.00338.pdf,Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition,2018 diff --git a/site/datasets/final/images_of_groups.csv b/site/datasets/final/images_of_groups.csv new file mode 100644 index 00000000..856d97b1 --- /dev/null +++ b/site/datasets/final/images_of_groups.csv 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University,edu,27a299b834a18e45d73e0bf784bbb5b304c197b3,citation,http://ai.stanford.edu/~vigneshr/cvpr_13/cvpr13_social_roles.pdf,Social Role Discovery in Human Events,2013 +4,Images of Groups,images_of_groups,37.43131385,-122.16936535,Stanford University,edu,d84230a2fc9950fccfd37f0291d65e634b5ffc32,citation,http://pdfs.semanticscholar.org/d842/30a2fc9950fccfd37f0291d65e634b5ffc32.pdf,Historical and Modern Image-to-Image Translation with Generative Adversarial Networks,2017 +5,Images of Groups,images_of_groups,25.01682835,121.53846924,National Taiwan University,edu,046865a5f822346c77e2865668ec014ec3282033,citation,http://www.csie.ntu.edu.tw/~winston/papers/chen12discovering.pdf,Discovering informative social subgraphs and predicting pairwise relationships from group photos,2012 +6,Images of Groups,images_of_groups,28.59899755,-81.19712501,University of Central Florida,edu,0aa303109a3402aa5a203877847d549c4a24d933,citation,http://crcv-web.eecs.ucf.edu/papers/cvpr2014/Resemblance_CVPR14.pdf,Who Do I Look Like? Determining Parent-Offspring Resemblance via Gated Autoencoders,2014 +7,Images of Groups,images_of_groups,37.4102193,-122.05965487,Carnegie Mellon University,edu,c6096986b4d6c374ab2d20031e026b581e7bf7e9,citation,http://pdfs.semanticscholar.org/c609/6986b4d6c374ab2d20031e026b581e7bf7e9.pdf,A Framework for Using Context to Understand Images of People,2009 +8,Images of Groups,images_of_groups,51.5231607,-0.1282037,University College London,edu,6aaa77e241fe55ae0c4ad281e27886ea778f9e23,citation,http://pdfs.semanticscholar.org/b562/ad2ae12920cb318c5309a35000b4d5eb27b8.pdf,F-Formation Detection: Individuating Free-Standing Conversational Groups in Images,2015 +9,Images of Groups,images_of_groups,43.7743911,-79.50481085,York University,edu,ffe4bb47ec15f768e1744bdf530d5796ba56cfc1,citation,https://arxiv.org/pdf/1706.04277.pdf,AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces,2017 +10,Images of Groups,images_of_groups,27.18794105,31.17009498,Assiut University,edu,ffe4bb47ec15f768e1744bdf530d5796ba56cfc1,citation,https://arxiv.org/pdf/1706.04277.pdf,AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces,2017 +11,Images of Groups,images_of_groups,40.9153196,-73.1270626,Stony Brook University,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,http://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017 +12,Images of Groups,images_of_groups,40.90826665,-73.11520891,Stony Brook University Hospital,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,http://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017 +13,Images of Groups,images_of_groups,-22.8148374,-47.0647708,University of Campinas (UNICAMP),edu,b161d261fabb507803a9e5834571d56a3b87d147,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8122913,Gender recognition from face images using a geometric descriptor,2017 +14,Images of Groups,images_of_groups,40.9153196,-73.1270626,Stony Brook University,edu,1190cba0cae3c8bb81bf80d6a0a83ae8c41240bc,citation,https://pdfs.semanticscholar.org/1190/cba0cae3c8bb81bf80d6a0a83ae8c41240bc.pdf,Squared Earth Mover ’ s Distance Loss for Training Deep Neural Networks on Ordered-Classes,2017 +15,Images of Groups,images_of_groups,24.94314825,121.36862979,National Taipei University,edu,30cc1ddd7a9b4878cca7783a59086bdc49dc4044,citation,https://doi.org/10.1007/s11042-015-2599-0,Intensity contrast masks for gender classification,2015 +16,Images of Groups,images_of_groups,-35.2776999,149.118527,Australian National University,edu,49e541e0bbc7a082e5c952fc70716e66e5713080,citation,http://ieeexplore.ieee.org/document/6460925/,Group expression intensity estimation in videos via Gaussian Processes,2012 +17,Images of Groups,images_of_groups,50.3755269,-4.13937687,Plymouth University,edu,8bed7ff2f75d956652320270eaf331e1f73efb35,citation,https://arxiv.org/pdf/1709.03820.pdf,Emotion recognition in the wild using deep neural networks and Bayesian classifiers,2017 +18,Images of Groups,images_of_groups,39.3650216,16.2257177,University of Calabria,edu,8bed7ff2f75d956652320270eaf331e1f73efb35,citation,https://arxiv.org/pdf/1709.03820.pdf,Emotion recognition in the wild using deep neural networks and Bayesian classifiers,2017 +19,Images of Groups,images_of_groups,51.7534538,-1.25400997,University of Oxford,edu,0be8b12f194fb604be69c139a195799e8ab53fd3,citation,http://www.robots.ox.ac.uk/~vgg/publications/2014/Hoai14/poster.pdf,Talking Heads: Detecting Humans and Recognizing Their Interactions,2014 +20,Images of Groups,images_of_groups,-35.2776999,149.118527,Australian National University,edu,0d3068b352c3733c9e1cc75e449bf7df1f7b10a4,citation,http://doi.ieeecomputersociety.org/10.1109/ACII.2013.111,Context Based Facial Expression Analysis in the Wild,2013 +21,Images of Groups,images_of_groups,45.42580475,-75.68740118,University of Ottawa,edu,16820ccfb626dcdc893cc7735784aed9f63cbb70,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W12/papers/Azarmehr_Real-Time_Embedded_Age_2015_CVPR_paper.pdf,Real-time embedded age and gender classification in unconstrained video,2015 +22,Images of Groups,images_of_groups,51.5247272,-0.03931035,Queen Mary University of London,edu,fcc82154067dfe778423c2df4ed69f0bec6e1534,citation,https://pdfs.semanticscholar.org/fcc8/2154067dfe778423c2df4ed69f0bec6e1534.pdf,Automatic Analysis of Affect and Membership in Group Settings,2017 +23,Images of Groups,images_of_groups,52.17638955,0.14308882,University of Cambridge,edu,fcc82154067dfe778423c2df4ed69f0bec6e1534,citation,https://pdfs.semanticscholar.org/fcc8/2154067dfe778423c2df4ed69f0bec6e1534.pdf,Automatic Analysis of Affect and Membership in Group Settings,2017 +24,Images of Groups,images_of_groups,30.284151,-97.73195598,University of Texas at Austin,edu,45513d0f2f5c0dac5b61f9ff76c7e46cce62f402,citation,http://pdfs.semanticscholar.org/4551/3d0f2f5c0dac5b61f9ff76c7e46cce62f402.pdf,Face Discovery with Social Context,2011 +25,Images of Groups,images_of_groups,37.26728,126.9841151,Seoul National University,edu,282503fa0285240ef42b5b4c74ae0590fe169211,citation,http://pdfs.semanticscholar.org/2825/03fa0285240ef42b5b4c74ae0590fe169211.pdf,Feeding Hand-Crafted Features for Enhancing the Performance of Convolutional Neural Networks,2018 +26,Images of Groups,images_of_groups,-35.2776999,149.118527,Australian National University,edu,1ab881ec87167af9071b2ad8ff6d4ce3eee38477,citation,http://pdfs.semanticscholar.org/1ab8/81ec87167af9071b2ad8ff6d4ce3eee38477.pdf,Finding Happiest Moments in a Social Context,2012 +27,Images of Groups,images_of_groups,-35.23656905,149.08446994,University of Canberra,edu,1ab881ec87167af9071b2ad8ff6d4ce3eee38477,citation,http://pdfs.semanticscholar.org/1ab8/81ec87167af9071b2ad8ff6d4ce3eee38477.pdf,Finding Happiest Moments in a Social Context,2012 +28,Images of Groups,images_of_groups,-35.23656905,149.08446994,University of Canberra,edu,572dbaee6648eefa4c9de9b42551204b985ff863,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7163151,The more the merrier: Analysing the affect of a group of people in images,2015 +29,Images of Groups,images_of_groups,32.87935255,-117.23110049,"University of California, San Diego",edu,572dbaee6648eefa4c9de9b42551204b985ff863,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7163151,The more the merrier: Analysing the affect of a group of people in images,2015 +30,Images of Groups,images_of_groups,46.0658836,11.1159894,University of Trento,edu,572dbaee6648eefa4c9de9b42551204b985ff863,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7163151,The more the merrier: Analysing the affect of a group of people in images,2015 +31,Images of Groups,images_of_groups,35.90503535,-79.04775327,University of North Carolina,edu,dbf6d2619bd41ce4c36488e15d114a2da31b51c9,citation,https://arxiv.org/pdf/1810.00028.pdf,Data-Driven Modeling of Group Entitativity in Virtual Environments,2018 +32,Images of Groups,images_of_groups,39.2899685,-76.62196103,University of Maryland,edu,dbf6d2619bd41ce4c36488e15d114a2da31b51c9,citation,https://arxiv.org/pdf/1810.00028.pdf,Data-Driven Modeling of Group Entitativity in Virtual Environments,2018 +33,Images of Groups,images_of_groups,37.4102193,-122.05965487,Carnegie Mellon University,edu,b593f13f974cf444a5781bbd487e1c69e056a1f7,citation,https://pdfs.semanticscholar.org/b593/f13f974cf444a5781bbd487e1c69e056a1f7.pdf,Query Image Query Image Retrievals Retrievals Transferred Poses Transferred Poses,2018 +34,Images of Groups,images_of_groups,43.7776426,11.259765,University of Florence,edu,02cc96ad997102b7c55e177ac876db3b91b4e72c,citation,http://www.micc.unifi.it/wp-content/uploads/2015/12/2015_museum-visitors-dataset.pdf,"MuseumVisitors: A dataset for pedestrian and group detection, gaze estimation and behavior understanding",2015 +35,Images of Groups,images_of_groups,40.8419836,-73.94368971,Columbia University,edu,02cc96ad997102b7c55e177ac876db3b91b4e72c,citation,http://www.micc.unifi.it/wp-content/uploads/2015/12/2015_museum-visitors-dataset.pdf,"MuseumVisitors: A dataset for pedestrian and group detection, gaze estimation and behavior understanding",2015 +36,Images of Groups,images_of_groups,58.38131405,26.72078081,University of Tartu,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0 +37,Images of Groups,images_of_groups,41.3868913,2.16352385,University of Barcelona,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0 +38,Images of Groups,images_of_groups,51.49887085,-0.17560797,Imperial College London,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +39,Images of Groups,images_of_groups,51.59029705,-0.22963221,Middlesex University,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +40,Images of Groups,images_of_groups,39.2899685,-76.62196103,University of Maryland,edu,3b092733f428b12f1f920638f868ed1e8663fe57,citation,http://www.math.jhu.edu/~data/RamaPapers/PerformanceBounds.pdf,On the size of Convolutional Neural Networks and generalization performance,2016 +41,Images of Groups,images_of_groups,33.6431901,-117.84016494,"University of California, Irvine",edu,3991223b1dc3b87883cec7af97cf56534178f74a,citation,http://doi.acm.org/10.1145/2461466.2461469,A unified framework for context assisted face clustering,2013 +42,Images of Groups,images_of_groups,65.0592157,25.46632601,University of Oulu,edu,1e516273554d87bbe1902fa0298179c493299035,citation,http://www.ee.oulu.fi/~hadid/Age-ICPR2012.pdf,Age Classification in Unconstrained Conditions Using LBP Variants,2012 +43,Images of Groups,images_of_groups,50.89273635,-1.39464295,University of Southampton,edu,fd67d0efbd94c9d8f9d2f0a972edd7320bc7604f,citation,http://pdfs.semanticscholar.org/fd67/d0efbd94c9d8f9d2f0a972edd7320bc7604f.pdf,Real-Time Semantic Clothing Segmentation,2012 +44,Images of Groups,images_of_groups,47.6543238,-122.30800894,University of Washington,edu,f2c30594d917ea915028668bc2a481371a72a14d,citation,http://pdfs.semanticscholar.org/f2c3/0594d917ea915028668bc2a481371a72a14d.pdf,Scene Understanding Using Internet Photo Collections,2010 +45,Images of Groups,images_of_groups,40.47913175,-74.43168868,Rutgers University,edu,31f1e711fcf82c855f27396f181bf5e565a2f58d,citation,http://doi.ieeecomputersociety.org/10.1109/ICCVW.2015.54,Unconstrained Age Estimation with Deep Convolutional Neural Networks,2015 +46,Images of Groups,images_of_groups,39.2899685,-76.62196103,University of Maryland,edu,31f1e711fcf82c855f27396f181bf5e565a2f58d,citation,http://doi.ieeecomputersociety.org/10.1109/ICCVW.2015.54,Unconstrained Age Estimation with Deep Convolutional Neural Networks,2015 +47,Images of Groups,images_of_groups,35.93006535,-84.31240032,Oak Ridge National Laboratory,edu,2cf3564d7421b661e84251d280d159d4b3ebb336,citation,https://doi.org/10.1109/BTAS.2014.6996287,Discriminating projections for estimating face age in wild images,2014 +48,Images of Groups,images_of_groups,34.2239869,-77.8701325,"UNCW, USA",edu,2cf3564d7421b661e84251d280d159d4b3ebb336,citation,https://doi.org/10.1109/BTAS.2014.6996287,Discriminating projections for estimating face age in wild images,2014 +49,Images of Groups,images_of_groups,34.2249827,-77.86907744,University of North Carolina at Wilmington,edu,2cf3564d7421b661e84251d280d159d4b3ebb336,citation,https://doi.org/10.1109/BTAS.2014.6996287,Discriminating projections for estimating face age in wild images,2014 +50,Images of Groups,images_of_groups,41.3868913,2.16352385,University of Barcelona,edu,500fbe18afd44312738cab91b4689c12b4e0eeee,citation,http://www.maia.ub.es/~sergio/linked/ijcnn_age_and_cultural_2015.pdf,ChaLearn looking at people 2015 new competitions: Age estimation and cultural event recognition,2015 +51,Images of Groups,images_of_groups,45.4312742,12.3265377,University of Venezia,edu,500fbe18afd44312738cab91b4689c12b4e0eeee,citation,http://www.maia.ub.es/~sergio/linked/ijcnn_age_and_cultural_2015.pdf,ChaLearn looking at people 2015 new competitions: Age estimation and cultural event recognition,2015 +52,Images of Groups,images_of_groups,42.4505507,-76.4783513,Cornell University,edu,0d57d3d2d04fc96d731cac99a7a8ef79050dac75,citation,http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/Workshops/4990a269.pdf,Not Everybody's Special: Using Neighbors in Referring Expressions with Uncertain Attributes,2013 +53,Images of Groups,images_of_groups,42.4505507,-76.4783513,Cornell University,edu,fbc9ba70e36768efff130c7d970ce52810b044ff,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6738500,Face-graph matching for classifying groups of people,2013 +54,Images of Groups,images_of_groups,37.43131385,-122.16936535,Stanford University,edu,fbc9ba70e36768efff130c7d970ce52810b044ff,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6738500,Face-graph matching for classifying groups of people,2013 +55,Images of Groups,images_of_groups,1.340216,103.965089,Singapore University of Technology and Design,edu,00823e6c0b6f1cf22897b8d0b2596743723ec51c,citation,https://arxiv.org/pdf/1708.07689.pdf,Understanding and Comparing Deep Neural Networks for Age and Gender Classification,2017 +56,Images of Groups,images_of_groups,41.1664858,-73.1920564,University of Bridgeport,edu,ac9a331327cceda4e23f9873f387c9fd161fad76,citation,http://pdfs.semanticscholar.org/ac9a/331327cceda4e23f9873f387c9fd161fad76.pdf,Deep Convolutional Neural Network for Age Estimation based on VGG-Face Model,2017 +57,Images of Groups,images_of_groups,42.4505507,-76.4783513,Cornell University,edu,5aad56cfa2bac5d6635df4184047e809f8fecca2,citation,http://chenlab.ece.cornell.edu/people/Amir/publications/picture_password.pdf,A visual dictionary attack on Picture Passwords,2013 +58,Images of Groups,images_of_groups,42.9336278,-78.88394479,SUNY Buffalo,edu,4793f11fbca4a7dba898b9fff68f70d868e2497c,citation,http://pdfs.semanticscholar.org/4793/f11fbca4a7dba898b9fff68f70d868e2497c.pdf,Kinship Verification through Transfer Learning,2011 +59,Images of Groups,images_of_groups,37.4102193,-122.05965487,Carnegie Mellon University,edu,eddc4989cdb20c8cdfb22e989bdb2cb9031d0439,citation,https://arxiv.org/pdf/1804.03080.pdf,Binge Watching: Scaling Affordance Learning from Sitcoms,2017 +60,Images of Groups,images_of_groups,42.3383668,-71.08793524,Northeastern University,edu,090e4713bcccff52dcd0c01169591affd2af7e76,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Shao_What_Do_You_2013_ICCV_paper.pdf,What Do You Do? Occupation Recognition in a Photo via Social Context,2013 +61,Images of Groups,images_of_groups,53.21967825,6.56251482,University of Groningen,edu,4ff4c27e47b0aa80d6383427642bb8ee9d01c0ac,citation,https://doi.org/10.1109/SSCI.2015.37,Deep Convolutional Neural Networks and Support Vector Machines for Gender Recognition,2015 +62,Images of Groups,images_of_groups,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,291f527598c589fb0519f890f1beb2749082ddfd,citation,http://pdfs.semanticscholar.org/3215/ceb94227451a958bcf6b1205c710d17e53f5.pdf,Seeing People in Social Context: Recognizing People and Social Relationships,2010 +63,Images of Groups,images_of_groups,42.4505507,-76.4783513,Cornell University,edu,28d06fd508d6f14cd15f251518b36da17909b79e,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Chen_Whats_in_a_2013_CVPR_paper.pdf,What's in a Name? First Names as Facial Attributes,2013 +64,Images of Groups,images_of_groups,37.43131385,-122.16936535,Stanford University,edu,28d06fd508d6f14cd15f251518b36da17909b79e,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Chen_Whats_in_a_2013_CVPR_paper.pdf,What's in a Name? First Names as Facial Attributes,2013 +65,Images of Groups,images_of_groups,47.0570222,21.922709,Queen Mary University,edu,34022637860443c052375c45c4f700afcb438cd0,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.185,Automatic Recognition of Emotions and Membership in Group Videos,2016 +66,Images of Groups,images_of_groups,52.17638955,0.14308882,University of Cambridge,edu,34022637860443c052375c45c4f700afcb438cd0,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.185,Automatic Recognition of Emotions and Membership in Group Videos,2016 +67,Images of Groups,images_of_groups,38.8964679,-104.8050594,University of Colorado at Colorado Springs,edu,e3e2c106ccbd668fb9fca851498c662add257036,citation,http://www.vast.uccs.edu/~tboult/PAPERS/BTAS13-Sapkota-et-al-Ensembles.pdf,"Appearance, context and co-occurrence ensembles for identity recognition in personal photo collections",2013 +68,Images of Groups,images_of_groups,25.01682835,121.53846924,National Taiwan University,edu,8ba67f45fbb1ce47a90df38f21834db37c840079,citation,http://www.cmlab.csie.ntu.edu.tw/~yanying/paper/dsp006-chen.pdf,People search and activity mining in large-scale community-contributed photos,2012 +69,Images of Groups,images_of_groups,1.2962018,103.77689944,National University of Singapore,edu,a5219fff98dfe3ec81dee95c4ead69a8e24cc802,citation,https://arxiv.org/pdf/1708.00634.pdf,Dual-Glance Model for Deciphering Social Relationships,2017 +70,Images of Groups,images_of_groups,44.97308605,-93.23708813,University of Minnesota,edu,a5219fff98dfe3ec81dee95c4ead69a8e24cc802,citation,https://arxiv.org/pdf/1708.00634.pdf,Dual-Glance Model for Deciphering Social Relationships,2017 +71,Images of Groups,images_of_groups,40.742252,-74.0270949,Stevens Institute of Technology,edu,1e1d7cbbef67e9e042a3a0a9a1bcefcc4a9adacf,citation,http://personal.stevens.edu/~hli18//data/papers/CVPR2016_CameraReady.pdf,A Multi-level Contextual Model for Person Recognition in Photo Albums,2016 +72,Images of Groups,images_of_groups,-34.9189226,138.60423668,University of Adelaide,edu,3d24b386d003bee176a942c26336dbe8f427aadd,citation,http://arxiv.org/abs/1611.09967,Sequential Person Recognition in Photo Albums with a Recurrent Network,2017 +73,Images of Groups,images_of_groups,37.43131385,-122.16936535,Stanford University,edu,111ae23b60284927f2545dfc59b0147bb3423792,citation,https://pdfs.semanticscholar.org/111a/e23b60284927f2545dfc59b0147bb3423792.pdf,Classroom Data Collection and Analysis using Computer Vision,2016 +74,Images of Groups,images_of_groups,51.99882735,4.37396037,Delft University of Technology,edu,dfbf941adeea19f5dff4a70a466ddd1b77f3b727,citation,https://pdfs.semanticscholar.org/dfbf/941adeea19f5dff4a70a466ddd1b77f3b727.pdf,Models for supervised learning in sequence data,2018 +75,Images of Groups,images_of_groups,40.8419836,-73.94368971,Columbia University,edu,774cbb45968607a027ae4729077734db000a1ec5,citation,http://pdfs.semanticscholar.org/774c/bb45968607a027ae4729077734db000a1ec5.pdf,From Bikers to Surfers: Visual Recognition of Urban Tribes,2013 +76,Images of Groups,images_of_groups,32.87935255,-117.23110049,"University of California, San Diego",edu,774cbb45968607a027ae4729077734db000a1ec5,citation,http://pdfs.semanticscholar.org/774c/bb45968607a027ae4729077734db000a1ec5.pdf,From Bikers to Surfers: Visual Recognition of Urban Tribes,2013 +77,Images of Groups,images_of_groups,47.6543238,-122.30800894,University of Washington,edu,5b2bc289b607ca1a0634555158464f28fe68a6d3,citation,http://vision.ics.uci.edu/papers/GargRSS_CVPR_2011/GargRSS_CVPR_2011.pdf,Where's Waldo: Matching people in images of crowds,2011 +78,Images of Groups,images_of_groups,42.4505507,-76.4783513,Cornell University,edu,5b2bc289b607ca1a0634555158464f28fe68a6d3,citation,http://vision.ics.uci.edu/papers/GargRSS_CVPR_2011/GargRSS_CVPR_2011.pdf,Where's Waldo: Matching people in images of crowds,2011 +79,Images of Groups,images_of_groups,42.4505507,-76.4783513,Cornell University,edu,b185f0a39384ceb3c4923196aeed6d68830a069f,citation,http://pdfs.semanticscholar.org/b185/f0a39384ceb3c4923196aeed6d68830a069f.pdf,Describing Clothing by Semantic Attributes,2012 +80,Images of Groups,images_of_groups,37.43131385,-122.16936535,Stanford University,edu,b185f0a39384ceb3c4923196aeed6d68830a069f,citation,http://pdfs.semanticscholar.org/b185/f0a39384ceb3c4923196aeed6d68830a069f.pdf,Describing Clothing by Semantic Attributes,2012 +81,Images of Groups,images_of_groups,42.4505507,-76.4783513,Cornell University,edu,14c37ea85ba8d74d053a34aedd7e484659fd54d4,citation,http://users.ece.cmu.edu/~dbatra/publications/assets/opd_cvpr10.pdf,Beyond trees: MRF inference via outer-planar decomposition,2010 +82,Images of Groups,images_of_groups,65.0592157,25.46632601,University of Oulu,edu,8d95317d0e366cecae1dd3f7c1ba69fe3fc4a8e0,citation,http://pdfs.semanticscholar.org/f7c7/f4494f73f2fe845be3b82ee711bc00be7508.pdf,Riesz-based Volume Local Binary Pattern and A Novel Group Expression Model for Group Happiness Intensity Analysis,2015 +83,Images of Groups,images_of_groups,-35.23656905,149.08446994,University of Canberra,edu,8d95317d0e366cecae1dd3f7c1ba69fe3fc4a8e0,citation,http://pdfs.semanticscholar.org/f7c7/f4494f73f2fe845be3b82ee711bc00be7508.pdf,Riesz-based Volume Local Binary Pattern and A Novel Group Expression Model for Group Happiness Intensity Analysis,2015 +84,Images of Groups,images_of_groups,-35.2776999,149.118527,Australian National University,edu,8d95317d0e366cecae1dd3f7c1ba69fe3fc4a8e0,citation,http://pdfs.semanticscholar.org/f7c7/f4494f73f2fe845be3b82ee711bc00be7508.pdf,Riesz-based Volume Local Binary Pattern and A Novel Group Expression Model for Group Happiness Intensity Analysis,2015 +85,Images of Groups,images_of_groups,45.42580475,-75.68740118,University of Ottawa,edu,65293ecf6a4c5ab037a2afb4a9a1def95e194e5f,citation,http://pdfs.semanticscholar.org/6529/3ecf6a4c5ab037a2afb4a9a1def95e194e5f.pdf,"Face , Age and Gender Recognition using Local Descriptors",2014 +86,Images of Groups,images_of_groups,32.8536333,-117.2035286,Kyung Hee University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +87,Images of Groups,images_of_groups,24.7246403,46.62335012,King Saud University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +88,Images of Groups,images_of_groups,23.7289899,90.3982682,Institute of Information Technology,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +89,Images of Groups,images_of_groups,42.9336278,-78.88394479,SUNY Buffalo,edu,2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d,citation,https://doi.org/10.1109/CVPRW.2011.5981801,Genealogical face recognition based on UB KinFace database,2011 +90,Images of Groups,images_of_groups,35.9042272,-78.85565763,"IBM Research, North Carolina",company,aea50d3414ecb20dc2ba77b0277d0df59bde2c2c,citation,http://pdfs.semanticscholar.org/aea5/0d3414ecb20dc2ba77b0277d0df59bde2c2c.pdf,The #selfiestation: Design and Use of a Kiosk for Taking Selfies in the Enterprise,2015 +91,Images of Groups,images_of_groups,40.00229045,116.32098908,Tsinghua University,edu,0a0d5283439f088c158fcec732e2593bb3cd57ad,citation,http://media.cs.tsinghua.edu.cn/~ahz/papers/whoblockswho_iccv2011_final.pdf,Who Blocks Who: Simultaneous clothing segmentation for grouping images,2011 +92,Images of Groups,images_of_groups,42.36782045,-71.12666653,Harvard University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 +93,Images of Groups,images_of_groups,40.9153196,-73.1270626,Stony Brook University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 +94,Images of Groups,images_of_groups,51.5217668,-0.13019072,University of London,edu,b9eb55c2c573e2fffd686b00a39185f0142ef816,citation,http://elvera.nue.tu-berlin.de/files/1241Ramzan2010.pdf,The participation payoff: challenges and opportunities for multimedia access in networked communities,2010 +95,Images of Groups,images_of_groups,51.99882735,4.37396037,Delft University of Technology,edu,b9eb55c2c573e2fffd686b00a39185f0142ef816,citation,http://elvera.nue.tu-berlin.de/files/1241Ramzan2010.pdf,The participation payoff: challenges and opportunities for multimedia access in networked communities,2010 +96,Images of Groups,images_of_groups,1.2962018,103.77689944,National University of Singapore,edu,cc3ef62b4a7eb6c4e45302deb89df2e547b6efcc,citation,http://pdfs.semanticscholar.org/cc3e/f62b4a7eb6c4e45302deb89df2e547b6efcc.pdf,Creating Picture Legends for Group Photos,2012 +97,Images of Groups,images_of_groups,37.4585796,-122.17560525,SRI International,edu,683f5c838ea2c9c50f3f5c5fa064c00868751733,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Chakraborty_3D_Visual_Proxemics_2013_CVPR_paper.pdf,3D Visual Proxemics: Recognizing Human Interactions in 3D from a Single Image,2013 +98,Images of Groups,images_of_groups,40.51865195,-74.44099801,State University of New Jersey,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791154,A cascaded convolutional neural network for age estimation of unconstrained faces,2016 +99,Images of Groups,images_of_groups,39.2899685,-76.62196103,University of Maryland,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791154,A cascaded convolutional neural network for age estimation of unconstrained faces,2016 +100,Images of Groups,images_of_groups,31.2284923,121.40211389,East China Normal University,edu,5364e58ba1f4cdfcffb247c2421e8f56a75fad8d,citation,https://doi.org/10.1109/VCIP.2017.8305113,Facial age estimation through self-paced learning,2017 +101,Images of Groups,images_of_groups,42.3383668,-71.08793524,Northeastern University,edu,c9f588d295437009994ddaabb64fd4e4c499b294,citation,http://pdfs.semanticscholar.org/c9f5/88d295437009994ddaabb64fd4e4c499b294.pdf,Predicting Professions through 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emotion recognition,2016 +6,IMDB,imdb_wiki,44.812384,20.453501,Singidunum University,edu,b7845e0b0ce17cde7db37d5524ef2a61dee3e540,citation,https://doi.org/10.1109/ICPR.2016.7899608,Fusion of classifier predictions for audio-visual emotion recognition,2016 +7,IMDB,imdb_wiki,58.38131405,26.72078081,University of Tartu,edu,b7845e0b0ce17cde7db37d5524ef2a61dee3e540,citation,https://doi.org/10.1109/ICPR.2016.7899608,Fusion of classifier predictions for audio-visual emotion recognition,2016 +8,IMDB,imdb_wiki,1.2962018,103.77689944,National University of Singapore,edu,fca9ebaa30d69ccec8bb577c31693c936c869e72,citation,https://arxiv.org/pdf/1809.00338.pdf,Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition,2018 +9,IMDB,imdb_wiki,40.0044795,116.370238,Chinese Academy of Sciences,edu,fca9ebaa30d69ccec8bb577c31693c936c869e72,citation,https://arxiv.org/pdf/1809.00338.pdf,Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition,2018 +10,IMDB,imdb_wiki,55.91029135,-3.32345777,Heriot-Watt University,edu,2cdc40f20b70ca44d9fd8e7716080ee05ca7924a,citation,http://pdfs.semanticscholar.org/2cdc/40f20b70ca44d9fd8e7716080ee05ca7924a.pdf,Real-time Convolutional Neural Networks for Emotion and Gender Classification,2017 +11,IMDB,imdb_wiki,56.45796755,-2.98214831,University of Dundee,edu,d5b0e73b584be507198b6665bcddeba92b62e1e5,citation,http://pdfs.semanticscholar.org/d5b0/e73b584be507198b6665bcddeba92b62e1e5.pdf,Multi-Region Ensemble Convolutional Neural Networks for High-Accuracy Age Estimation,2017 +12,IMDB,imdb_wiki,22.15263985,113.56803206,Macau University of Science and Technology,edu,d5b0e73b584be507198b6665bcddeba92b62e1e5,citation,http://pdfs.semanticscholar.org/d5b0/e73b584be507198b6665bcddeba92b62e1e5.pdf,Multi-Region Ensemble Convolutional Neural Networks for High-Accuracy Age Estimation,2017 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to Ranking,2018 +17,IMDB,imdb_wiki,45.47567215,9.23336232,Università degli Studi di Milano,edu,a713a01971e73d0c3118d0409dc7699a24f521d6,citation,https://doi.org/10.1109/SSCI.2017.8285381,Age estimation based on face images and pre-trained convolutional neural networks,2017 +18,IMDB,imdb_wiki,35.6894875,139.6917064,"IBJ, Inc., Tokyo, Japan",company,df7af280771a6c8302b75ed0a14ffe7854cca679,citation,http://doi.ieeecomputersociety.org/10.1109/ICMEW.2017.8026293,Prediction of users' facial attractiveness on an online dating website,2017 +19,IMDB,imdb_wiki,35.9020448,139.93622009,University of Tokyo,edu,df7af280771a6c8302b75ed0a14ffe7854cca679,citation,http://doi.ieeecomputersociety.org/10.1109/ICMEW.2017.8026293,Prediction of users' facial attractiveness on an online dating website,2017 +20,IMDB,imdb_wiki,35.9990522,-78.9290629,Duke University,edu,cca9ae621e8228cfa787ec7954bb375536160e0d,citation,https://arxiv.org/pdf/1805.07410.pdf,Learning to Collaborate for User-Controlled Privacy,2018 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orientation fields,2017 +25,IMDB,imdb_wiki,51.49887085,-0.17560797,Imperial College London,edu,56e079f4eb40744728fd1d7665938b06426338e5,citation,https://arxiv.org/pdf/1705.04293.pdf,Bayesian Approaches to Distribution Regression,2018 +26,IMDB,imdb_wiki,51.5231607,-0.1282037,University College London,edu,56e079f4eb40744728fd1d7665938b06426338e5,citation,https://arxiv.org/pdf/1705.04293.pdf,Bayesian Approaches to Distribution Regression,2018 +27,IMDB,imdb_wiki,51.7534538,-1.25400997,University of Oxford,edu,56e079f4eb40744728fd1d7665938b06426338e5,citation,https://arxiv.org/pdf/1705.04293.pdf,Bayesian Approaches to Distribution Regression,2018 +28,IMDB,imdb_wiki,45.5039761,-73.5749687,McGill University,edu,407bb798ab153bf6156ba2956f8cf93256b6910a,citation,http://pdfs.semanticscholar.org/407b/b798ab153bf6156ba2956f8cf93256b6910a.pdf,Fisher Pruning of Deep Nets for Facial Trait Classification,2018 +29,IMDB,imdb_wiki,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,39c10888a470b92b917788c57a6fd154c97b421c,citation,https://doi.org/10.1109/VCIP.2017.8305036,Joint multi-feature fusion and attribute relationships for facial attribute prediction,2017 +30,IMDB,imdb_wiki,51.7534538,-1.25400997,University of Oxford,edu,eb027969f9310e0ae941e2adee2d42cdf07d938c,citation,https://arxiv.org/pdf/1710.08092.pdf,VGGFace2: A Dataset for Recognising Faces across Pose and Age,2018 +31,IMDB,imdb_wiki,45.5039761,-73.5749687,McGill University,edu,ed9d11e995baeec17c5d2847ec1a8d5449254525,citation,https://pdfs.semanticscholar.org/ed9d/11e995baeec17c5d2847ec1a8d5449254525.pdf,Efficient Gender Classification Using a Deep LDA-Pruned Net,2017 +32,IMDB,imdb_wiki,31.32235655,121.38400941,Shanghai University,edu,d454ad60b061c1a1450810a0f335fafbfeceeccc,citation,https://arxiv.org/pdf/1712.07195.pdf,Deep Regression Forests for Age Estimation,2017 +33,IMDB,imdb_wiki,40.0044795,116.370238,Chinese Academy of Sciences,edu,288964068cd87d97a98b8bc927d6e0d2349458a2,citation,https://pdfs.semanticscholar.org/2889/64068cd87d97a98b8bc927d6e0d2349458a2.pdf,Mean-Variance Loss for Deep Age Estimation from a Face,0 +34,IMDB,imdb_wiki,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,288964068cd87d97a98b8bc927d6e0d2349458a2,citation,https://pdfs.semanticscholar.org/2889/64068cd87d97a98b8bc927d6e0d2349458a2.pdf,Mean-Variance Loss for Deep Age Estimation from a Face,0 +35,IMDB,imdb_wiki,40.51865195,-74.44099801,State University of New Jersey,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791154,A cascaded convolutional neural network for age estimation of unconstrained faces,2016 +36,IMDB,imdb_wiki,39.2899685,-76.62196103,University of Maryland,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791154,A cascaded convolutional neural network for age estimation of unconstrained faces,2016 +37,IMDB,imdb_wiki,37.2830003,127.04548469,Ajou University,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018 +38,IMDB,imdb_wiki,37.403917,127.159786,Korea Electronics Technology Institute,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018 +39,IMDB,imdb_wiki,37.26728,126.9841151,Seoul National University,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018 +40,IMDB,imdb_wiki,1.2962018,103.77689944,National University of Singapore,edu,5f94969b9491db552ffebc5911a45def99026afe,citation,https://pdfs.semanticscholar.org/5f94/969b9491db552ffebc5911a45def99026afe.pdf,Multimodal Learning and Reasoning for Visual Question Answering,2017 +41,IMDB,imdb_wiki,42.357757,-83.06286711,Wayne State University,edu,28d99dc2d673d62118658f8375b414e5192eac6f,citation,http://www.cs.wayne.edu/~mdong/cvpr17.pdf,Using Ranking-CNN for Age Estimation,2017 +42,IMDB,imdb_wiki,49.2767454,-122.91777375,Simon Fraser University,edu,975978ee6a32383d6f4f026b944099e7739e5890,citation,https://pdfs.semanticscholar.org/9759/78ee6a32383d6f4f026b944099e7739e5890.pdf,Privacy-Preserving Age Estimation for Content Rating,2018 +43,IMDB,imdb_wiki,49.8091536,-97.13304179,University of Manitoba,edu,975978ee6a32383d6f4f026b944099e7739e5890,citation,https://pdfs.semanticscholar.org/9759/78ee6a32383d6f4f026b944099e7739e5890.pdf,Privacy-Preserving Age Estimation for Content Rating,2018 +44,IMDB,imdb_wiki,43.66333345,-79.39769975,University of Toronto,edu,36a3a96ef54000a0cd63de867a5eb7e84396de09,citation,http://www.cs.toronto.edu/~guerzhoy/oriviz/crv17.pdf,Automatic Photo Orientation Detection with Convolutional Neural Networks,2017 +45,IMDB,imdb_wiki,31.32235655,121.38400941,Shanghai University,edu,5f0d4a0b5f72d8700cdf8cb179263a8fa866b59b,citation,https://pdfs.semanticscholar.org/5f0d/4a0b5f72d8700cdf8cb179263a8fa866b59b.pdf,Memo No . 85 06 / 2018 Deep Regression Forests for Age Estimation,2018 +46,IMDB,imdb_wiki,51.5247272,-0.03931035,Queen Mary University of London,edu,6cefb70f4668ee6c0bf0c18ea36fd49dd60e8365,citation,http://pdfs.semanticscholar.org/6cef/b70f4668ee6c0bf0c18ea36fd49dd60e8365.pdf,Privacy-Preserving Deep Inference for Rich User Data on The Cloud,2017 +47,IMDB,imdb_wiki,35.7036227,51.35125097,Sharif University of Technology,edu,6cefb70f4668ee6c0bf0c18ea36fd49dd60e8365,citation,http://pdfs.semanticscholar.org/6cef/b70f4668ee6c0bf0c18ea36fd49dd60e8365.pdf,Privacy-Preserving Deep Inference for Rich User Data on The Cloud,2017 +48,IMDB,imdb_wiki,51.99882735,4.37396037,Delft University of Technology,edu,dfbf941adeea19f5dff4a70a466ddd1b77f3b727,citation,https://pdfs.semanticscholar.org/dfbf/941adeea19f5dff4a70a466ddd1b77f3b727.pdf,Models for supervised learning in sequence data,2018 +49,IMDB,imdb_wiki,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,cb27b45329d61f5f95ed213798d4b2a615e76be2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8329236,Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion,2018 +50,IMDB,imdb_wiki,37.2520226,127.0555019,"Samsung SAIT, Korea",company,cb27b45329d61f5f95ed213798d4b2a615e76be2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8329236,Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion,2018 +51,IMDB,imdb_wiki,35.9042272,-78.85565763,"IBM Research, North Carolina",company,00a967cb2d18e1394226ad37930524a31351f6cf,citation,https://arxiv.org/pdf/1611.05377v1.pdf,Fully-Adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification,2017 +52,IMDB,imdb_wiki,12.9803537,77.6975101,"Samsung R&D Institute, Bangalore, India",company,cf736f596bf881ca97ec4b29776baaa493b9d50e,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7952629,Low Dimensional Deep Features for facial landmark alignment,2017 +53,IMDB,imdb_wiki,-35.0636071,147.3552234,Charles Sturt University,edu,2e231f1e7e641dd3619bec59e14d02e91360ac01,citation,https://arxiv.org/pdf/1807.10421.pdf,Fusion Network for Face-Based Age Estimation,2018 +54,IMDB,imdb_wiki,51.3791442,-2.3252332,University of Bath,edu,2e231f1e7e641dd3619bec59e14d02e91360ac01,citation,https://arxiv.org/pdf/1807.10421.pdf,Fusion Network for Face-Based Age Estimation,2018 +55,IMDB,imdb_wiki,1.340216,103.965089,Singapore University of Technology and Design,edu,00823e6c0b6f1cf22897b8d0b2596743723ec51c,citation,https://arxiv.org/pdf/1708.07689.pdf,Understanding and Comparing Deep Neural Networks for Age and Gender Classification,2017 +56,IMDB,imdb_wiki,31.2284923,121.40211389,East China Normal University,edu,5364e58ba1f4cdfcffb247c2421e8f56a75fad8d,citation,https://doi.org/10.1109/VCIP.2017.8305113,Facial age estimation through self-paced learning,2017 +57,IMDB,imdb_wiki,61.44964205,23.85877462,Tampere University of Technology,edu,7f21a7441c6ded38008c1fd0b91bdd54425d3f80,citation,https://arxiv.org/pdf/1809.05474.pdf,Real Time System for Facial Analysis,2018 +58,IMDB,imdb_wiki,55.94951105,-3.19534913,University of Edinburgh,edu,f5fae7810a33ed67852ad6a3e0144cb278b24b41,citation,https://pdfs.semanticscholar.org/f5fa/e7810a33ed67852ad6a3e0144cb278b24b41.pdf,Multilingual Gender Classification with Multi-view Deep Learning: Notebook for PAN at CLEF 2018,2018 +59,IMDB,imdb_wiki,40.9153196,-73.1270626,Stony Brook University,edu,1190cba0cae3c8bb81bf80d6a0a83ae8c41240bc,citation,https://pdfs.semanticscholar.org/1190/cba0cae3c8bb81bf80d6a0a83ae8c41240bc.pdf,Squared Earth Mover ’ s Distance Loss for Training Deep Neural Networks on Ordered-Classes,2017 +60,IMDB,imdb_wiki,26.88111275,112.62850666,Hunan University,edu,86d0127e1fd04c3d8ea78401c838af621647dc95,citation,https://arxiv.org/pdf/1804.02810.pdf,A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction,2018 +61,IMDB,imdb_wiki,28.2290209,112.99483204,"National University of Defense Technology, China",edu,86d0127e1fd04c3d8ea78401c838af621647dc95,citation,https://arxiv.org/pdf/1804.02810.pdf,A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction,2018 +62,IMDB,imdb_wiki,29.58333105,-98.61944505,University of Texas at San Antonio,edu,86d0127e1fd04c3d8ea78401c838af621647dc95,citation,https://arxiv.org/pdf/1804.02810.pdf,A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction,2018 +63,IMDB,imdb_wiki,22.42031295,114.20788644,Chinese University of Hong Kong,edu,d80a3d1f3a438e02a6685e66ee908446766fefa9,citation,https://arxiv.org/pdf/1708.09687.pdf,Quantifying Facial Age by Posterior of Age Comparisons,2017 +64,IMDB,imdb_wiki,51.49887085,-0.17560797,Imperial College London,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +65,IMDB,imdb_wiki,51.59029705,-0.22963221,Middlesex University,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +66,IMDB,imdb_wiki,40.00229045,116.32098908,Tsinghua University,edu,493c8591d6a1bef5d7b84164a73761cefb9f5a25,citation,http://dl.acm.org/citation.cfm?id=3159691,User Profiling through Deep Multimodal Fusion,2018 +67,IMDB,imdb_wiki,47.6543238,-122.30800894,University of Washington,edu,493c8591d6a1bef5d7b84164a73761cefb9f5a25,citation,http://dl.acm.org/citation.cfm?id=3159691,User Profiling through Deep Multimodal Fusion,2018 +68,IMDB,imdb_wiki,30.44235995,-84.29747867,Florida State University,edu,b8c08c1330779283b3fbf06d133faf8bd55ea941,citation,https://arxiv.org/pdf/1803.11521.pdf,Online Regression with Feature Selection in Stochastic Data Streams,2018 +69,IMDB,imdb_wiki,30.44235995,-84.29747867,Florida State University,edu,1cfca6b71b0ead87bbb79a8614ddec3a10100faa,citation,https://arxiv.org/pdf/1809.05465.pdf,Are screening methods useful in feature selection? An empirical study,2018 +70,IMDB,imdb_wiki,51.49887085,-0.17560797,Imperial College London,edu,a06b6d30e2b31dc600f622ab15afe5e2929581a7,citation,https://ibug.doc.ic.ac.uk/media/uploads/documents/2209.pdf,Robust Joint and Individual Variance Explained,2017 +71,IMDB,imdb_wiki,51.59029705,-0.22963221,Middlesex University,edu,a06b6d30e2b31dc600f622ab15afe5e2929581a7,citation,https://ibug.doc.ic.ac.uk/media/uploads/documents/2209.pdf,Robust Joint and Individual Variance Explained,2017 +72,IMDB,imdb_wiki,32.0575279,118.78682252,Southeast University,edu,8ff8c64288a2f7e4e8bf8fda865820b04ab3dbe8,citation,https://pdfs.semanticscholar.org/0056/92b9fa6728df3a7f14578c43410867bba425.pdf,Age Estimation Using Expectation of Label Distribution Learning,2018 +73,IMDB,imdb_wiki,32.0565957,118.77408833,Nanjing University,edu,8ff8c64288a2f7e4e8bf8fda865820b04ab3dbe8,citation,https://pdfs.semanticscholar.org/0056/92b9fa6728df3a7f14578c43410867bba425.pdf,Age Estimation Using Expectation of Label Distribution Learning,2018 +74,IMDB,imdb_wiki,58.38131405,26.72078081,University of Tartu,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0 +75,IMDB,imdb_wiki,41.3868913,2.16352385,University of Barcelona,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0 +76,IMDB,imdb_wiki,35.9542493,-83.9307395,University of Tennessee,edu,7fab17ef7e25626643f1d55257a3e13348e435bd,citation,https://arxiv.org/pdf/1702.08423.pdf,Age Progression/Regression by Conditional Adversarial Autoencoder,2017 +77,IMDB,imdb_wiki,37.4102193,-122.05965487,Carnegie Mellon University,edu,ec05078be14a11157ac0e1c6b430ac886124589b,citation,http://pdfs.semanticscholar.org/ec05/078be14a11157ac0e1c6b430ac886124589b.pdf,Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches,2018 +78,IMDB,imdb_wiki,45.57022705,-122.63709346,Concordia University,edu,ec05078be14a11157ac0e1c6b430ac886124589b,citation,http://pdfs.semanticscholar.org/ec05/078be14a11157ac0e1c6b430ac886124589b.pdf,Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches,2018 +79,IMDB,imdb_wiki,40.00229045,116.32098908,Tsinghua University,edu,2149d49c84a83848d6051867290d9c8bfcef0edb,citation,https://doi.org/10.1109/TIFS.2017.2746062,Label-Sensitive Deep Metric Learning for Facial Age Estimation,2018 +80,IMDB,imdb_wiki,42.36782045,-71.12666653,Harvard University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 +81,IMDB,imdb_wiki,40.9153196,-73.1270626,Stony Brook University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 +82,IMDB,imdb_wiki,46.0658836,11.1159894,University of Trento,edu,df31e9c882dfb3ea5a3abe3b139ceacb1d90a302,citation,https://arxiv.org/pdf/1808.09211.pdf,DeepGUM: Learning Deep Robust Regression with a Gaussian-Uniform Mixture Model,2018 +83,IMDB,imdb_wiki,51.7534538,-1.25400997,University of Oxford,edu,523854a7d8755e944bd50217c14481fe1329a969,citation,https://arxiv.org/pdf/1808.00380.pdf,A Differentially Private Kernel Two-Sample Test,2018 +84,IMDB,imdb_wiki,51.49887085,-0.17560797,Imperial College London,edu,9b0489f2d5739213ef8c3e2e18739c4353c3a3b7,citation,http://pdfs.semanticscholar.org/9b04/89f2d5739213ef8c3e2e18739c4353c3a3b7.pdf,Visual Data Augmentation through Learning,2018 +85,IMDB,imdb_wiki,51.59029705,-0.22963221,Middlesex University,edu,9b0489f2d5739213ef8c3e2e18739c4353c3a3b7,citation,http://pdfs.semanticscholar.org/9b04/89f2d5739213ef8c3e2e18739c4353c3a3b7.pdf,Visual Data Augmentation through Learning,2018 +86,IMDB,imdb_wiki,40.00229045,116.32098908,Tsinghua University,edu,51f626540860ad75b68206025a45466a6d087aa6,citation,https://doi.org/10.1109/ICIP.2017.8296595,Cluster convolutional neural networks for facial age estimation,2017 +87,IMDB,imdb_wiki,49.2593879,-122.9151893,"AltumView Systems Inc., Burnaby, BC, Canada",company,b44f03b5fa8c6275238c2d13345652e6ff7e6ea9,citation,https://doi.org/10.1109/GlobalSIP.2017.8309138,Lapped convolutional neural networks for embedded systems,2017 +88,IMDB,imdb_wiki,39.2899685,-76.62196103,University of Maryland,edu,93420d9212dd15b3ef37f566e4d57e76bb2fab2f,citation,https://arxiv.org/pdf/1611.00851.pdf,An All-In-One Convolutional Neural Network for Face Analysis,2017 +89,IMDB,imdb_wiki,22.15263985,113.56803206,Macau University of Science and Technology,edu,56f231fc40424ed9a7c93cbc9f5a99d022e1d242,citation,http://pdfs.semanticscholar.org/d060/f2f3641c6a89ade021eea749414a5c6b443f.pdf,Age Estimation Based on a Single Network with Soft Softmax of Aging Modeling,2016 +90,IMDB,imdb_wiki,40.0044795,116.370238,Chinese Academy of Sciences,edu,56f231fc40424ed9a7c93cbc9f5a99d022e1d242,citation,http://pdfs.semanticscholar.org/d060/f2f3641c6a89ade021eea749414a5c6b443f.pdf,Age Estimation Based on a Single Network with Soft Softmax of Aging Modeling,2016 +91,IMDB,imdb_wiki,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,56f231fc40424ed9a7c93cbc9f5a99d022e1d242,citation,http://pdfs.semanticscholar.org/d060/f2f3641c6a89ade021eea749414a5c6b443f.pdf,Age Estimation Based on a Single Network with Soft Softmax of Aging Modeling,2016 +92,IMDB,imdb_wiki,43.614386,7.071125,EURECOM,edu,1648cf24c042122af2f429641ba9599a2187d605,citation,https://doi.org/10.1109/BTAS.2017.8272698,Boosting cross-age face verification via generative age normalization,2017 +93,IMDB,imdb_wiki,21.003952,105.84360183,Hanoi University of Science and Technology,edu,ca37933b6297cdca211aa7250cbe6b59f8be40e5,citation,http://doi.acm.org/10.1145/3155133.3155207,"Multi-task learning for smile detection, emotion recognition and gender classification",2017 +94,IMDB,imdb_wiki,51.49887085,-0.17560797,Imperial College London,edu,cf2002fac81ccdccdadb5cc43f7b1cd30882d2c2,citation,https://arxiv.org/pdf/1803.09546.pdf,Calibrated Prediction Intervals for Neural Network Regressors,2018 +95,IMDB,imdb_wiki,51.7534538,-1.25400997,University of Oxford,edu,75f9d3533f175943e33c9155f4038488f32a24bc,citation,https://arxiv.org/pdf/1811.06817.pdf,Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control,2018 +96,IMDB,imdb_wiki,32.8536333,-117.2035286,Kyung Hee University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +97,IMDB,imdb_wiki,24.7246403,46.62335012,King Saud University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +98,IMDB,imdb_wiki,23.7289899,90.3982682,Institute of Information Technology,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +99,IMDB,imdb_wiki,28.5456282,77.2731505,"IIIT Delhi, India",edu,f726738954e7055bb3615fa7e8f59f136d3e0bdc,citation,https://arxiv.org/pdf/1803.07385.pdf,Are you eligible? Predicting adulthood from face images via class specific mean autoencoder,2018 +100,IMDB,imdb_wiki,42.0551164,-87.67581113,Northwestern University,edu,c1586ee25e660f31cba0ca9ba5bf39ffcc020aab,citation,https://arxiv.org/pdf/1807.06708.pdf,A Modulation Module for Multi-task Learning with Applications in Image Retrieval,2018 +101,IMDB,imdb_wiki,37.4102193,-122.05965487,Carnegie Mellon University,edu,c1586ee25e660f31cba0ca9ba5bf39ffcc020aab,citation,https://arxiv.org/pdf/1807.06708.pdf,A Modulation Module for Multi-task Learning with Applications in Image Retrieval,2018 +102,IMDB,imdb_wiki,30.04287695,31.23664139,American University in Cairo,edu,3a2c90e0963bfb07fc7cd1b5061383e9a99c39d2,citation,https://arxiv.org/pdf/1710.03804.pdf,End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies,2017 +103,IMDB,imdb_wiki,31.83907195,117.26420748,University of Science and Technology of China,edu,47cd161546c59ab1e05f8841b82e985f72e5ddcb,citation,https://doi.org/10.1109/ICIP.2017.8296552,Gender classification in live videos,2017 +104,IMDB,imdb_wiki,39.2899685,-76.62196103,University of Maryland,edu,1491d0938bb4183bd19f2fee3b61997e1918160d,citation,https://arxiv.org/pdf/1807.00453.pdf,Elastic Neural Networks: A Scalable Framework for Embedded Computer Vision,2018 +105,IMDB,imdb_wiki,30.44235995,-84.29747867,Florida State University,edu,b88bace97d214d279e3a2053ccff0b6425295708,citation,https://arxiv.org/pdf/1803.11521.pdf,A Novel Framework for Online Supervised Learning with Feature Selection,2018 +106,IMDB,imdb_wiki,61.44964205,23.85877462,Tampere University of Technology,edu,b20cfbb2348984b4e25b6b9174f3c7b65b6aed9e,citation,http://pdfs.semanticscholar.org/b20c/fbb2348984b4e25b6b9174f3c7b65b6aed9e.pdf,Learning with Ambiguous Label Distribution for Apparent Age Estimation,2016 +107,IMDB,imdb_wiki,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,f3ec43a7b22f6e5414fec473acda8ffd843e7baf,citation,https://arxiv.org/pdf/1809.07447.pdf,A Coupled Evolutionary Network for Age Estimation,2018 +108,IMDB,imdb_wiki,39.94976005,116.33629046,Beijing Jiaotong University,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017 +109,IMDB,imdb_wiki,43.1576969,-77.58829158,University of Rochester,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017 +110,IMDB,imdb_wiki,1.2962018,103.77689944,National University of Singapore,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017 +111,IMDB,imdb_wiki,31.846918,117.29053367,Hefei University of Technology,edu,dc9c0527f8d4461b1742cccc7317fec8dd96d81a,citation,https://arxiv.org/pdf/1805.08373.pdf,Speeding-Up Age Estimation in Intelligent Demographics System via Network Optimization,2018 +112,IMDB,imdb_wiki,1.3484104,103.68297965,Nanyang Technological University,edu,dc9c0527f8d4461b1742cccc7317fec8dd96d81a,citation,https://arxiv.org/pdf/1805.08373.pdf,Speeding-Up Age Estimation in Intelligent Demographics System via Network Optimization,2018 +113,IMDB,imdb_wiki,43.614386,7.071125,EURECOM,edu,f7b422df567ce9813926461251517761e3e6cda0,citation,https://arxiv.org/pdf/1702.01983.pdf,Face aging with conditional generative adversarial networks,2017 +114,IMDB,imdb_wiki,21.003952,105.84360183,Hanoi University of Science and Technology,edu,68573e296f069071d071fc158e974e8bc70c893f,citation,https://pdfs.semanticscholar.org/6857/3e296f069071d071fc158e974e8bc70c893f.pdf,"Effective Deep Multi-source Multi-task 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Commercial,morph,25.0410728,121.6147562,Institute of Information Science,edu,4c71b0cdb6b80889b976e8eb4457942bd4dd7b66,citation,https://doi.org/10.1109/TIP.2014.2387379,A Learning Framework for Age Rank Estimation Based on Face Images With Scattering Transform,2015 +5,MORPH Commercial,morph,51.0267513,-1.3972576,"IBM Hursley Labs, UK",company,7123e510dea783035b02f6c35e35a1a09677c5ab,citation,https://doi.org/10.1109/ICPR.2016.7900297,Back to the future: A fully automatic method for robust age progression,2016 +6,MORPH Commercial,morph,35.9042272,-78.85565763,"IBM Research, North Carolina",company,7123e510dea783035b02f6c35e35a1a09677c5ab,citation,https://doi.org/10.1109/ICPR.2016.7900297,Back to the future: A fully automatic method for robust age progression,2016 +7,MORPH Commercial,morph,51.49887085,-0.17560797,Imperial College London,edu,7123e510dea783035b02f6c35e35a1a09677c5ab,citation,https://doi.org/10.1109/ICPR.2016.7900297,Back to the future: A fully automatic method for robust age 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University,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017 +34,MORPH Commercial,morph,43.1576969,-77.58829158,University of Rochester,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017 +35,MORPH Commercial,morph,1.2962018,103.77689944,National University of Singapore,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017 +36,MORPH Commercial,morph,40.0044795,116.370238,Chinese Academy of Sciences,edu,bd8b7599acf53e3053aa27cfd522764e28474e57,citation,http://www.jdl.ac.cn/doc/2009/iccv09_Learning%20Long%20Term%20Face%20Aging%20Patterns%20from%20Partially%20Dense%20Aging%20Databases.pdf,Learning long term face aging patterns from partially dense aging databases,2009 +37,MORPH Commercial,morph,39.9922379,116.30393816,Peking University,edu,bd8b7599acf53e3053aa27cfd522764e28474e57,citation,http://www.jdl.ac.cn/doc/2009/iccv09_Learning%20Long%20Term%20Face%20Aging%20Patterns%20from%20Partially%20Dense%20Aging%20Databases.pdf,Learning long term face aging patterns from partially dense aging databases,2009 +38,MORPH Commercial,morph,43.614386,7.071125,EURECOM,edu,70569810e46f476515fce80a602a210f8d9a2b95,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.105,Apparent Age Estimation from Face Images Combining General and Children-Specialized Deep Learning Models,2016 +39,MORPH Commercial,morph,39.9213097,32.7988233,"TOBB Economy and Technology University, Ankara, Turkey",edu,cc1ed45b02d7fffb42a0fd8cffe5f11792b6ea74,citation,https://doi.org/10.1109/SIU.2016.7495874,Analysis of the effect of image resolution on automatic face gender and age classification,2016 +40,MORPH Commercial,morph,-33.91758275,151.23124025,University of New South Wales,edu,29631ca6cff21c9199c70bcdbbcd5f812d331a96,citation,http://pdfs.semanticscholar.org/2963/1ca6cff21c9199c70bcdbbcd5f812d331a96.pdf,Error Rates in Users of Automatic Face Recognition Software,2015 +41,MORPH Commercial,morph,-33.88890695,151.18943366,University of Sydney,edu,29631ca6cff21c9199c70bcdbbcd5f812d331a96,citation,http://pdfs.semanticscholar.org/2963/1ca6cff21c9199c70bcdbbcd5f812d331a96.pdf,Error Rates in Users of Automatic Face Recognition Software,2015 +42,MORPH Commercial,morph,42.718568,-84.47791571,Michigan State University,edu,1a53ca294bbe5923c46a339955e8207907e9c8c6,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7273870,What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics,2016 +43,MORPH Commercial,morph,43.614386,7.071125,EURECOM,edu,1a53ca294bbe5923c46a339955e8207907e9c8c6,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7273870,What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics,2016 +44,MORPH Commercial,morph,40.4319722,-86.92389368,Purdue University,edu,c7c53d75f6e963b403057d8ba5952e4974a779ad,citation,https://pdfs.semanticscholar.org/c7c5/3d75f6e963b403057d8ba5952e4974a779ad.pdf,Aging effects in automated face recognition,2018 +45,MORPH Commercial,morph,41.02451875,28.97697953,Bahçeşehir University,edu,0c2370e156a4eb8d84a5fdb049c5a894c3431f1c,citation,https://doi.org/10.1109/CIBIM.2014.7015437,Biometric template update under facial aging,2014 +46,MORPH Commercial,morph,53.22853665,-0.54873472,University of Lincoln,edu,0c2370e156a4eb8d84a5fdb049c5a894c3431f1c,citation,https://doi.org/10.1109/CIBIM.2014.7015437,Biometric template update under facial aging,2014 +47,MORPH Commercial,morph,46.0810723,13.2119474,University of Udine,edu,0c2370e156a4eb8d84a5fdb049c5a894c3431f1c,citation,https://doi.org/10.1109/CIBIM.2014.7015437,Biometric template update under facial aging,2014 +48,MORPH Commercial,morph,25.0410728,121.6147562,Institute 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Technology,edu,3cc46bf79fb9225cf308815c7d41c8dd5625cc29,citation,http://poseidon.csd.auth.gr/papers/PUBLISHED/CONFERENCE/pdf/2016/Pantraki2016.pdf,Age interval and gender prediction using PARAFAC2 applied to speech utterances,2016 +132,MORPH Commercial,morph,23.09461185,113.28788994,Sun Yat-Sen University,edu,189e5a2fa51ed471c0e7227d82dffb52736070d8,citation,https://doi.org/10.1109/ICIP.2017.8296995,Cross-age face recognition using reference coding with kernel direct discriminant analysis,2017 +133,MORPH Commercial,morph,42.357757,-83.06286711,Wayne State University,edu,4f1249369127cc2e2894f6b2f1052d399794919a,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8239663,Deep Age Estimation: From Classification to Ranking,2018 +134,MORPH Commercial,morph,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,cd6aaa37fffd0b5c2320f386be322b8adaa1cc68,citation,https://arxiv.org/pdf/1804.06655.pdf,Deep Face Recognition: A Survey,2018 +135,MORPH 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Rome,edu,62750d78e819d745b9200b0c5c35fcae6fb9f404,citation,http://doi.org/10.1007/s11042-016-4085-8,Leveraging implicit demographic information for face recognition using a multi-expert system,2016 +146,MORPH Commercial,morph,40.845492,14.2578058,University of Naples Federico II,edu,62750d78e819d745b9200b0c5c35fcae6fb9f404,citation,http://doi.org/10.1007/s11042-016-4085-8,Leveraging implicit demographic information for face recognition using a multi-expert system,2016 +147,MORPH Commercial,morph,25.01353105,121.54173736,National Taiwan University of Science and Technology,edu,e4c3587392d477b7594086c6f28a00a826abf004,citation,https://doi.org/10.1109/ICIP.2017.8296998,Face recognition by facial attribute assisted network,2017 +148,MORPH Commercial,morph,39.9922379,116.30393816,Peking University,edu,c4ca092972abb74ee1c20b7cae6e69c654479e2c,citation,https://doi.org/10.1109/ICIP.2016.7532960,Linear canonical correlation analysis based ranking approach for facial age estimation,2016 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Thessaloniki,edu,1fd3dbb6e910708fa85c8a86e17ba0b6fef5617c,citation,http://pdfs.semanticscholar.org/1fd3/dbb6e910708fa85c8a86e17ba0b6fef5617c.pdf,Age interval and gender prediction using PARAFAC2 on speech recordings and face images,2016 +164,MORPH Commercial,morph,40.00229045,116.32098908,Tsinghua University,edu,6c6f0e806e4e286f3b18b934f42c72b67030ce17,citation,https://doi.org/10.1109/FG.2011.5771345,Combination of age and head pose for adult face verification,2011 +165,MORPH Commercial,morph,46.5190557,6.5667576,"Swiss Federal, Institute of Technology, Lausanne",edu,6c6f0e806e4e286f3b18b934f42c72b67030ce17,citation,https://doi.org/10.1109/FG.2011.5771345,Combination of age and head pose for adult face verification,2011 +166,MORPH Commercial,morph,52.6221571,1.2409136,University of East Anglia,edu,05a0d04693b2a51a8131d195c68ad9f5818b2ce1,citation,http://pdfs.semanticscholar.org/05a0/d04693b2a51a8131d195c68ad9f5818b2ce1.pdf,Dual-reference Face Retrieval: What Does He/She Look Like at 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is the influence across race and gender?,2010 +171,MORPH Commercial,morph,1.3484104,103.68297965,Nanyang Technological University,edu,b6a23f72007cb40223d7e1e1cc47e466716de945,citation,https://doi.org/10.1109/CVPRW.2010.5544598,Ordinary preserving manifold analysis for human age estimation,2010 +172,MORPH Commercial,morph,60.7897318,10.6821927,"Norwegian Biometrics Lab, NTNU, Norway",edu,0647c9d56cf11215894d57d677997826b22f6a13,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8401557,Transgender face recognition with off-the-shelf pre-trained CNNs: A comprehensive study,2018 +173,MORPH Commercial,morph,52.3553655,4.9501644,University of Amsterdam,edu,935a7793cbb8f102924fa34fce1049727de865c2,citation,https://doi.org/10.1109/ICIP.2015.7351554,Age estimation under changes in image quality: An experimental study,2015 +174,MORPH Commercial,morph,40.01407945,-105.26695944,"University of Colorado, 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University,edu,2a6783ae51d7ee781d584ef9a3eb8ab1997d0489,citation,https://doi.org/10.1109/CVPRW.2010.5543608,A study of large-scale ethnicity estimation with gender and age variations,2010 +186,MORPH Commercial,morph,39.65404635,-79.96475355,West Virginia University,edu,2a6783ae51d7ee781d584ef9a3eb8ab1997d0489,citation,https://doi.org/10.1109/CVPRW.2010.5543608,A study of large-scale ethnicity estimation with gender and age variations,2010 +187,MORPH Commercial,morph,40.00229045,116.32098908,Tsinghua University,edu,a53d13b9110cddb2a5f38b9d7ed69d328e3c6db9,citation,https://doi.org/10.1109/TIP.2015.2481327,Cost-Sensitive Local Binary Feature Learning for Facial Age Estimation,2015 +188,MORPH Commercial,morph,1.3484104,103.68297965,Nanyang Technological University,edu,a53d13b9110cddb2a5f38b9d7ed69d328e3c6db9,citation,https://doi.org/10.1109/TIP.2015.2481327,Cost-Sensitive Local Binary Feature Learning for Facial Age Estimation,2015 +189,MORPH 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Caen,edu,0ad8149318912b5449085187eb3521786a37bc78,citation,http://arxiv.org/abs/1604.02975,CP-mtML: Coupled Projection Multi-Task Metric Learning for Large Scale Face Retrieval,2016 +258,MORPH Commercial,morph,51.44415765,7.26096541,Ruhr-University Bochum,edu,7e1ea2679a110241ed0dd38ff45cd4dfeb7a8e83,citation,http://pdfs.semanticscholar.org/7e1e/a2679a110241ed0dd38ff45cd4dfeb7a8e83.pdf,Extensions of Hierarchical Slow Feature Analysis for Efficient Classification and Regression on High-Dimensional Data,2017 +259,MORPH Commercial,morph,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,2e27667421a7eeab278e0b761db4d2c725683c3f,citation,https://doi.org/10.1007/s11042-013-1815-z,Effective human age estimation using a two-stage approach based on Lie Algebrized Gaussians feature,2013 +260,MORPH Commercial,morph,32.0565957,118.77408833,Nanjing University,edu,0c741fa0966ba3ee4fc326e919bf2f9456d0cd74,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.51,Facial 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Sciences,edu,fca9ebaa30d69ccec8bb577c31693c936c869e72,citation,https://arxiv.org/pdf/1809.00338.pdf,Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition,2018 +265,MORPH Commercial,morph,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,cfdc4d0f8e1b4b9ced35317d12b4229f2e3311ab,citation,https://pdfs.semanticscholar.org/cfdc/4d0f8e1b4b9ced35317d12b4229f2e3311ab.pdf,Quaero at TRECVID 2010: Semantic Indexing,2010 +266,MORPH Commercial,morph,42.718568,-84.47791571,Michigan State University,edu,02d650d8a3a9daaba523433fbe93705df0a7f4b1,citation,http://pdfs.semanticscholar.org/02d6/50d8a3a9daaba523433fbe93705df0a7f4b1.pdf,How Does Aging Affect Facial Components?,2012 +267,MORPH Commercial,morph,34.67567405,33.04577648,Cyprus University of Technology,edu,70db3a0d2ca8a797153cc68506b8650908cb0ada,citation,http://pdfs.semanticscholar.org/70db/3a0d2ca8a797153cc68506b8650908cb0ada.pdf,An Overview of Research 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Science,edu,56359d2b4508cc267d185c1d6d310a1c4c2cc8c2,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2015.7298618,Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition,2015 +276,MORPH Commercial,morph,1.2962018,103.77689944,National University of Singapore,edu,56359d2b4508cc267d185c1d6d310a1c4c2cc8c2,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2015.7298618,Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition,2015 +277,MORPH Commercial,morph,32.0565957,118.77408833,Nanjing University,edu,a6e43b73f9f87588783988333997a81b4487e2d5,citation,http://pdfs.semanticscholar.org/a6e4/3b73f9f87588783988333997a81b4487e2d5.pdf,Facial Age Estimation by Total Ordering Preserving Projection,2016 +278,MORPH Commercial,morph,1.2988926,103.7873107,"Institution for Infocomm Research, Singapore",edu,8229f2735a0db0ad41f4d7252129311f06959907,citation,https://doi.org/10.1109/TIP.2011.2106794,Active Learning for Solving the Incomplete Data Problem in Facial Age Classification by the Furthest Nearest-Neighbor Criterion,2011 +279,MORPH Commercial,morph,1.3484104,103.68297965,Nanyang Technological University,edu,8229f2735a0db0ad41f4d7252129311f06959907,citation,https://doi.org/10.1109/TIP.2011.2106794,Active Learning for Solving the Incomplete Data Problem in Facial Age Classification by the Furthest Nearest-Neighbor Criterion,2011 +280,MORPH Commercial,morph,39.2899685,-76.62196103,University of Maryland,edu,963a004e208ce4bd26fa79a570af61d31651b3c3,citation,https://doi.org/10.1016/j.jvlc.2009.01.011,Computational methods for modeling facial aging: A survey,2009 +281,MORPH Commercial,morph,40.48256135,-3.6906079,Universidad Autonoma de Madrid,edu,4b5ff8c67f3496a414f94e35cb35a601ec98e5cf,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6547306,Understanding the discrimination power of facial regions in forensic casework,2013 +282,MORPH Commercial,morph,40.4445565,-3.7122785,"Dirección General de la Guardia Civil, Madrid, Spain",edu,4b5ff8c67f3496a414f94e35cb35a601ec98e5cf,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6547306,Understanding the discrimination power of facial regions in forensic casework,2013 +283,MORPH Commercial,morph,-37.8087465,144.9638875,RMIT University,edu,c49075ead6eb07ede5ada4fe372899bd0cfb83ac,citation,https://doi.org/10.1109/ICSPCS.2015.7391782,Multi-stage classification network for automatic age estimation from facial images,2015 +284,MORPH Commercial,morph,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,00301c250d667700276b1e573640ff2fd7be574d,citation,https://doi.org/10.1109/BTAS.2014.6996242,Establishing a test set and initial comparisons for quantitatively evaluating synthetic age progression for adult aging,2014 diff --git a/site/datasets/final/morph_nc.csv b/site/datasets/final/morph_nc.csv new file mode 100644 index 00000000..6ff0320b --- 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China,edu,ddd0f1c53f76d7fc20e11b7e33bbdc0437516d2b,citation,https://doi.org/10.1109/ICDSP.2016.7868598,Deep learning-based learning to rank with ties for image re-ranking,2016 +4,MORPH Non-Commercial,morph_nc,25.0410728,121.6147562,Institute of Information Science,edu,4c71b0cdb6b80889b976e8eb4457942bd4dd7b66,citation,https://doi.org/10.1109/TIP.2014.2387379,A Learning Framework for Age Rank Estimation Based on Face Images With Scattering Transform,2015 +5,MORPH Non-Commercial,morph_nc,51.0267513,-1.3972576,"IBM Hursley Labs, UK",company,7123e510dea783035b02f6c35e35a1a09677c5ab,citation,https://doi.org/10.1109/ICPR.2016.7900297,Back to the future: A fully automatic method for robust age progression,2016 +6,MORPH Non-Commercial,morph_nc,35.9042272,-78.85565763,"IBM Research, North Carolina",company,7123e510dea783035b02f6c35e35a1a09677c5ab,citation,https://doi.org/10.1109/ICPR.2016.7900297,Back to the future: A fully automatic method for robust age progression,2016 +7,MORPH Non-Commercial,morph_nc,51.49887085,-0.17560797,Imperial College London,edu,7123e510dea783035b02f6c35e35a1a09677c5ab,citation,https://doi.org/10.1109/ICPR.2016.7900297,Back to the future: A fully automatic method for robust age progression,2016 +8,MORPH Non-Commercial,morph_nc,35.5167538,139.48342251,Tokyo Institute of Technology,edu,3083d2c6d4f456e01cbb72930dc2207af98a6244,citation,http://pdfs.semanticscholar.org/3083/d2c6d4f456e01cbb72930dc2207af98a6244.pdf,Perceived Age Estimation from Face Images,2011 +9,MORPH Non-Commercial,morph_nc,41.3868913,2.16352385,University of Barcelona,edu,500fbe18afd44312738cab91b4689c12b4e0eeee,citation,http://www.maia.ub.es/~sergio/linked/ijcnn_age_and_cultural_2015.pdf,ChaLearn looking at people 2015 new competitions: Age estimation and cultural event recognition,2015 +10,MORPH Non-Commercial,morph_nc,45.4312742,12.3265377,University of Venezia,edu,500fbe18afd44312738cab91b4689c12b4e0eeee,citation,http://www.maia.ub.es/~sergio/linked/ijcnn_age_and_cultural_2015.pdf,ChaLearn looking at people 2015 new competitions: Age estimation and cultural event recognition,2015 +11,MORPH Non-Commercial,morph_nc,41.10427915,29.02231159,Istanbul Technical University,edu,fd53be2e0a9f33080a9db4b5a5e416e24ae8e198,citation,https://arxiv.org/pdf/1606.02909.pdf,Apparent Age Estimation Using Ensemble of Deep Learning Models,2016 +12,MORPH Non-Commercial,morph_nc,40.6341322,-8.6599726,"University of Beira Interior, Portugal",edu,81c21f4aafab39b7f5965829ec9e0f828d6a6182,citation,https://doi.org/10.1109/BTAS.2015.7358744,Acquiring high-resolution face images in outdoor environments: A master-slave calibration algorithm,2015 +13,MORPH Non-Commercial,morph_nc,42.36782045,-71.12666653,Harvard University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 +14,MORPH Non-Commercial,morph_nc,40.9153196,-73.1270626,Stony Brook University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 +15,MORPH Non-Commercial,morph_nc,40.47913175,-74.43168868,Rutgers University,edu,31f1e711fcf82c855f27396f181bf5e565a2f58d,citation,http://doi.ieeecomputersociety.org/10.1109/ICCVW.2015.54,Unconstrained Age Estimation with Deep Convolutional Neural Networks,2015 +16,MORPH Non-Commercial,morph_nc,39.2899685,-76.62196103,University of Maryland,edu,31f1e711fcf82c855f27396f181bf5e565a2f58d,citation,http://doi.ieeecomputersociety.org/10.1109/ICCVW.2015.54,Unconstrained Age Estimation with Deep Convolutional Neural Networks,2015 +17,MORPH Non-Commercial,morph_nc,39.65404635,-79.96475355,West Virginia University,edu,af12a79892bd030c19dfea392f7a7ccb0e7ebb72,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6247972,A study on human age estimation under facial expression changes,2012 +18,MORPH Non-Commercial,morph_nc,23.09461185,113.28788994,Sun Yat-Sen University,edu,2d7c2c015053fff5300515a7addcd74b523f3f66,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8323422,Age-Related Factor Guided Joint Task Modeling Convolutional Neural Network for Cross-Age Face Recognition,2018 +19,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,b234d429c9ea682e54fca52f4b889b3170f65ffc,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2012.22,A Concatenational Graph Evolution Aging Model,2012 +20,MORPH Non-Commercial,morph_nc,39.9922379,116.30393816,Peking University,edu,b234d429c9ea682e54fca52f4b889b3170f65ffc,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2012.22,A Concatenational Graph Evolution Aging Model,2012 +21,MORPH Non-Commercial,morph_nc,40.00229045,116.32098908,Tsinghua University,edu,b234d429c9ea682e54fca52f4b889b3170f65ffc,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2012.22,A Concatenational Graph Evolution Aging Model,2012 +22,MORPH Non-Commercial,morph_nc,30.19331415,120.11930822,Zhejiang University,edu,ee65cee5151928c63d3ef36fcbb582fabb2b6d2c,citation,https://doi.org/10.1109/LSP.2016.2602538,Structure-Aware Slow Feature Analysis for Age Estimation,2016 +23,MORPH Non-Commercial,morph_nc,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,ee65cee5151928c63d3ef36fcbb582fabb2b6d2c,citation,https://doi.org/10.1109/LSP.2016.2602538,Structure-Aware Slow Feature Analysis for Age Estimation,2016 +24,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,288964068cd87d97a98b8bc927d6e0d2349458a2,citation,https://pdfs.semanticscholar.org/2889/64068cd87d97a98b8bc927d6e0d2349458a2.pdf,Mean-Variance Loss for Deep Age Estimation 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University,edu,ffc81ced9ee8223ab0adb18817321cbee99606e6,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791157,A multibiometrics-based CAPTCHA for improved online security,2016 +29,MORPH Non-Commercial,morph_nc,41.25713055,-72.9896696,Yale University,edu,df7312cbabb7d75d915ba0d91dea77100ded5c56,citation,https://arxiv.org/pdf/1811.06446.pdf,Preliminary Studies on a Large Face Database,2018 +30,MORPH Non-Commercial,morph_nc,29.6328784,-82.3490133,University of Florida,edu,df7312cbabb7d75d915ba0d91dea77100ded5c56,citation,https://arxiv.org/pdf/1811.06446.pdf,Preliminary Studies on a Large Face Database,2018 +31,MORPH Non-Commercial,morph_nc,31.83907195,117.26420748,University of Science and Technology of China,edu,56c700693b63e3da3b985777da6d9256e2e0dc21,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1A_079.pdf,Global refinement of random forest,2015 +32,MORPH Non-Commercial,morph_nc,40.00229045,116.32098908,Tsinghua University,edu,1e344b99583b782e3eaf152cdfa15f217b781181,citation,http://doi.acm.org/10.1145/2499788.2499789,A new biologically inspired active appearance model for face age estimation by using local ordinal ranking,2013 +33,MORPH Non-Commercial,morph_nc,39.94976005,116.33629046,Beijing Jiaotong University,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017 +34,MORPH Non-Commercial,morph_nc,43.1576969,-77.58829158,University of Rochester,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017 +35,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017 +36,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,bd8b7599acf53e3053aa27cfd522764e28474e57,citation,http://www.jdl.ac.cn/doc/2009/iccv09_Learning%20Long%20Term%20Face%20Aging%20Patterns%20from%20Partially%20Dense%20Aging%20Databases.pdf,Learning long term face aging patterns from partially dense aging databases,2009 +37,MORPH Non-Commercial,morph_nc,39.9922379,116.30393816,Peking University,edu,bd8b7599acf53e3053aa27cfd522764e28474e57,citation,http://www.jdl.ac.cn/doc/2009/iccv09_Learning%20Long%20Term%20Face%20Aging%20Patterns%20from%20Partially%20Dense%20Aging%20Databases.pdf,Learning long term face aging patterns from partially dense aging databases,2009 +38,MORPH Non-Commercial,morph_nc,43.614386,7.071125,EURECOM,edu,70569810e46f476515fce80a602a210f8d9a2b95,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.105,Apparent Age Estimation from Face Images Combining General and Children-Specialized Deep Learning Models,2016 +39,MORPH Non-Commercial,morph_nc,39.9213097,32.7988233,"TOBB Economy and Technology University, Ankara, Turkey",edu,cc1ed45b02d7fffb42a0fd8cffe5f11792b6ea74,citation,https://doi.org/10.1109/SIU.2016.7495874,Analysis of the effect of image resolution on automatic face gender and age classification,2016 +40,MORPH Non-Commercial,morph_nc,-33.91758275,151.23124025,University of New South Wales,edu,29631ca6cff21c9199c70bcdbbcd5f812d331a96,citation,http://pdfs.semanticscholar.org/2963/1ca6cff21c9199c70bcdbbcd5f812d331a96.pdf,Error Rates in Users of Automatic Face Recognition Software,2015 +41,MORPH Non-Commercial,morph_nc,-33.88890695,151.18943366,University of Sydney,edu,29631ca6cff21c9199c70bcdbbcd5f812d331a96,citation,http://pdfs.semanticscholar.org/2963/1ca6cff21c9199c70bcdbbcd5f812d331a96.pdf,Error Rates in Users of Automatic Face Recognition Software,2015 +42,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,1a53ca294bbe5923c46a339955e8207907e9c8c6,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7273870,What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics,2016 +43,MORPH Non-Commercial,morph_nc,43.614386,7.071125,EURECOM,edu,1a53ca294bbe5923c46a339955e8207907e9c8c6,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7273870,What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics,2016 +44,MORPH Non-Commercial,morph_nc,40.4319722,-86.92389368,Purdue University,edu,c7c53d75f6e963b403057d8ba5952e4974a779ad,citation,https://pdfs.semanticscholar.org/c7c5/3d75f6e963b403057d8ba5952e4974a779ad.pdf,Aging effects in automated face recognition,2018 +45,MORPH Non-Commercial,morph_nc,41.02451875,28.97697953,Bahçeşehir University,edu,0c2370e156a4eb8d84a5fdb049c5a894c3431f1c,citation,https://doi.org/10.1109/CIBIM.2014.7015437,Biometric template update under facial aging,2014 +46,MORPH Non-Commercial,morph_nc,53.22853665,-0.54873472,University of Lincoln,edu,0c2370e156a4eb8d84a5fdb049c5a894c3431f1c,citation,https://doi.org/10.1109/CIBIM.2014.7015437,Biometric template update under facial aging,2014 +47,MORPH Non-Commercial,morph_nc,46.0810723,13.2119474,University of Udine,edu,0c2370e156a4eb8d84a5fdb049c5a894c3431f1c,citation,https://doi.org/10.1109/CIBIM.2014.7015437,Biometric template update under facial aging,2014 +48,MORPH Non-Commercial,morph_nc,25.0410728,121.6147562,Institute of Information Science,edu,1c17450c4d616e1e1eece248c42eba4f87de9e0d,citation,http://pdfs.semanticscholar.org/d269/39a00a8d3964de612cd3faa86764343d5622.pdf,Automatic Age Estimation from Face Images via Deep Ranking,2015 +49,MORPH Non-Commercial,morph_nc,43.47061295,-80.54724732,University of Waterloo,edu,f2902f5956d7e2dca536d9131d4334f85f52f783,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6460191,Facial age estimation using Clustered Multi-task Support Vector Regression Machine,2012 +50,MORPH Non-Commercial,morph_nc,39.65404635,-79.96475355,West Virginia University,edu,ba2bbef34f05551291410103e3de9e82fdf9dddd,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Guo_A_Study_on_2014_CVPR_paper.pdf,A Study on Cross-Population Age Estimation,2014 +51,MORPH Non-Commercial,morph_nc,31.32235655,121.38400941,Shanghai University,edu,d454ad60b061c1a1450810a0f335fafbfeceeccc,citation,https://arxiv.org/pdf/1712.07195.pdf,Deep Regression Forests for Age Estimation,2017 +52,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,ad2cb5c255e555d9767d526721a4c7053fa2ac58,citation,https://arxiv.org/pdf/1711.03990.pdf,Longitudinal Study of Child Face Recognition,2018 +53,MORPH Non-Commercial,morph_nc,39.95472495,-75.15346905,Temple University,edu,0cf2eecf20cfbcb7f153713479e3206670ea0e9c,citation,https://arxiv.org/pdf/1806.08906.pdf,Privacy-Protective-GAN for Face De-identification,2018 +54,MORPH Non-Commercial,morph_nc,31.32235655,121.38400941,Shanghai University,edu,c0b02be66a5a1907e8cfb8117de50f80b90a65a8,citation,http://doi.acm.org/10.1145/2808492.2808523,Manifold learning in sparse selected feature subspaces,2015 +55,MORPH Non-Commercial,morph_nc,47.6423318,-122.1369302,Microsoft,company,ff012c56b9b1de969328dacd13e26b7138ff298b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7762921,Facial Age Estimation With Age Difference,2017 +56,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,ff012c56b9b1de969328dacd13e26b7138ff298b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7762921,Facial Age Estimation With Age Difference,2017 +57,MORPH Non-Commercial,morph_nc,31.846918,117.29053367,Hefei University of Technology,edu,ff012c56b9b1de969328dacd13e26b7138ff298b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7762921,Facial Age Estimation With Age Difference,2017 +58,MORPH Non-Commercial,morph_nc,1.3484104,103.68297965,Nanyang Technological University,edu,ff012c56b9b1de969328dacd13e26b7138ff298b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7762921,Facial Age Estimation With Age Difference,2017 +59,MORPH Non-Commercial,morph_nc,40.00229045,116.32098908,Tsinghua University,edu,2149d49c84a83848d6051867290d9c8bfcef0edb,citation,https://doi.org/10.1109/TIFS.2017.2746062,Label-Sensitive Deep Metric Learning for Facial Age Estimation,2018 +60,MORPH Non-Commercial,morph_nc,25.0410728,121.6147562,Institute of Information Science,edu,c44c84540db1c38ace232ef34b03bda1c81ba039,citation,http://pdfs.semanticscholar.org/c44c/84540db1c38ace232ef34b03bda1c81ba039.pdf,Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval,2014 +61,MORPH Non-Commercial,morph_nc,25.01682835,121.53846924,National Taiwan University,edu,c44c84540db1c38ace232ef34b03bda1c81ba039,citation,http://pdfs.semanticscholar.org/c44c/84540db1c38ace232ef34b03bda1c81ba039.pdf,Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval,2014 +62,MORPH Non-Commercial,morph_nc,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018 +63,MORPH Non-Commercial,morph_nc,39.2899685,-76.62196103,University of Maryland,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018 +64,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,2f2406551c693d616a840719ae1e6ea448e2f5d3,citation,http://biometrics.cse.msu.edu/Presentations/CharlesOtto_ICB13_AgeEstimationFaceImages_HumanVsMachinePerformance.pdf,Age estimation from face images: Human vs. machine performance,2013 +65,MORPH Non-Commercial,morph_nc,1.3037257,103.7737763,"Advanced Digital Sciences Center, Singapore",edu,15fbb5fc3bdd692a6b2dd737cce7f39f7c89a25c,citation,https://doi.org/10.1109/TMM.2011.2167317,Web Image and Video Mining Towards Universal and Robust Age Estimator,2011 +66,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,15fbb5fc3bdd692a6b2dd737cce7f39f7c89a25c,citation,https://doi.org/10.1109/TMM.2011.2167317,Web Image and Video Mining Towards Universal and Robust Age Estimator,2011 +67,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,b446bcd7fb78adfe346cf7a01a38e4f43760f363,citation,http://pdfs.semanticscholar.org/b446/bcd7fb78adfe346cf7a01a38e4f43760f363.pdf,To appear in ICB 2018 Longitudinal Study of Child Face Recognition,2017 +68,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,c035c193eed5d72c7f187f0bc880a17d217dada0,citation,http://pdfs.semanticscholar.org/c035/c193eed5d72c7f187f0bc880a17d217dada0.pdf,"Local Gradient Gabor Pattern (LGGP) with Applications in Face Recognition, Cross-spectral Matching and Soft Biometrics",2013 +69,MORPH Non-Commercial,morph_nc,39.65404635,-79.96475355,West Virginia University,edu,c035c193eed5d72c7f187f0bc880a17d217dada0,citation,http://pdfs.semanticscholar.org/c035/c193eed5d72c7f187f0bc880a17d217dada0.pdf,"Local Gradient Gabor Pattern (LGGP) with Applications in Face Recognition, Cross-spectral Matching and Soft Biometrics",2013 +70,MORPH Non-Commercial,morph_nc,34.66869155,-82.83743476,Clemson University,edu,c907104680ad53bdc673f2648d713e4d26335825,citation,http://doi.acm.org/10.1145/3077286.3077304,Dataset and Metrics for Adult Age-Progression Evaluation,2017 +71,MORPH Non-Commercial,morph_nc,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,c907104680ad53bdc673f2648d713e4d26335825,citation,http://doi.acm.org/10.1145/3077286.3077304,Dataset and Metrics for Adult Age-Progression Evaluation,2017 +72,MORPH Non-Commercial,morph_nc,37.5600406,126.9369248,Yonsei University,edu,fde41dc4ec6ac6474194b99e05b43dd6a6c4f06f,citation,https://arxiv.org/pdf/1809.01990.pdf,Multi-Expert Gender Classification on Age Group by Integrating Deep Neural Networks,2018 +73,MORPH Non-Commercial,morph_nc,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,31a36014354ee7c89aa6d94e656db77922b180a5,citation,http://doi.acm.org/10.1145/2304496.2304509,An interactive tool for extremely dense landmarking of faces,2012 +74,MORPH Non-Commercial,morph_nc,37.5901411,127.0362318,Korea University,edu,4b519e2e88ccd45718b0fc65bfd82ebe103902f7,citation,http://biometrics.cse.msu.edu/Publications/Face/LiParkJain_DiscriminativeModelAgeInvariantFR_TIFS11.pdf,A Discriminative Model for Age Invariant Face Recognition,2011 +75,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,4b519e2e88ccd45718b0fc65bfd82ebe103902f7,citation,http://biometrics.cse.msu.edu/Publications/Face/LiParkJain_DiscriminativeModelAgeInvariantFR_TIFS11.pdf,A 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USA",company,604a281100784b4d5bc1a6db993d423abc5dc8f0,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5353681,Face Verification Across Age Progression Using Discriminative Methods,2010 +80,MORPH Non-Commercial,morph_nc,39.2899685,-76.62196103,University of Maryland,edu,604a281100784b4d5bc1a6db993d423abc5dc8f0,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5353681,Face Verification Across Age Progression Using Discriminative Methods,2010 +81,MORPH Non-Commercial,morph_nc,39.95472495,-75.15346905,Temple University,edu,604a281100784b4d5bc1a6db993d423abc5dc8f0,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5353681,Face Verification Across Age Progression Using Discriminative Methods,2010 +82,MORPH Non-Commercial,morph_nc,51.2975344,1.07296165,University of Kent,edu,6486b36c6f7fd7675257d26e896223a02a1881d9,citation,https://doi.org/10.1109/THMS.2014.2376874,Selective Review and Analysis of Aging Effects in Biometric System Implementation,2015 +83,MORPH Non-Commercial,morph_nc,22.42031295,114.20788644,Chinese University of Hong Kong,edu,16bce9f940bb01aa5ec961892cc021d4664eb9e4,citation,http://www.cise.ufl.edu/~dihong/assets/TIST-2014-10-0214.R2.pdf,Mutual Component Analysis for Heterogeneous Face Recognition,2016 +84,MORPH Non-Commercial,morph_nc,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,16bce9f940bb01aa5ec961892cc021d4664eb9e4,citation,http://www.cise.ufl.edu/~dihong/assets/TIST-2014-10-0214.R2.pdf,Mutual Component Analysis for Heterogeneous Face Recognition,2016 +85,MORPH Non-Commercial,morph_nc,34.67567405,33.04577648,Cyprus University of Technology,edu,9d3aa3b7d392fad596b067b13b9e42443bbc377c,citation,http://pdfs.semanticscholar.org/9d3a/a3b7d392fad596b067b13b9e42443bbc377c.pdf,Facial Biometric Templates and Aging: Problems and Challenges for Artificial Intelligence,2009 +86,MORPH Non-Commercial,morph_nc,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,217a21d60bb777d15cd9328970cab563d70b5d23,citation,http://www.cise.ufl.edu/~dihong/assets/iccv2013.pdf,Hidden Factor Analysis for Age Invariant Face Recognition,2013 +87,MORPH Non-Commercial,morph_nc,22.42031295,114.20788644,Chinese University of Hong Kong,edu,217a21d60bb777d15cd9328970cab563d70b5d23,citation,http://www.cise.ufl.edu/~dihong/assets/iccv2013.pdf,Hidden Factor Analysis for Age Invariant Face Recognition,2013 +88,MORPH Non-Commercial,morph_nc,32.0565957,118.77408833,Nanjing University,edu,b1bb517bd87a1212174033fc786b2237844b04e6,citation,https://doi.org/10.1016/j.neucom.2015.03.078,Cumulative attribute relation regularization learning for human age estimation,2015 +89,MORPH Non-Commercial,morph_nc,40.8419836,-73.94368971,Columbia University,edu,a0dc68c546e0fc72eb0d9ca822cf0c9ccb4b4c4f,citation,http://www.cs.columbia.edu/~neeraj/base/papers/nk_ijcb2011_fusion.pdf,Fusing with context: A Bayesian approach to combining descriptive attributes,2011 +90,MORPH Non-Commercial,morph_nc,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,a0dc68c546e0fc72eb0d9ca822cf0c9ccb4b4c4f,citation,http://www.cs.columbia.edu/~neeraj/base/papers/nk_ijcb2011_fusion.pdf,Fusing with context: A Bayesian approach to combining descriptive attributes,2011 +91,MORPH Non-Commercial,morph_nc,1.3484104,103.68297965,Nanyang Technological University,edu,d119443de1d75cad384d897c2ed5a7b9c1661d98,citation,https://doi.org/10.1109/ICIP.2010.5650873,Cost-sensitive subspace learning for human age estimation,2010 +92,MORPH Non-Commercial,morph_nc,34.2249827,-77.86907744,University of North Carolina at Wilmington,edu,97c59db934ff85c60c460a4591106682b5ab9caa,citation,https://doi.org/10.1109/BTAS.2012.6374568,Extremely dense face registration: Comparing automatic landmarking algorithms for general and ethno-gender models,2012 +93,MORPH Non-Commercial,morph_nc,43.2213516,-75.4085577,"Air Force Research Lab, Rome, NY",mil,834736698f2cc5c221c22369abe95515243a9fc3,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6996249,GARP-face: Balancing privacy protection and utility preservation in face de-identification,2014 +94,MORPH Non-Commercial,morph_nc,39.95472495,-75.15346905,Temple University,edu,834736698f2cc5c221c22369abe95515243a9fc3,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6996249,GARP-face: Balancing privacy protection and utility preservation in face de-identification,2014 +95,MORPH Non-Commercial,morph_nc,32.0575279,118.78682252,Southeast University,edu,3cb488a3b71f221a8616716a1fc2b951dd0de549,citation,http://doi.ieeecomputersociety.org/10.1109/ICPR.2014.764,Facial Age Estimation by Adaptive Label Distribution Learning,2014 +96,MORPH Non-Commercial,morph_nc,22.3386304,114.2620337,Hong Kong University of Science and Technology,edu,8000c4f278e9af4d087c0d0895fff7012c5e3d78,citation,https://www.cse.ust.hk/~yuzhangcse/papers/Zhang_Yeung_CVPR10.pdf,Multi-task warped Gaussian process for personalized age estimation,2010 +97,MORPH Non-Commercial,morph_nc,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,59fe66eeb06d1a7e1496a85f7ffc7b37512cd7e5,citation,http://doi.ieeecomputersociety.org/10.1109/ICME.2016.7552862,Robust feature encoding for age-invariant face recognition,2016 +98,MORPH Non-Commercial,morph_nc,23.0502042,113.39880323,South China University of Technology,edu,4bd3de97b256b96556d19a5db71dda519934fd53,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.529,Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition,2016 +99,MORPH Non-Commercial,morph_nc,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,4bd3de97b256b96556d19a5db71dda519934fd53,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.529,Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition,2016 +100,MORPH Non-Commercial,morph_nc,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,1d3dd9aba79a53390317ec1e0b7cd742cba43132,citation,http://www.cise.ufl.edu/~dihong/assets/Gong_A_Maximum_Entropy_2015_CVPR_paper.pdf,A maximum entropy feature descriptor for age invariant face recognition,2015 +101,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,1d3dd9aba79a53390317ec1e0b7cd742cba43132,citation,http://www.cise.ufl.edu/~dihong/assets/Gong_A_Maximum_Entropy_2015_CVPR_paper.pdf,A maximum entropy feature descriptor for age invariant face recognition,2015 +102,MORPH Non-Commercial,morph_nc,22.42031295,114.20788644,Chinese University of Hong Kong,edu,1d3dd9aba79a53390317ec1e0b7cd742cba43132,citation,http://www.cise.ufl.edu/~dihong/assets/Gong_A_Maximum_Entropy_2015_CVPR_paper.pdf,A maximum entropy feature descriptor for age invariant face recognition,2015 +103,MORPH Non-Commercial,morph_nc,32.0575279,118.78682252,Southeast University,edu,1c530de1a94ac70bf9086e39af1712ea8d2d2781,citation,http://pdfs.semanticscholar.org/1c53/0de1a94ac70bf9086e39af1712ea8d2d2781.pdf,Sparsity Conditional Energy Label Distribution Learning for Age Estimation,2016 +104,MORPH Non-Commercial,morph_nc,37.4102193,-122.05965487,Carnegie Mellon University,edu,eb8519cec0d7a781923f68fdca0891713cb81163,citation,https://arxiv.org/pdf/1703.08617.pdf,Temporal Non-volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition,2017 +105,MORPH Non-Commercial,morph_nc,45.57022705,-122.63709346,Concordia University,edu,eb8519cec0d7a781923f68fdca0891713cb81163,citation,https://arxiv.org/pdf/1703.08617.pdf,Temporal Non-volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition,2017 +106,MORPH Non-Commercial,morph_nc,57.6252103,39.8845656,Yaroslavl State University,edu,cfaf61bacf61901b7e1ac25b779a1f87c1e8cf7f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6737950,Application 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Process for Automatic Age Estimation,2017 +118,MORPH Non-Commercial,morph_nc,-33.88890695,151.18943366,University of Sydney,edu,2a7e6a1b2638550370a47f2f6f6e38e76fe9ac13,citation,http://doi.acm.org/10.1145/3090311,Multifeature Anisotropic Orthogonal Gaussian Process for Automatic Age Estimation,2017 +119,MORPH Non-Commercial,morph_nc,51.2975344,1.07296165,University of Kent,edu,2336de3a81dada63eb00ea82f7570c4069342fb5,citation,http://doi.acm.org/10.1145/2361407.2361428,A methodological framework for investigating age factors on the performance of biometric systems,2012 +120,MORPH Non-Commercial,morph_nc,39.2899685,-76.62196103,University of Maryland,edu,93420d9212dd15b3ef37f566e4d57e76bb2fab2f,citation,https://arxiv.org/pdf/1611.00851.pdf,An All-In-One Convolutional Neural Network for Face Analysis,2017 +121,MORPH Non-Commercial,morph_nc,39.95472495,-75.15346905,Temple University,edu,019e471667c72b5b3728b4a9ba9fe301a7426fb2,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/2A_012.pdf,Cross-age face verification by coordinating with cross-face age verification,2015 +122,MORPH Non-Commercial,morph_nc,45.57022705,-122.63709346,Concordia University,edu,c418a3441f992fea523926f837f4bfb742548c16,citation,http://pdfs.semanticscholar.org/c418/a3441f992fea523926f837f4bfb742548c16.pdf,A Computer Approach for Face Aging Problems,2010 +123,MORPH Non-Commercial,morph_nc,22.42031295,114.20788644,Chinese University of Hong Kong,edu,d80a3d1f3a438e02a6685e66ee908446766fefa9,citation,https://arxiv.org/pdf/1708.09687.pdf,Quantifying Facial Age by Posterior of Age Comparisons,2017 +124,MORPH Non-Commercial,morph_nc,34.67567405,33.04577648,Cyprus University of Technology,edu,ebbceab4e15bf641f74e335b70c6c4490a043961,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4813349,Evaluating the performance of face-aging algorithms,2008 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University,edu,2050847bc7a1a0453891f03aeeb4643e360fde7d,citation,https://cvhci.anthropomatik.kit.edu/~mtapaswi/papers/ICMR2015.pdf,Accio: A Data Set for Face Track Retrieval in Movies Across Age,2015 +129,MORPH Non-Commercial,morph_nc,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,2050847bc7a1a0453891f03aeeb4643e360fde7d,citation,https://cvhci.anthropomatik.kit.edu/~mtapaswi/papers/ICMR2015.pdf,Accio: A Data Set for Face Track Retrieval in Movies Across Age,2015 +130,MORPH Non-Commercial,morph_nc,40.62984145,22.9588935,Aristotle University of Thessaloniki,edu,3cc46bf79fb9225cf308815c7d41c8dd5625cc29,citation,http://poseidon.csd.auth.gr/papers/PUBLISHED/CONFERENCE/pdf/2016/Pantraki2016.pdf,Age interval and gender prediction using PARAFAC2 applied to speech utterances,2016 +131,MORPH Non-Commercial,morph_nc,34.67567405,33.04577648,Cyprus University of Technology,edu,3cc46bf79fb9225cf308815c7d41c8dd5625cc29,citation,http://poseidon.csd.auth.gr/papers/PUBLISHED/CONFERENCE/pdf/2016/Pantraki2016.pdf,Age interval and gender prediction using PARAFAC2 applied to speech utterances,2016 +132,MORPH Non-Commercial,morph_nc,23.09461185,113.28788994,Sun Yat-Sen University,edu,189e5a2fa51ed471c0e7227d82dffb52736070d8,citation,https://doi.org/10.1109/ICIP.2017.8296995,Cross-age face recognition using reference coding with kernel direct discriminant analysis,2017 +133,MORPH Non-Commercial,morph_nc,42.357757,-83.06286711,Wayne State University,edu,4f1249369127cc2e2894f6b2f1052d399794919a,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8239663,Deep Age Estimation: From Classification to Ranking,2018 +134,MORPH Non-Commercial,morph_nc,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,cd6aaa37fffd0b5c2320f386be322b8adaa1cc68,citation,https://arxiv.org/pdf/1804.06655.pdf,Deep Face Recognition: A Survey,2018 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California,edu,eb6ee56e085ebf473da990d032a4249437a3e462,citation,http://www-scf.usc.edu/~chuntinh/doc/Age_Gender_Classification_APSIPA_2017.pdf,Age/gender classification with whole-component convolutional neural networks (WC-CNN),2017 +142,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,e506cdb250eba5e70c5147eb477fbd069714765b,citation,https://pdfs.semanticscholar.org/e506/cdb250eba5e70c5147eb477fbd069714765b.pdf,Heterogeneous Face Recognition,2012 +143,MORPH Non-Commercial,morph_nc,35.90503535,-79.04775327,University of North Carolina,edu,f374ac9307be5f25145b44931f5a53b388a77e49,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5339060,Improvements in Active Appearance Model based synthetic age progression for adult aging,2009 +144,MORPH Non-Commercial,morph_nc,38.83133325,-77.30798839,George Mason University,edu,62750d78e819d745b9200b0c5c35fcae6fb9f404,citation,http://doi.org/10.1007/s11042-016-4085-8,Leveraging implicit demographic information for face recognition using a multi-expert system,2016 +145,MORPH Non-Commercial,morph_nc,41.9037626,12.5144384,Sapienza University of Rome,edu,62750d78e819d745b9200b0c5c35fcae6fb9f404,citation,http://doi.org/10.1007/s11042-016-4085-8,Leveraging implicit demographic information for face recognition using a multi-expert system,2016 +146,MORPH Non-Commercial,morph_nc,40.845492,14.2578058,University of Naples Federico II,edu,62750d78e819d745b9200b0c5c35fcae6fb9f404,citation,http://doi.org/10.1007/s11042-016-4085-8,Leveraging implicit demographic information for face recognition using a multi-expert system,2016 +147,MORPH Non-Commercial,morph_nc,25.01353105,121.54173736,National Taiwan University of Science and Technology,edu,e4c3587392d477b7594086c6f28a00a826abf004,citation,https://doi.org/10.1109/ICIP.2017.8296998,Face recognition by facial attribute assisted network,2017 +148,MORPH Non-Commercial,morph_nc,39.9922379,116.30393816,Peking University,edu,c4ca092972abb74ee1c20b7cae6e69c654479e2c,citation,https://doi.org/10.1109/ICIP.2016.7532960,Linear canonical correlation analysis based ranking approach for facial age estimation,2016 +149,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,575141e42740564f64d9be8ab88d495192f5b3bc,citation,http://pdfs.semanticscholar.org/5751/41e42740564f64d9be8ab88d495192f5b3bc.pdf,Age Estimation Based on Multi-Region Convolutional Neural Network,2016 +150,MORPH Non-Commercial,morph_nc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,575141e42740564f64d9be8ab88d495192f5b3bc,citation,http://pdfs.semanticscholar.org/5751/41e42740564f64d9be8ab88d495192f5b3bc.pdf,Age Estimation Based on Multi-Region Convolutional Neural Network,2016 +151,MORPH Non-Commercial,morph_nc,56.66340325,12.87929727,Halmstad University,edu,555f75077a02f33a05841f9b63a1388ec5fbcba5,citation,https://arxiv.org/pdf/1810.03360.pdf,A Survey on Periocular Biometrics Research,2016 +152,MORPH Non-Commercial,morph_nc,39.94976005,116.33629046,Beijing Jiaotong University,edu,0821028073981f9bd2dba2ad2557b25403fe7d7d,citation,http://doi.acm.org/10.1145/2733373.2806318,Facial Age Estimation Based on Structured Low-rank Representation,2015 +153,MORPH Non-Commercial,morph_nc,46.109237,7.08453549,IDIAP Research Institute,edu,939123cf21dc9189a03671484c734091b240183e,citation,http://publications.idiap.ch/downloads/papers/2015/Erdogmus_MMSP_2015.pdf,Within- and cross- database evaluations for face gender classification via befit protocols,2014 +154,MORPH Non-Commercial,morph_nc,36.689487,2.981877,"Center for Development of Advanced Technologies, Algeria",edu,4551194408383b12db19a22cca5db0f185cced5c,citation,https://doi.org/10.1109/TNNLS.2014.2341634,Nonlinear Topological Component Analysis: Application to Age-Invariant Face Recognition,2015 +155,MORPH Non-Commercial,morph_nc,56.45796755,-2.98214831,University of 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Korea",edu,abbc6dcbd032ff80e0535850f1bc27c4610b0d45,citation,https://doi.org/10.1109/ICIP.2015.7350983,Facial age estimation via extended curvature Gabor filter,2015 +159,MORPH Non-Commercial,morph_nc,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,abbc6dcbd032ff80e0535850f1bc27c4610b0d45,citation,https://doi.org/10.1109/ICIP.2015.7350983,Facial age estimation via extended curvature Gabor filter,2015 +160,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,989332c5f1b22604d6bb1f78e606cb6b1f694e1a,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Wang_Recurrent_Face_Aging_CVPR_2016_paper.pdf,Recurrent Face Aging,2016 +161,MORPH Non-Commercial,morph_nc,32.0575279,118.78682252,Southeast University,edu,989332c5f1b22604d6bb1f78e606cb6b1f694e1a,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Wang_Recurrent_Face_Aging_CVPR_2016_paper.pdf,Recurrent Face Aging,2016 +162,MORPH 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Lausanne",edu,6c6f0e806e4e286f3b18b934f42c72b67030ce17,citation,https://doi.org/10.1109/FG.2011.5771345,Combination of age and head pose for adult face verification,2011 +166,MORPH Non-Commercial,morph_nc,52.6221571,1.2409136,University of East Anglia,edu,05a0d04693b2a51a8131d195c68ad9f5818b2ce1,citation,http://pdfs.semanticscholar.org/05a0/d04693b2a51a8131d195c68ad9f5818b2ce1.pdf,Dual-reference Face Retrieval: What Does He/She Look Like at Age 'X'?,2017 +167,MORPH Non-Commercial,morph_nc,40.44415295,-79.96243993,University of Pittsburgh,edu,05a0d04693b2a51a8131d195c68ad9f5818b2ce1,citation,http://pdfs.semanticscholar.org/05a0/d04693b2a51a8131d195c68ad9f5818b2ce1.pdf,Dual-reference Face Retrieval: What Does He/She Look Like at Age 'X'?,2017 +168,MORPH Non-Commercial,morph_nc,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,387b54cf6c186c12d83f95df6bd458c5eb1254ee,citation,https://doi.org/10.1109/VCIP.2017.8305123,Deep probabilities for age estimation,2017 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Norway",edu,0647c9d56cf11215894d57d677997826b22f6a13,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8401557,Transgender face recognition with off-the-shelf pre-trained CNNs: A comprehensive study,2018 +173,MORPH Non-Commercial,morph_nc,52.3553655,4.9501644,University of Amsterdam,edu,935a7793cbb8f102924fa34fce1049727de865c2,citation,https://doi.org/10.1109/ICIP.2015.7351554,Age estimation under changes in image quality: An experimental study,2015 +174,MORPH Non-Commercial,morph_nc,40.01407945,-105.26695944,"University of Colorado, Boulder",edu,4aabd6db4594212019c9af89b3e66f39f3108aac,citation,http://pdfs.semanticscholar.org/4aab/d6db4594212019c9af89b3e66f39f3108aac.pdf,The Mere Exposure Effect and Classical Conditioning,2015 +175,MORPH Non-Commercial,morph_nc,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,73d15a975b0595e0cc2e0981a9396a89c474dc7e,citation,https://arxiv.org/pdf/1811.03680.pdf,Gender Effect on Face Recognition for a Large Longitudinal 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Singapore,edu,8cffe360a05085d4bcba111a3a3cd113d96c0369,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2011.6126248,Learning universal multi-view age estimator using video context,2011 +180,MORPH Non-Commercial,morph_nc,23.143197,113.34009651,South China Normal University,edu,dc6ad30c7a4bc79bb06b4725b16e202d3d7d8935,citation,http://doi.org/10.1007/s11042-017-4646-5,Age classification with deep learning face representation,2017 +181,MORPH Non-Commercial,morph_nc,23.0502042,113.39880323,South China University of Technology,edu,dc6ad30c7a4bc79bb06b4725b16e202d3d7d8935,citation,http://doi.org/10.1007/s11042-017-4646-5,Age classification with deep learning face representation,2017 +182,MORPH Non-Commercial,morph_nc,50.0764296,14.41802312,Czech Technical University,edu,023ed32ac3ea6029f09b8c582efbe3866de7d00a,citation,http://pdfs.semanticscholar.org/023e/d32ac3ea6029f09b8c582efbe3866de7d00a.pdf,Discriminative learning from partially annotated examples,2016 +183,MORPH 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University,edu,e22adcd2a6a7544f017ec875ce8f89d5c59e09c8,citation,https://arxiv.org/pdf/1807.11936.pdf,Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers,2018 +191,MORPH Non-Commercial,morph_nc,25.01682835,121.53846924,National Taiwan University,edu,6ab33fa51467595f18a7a22f1d356323876f8262,citation,http://www.iis.sinica.edu.tw/~kuangyu/OHRank_files/0523.pdf,Ordinal hyperplanes ranker with cost sensitivities for age estimation,2011 +192,MORPH Non-Commercial,morph_nc,25.0410728,121.6147562,Institute of Information Science,edu,6ab33fa51467595f18a7a22f1d356323876f8262,citation,http://www.iis.sinica.edu.tw/~kuangyu/OHRank_files/0523.pdf,Ordinal hyperplanes ranker with cost sensitivities for age estimation,2011 +193,MORPH Non-Commercial,morph_nc,25.0411727,121.6146518,"Academia Sinica, Taiwan",edu,6ab33fa51467595f18a7a22f1d356323876f8262,citation,http://www.iis.sinica.edu.tw/~kuangyu/OHRank_files/0523.pdf,Ordinal hyperplanes ranker with cost 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University,edu,fcb97ede372c5bddde7a61924ac2fd29788c82ce,citation,https://doi.org/10.1109/TSMCC.2012.2192727,Ordinary Preserving Manifold Analysis for Human Age and Head Pose Estimation,2013 +198,MORPH Non-Commercial,morph_nc,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,cb27b45329d61f5f95ed213798d4b2a615e76be2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8329236,Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion,2018 +199,MORPH Non-Commercial,morph_nc,37.2520226,127.0555019,"Samsung SAIT, Korea",company,cb27b45329d61f5f95ed213798d4b2a615e76be2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8329236,Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion,2018 +200,MORPH Non-Commercial,morph_nc,35.14479945,33.90492318,Eastern Mediterranean University,edu,c5421a18583f629b49ca20577022f201692c4f5d,citation,http://pdfs.semanticscholar.org/c542/1a18583f629b49ca20577022f201692c4f5d.pdf,Facial Age Classification using Subpattern-based Approaches,2011 +201,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,68c4a1d438ea1c6dfba92e3aee08d48f8e7f7090,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Liu_AgeNet_Deeply_Learned_ICCV_2015_paper.pdf,AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation,2015 +202,MORPH Non-Commercial,morph_nc,31.32235655,121.38400941,Shanghai University,edu,5f0d4a0b5f72d8700cdf8cb179263a8fa866b59b,citation,https://pdfs.semanticscholar.org/5f0d/4a0b5f72d8700cdf8cb179263a8fa866b59b.pdf,Memo No . 85 06 / 2018 Deep Regression Forests for Age Estimation,2018 +203,MORPH Non-Commercial,morph_nc,24.96841805,121.19139696,National Central University,edu,c58ece1a3fa23608f022e424ec5a93cddda31308,citation,https://doi.org/10.1109/JSYST.2014.2325957,Extraction of Visual Facial Features for Health Management,2016 +204,MORPH Non-Commercial,morph_nc,50.0764296,14.41802312,Czech Technical University,edu,56e25358ebfaf8a8b3c7c33ed007e24f026065d0,citation,https://doi.org/10.1007/s10994-015-5541-9,V-shaped interval insensitive loss for ordinal classification,2015 +205,MORPH Non-Commercial,morph_nc,5.7648848,102.6281702,"University Sultan Zainal Abidin, Malaysia",edu,3337cfc3de2c16dee6f7cbeda5f263409a9ad81e,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8398675,Age prediction on face features via multiple classifiers,2018 +206,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,2836d68c86f29bb87537ea6066d508fde838ad71,citation,http://arxiv.org/pdf/1510.06503v1.pdf,Personalized Age Progression with Aging Dictionary,2015 +207,MORPH 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Sciences,edu,d492dbfaa42b4f8b8a74786d7343b3be6a3e9a1d,citation,https://pdfs.semanticscholar.org/d492/dbfaa42b4f8b8a74786d7343b3be6a3e9a1d.pdf,Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation,0 +211,MORPH Non-Commercial,morph_nc,34.67567405,33.04577648,Cyprus University of Technology,edu,fa518a033b1f6299d1826389bd1520cf52291b56,citation,https://pdfs.semanticscholar.org/fa51/8a033b1f6299d1826389bd1520cf52291b56.pdf,Facial Age Simulation using Age-specific 3D Models and Recursive PCA,2013 +212,MORPH Non-Commercial,morph_nc,38.83133325,-77.30798839,George Mason University,edu,1c147261f5ab1b8ee0a54021a3168fa191096df8,citation,http://pdfs.semanticscholar.org/1c14/7261f5ab1b8ee0a54021a3168fa191096df8.pdf,Face Recognition across Time Lapse Using Convolutional Neural Networks,2016 +213,MORPH Non-Commercial,morph_nc,32.05765485,118.7550004,HoHai University,edu,b84b7b035c574727e4c30889e973423fe15560d7,citation,http://pdfs.semanticscholar.org/b84b/7b035c574727e4c30889e973423fe15560d7.pdf,Human Age Estimation Using Ranking SVM,2012 +214,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,b84b7b035c574727e4c30889e973423fe15560d7,citation,http://pdfs.semanticscholar.org/b84b/7b035c574727e4c30889e973423fe15560d7.pdf,Human Age Estimation Using Ranking SVM,2012 +215,MORPH Non-Commercial,morph_nc,39.6810328,-75.7540184,University of Delaware,edu,19da9f3532c2e525bf92668198b8afec14f9efea,citation,http://pdfs.semanticscholar.org/19da/9f3532c2e525bf92668198b8afec14f9efea.pdf,Challenge: Face verification across age progression using real-world data,2011 +216,MORPH Non-Commercial,morph_nc,39.95472495,-75.15346905,Temple University,edu,f24e379e942e134d41c4acec444ecf02b9d0d3a9,citation,http://pdfs.semanticscholar.org/f24e/379e942e134d41c4acec444ecf02b9d0d3a9.pdf,Analysis of Facial Images across Age Progression 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London",edu,0e5557a0cc58194ad53fab5dd6f4d4195d19ce4e,citation,https://doi.org/10.1109/TMM.2015.2500730,Deep Aging Face Verification With Large Gaps,2016 +225,MORPH Non-Commercial,morph_nc,31.846918,117.29053367,Hefei University of Technology,edu,0e5557a0cc58194ad53fab5dd6f4d4195d19ce4e,citation,https://doi.org/10.1109/TMM.2015.2500730,Deep Aging Face Verification With Large Gaps,2016 +226,MORPH Non-Commercial,morph_nc,29.58333105,-98.61944505,University of Texas at San Antonio,edu,f2896dd2701fbb3564492a12c64f11a5ad456a67,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5495414,Cross-database age estimation based on transfer learning,2010 +227,MORPH Non-Commercial,morph_nc,34.1235825,108.83546,Xidian University,edu,f2896dd2701fbb3564492a12c64f11a5ad456a67,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5495414,Cross-database age estimation based on transfer learning,2010 +228,MORPH Non-Commercial,morph_nc,56.66340325,12.87929727,Halmstad University,edu,9cda3e56cec21bd8f91f7acfcefc04ac10973966,citation,https://doi.org/10.1109/IWBF.2016.7449688,"Periocular biometrics: databases, algorithms and directions",2016 +229,MORPH Non-Commercial,morph_nc,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,13aef395f426ca8bd93640c9c3f848398b189874,citation,https://pdfs.semanticscholar.org/13ae/f395f426ca8bd93640c9c3f848398b189874.pdf,1 Image Preprocessing and Complete 2 DPCA with Feature Extraction for Gender Recognition NSF REU 2017 : Statistical Learning and Data Mining,2017 +230,MORPH Non-Commercial,morph_nc,24.7925484,120.9951183,National Tsing Hua University,edu,cfa40560fa74b2fb5c26bdd6ea7c610ba5130e2f,citation,https://doi.org/10.1109/TIFS.2013.2286265,Subspace Learning for Facial Age Estimation Via Pairwise Age Ranking,2013 +231,MORPH Non-Commercial,morph_nc,58.38131405,26.72078081,University of Tartu,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0 +232,MORPH Non-Commercial,morph_nc,41.3868913,2.16352385,University of Barcelona,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0 +233,MORPH Non-Commercial,morph_nc,39.65404635,-79.96475355,West Virginia University,edu,86a8b3d0f753cb49ac3250fa14d277983e30a4b7,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2013.75,Exploiting Unlabeled Ages for Aging Pattern Analysis on a Large Database,2013 +234,MORPH Non-Commercial,morph_nc,34.2239869,-77.8701325,"UNCW, USA",edu,2b5cb5466eecb131f06a8100dcaf0c7a0e30d391,citation,http://doi.acm.org/10.1145/1924559.1924608,A comparative study of active appearance model annotation schemes for the face,2010 +235,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,fc798314994bf94d1cde8d615ba4d5e61b6268b6,citation,http://pdfs.semanticscholar.org/fc79/8314994bf94d1cde8d615ba4d5e61b6268b6.pdf,"Face Recognition : face in video , age invariance , and facial marks",2009 +236,MORPH Non-Commercial,morph_nc,24.12084345,120.67571165,National Chung Hsing University,edu,635d2696aa597a278dd6563f079be06aa76a33c0,citation,https://doi.org/10.1109/ICIP.2016.7532429,Age estimation via fusion of multiple binary age grouping systems,2016 +237,MORPH Non-Commercial,morph_nc,25.01682835,121.53846924,National Taiwan University,edu,635d2696aa597a278dd6563f079be06aa76a33c0,citation,https://doi.org/10.1109/ICIP.2016.7532429,Age estimation via fusion of multiple binary age grouping systems,2016 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and local binary patterns,2010 +242,MORPH Non-Commercial,morph_nc,23.09461185,113.28788994,Sun Yat-Sen University,edu,d1b5b3e4b803dc4e50c5b80c1bc69c6d98751698,citation,https://doi.org/10.1109/LSP.2017.2661983,Modified Hidden Factor Analysis for Cross-Age Face Recognition,2017 +243,MORPH Non-Commercial,morph_nc,39.65404635,-79.96475355,West Virginia University,edu,55bc7abcef8266d76667896bbc652d081d00f797,citation,http://www.cse.msu.edu/~rossarun/pubs/ChenCosmeticsGenderAge_VISAPP2014.pdf,Impact of facial cosmetics on automatic gender and age estimation algorithms,2014 +244,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,55bc7abcef8266d76667896bbc652d081d00f797,citation,http://www.cse.msu.edu/~rossarun/pubs/ChenCosmeticsGenderAge_VISAPP2014.pdf,Impact of facial cosmetics on automatic gender and age estimation algorithms,2014 +245,MORPH Non-Commercial,morph_nc,39.65404635,-79.96475355,West Virginia University,edu,7a65fc9e78eff3ab6062707deaadde024d2fad40,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Zhu_A_Study_on_ICCV_2015_paper.pdf,A Study on Apparent Age Estimation,2015 +246,MORPH Non-Commercial,morph_nc,42.357757,-83.06286711,Wayne State University,edu,28d99dc2d673d62118658f8375b414e5192eac6f,citation,http://www.cs.wayne.edu/~mdong/cvpr17.pdf,Using Ranking-CNN for Age Estimation,2017 +247,MORPH Non-Commercial,morph_nc,37.4102193,-122.05965487,Carnegie Mellon University,edu,ec05078be14a11157ac0e1c6b430ac886124589b,citation,http://pdfs.semanticscholar.org/ec05/078be14a11157ac0e1c6b430ac886124589b.pdf,Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches,2018 +248,MORPH Non-Commercial,morph_nc,45.57022705,-122.63709346,Concordia University,edu,ec05078be14a11157ac0e1c6b430ac886124589b,citation,http://pdfs.semanticscholar.org/ec05/078be14a11157ac0e1c6b430ac886124589b.pdf,Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches,2018 +249,MORPH Non-Commercial,morph_nc,46.5190557,6.5667576,"Swiss Federal Institute of Technology Lausanne, Switzerland",edu,d7a84db2a1bf7b97657b0250f354f249394dd700,citation,https://doi.org/10.1109/ICIP.2010.5653518,Global and local feature based multi-classifier A-stack model for aging face identification,2010 +250,MORPH Non-Commercial,morph_nc,39.65404635,-79.96475355,West Virginia University,edu,d3c004125c71942846a9b32ae565c5216c068d1e,citation,http://pdfs.semanticscholar.org/d3c0/04125c71942846a9b32ae565c5216c068d1e.pdf,Recognizing Age-Separated Face Images: Humans and Machines,2014 +251,MORPH Non-Commercial,morph_nc,52.3553655,4.9501644,University of Amsterdam,edu,999289b0ef76c4c6daa16a4f42df056bf3d68377,citation,http://pdfs.semanticscholar.org/9992/89b0ef76c4c6daa16a4f42df056bf3d68377.pdf,The Role of Color and Contrast in Facial Age Estimation,2014 +252,MORPH Non-Commercial,morph_nc,51.99882735,4.37396037,Delft University of Technology,edu,999289b0ef76c4c6daa16a4f42df056bf3d68377,citation,http://pdfs.semanticscholar.org/9992/89b0ef76c4c6daa16a4f42df056bf3d68377.pdf,The Role of Color and Contrast in Facial Age Estimation,2014 +253,MORPH Non-Commercial,morph_nc,28.5456282,77.2731505,"IIIT Delhi, India",edu,f726738954e7055bb3615fa7e8f59f136d3e0bdc,citation,https://arxiv.org/pdf/1803.07385.pdf,Are you eligible? Predicting adulthood from face images via class specific mean autoencoder,2018 +254,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,b9d68dbeb8e5fdc5984b49a317ea6798b378e5ae,citation,http://doi.acm.org/10.1145/2733373.2807962,What Shall I Look Like after N Years?,2015 +255,MORPH Non-Commercial,morph_nc,32.0565957,118.77408833,Nanjing University,edu,b9d68dbeb8e5fdc5984b49a317ea6798b378e5ae,citation,http://doi.acm.org/10.1145/2733373.2807962,What Shall I Look Like after N Years?,2015 +256,MORPH Non-Commercial,morph_nc,45.42580475,-75.68740118,University of Ottawa,edu,16820ccfb626dcdc893cc7735784aed9f63cbb70,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W12/papers/Azarmehr_Real-Time_Embedded_Age_2015_CVPR_paper.pdf,Real-time embedded age and gender classification in unconstrained video,2015 +257,MORPH Non-Commercial,morph_nc,35.0274996,135.78154513,University of Caen,edu,0ad8149318912b5449085187eb3521786a37bc78,citation,http://arxiv.org/abs/1604.02975,CP-mtML: Coupled Projection Multi-Task Metric Learning for Large Scale Face Retrieval,2016 +258,MORPH Non-Commercial,morph_nc,51.44415765,7.26096541,Ruhr-University Bochum,edu,7e1ea2679a110241ed0dd38ff45cd4dfeb7a8e83,citation,http://pdfs.semanticscholar.org/7e1e/a2679a110241ed0dd38ff45cd4dfeb7a8e83.pdf,Extensions of Hierarchical Slow Feature Analysis for Efficient Classification and Regression on High-Dimensional Data,2017 +259,MORPH Non-Commercial,morph_nc,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,2e27667421a7eeab278e0b761db4d2c725683c3f,citation,https://doi.org/10.1007/s11042-013-1815-z,Effective human age estimation using a two-stage approach based on Lie Algebrized Gaussians feature,2013 +260,MORPH Non-Commercial,morph_nc,32.0565957,118.77408833,Nanjing University,edu,0c741fa0966ba3ee4fc326e919bf2f9456d0cd74,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.51,Facial Age Estimation by Learning from Label Distributions,2010 +261,MORPH Non-Commercial,morph_nc,32.0575279,118.78682252,Southeast University,edu,0c741fa0966ba3ee4fc326e919bf2f9456d0cd74,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.51,Facial Age Estimation by Learning from Label Distributions,2010 +262,MORPH Non-Commercial,morph_nc,-37.78397455,144.95867433,Monash University,edu,0c741fa0966ba3ee4fc326e919bf2f9456d0cd74,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.51,Facial Age Estimation by Learning from Label Distributions,2010 +263,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,fca9ebaa30d69ccec8bb577c31693c936c869e72,citation,https://arxiv.org/pdf/1809.00338.pdf,Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition,2018 +264,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,fca9ebaa30d69ccec8bb577c31693c936c869e72,citation,https://arxiv.org/pdf/1809.00338.pdf,Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition,2018 +265,MORPH Non-Commercial,morph_nc,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,cfdc4d0f8e1b4b9ced35317d12b4229f2e3311ab,citation,https://pdfs.semanticscholar.org/cfdc/4d0f8e1b4b9ced35317d12b4229f2e3311ab.pdf,Quaero at TRECVID 2010: Semantic Indexing,2010 +266,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,02d650d8a3a9daaba523433fbe93705df0a7f4b1,citation,http://pdfs.semanticscholar.org/02d6/50d8a3a9daaba523433fbe93705df0a7f4b1.pdf,How Does Aging Affect Facial Components?,2012 +267,MORPH Non-Commercial,morph_nc,34.67567405,33.04577648,Cyprus University of Technology,edu,70db3a0d2ca8a797153cc68506b8650908cb0ada,citation,http://pdfs.semanticscholar.org/70db/3a0d2ca8a797153cc68506b8650908cb0ada.pdf,An Overview of Research Activities in Facial Age Estimation Using the FG-NET Aging Database,2014 +268,MORPH Non-Commercial,morph_nc,22.5447154,113.9357164,Tencent,company,a2d1818eb461564a5153c74028e53856cf0b40fd,citation,https://arxiv.org/pdf/1810.07599.pdf,Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition,2018 +269,MORPH Non-Commercial,morph_nc,57.6252103,39.8845656,Yaroslavl State University,edu,05318a267226f6d855d83e9338eaa9e718b2a8dd,citation,https://fruct.org/publications/fruct16/files/Khr.pdf,Age estimation from face images: challenging problem for audience measurement systems,2014 +270,MORPH Non-Commercial,morph_nc,41.5381124,2.4447406,"EUP Mataró, Spain",edu,1f5725a4a2eb6cdaefccbc20dccadf893936df12,citation,https://doi.org/10.1109/CCST.2012.6393544,On the relevance of age in handwritten biometric recognition,2012 +271,MORPH Non-Commercial,morph_nc,34.67567405,33.04577648,Cyprus University of Technology,edu,876583a059154def7a4bc503b21542f80859affd,citation,https://doi.org/10.1109/IWBF.2016.7449697,On the analysis of factors influencing the performance of facial age progression,2016 +272,MORPH Non-Commercial,morph_nc,-35.0636071,147.3552234,Charles Sturt University,edu,2e231f1e7e641dd3619bec59e14d02e91360ac01,citation,https://arxiv.org/pdf/1807.10421.pdf,Fusion Network for Face-Based Age Estimation,2018 +273,MORPH Non-Commercial,morph_nc,51.3791442,-2.3252332,University of Bath,edu,2e231f1e7e641dd3619bec59e14d02e91360ac01,citation,https://arxiv.org/pdf/1807.10421.pdf,Fusion Network for Face-Based Age Estimation,2018 +274,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,56359d2b4508cc267d185c1d6d310a1c4c2cc8c2,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2015.7298618,Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition,2015 +275,MORPH Non-Commercial,morph_nc,39.9041999,116.4073963,Chinese Academy of Science,edu,56359d2b4508cc267d185c1d6d310a1c4c2cc8c2,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2015.7298618,Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition,2015 +276,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,56359d2b4508cc267d185c1d6d310a1c4c2cc8c2,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2015.7298618,Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition,2015 +277,MORPH Non-Commercial,morph_nc,32.0565957,118.77408833,Nanjing University,edu,a6e43b73f9f87588783988333997a81b4487e2d5,citation,http://pdfs.semanticscholar.org/a6e4/3b73f9f87588783988333997a81b4487e2d5.pdf,Facial Age Estimation by Total Ordering Preserving Projection,2016 +278,MORPH Non-Commercial,morph_nc,1.2988926,103.7873107,"Institution for Infocomm Research, Singapore",edu,8229f2735a0db0ad41f4d7252129311f06959907,citation,https://doi.org/10.1109/TIP.2011.2106794,Active Learning for Solving the Incomplete Data Problem in Facial Age Classification by the Furthest Nearest-Neighbor Criterion,2011 +279,MORPH Non-Commercial,morph_nc,1.3484104,103.68297965,Nanyang Technological University,edu,8229f2735a0db0ad41f4d7252129311f06959907,citation,https://doi.org/10.1109/TIP.2011.2106794,Active Learning for Solving the Incomplete Data Problem in Facial Age Classification by the Furthest Nearest-Neighbor Criterion,2011 +280,MORPH Non-Commercial,morph_nc,39.2899685,-76.62196103,University of Maryland,edu,963a004e208ce4bd26fa79a570af61d31651b3c3,citation,https://doi.org/10.1016/j.jvlc.2009.01.011,Computational methods for modeling facial aging: A survey,2009 +281,MORPH Non-Commercial,morph_nc,40.48256135,-3.6906079,Universidad Autonoma de Madrid,edu,4b5ff8c67f3496a414f94e35cb35a601ec98e5cf,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6547306,Understanding the discrimination power of facial regions in forensic casework,2013 +282,MORPH Non-Commercial,morph_nc,40.4445565,-3.7122785,"Dirección General de la Guardia Civil, Madrid, Spain",edu,4b5ff8c67f3496a414f94e35cb35a601ec98e5cf,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6547306,Understanding the discrimination power of facial regions in forensic casework,2013 +283,MORPH Non-Commercial,morph_nc,-37.8087465,144.9638875,RMIT University,edu,c49075ead6eb07ede5ada4fe372899bd0cfb83ac,citation,https://doi.org/10.1109/ICSPCS.2015.7391782,Multi-stage classification network for automatic age estimation from facial images,2015 +284,MORPH Non-Commercial,morph_nc,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,00301c250d667700276b1e573640ff2fd7be574d,citation,https://doi.org/10.1109/BTAS.2014.6996242,Establishing a test set 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University,edu,9f65319b8a33c8ec11da2f034731d928bf92e29d,citation,http://pdfs.semanticscholar.org/9f65/319b8a33c8ec11da2f034731d928bf92e29d.pdf,Taking Roll: a Pipeline for Face Recognition,2018 +6,MsCeleb,msceleb,51.49887085,-0.17560797,Imperial College London,edu,809ea255d144cff780300440d0f22c96e98abd53,citation,http://pdfs.semanticscholar.org/809e/a255d144cff780300440d0f22c96e98abd53.pdf,ArcFace: Additive Angular Margin Loss for Deep Face Recognition,2018 +7,MsCeleb,msceleb,31.28473925,121.49694909,Tongji University,edu,fe0cf8eaa5a5f59225197ef1bb8613e603cd96d4,citation,https://pdfs.semanticscholar.org/4e20/8cfff33327863b5aeef0bf9b327798a5610c.pdf,Improved Face Verification with Simple Weighted Feature Combination,2017 +8,MsCeleb,msceleb,45.7835966,4.7678948,École Centrale de Lyon,edu,486840f4f524e97f692a7f6b42cd19019ee71533,citation,https://arxiv.org/pdf/1703.08388v2.pdf,DeepVisage: Making Face Recognition Simple Yet With Powerful Generalization Skills,2017 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Technology,edu,b73795963dc623a634d218d29e4a5b74dfbc79f1,citation,https://arxiv.org/pdf/1807.08772.pdf,Identity Preserving Face Completion for Large Ocular Region Occlusion,2018 +17,MsCeleb,msceleb,38.0333742,-84.5017758,University of Kentucky,edu,b73795963dc623a634d218d29e4a5b74dfbc79f1,citation,https://arxiv.org/pdf/1807.08772.pdf,Identity Preserving Face Completion for Large Ocular Region Occlusion,2018 +18,MsCeleb,msceleb,34.0224149,-118.28634407,University of Southern California,edu,b73795963dc623a634d218d29e4a5b74dfbc79f1,citation,https://arxiv.org/pdf/1807.08772.pdf,Identity Preserving Face Completion for Large Ocular Region Occlusion,2018 +19,MsCeleb,msceleb,35.6924853,139.7582533,"National Institute of Informatics, Japan",edu,102280e80470ace006e14d6ec9adda082603dea1,citation,https://arxiv.org/pdf/1804.04418.pdf,Transformation on Computer-Generated Facial Image to Avoid Detection by Spoofing Detector,2018 +20,MsCeleb,msceleb,55.94951105,-3.19534913,University of Edinburgh,edu,102280e80470ace006e14d6ec9adda082603dea1,citation,https://arxiv.org/pdf/1804.04418.pdf,Transformation on Computer-Generated Facial Image to Avoid Detection by Spoofing Detector,2018 +21,MsCeleb,msceleb,42.4505507,-76.4783513,Cornell University,edu,bd379f8e08f88729a9214260e05967f4ca66cd65,citation,https://arxiv.org/pdf/1711.06148.pdf,Learning Compositional Visual Concepts with Mutual Consistency,2017 +22,MsCeleb,msceleb,22.42031295,114.20788644,Chinese University of Hong Kong,edu,58d76380d194248b3bb291b8c7c5137a0a376897,citation,https://pdfs.semanticscholar.org/58d7/6380d194248b3bb291b8c7c5137a0a376897.pdf,FaceID-GAN : Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis,2018 +23,MsCeleb,msceleb,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,58d76380d194248b3bb291b8c7c5137a0a376897,citation,https://pdfs.semanticscholar.org/58d7/6380d194248b3bb291b8c7c5137a0a376897.pdf,FaceID-GAN : Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis,2018 +24,MsCeleb,msceleb,42.3383668,-71.08793524,Northeastern University,edu,f74917fc0e55f4f5682909dcf6929abd19d33e2e,citation,http://pdfs.semanticscholar.org/f749/17fc0e55f4f5682909dcf6929abd19d33e2e.pdf,Gan Quality Index (gqi) by Gan-induced Classifier,2018 +25,MsCeleb,msceleb,40.8722825,-73.89489171,City University of New York,edu,f74917fc0e55f4f5682909dcf6929abd19d33e2e,citation,http://pdfs.semanticscholar.org/f749/17fc0e55f4f5682909dcf6929abd19d33e2e.pdf,Gan Quality Index (gqi) by Gan-induced Classifier,2018 +26,MsCeleb,msceleb,39.2899685,-76.62196103,University of Maryland,edu,def2983576001bac7d6461d78451159800938112,citation,https://arxiv.org/pdf/1705.07426.pdf,The Do’s and Don’ts for CNN-Based Face Verification,2017 +27,MsCeleb,msceleb,37.4102193,-122.05965487,Carnegie Mellon University,edu,2b869d5551b10f13bf6fcdb8d13f0aa4d1f59fc4,citation,https://arxiv.org/pdf/1803.00130.pdf,Ring loss: Convex Feature Normalization for Face Recognition,2018 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Learning to Group Faces via Imitation Learning,2018 +51,MsCeleb,msceleb,40.47913175,-74.43168868,Rutgers University,edu,d4448f8aa320f04066cc43201d55ddd023eb712e,citation,https://pdfs.semanticscholar.org/d444/8f8aa320f04066cc43201d55ddd023eb712e.pdf,Clothing Change Aware Person Identification,0 +52,MsCeleb,msceleb,33.9928298,-81.02685168,University of South Carolina,edu,d4448f8aa320f04066cc43201d55ddd023eb712e,citation,https://pdfs.semanticscholar.org/d444/8f8aa320f04066cc43201d55ddd023eb712e.pdf,Clothing Change Aware Person Identification,0 +53,MsCeleb,msceleb,39.94976005,116.33629046,Beijing Jiaotong University,edu,d7cbedbee06293e78661335c7dd9059c70143a28,citation,https://arxiv.org/pdf/1804.07573.pdf,MobileFaceNets: Efficient CNNs for Accurate Real-time Face Verification on Mobile Devices,2018 +54,MsCeleb,msceleb,51.49887085,-0.17560797,Imperial College London,edu,40bb090a4e303f11168dce33ed992f51afe02ff7,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Deng_Marginal_Loss_for_CVPR_2017_paper.pdf,Marginal Loss for Deep Face Recognition,2017 +55,MsCeleb,msceleb,29.7207902,-95.34406271,University of Houston,edu,38d8ff137ff753f04689e6b76119a44588e143f3,citation,http://pdfs.semanticscholar.org/38d8/ff137ff753f04689e6b76119a44588e143f3.pdf,When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition,2017 +56,MsCeleb,msceleb,42.3383668,-71.08793524,Northeastern University,edu,c9efcd8e32dced6efa2bba64789df8d0a8e4996a,citation,http://dl.acm.org/citation.cfm?id=2984060,Deep Convolutional Neural Network with Independent Softmax for Large Scale Face Recognition,2016 +57,MsCeleb,msceleb,40.0044795,116.370238,Chinese Academy of Sciences,edu,43fe03ec1acb6ea9d05d2b22eeddb2631bd30437,citation,https://doi.org/10.1109/ICIP.2017.8296394,Weakly supervised multiscale-inception learning for web-scale face recognition,2017 +58,MsCeleb,msceleb,1.3484104,103.68297965,Nanyang Technological University,edu,a322479a6851f57a3d74d017a9cb6d71395ed806,citation,https://pdfs.semanticscholar.org/a322/479a6851f57a3d74d017a9cb6d71395ed806.pdf,Towards Pose Invariant Face Recognition in the Wild,0 +59,MsCeleb,msceleb,40.0044795,116.370238,Chinese Academy of Sciences,edu,a322479a6851f57a3d74d017a9cb6d71395ed806,citation,https://pdfs.semanticscholar.org/a322/479a6851f57a3d74d017a9cb6d71395ed806.pdf,Towards Pose Invariant Face Recognition in the Wild,0 +60,MsCeleb,msceleb,28.2290209,112.99483204,"National University of Defense Technology, China",edu,a322479a6851f57a3d74d017a9cb6d71395ed806,citation,https://pdfs.semanticscholar.org/a322/479a6851f57a3d74d017a9cb6d71395ed806.pdf,Towards Pose Invariant Face Recognition in the Wild,0 +61,MsCeleb,msceleb,1.2962018,103.77689944,National University of Singapore,edu,a322479a6851f57a3d74d017a9cb6d71395ed806,citation,https://pdfs.semanticscholar.org/a322/479a6851f57a3d74d017a9cb6d71395ed806.pdf,Towards Pose Invariant Face Recognition in the Wild,0 +62,MsCeleb,msceleb,22.42031295,114.20788644,Chinese University of Hong Kong,edu,d949fadc9b6c5c8b067fa42265ad30945f9caa99,citation,https://arxiv.org/pdf/1710.00870.pdf,Rethinking Feature Discrimination and Polymerization for Large-scale Recognition,2017 +63,MsCeleb,msceleb,22.42031295,114.20788644,Chinese University of Hong Kong,edu,6fed504da4e192fe4c2d452754d23d3db4a4e5e3,citation,http://pdfs.semanticscholar.org/85ee/d639f7367c794a6d8b38619697af3efaacfe.pdf,Learning Deep Features via Congenerous Cosine Loss for Person Recognition,2017 +64,MsCeleb,msceleb,39.9586652,116.30971281,Beijing Institute of Technology,edu,0ea7b7fff090c707684fd4dc13e0a8f39b300a97,citation,http://arxiv.org/abs/1711.06055,Integrated Face Analytics Networks through Cross-Dataset Hybrid Training,2017 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Face Verification,2018 +69,MsCeleb,msceleb,42.718568,-84.47791571,Michigan State University,edu,ad2cb5c255e555d9767d526721a4c7053fa2ac58,citation,https://arxiv.org/pdf/1711.03990.pdf,Longitudinal Study of Child Face Recognition,2018 +70,MsCeleb,msceleb,50.7791703,6.06728733,RWTH Aachen University,edu,f02f0f6fcd56a9b1407045de6634df15c60a85cd,citation,http://pdfs.semanticscholar.org/f02f/0f6fcd56a9b1407045de6634df15c60a85cd.pdf,Learning Low-shot facial representations via 2D warping,2017 +71,MsCeleb,msceleb,25.01682835,121.53846924,National Taiwan University,edu,17423fe480b109e1d924314c1dddb11b084e8a42,citation,https://pdfs.semanticscholar.org/1742/3fe480b109e1d924314c1dddb11b084e8a42.pdf,Deep Disguised Faces Recognition,0 +72,MsCeleb,msceleb,42.718568,-84.47791571,Michigan State University,edu,d29eec5e047560627c16803029d2eb8a4e61da75,citation,http://pdfs.semanticscholar.org/d29e/ec5e047560627c16803029d2eb8a4e61da75.pdf,Feature Transfer Learning for Deep Face Recognition with Long-Tail 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Sciences,edu,01dfd60c0851c4e5a99176e99aa369e1b5f606b7,citation,https://arxiv.org/pdf/1809.01936.pdf,Disentangled Variational Representation for Heterogeneous Face Recognition,2018 +93,MsCeleb,msceleb,39.329053,-76.619425,Johns Hopkins University,edu,01dfd60c0851c4e5a99176e99aa369e1b5f606b7,citation,https://arxiv.org/pdf/1809.01936.pdf,Disentangled Variational Representation for Heterogeneous Face Recognition,2018 +94,MsCeleb,msceleb,32.0575279,118.78682252,Southeast University,edu,8ff8c64288a2f7e4e8bf8fda865820b04ab3dbe8,citation,https://pdfs.semanticscholar.org/0056/92b9fa6728df3a7f14578c43410867bba425.pdf,Age Estimation Using Expectation of Label Distribution Learning,2018 +95,MsCeleb,msceleb,32.0565957,118.77408833,Nanjing University,edu,8ff8c64288a2f7e4e8bf8fda865820b04ab3dbe8,citation,https://pdfs.semanticscholar.org/0056/92b9fa6728df3a7f14578c43410867bba425.pdf,Age Estimation Using Expectation of Label Distribution Learning,2018 +96,MsCeleb,msceleb,1.3484104,103.68297965,Nanyang Technological University,edu,47190d213caef85e8b9dd0d271dbadc29ed0a953,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018 +97,MsCeleb,msceleb,32.87935255,-117.23110049,"University of California, San Diego",edu,47190d213caef85e8b9dd0d271dbadc29ed0a953,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018 +98,MsCeleb,msceleb,39.2899685,-76.62196103,University of Maryland,edu,872dfdeccf99bbbed7c8f1ea08afb2d713ebe085,citation,https://arxiv.org/pdf/1703.09507.pdf,L2-constrained Softmax Loss for Discriminative Face Verification,2017 +99,MsCeleb,msceleb,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018 +100,MsCeleb,msceleb,39.2899685,-76.62196103,University of Maryland,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018 +101,MsCeleb,msceleb,37.4102193,-122.05965487,Carnegie Mellon University,edu,d35534f3f59631951011539da2fe83f2844ca245,citation,https://arxiv.org/pdf/1705.07904.pdf,Semantically Decomposing the Latent Spaces of Generative Adversarial Networks,2017 +102,MsCeleb,msceleb,37.43131385,-122.16936535,Stanford University,edu,d35534f3f59631951011539da2fe83f2844ca245,citation,https://arxiv.org/pdf/1705.07904.pdf,Semantically Decomposing the Latent Spaces of Generative Adversarial Networks,2017 +103,MsCeleb,msceleb,32.87935255,-117.23110049,"University of California, San Diego",edu,d35534f3f59631951011539da2fe83f2844ca245,citation,https://arxiv.org/pdf/1705.07904.pdf,Semantically Decomposing the Latent Spaces of Generative Adversarial Networks,2017 +104,MsCeleb,msceleb,37.3936717,-122.0807262,Facebook,company,628a3f027b7646f398c68a680add48c7969ab1d9,citation,https://pdfs.semanticscholar.org/628a/3f027b7646f398c68a680add48c7969ab1d9.pdf,Plan for Final Year Project : HKU-Face : A Large Scale Dataset for Deep Face Recognition,2017 +105,MsCeleb,msceleb,51.7534538,-1.25400997,University of Oxford,edu,313d5eba97fe064bdc1f00b7587a4b3543ef712a,citation,https://pdfs.semanticscholar.org/cb7f/93467b0ec1afd43d995e511f5d7bf052a5af.pdf,Compact Deep Aggregation for Set Retrieval,2018 +106,MsCeleb,msceleb,42.718568,-84.47791571,Michigan State University,edu,b446bcd7fb78adfe346cf7a01a38e4f43760f363,citation,http://pdfs.semanticscholar.org/b446/bcd7fb78adfe346cf7a01a38e4f43760f363.pdf,To appear in ICB 2018 Longitudinal Study of Child Face Recognition,2017 +107,MsCeleb,msceleb,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,cd6aaa37fffd0b5c2320f386be322b8adaa1cc68,citation,https://arxiv.org/pdf/1804.06655.pdf,Deep Face Recognition: A Survey,2018 +108,MsCeleb,msceleb,22.42031295,114.20788644,Chinese University of Hong Kong,edu,831b4d8b0c0173b0bac0e328e844a0fbafae6639,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018 +109,MsCeleb,msceleb,1.3484104,103.68297965,Nanyang Technological University,edu,831b4d8b0c0173b0bac0e328e844a0fbafae6639,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018 +110,MsCeleb,msceleb,29.7207902,-95.34406271,University of Houston,edu,3cb2841302af1fb9656f144abc79d4f3d0b27380,citation,https://pdfs.semanticscholar.org/3cb2/841302af1fb9656f144abc79d4f3d0b27380.pdf,When 3 D-Aided 2 D Face Recognition Meets Deep Learning : An extended UR 2 D for Pose-Invariant Face Recognition,2017 +111,MsCeleb,msceleb,39.2899685,-76.62196103,University of Maryland,edu,23dd8d17ce09c22d367e4d62c1ccf507bcbc64da,citation,https://pdfs.semanticscholar.org/23dd/8d17ce09c22d367e4d62c1ccf507bcbc64da.pdf,Deep Density Clustering of Unconstrained Faces ( Supplementary Material ),2018 diff --git a/site/datasets/final/pipa.csv b/site/datasets/final/pipa.csv new file mode 100644 index 00000000..c68e70b6 --- /dev/null +++ b/site/datasets/final/pipa.csv @@ -0,0 +1,37 @@ +index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,PIPA,pipa,0.0,0.0,,,0a85bdff552615643dd74646ac881862a7c7072d,main,https://doi.org/10.1109/CVPR.2015.7299113,Beyond frontal faces: Improving Person Recognition using multiple cues,2015 +1,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,bfc04ce7752fac884cf5a78b30ededfd5a0ad109,citation,https://arxiv.org/pdf/1804.04779.pdf,A Hybrid Model for Identity Obfuscation by Face Replacement,2018 +2,PIPA,pipa,28.59899755,-81.19712501,University of Central Florida,edu,2b339ece73e3787f445c5b92078e8f82c9b1c522,citation,http://pdfs.semanticscholar.org/7a2e/e06aaa3f342937225272951c0b6dd4309a7a.pdf,"Human Re-identification in Crowd Videos Using Personal, Social and Environmental Constraints",2016 +3,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,bc27434e376db89fe0e6ef2d2fabc100d2575ec6,citation,https://arxiv.org/pdf/1607.08438.pdf,Faceless Person Recognition; Privacy Implications in Social Media,2016 +4,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,0c59071ddd33849bd431165bc2d21bbe165a81e0,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Oh_Person_Recognition_in_ICCV_2015_paper.pdf,Person Recognition in Personal Photo Collections,2015 +5,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,23429ef60e7a9c0e2f4d81ed1b4e47cc2616522f,citation,https://arxiv.org/pdf/1704.06456.pdf,A Domain Based Approach to Social Relation Recognition,2017 +6,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,ba7b12c8e2ff3c5e4e0f70b58215b41b18ff8feb,citation,https://arxiv.org/pdf/1711.09001.pdf,Natural and Effective Obfuscation by Head Inpainting,2017 +7,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,808b685d09912cbef4a009e74e10476304b4cccf,citation,http://pdfs.semanticscholar.org/808b/685d09912cbef4a009e74e10476304b4cccf.pdf,From Understanding to Controlling Privacy against Automatic Person Recognition in Social Media,2017 +8,PIPA,pipa,42.4505507,-76.4783513,Cornell University,edu,0da75b0d341c8f945fae1da6c77b6ec345f47f2a,citation,https://pdfs.semanticscholar.org/0da7/5b0d341c8f945fae1da6c77b6ec345f47f2a.pdf,The Effect of Computer-Generated Descriptions on Photo-Sharing Experiences of People With Visual Impairments,2017 +9,PIPA,pipa,42.4505507,-76.4783513,Cornell University,edu,0aaf785d7f21d2b5ad582b456896495d30b0a4e2,citation,http://dl.acm.org/citation.cfm?id=3173789,A Face Recognition Application for People with Visual Impairments: Understanding Use Beyond the Lab,2018 +10,PIPA,pipa,22.42031295,114.20788644,Chinese University of Hong Kong,edu,c97a5f2241cc6cd99ef0c4527ea507a50841f60b,citation,https://arxiv.org/pdf/1807.10510.pdf,Person Search in Videos with One Portrait Through Visual and Temporal Links,2018 +11,PIPA,pipa,40.00229045,116.32098908,Tsinghua University,edu,c97a5f2241cc6cd99ef0c4527ea507a50841f60b,citation,https://arxiv.org/pdf/1807.10510.pdf,Person Search in Videos with One Portrait Through Visual and Temporal Links,2018 +12,PIPA,pipa,22.42031295,114.20788644,Chinese University of Hong Kong,edu,6fed504da4e192fe4c2d452754d23d3db4a4e5e3,citation,http://pdfs.semanticscholar.org/85ee/d639f7367c794a6d8b38619697af3efaacfe.pdf,Learning Deep Features via Congenerous Cosine Loss for Person Recognition,2017 +13,PIPA,pipa,-33.8809651,151.20107299,University of Technology Sydney,edu,0b84f07af44f964817675ad961def8a51406dd2e,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.357,Person Re-identification in the Wild,2017 +14,PIPA,pipa,17.4454957,78.34854698,International Institute of Information Technology,edu,eb8a3948c4be0d23eb7326d27f2271be893b3409,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7914701,A Probabilistic Approach to People-Centric Photo Selection and Sequencing,2017 +15,PIPA,pipa,1.3037257,103.7737763,University of Illinois’ Advanced Digital Sciences Center,edu,eb8a3948c4be0d23eb7326d27f2271be893b3409,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7914701,A Probabilistic Approach to People-Centric Photo Selection and Sequencing,2017 +16,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,3e0a1884448bfd7f416c6a45dfcdfc9f2e617268,citation,https://arxiv.org/pdf/1805.05838.pdf,Understanding and Controlling User Linkability in Decentralized Learning,2018 +17,PIPA,pipa,23.09461185,113.28788994,Sun Yat-Sen University,edu,725c3605c2d26d113637097358cd4c08c19ff9e1,citation,https://arxiv.org/pdf/1807.00504.pdf,Deep Reasoning with Knowledge Graph for Social Relationship Understanding,2018 +18,PIPA,pipa,51.7534538,-1.25400997,University of Oxford,edu,ff1f45bdad41d8b35435098041e009627e60d208,citation,http://pdfs.semanticscholar.org/ff1f/45bdad41d8b35435098041e009627e60d208.pdf,"NAGRANI, ZISSERMAN: FROM BENEDICT CUMBERBATCH TO SHERLOCK HOLMES 1 From Benedict Cumberbatch to Sherlock Holmes: Character Identification in TV series without a Script",2017 +19,PIPA,pipa,40.742252,-74.0270949,Stevens Institute of Technology,edu,1e1d7cbbef67e9e042a3a0a9a1bcefcc4a9adacf,citation,http://personal.stevens.edu/~hli18//data/papers/CVPR2016_CameraReady.pdf,A Multi-level Contextual Model for Person Recognition in Photo Albums,2016 +20,PIPA,pipa,42.4505507,-76.4783513,Cornell University,edu,269248eb8a44da5248cef840f7079b1294dbf237,citation,https://arxiv.org/pdf/1805.01515.pdf,The Effect of Computer-Generated Descriptions on Photo-Sharing Experiences of People with Visual Impairments,2017 +21,PIPA,pipa,40.47913175,-74.43168868,Rutgers University,edu,d4448f8aa320f04066cc43201d55ddd023eb712e,citation,https://pdfs.semanticscholar.org/d444/8f8aa320f04066cc43201d55ddd023eb712e.pdf,Clothing Change Aware Person Identification,0 +22,PIPA,pipa,33.9928298,-81.02685168,University of South Carolina,edu,d4448f8aa320f04066cc43201d55ddd023eb712e,citation,https://pdfs.semanticscholar.org/d444/8f8aa320f04066cc43201d55ddd023eb712e.pdf,Clothing Change Aware Person Identification,0 +23,PIPA,pipa,39.94976005,116.33629046,Beijing Jiaotong University,edu,b5968e7bb23f5f03213178c22fd2e47af3afa04c,citation,https://arxiv.org/pdf/1705.07206.pdf,Multiple-Human Parsing in the Wild,2017 +24,PIPA,pipa,1.2962018,103.77689944,National University of Singapore,edu,b5968e7bb23f5f03213178c22fd2e47af3afa04c,citation,https://arxiv.org/pdf/1705.07206.pdf,Multiple-Human Parsing in the Wild,2017 +25,PIPA,pipa,42.4505507,-76.4783513,Cornell University,edu,537328af75f50d49696972a6c34bca97c14bc762,citation,https://arxiv.org/pdf/1805.04049.pdf,Exploiting Unintended Feature Leakage in Collaborative Learning,2018 +26,PIPA,pipa,22.42031295,114.20788644,Chinese University of Hong Kong,edu,1c9efb6c895917174ac6ccc3bae191152f90c625,citation,https://arxiv.org/pdf/1806.03084.pdf,Unifying Identification and Context Learning for Person Recognition,2018 +27,PIPA,pipa,37.21872455,-80.42542519,Virginia Polytechnic Institute and State University,edu,6d8eef8f8d6cd8436c55018e6ca5c5907b31ac19,citation,http://pdfs.semanticscholar.org/6d8e/ef8f8d6cd8436c55018e6ca5c5907b31ac19.pdf,Understanding Representations and Reducing their Redundancy in Deep Networks,2016 +28,PIPA,pipa,17.4454957,78.34854698,International Institute of Information Technology,edu,01e27c91c7cef926389f913d12410725e7dd35ab,citation,https://doi.org/10.1007/s11760-017-1140-5,Semi-supervised annotation of faces in image collection,2018 +29,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,b68150bfdec373ed8e025f448b7a3485c16e3201,citation,https://arxiv.org/pdf/1703.09471.pdf,Adversarial Image Perturbation for Privacy Protection A Game Theory Perspective,2017 +30,PIPA,pipa,30.284151,-97.73195598,University of Texas at Austin,edu,3c57e28a4eb463d532ea2b0b1ba4b426ead8d9a0,citation,http://pdfs.semanticscholar.org/73cc/fdedbd7d72a147925727ba1932f9488cfde3.pdf,Defeating Image Obfuscation with Deep Learning,2016 +31,PIPA,pipa,28.2290209,112.99483204,"National University of Defense Technology, China",edu,5f771fed91c8e4b666489ba2384d0705bcf75030,citation,https://arxiv.org/pdf/1804.03287.pdf,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018 +32,PIPA,pipa,1.2962018,103.77689944,National University of Singapore,edu,5f771fed91c8e4b666489ba2384d0705bcf75030,citation,https://arxiv.org/pdf/1804.03287.pdf,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018 +33,PIPA,pipa,22.42031295,114.20788644,Chinese University of Hong Kong,edu,d949fadc9b6c5c8b067fa42265ad30945f9caa99,citation,https://arxiv.org/pdf/1710.00870.pdf,Rethinking Feature Discrimination and Polymerization for Large-scale Recognition,2017 +34,PIPA,pipa,-34.9189226,138.60423668,University of Adelaide,edu,3d24b386d003bee176a942c26336dbe8f427aadd,citation,http://arxiv.org/abs/1611.09967,Sequential Person Recognition in Photo Albums with a Recurrent Network,2017 +35,PIPA,pipa,42.4505507,-76.4783513,Cornell University,edu,8bdf6f03bde08c424c214188b35be8b2dec7cdea,citation,https://arxiv.org/pdf/1805.04049.pdf,Inference Attacks Against Collaborative Learning,2018 diff --git a/site/datasets/final/umd_faces.csv b/site/datasets/final/umd_faces.csv new file mode 100644 index 00000000..53788401 --- /dev/null +++ b/site/datasets/final/umd_faces.csv @@ -0,0 +1,34 @@ +index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,UMD,umd_faces,0.0,0.0,,,31b05f65405534a696a847dd19c621b7b8588263,main,http://arxiv.org/abs/1611.01484,UMDFaces: An annotated face dataset for training deep networks,2017 +1,UMD,umd_faces,39.2899685,-76.62196103,University of Maryland,edu,19458454308a9f56b7de76bf7d8ff8eaa52b0173,citation,https://pdfs.semanticscholar.org/1945/8454308a9f56b7de76bf7d8ff8eaa52b0173.pdf,Deep Features for Recognizing Disguised Faces in the Wild,0 +2,UMD,umd_faces,28.2290209,112.99483204,"National University of Defense Technology, China",edu,511a8cdf2127ef8aa07cbdf9660fe9e0e2dfbde7,citation,https://pdfs.semanticscholar.org/511a/8cdf2127ef8aa07cbdf9660fe9e0e2dfbde7.pdf,A Community Detection Approach to Cleaning Extremely Large Face Database,2018 +3,UMD,umd_faces,25.01682835,121.53846924,National Taiwan University,edu,81884e1de00e59f24bc20254584d73a1a1806933,citation,https://arxiv.org/pdf/1811.02328.pdf,Super-Identity Convolutional Neural Network for Face Hallucination,2018 +4,UMD,umd_faces,39.993008,116.329882,SenseTime,company,81884e1de00e59f24bc20254584d73a1a1806933,citation,https://arxiv.org/pdf/1811.02328.pdf,Super-Identity Convolutional Neural Network for Face Hallucination,2018 +5,UMD,umd_faces,30.284151,-97.73195598,University of Texas at Austin,edu,81884e1de00e59f24bc20254584d73a1a1806933,citation,https://arxiv.org/pdf/1811.02328.pdf,Super-Identity Convolutional Neural Network for Face Hallucination,2018 +6,UMD,umd_faces,45.7835966,4.7678948,École Centrale de Lyon,edu,486840f4f524e97f692a7f6b42cd19019ee71533,citation,https://arxiv.org/pdf/1703.08388v2.pdf,DeepVisage: Making Face Recognition Simple Yet With Powerful Generalization Skills,2017 +7,UMD,umd_faces,48.832493,2.267474,Safran Identity and Security,company,486840f4f524e97f692a7f6b42cd19019ee71533,citation,https://arxiv.org/pdf/1703.08388v2.pdf,DeepVisage: Making Face Recognition Simple Yet With Powerful Generalization Skills,2017 +8,UMD,umd_faces,41.70456775,-86.23822026,University of Notre Dame,edu,73ea06787925157df519a15ee01cc3dc1982a7e0,citation,https://arxiv.org/pdf/1811.01474.pdf,Fast Face Image Synthesis with Minimal Training,2018 +9,UMD,umd_faces,30.40550035,-91.18620474,Louisiana State University,edu,9f65319b8a33c8ec11da2f034731d928bf92e29d,citation,http://pdfs.semanticscholar.org/9f65/319b8a33c8ec11da2f034731d928bf92e29d.pdf,Taking Roll: a Pipeline for Face Recognition,2018 +10,UMD,umd_faces,51.24303255,-0.59001382,University of Surrey,edu,ed07856461da6c7afa4f1782b5b607b45eebe9f6,citation,https://pdfs.semanticscholar.org/ed07/856461da6c7afa4f1782b5b607b45eebe9f6.pdf,D Morphable Models as Spatial Transformer Networks,2017 +11,UMD,umd_faces,53.94540365,-1.03138878,University of York,edu,ed07856461da6c7afa4f1782b5b607b45eebe9f6,citation,https://pdfs.semanticscholar.org/ed07/856461da6c7afa4f1782b5b607b45eebe9f6.pdf,D Morphable Models as Spatial Transformer Networks,2017 +12,UMD,umd_faces,53.94540365,-1.03138878,University of York,edu,6a4419ce2338ea30a570cf45624741b754fa52cb,citation,https://arxiv.org/pdf/1804.02541.pdf,Statistical transformer networks: learning shape and appearance models via self supervision,2018 +13,UMD,umd_faces,51.49887085,-0.17560797,Imperial College London,edu,809ea255d144cff780300440d0f22c96e98abd53,citation,http://pdfs.semanticscholar.org/809e/a255d144cff780300440d0f22c96e98abd53.pdf,ArcFace: Additive Angular Margin Loss for Deep Face Recognition,2018 +14,UMD,umd_faces,39.2899685,-76.62196103,University of Maryland,edu,def2983576001bac7d6461d78451159800938112,citation,https://arxiv.org/pdf/1705.07426.pdf,The Do’s and Don’ts for CNN-Based Face Verification,2017 +15,UMD,umd_faces,43.7776426,11.259765,University of Florence,edu,746c0205fdf191a737df7af000eaec9409ede73f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8423119,Investigating Nuisances in DCNN-Based Face Recognition,2018 +16,UMD,umd_faces,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018 +17,UMD,umd_faces,39.2899685,-76.62196103,University of Maryland,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018 +18,UMD,umd_faces,51.24303255,-0.59001382,University of Surrey,edu,c146aa6d56233ce700032f1cb179700778557601,citation,https://arxiv.org/pdf/1708.07199.pdf,3D Morphable Models as Spatial Transformer Networks,2017 +19,UMD,umd_faces,53.94540365,-1.03138878,University of York,edu,c146aa6d56233ce700032f1cb179700778557601,citation,https://arxiv.org/pdf/1708.07199.pdf,3D Morphable Models as Spatial Transformer Networks,2017 +20,UMD,umd_faces,25.01682835,121.53846924,National Taiwan University,edu,17423fe480b109e1d924314c1dddb11b084e8a42,citation,https://pdfs.semanticscholar.org/1742/3fe480b109e1d924314c1dddb11b084e8a42.pdf,Deep Disguised Faces Recognition,0 +21,UMD,umd_faces,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,7a131fafa7058fb75fdca32d0529bc7cb50429bd,citation,https://arxiv.org/pdf/1704.04086.pdf,Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis,2017 +22,UMD,umd_faces,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,cd6aaa37fffd0b5c2320f386be322b8adaa1cc68,citation,https://arxiv.org/pdf/1804.06655.pdf,Deep Face Recognition: A Survey,2018 +23,UMD,umd_faces,30.274084,120.15507,Alibaba,company,89497854eada7e32f06aa8f3c0ceedc0e91ecfef,citation,https://doi.org/10.1109/TIP.2017.2784571,Deep Context-Sensitive Facial Landmark Detection With Tree-Structured Modeling,2018 +24,UMD,umd_faces,30.19331415,120.11930822,Zhejiang University,edu,89497854eada7e32f06aa8f3c0ceedc0e91ecfef,citation,https://doi.org/10.1109/TIP.2017.2784571,Deep Context-Sensitive Facial Landmark Detection With Tree-Structured Modeling,2018 +25,UMD,umd_faces,38.8920756,-104.79716389,"University of Colorado, Colorado Springs",edu,d4f1eb008eb80595bcfdac368e23ae9754e1e745,citation,https://arxiv.org/pdf/1708.02337.pdf,Unconstrained Face Detection and Open-Set Face Recognition Challenge,2017 +26,UMD,umd_faces,51.7534538,-1.25400997,University of Oxford,edu,eb027969f9310e0ae941e2adee2d42cdf07d938c,citation,https://arxiv.org/pdf/1710.08092.pdf,VGGFace2: A Dataset for Recognising Faces across Pose and Age,2018 +27,UMD,umd_faces,51.49887085,-0.17560797,Imperial College London,edu,c43ed9b34cad1a3976bac7979808eb038d88af84,citation,https://arxiv.org/pdf/1804.03675.pdf,Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model,2018 +28,UMD,umd_faces,51.24303255,-0.59001382,University of Surrey,edu,c43ed9b34cad1a3976bac7979808eb038d88af84,citation,https://arxiv.org/pdf/1804.03675.pdf,Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model,2018 +29,UMD,umd_faces,41.70456775,-86.23822026,University of Notre Dame,edu,e64c166dc5bb33bc61462a8b5ac92edb24d905a1,citation,https://arxiv.org/pdf/1811.01474.pdf,Fast Face Image Synthesis with Minimal Training.,2018 +30,UMD,umd_faces,51.7534538,-1.25400997,University of Oxford,edu,70c59dc3470ae867016f6ab0e008ac8ba03774a1,citation,https://arxiv.org/pdf/1710.08092.pdf,VGGFace2: A Dataset for Recognising Faces across Pose and Age,2018 +31,UMD,umd_faces,38.99203005,-76.9461029,University of Maryland College Park,edu,3d2891950f1b76f783a9ba77b3c55b8e68b95fbe,citation,https://arxiv.org/pdf/1802.06713.pdf,Disentangling 3D Pose in a Dendritic CNN for Unconstrained 2D Face Alignment,2018 +32,UMD,umd_faces,51.49887085,-0.17560797,Imperial College London,edu,1929863fff917ee7f6dc428fc1ce732777668eca,citation,https://arxiv.org/pdf/1712.04695.pdf,UV-GAN: Adversarial Facial UV Map Completion for Pose-Invariant Face Recognition,2018 diff --git a/site/datasets/final/voc.csv b/site/datasets/final/voc.csv new file mode 100644 index 00000000..9400d7d6 --- /dev/null +++ b/site/datasets/final/voc.csv @@ -0,0 +1,401 @@ +index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,VOC,voc,0.0,0.0,,,abe9f3b91fd26fa1b50cd685c0d20debfb372f73,main,http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc14.pdf,The Pascal Visual Object Classes Challenge: A Retrospective,2014 +1,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,ed2f711cf9bcd9d7ab039d746af109ed9573421a,citation,https://pdfs.semanticscholar.org/ed2f/711cf9bcd9d7ab039d746af109ed9573421a.pdf,Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks,2018 +2,VOC,voc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,ed2f711cf9bcd9d7ab039d746af109ed9573421a,citation,https://pdfs.semanticscholar.org/ed2f/711cf9bcd9d7ab039d746af109ed9573421a.pdf,Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks,2018 +3,VOC,voc,13.0222347,77.56718325,Indian Institute of Science Bangalore,edu,a6ac6463b5c89ac9eb013c978f213b309cc6a5c7,citation,https://arxiv.org/pdf/1808.01134.pdf,iSPA-Net: Iterative Semantic Pose Alignment Network,2018 +4,VOC,voc,42.3583961,-71.09567788,MIT,edu,aaf4d938f2e66d158d5e635a9c1d279cdc7639c0,citation,http://pdfs.semanticscholar.org/aaf4/d938f2e66d158d5e635a9c1d279cdc7639c0.pdf,Toward visual understanding of everyday object,2015 +5,VOC,voc,42.2942142,-83.71003894,University of Michigan,edu,74dbcc09a3456ddacf5cece640b84045ebdf6be1,citation,https://arxiv.org/pdf/1810.05162.pdf,Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation,2018 +6,VOC,voc,49.2767454,-122.91777375,Simon Fraser University,edu,74dbcc09a3456ddacf5cece640b84045ebdf6be1,citation,https://arxiv.org/pdf/1810.05162.pdf,Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation,2018 +7,VOC,voc,46.109237,7.08453549,IDIAP Research Institute,edu,dedc7b080b8e13d72f8dc33e248e7637d191fdbf,citation,http://pdfs.semanticscholar.org/dedc/7b080b8e13d72f8dc33e248e7637d191fdbf.pdf,Beyond Dataset Bias: Multi-task Unaligned Shared Knowledge Transfer,2012 +8,VOC,voc,52.17638955,0.14308882,University of Cambridge,edu,dedc7b080b8e13d72f8dc33e248e7637d191fdbf,citation,http://pdfs.semanticscholar.org/dedc/7b080b8e13d72f8dc33e248e7637d191fdbf.pdf,Beyond Dataset Bias: Multi-task Unaligned Shared Knowledge Transfer,2012 +9,VOC,voc,39.00041165,-77.10327775,National Institutes of Health,edu,18c57ddc9c0164ee792661f43a5578f7a00d0330,citation,https://arxiv.org/pdf/1705.02315v2.pdf,ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases,2017 +10,VOC,voc,37.40253645,-122.11655107,Toyota Research Institute,edu,a825680aeb853fc34c65b5844c4c4391148f18c3,citation,https://arxiv.org/pdf/1711.10006.pdf,SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again,2017 +11,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,f249c266321d661ae398c26ddb8c7409f6455ba1,citation,https://pdfs.semanticscholar.org/f249/c266321d661ae398c26ddb8c7409f6455ba1.pdf,Revisiting Faster R-CNN: A Deeper Look at Region Proposal Network,2017 +12,VOC,voc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,7fa5ede4a34dbe604ce317d529eed78db6642bc0,citation,https://arxiv.org/pdf/1709.01829.pdf,Soft Proposal Networks for Weakly Supervised Object Localization,2017 +13,VOC,voc,35.9990522,-78.9290629,Duke University,edu,7fa5ede4a34dbe604ce317d529eed78db6642bc0,citation,https://arxiv.org/pdf/1709.01829.pdf,Soft Proposal Networks for Weakly Supervised Object Localization,2017 +14,VOC,voc,42.3583961,-71.09567788,MIT,edu,05fdd29536d55fe3ad00689b6f60ada8bc761e91,citation,http://people.csail.mit.edu/torralba/publications/ihog_iccv.pdf,HOGgles: Visualizing Object Detection Features,2013 +15,VOC,voc,24.7925484,120.9951183,National Tsing Hua University,edu,394bf41cd8578ec10cd34452c688c3e3de1c16a7,citation,https://pdfs.semanticscholar.org/394b/f41cd8578ec10cd34452c688c3e3de1c16a7.pdf,Multi-view to Novel View: Synthesizing Novel Views With Self-learned Confidence,2018 +16,VOC,voc,22.42031295,114.20788644,Chinese University of Hong Kong,edu,2453dd38cde21f3248b55d281405f11d58168fa9,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.342,Multi-scale Patch Aggregation (MPA) for Simultaneous Detection and Segmentation,2016 +17,VOC,voc,50.7791703,6.06728733,RWTH Aachen University,edu,ccb9ffa26b28dffc4f7d613821d1a9f0d60ea3f4,citation,https://arxiv.org/pdf/1706.09364.pdf,Online Adaptation of Convolutional Neural Networks for Video Object Segmentation,2017 +18,VOC,voc,39.87549675,32.78553506,Middle East Technical University,edu,d38af10096aa90dfccd7e4cec9757900bf6958bd,citation,https://arxiv.org/pdf/1807.04067.pdf,MultiPoseNet: Fast Multi-Person Pose Estimation Using Pose Residual Network,2018 +19,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,8c1e828a4826a1fb3eb47ee432f5333b974fa141,citation,http://pdfs.semanticscholar.org/8c1e/828a4826a1fb3eb47ee432f5333b974fa141.pdf,Spatial Graph for Image Classification,2012 +20,VOC,voc,38.88140235,121.52281098,Dalian University of Technology,edu,2a31b4bf2a294b6e67956a6cd5ed6d875af548e0,citation,https://arxiv.org/pdf/1710.01020.pdf,Learning Affinity via Spatial Propagation Networks,2017 +21,VOC,voc,39.2899685,-76.62196103,University of Maryland,edu,0790c400bfe6fbefe88ef7791476e1abf1952089,citation,https://arxiv.org/pdf/1511.04067v1.pdf,Deep Gaussian Conditional Random Field Network: A Model-Based Deep Network for Discriminative Denoising,2016 +22,VOC,voc,41.3868913,2.16352385,University of Barcelona,edu,442cf9b24661c9ea5c2a1dcabd4a5b8af1cd89da,citation,https://arxiv.org/pdf/1806.10805.pdf,Beyond One-hot Encoding: lower dimensional target embedding,2018 +23,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,04eda7eee3e0282de50e54554f50870dd17defa1,citation,https://arxiv.org/pdf/1705.08280v1.pdf,How Hard Can It Be? Estimating the Difficulty of Visual Search in an Image,2016 +24,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,90a4125974564a5ab6c2ce2ff685fc36e9cf0680,citation,https://arxiv.org/pdf/1703.08448.pdf,Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach,2017 +25,VOC,voc,39.94976005,116.33629046,Beijing Jiaotong University,edu,90a4125974564a5ab6c2ce2ff685fc36e9cf0680,citation,https://arxiv.org/pdf/1703.08448.pdf,Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach,2017 +26,VOC,voc,39.9922379,116.30393816,Peking University,edu,c3dd6c1ddbb9cfcc1bed6383ffaa0b1ce4d13625,citation,https://arxiv.org/pdf/1807.01544.pdf,TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes,2018 +27,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,2976605dc3b73377696537291d45f09f1ab1fbf5,citation,http://www.ri.cmu.edu/pub_files/2016/6/multi-task.pdf,Cross-Stitch Networks for Multi-task Learning,2016 +28,VOC,voc,28.54632595,77.27325504,Indian Institute of Technology Delhi,edu,25e9a2ec45c34d4610359196dc505a72c3833336,citation,http://pdfs.semanticscholar.org/25e9/a2ec45c34d4610359196dc505a72c3833336.pdf,Benchmarking KAZE and MCM for Multiclass Classification,2015 +29,VOC,voc,39.9808333,116.34101249,Beihang University,edu,935e639bebf905af2e35e8b1e7aa0538d7122185,citation,https://arxiv.org/pdf/1808.00313.pdf,A Network Structure to Explicitly Reduce Confusion Errors in Semantic Segmentation,2018 +30,VOC,voc,39.8011499,140.0459116,Akita Prefectural University,edu,211435a4e14d00f4aaed191acfb548185ee800b9,citation,http://pdfs.semanticscholar.org/2114/35a4e14d00f4aaed191acfb548185ee800b9.pdf,Visual Saliency Based Multiple Objects Segmentation and its Parallel Implementation for Real-Time Vision Processing,2015 +31,VOC,voc,49.25839375,-123.24658161,University of British Columbia,edu,9fae24003bbedecdb617f9779215d79d06b90dd8,citation,https://arxiv.org/pdf/1807.09856.pdf,Where Are the Blobs: Counting by Localization with Point Supervision,2018 +32,VOC,voc,40.72925325,-73.99625394,New York University,edu,c45681fa9d9c36a6a196017ef283ac38904f91bb,citation,https://arxiv.org/pdf/1711.07377.pdf,Pixel-wise object tracking,2017 +33,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,45f858f9e8d7713f60f52618e54089ba68dfcd6d,citation,http://openaccess.thecvf.com/content_ICCV_2017/papers/Sigurdsson_What_Actions_Are_ICCV_2017_paper.pdf,What Actions are Needed for Understanding Human Actions in Videos?,2017 +34,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,57bd01c042a5f64659b3a9f91c048b8594f762f6,citation,http://pdfs.semanticscholar.org/57bd/01c042a5f64659b3a9f91c048b8594f762f6.pdf,Advances in fine-grained visual categorization,2015 +35,VOC,voc,31.30104395,121.50045497,Fudan University,edu,9716416a15e79a36e3481bcdad79cdc905603e6d,citation,https://arxiv.org/pdf/1808.07016.pdf,Gaussian Word Embedding with a Wasserstein Distance Loss,2017 +36,VOC,voc,32.0565957,118.77408833,Nanjing University,edu,97265d64859e06900c11ae5bb5f03f3bd265f858,citation,https://arxiv.org/pdf/1612.01082.pdf,Multilabel Image Classification With Regional Latent Semantic Dependencies,2018 +37,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,97265d64859e06900c11ae5bb5f03f3bd265f858,citation,https://arxiv.org/pdf/1612.01082.pdf,Multilabel Image Classification With Regional Latent Semantic Dependencies,2018 +38,VOC,voc,-33.8809651,151.20107299,University of Technology Sydney,edu,97265d64859e06900c11ae5bb5f03f3bd265f858,citation,https://arxiv.org/pdf/1612.01082.pdf,Multilabel Image Classification With Regional Latent Semantic Dependencies,2018 +39,VOC,voc,42.3583961,-71.09567788,MIT,edu,a19904e76b5ded44e6aeb9af85997d160de6bb22,citation,http://pdfs.semanticscholar.org/a199/04e76b5ded44e6aeb9af85997d160de6bb22.pdf,TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation,2018 +40,VOC,voc,47.05821,15.46019568,Graz University of Technology,edu,96a9ca7a8366ae0efe6b58a515d15b44776faf6e,citation,https://arxiv.org/pdf/1609.00129.pdf,Grid Loss: Detecting Occluded Faces,2016 +41,VOC,voc,47.05821,15.46019568,Graz University of Technology,edu,513b8dc73a9fbc467e1ac130fe8c842b5839ca51,citation,http://pdfs.semanticscholar.org/513b/8dc73a9fbc467e1ac130fe8c842b5839ca51.pdf,Dissertation Scalable Visual Navigation for Micro Aerial Vehicles using Geometric Prior Knowledge,2013 +42,VOC,voc,37.8687126,-122.25586815,"University of California, Berkeley",edu,0ee3aa2a78f9680bb65a823bd9195c879572ec1c,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Dubey_What_Makes_an_ICCV_2015_paper.pdf,What Makes an Object Memorable?,2015 +43,VOC,voc,42.3583961,-71.09567788,MIT,edu,0ee3aa2a78f9680bb65a823bd9195c879572ec1c,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Dubey_What_Makes_an_ICCV_2015_paper.pdf,What Makes an Object Memorable?,2015 +44,VOC,voc,37.36566745,-120.42158888,"University of California, Merced",edu,0ee3aa2a78f9680bb65a823bd9195c879572ec1c,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Dubey_What_Makes_an_ICCV_2015_paper.pdf,What Makes an Object Memorable?,2015 +45,VOC,voc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,a776acc53591c3eb0b53501d9758d984e2e52a97,citation,https://arxiv.org/pdf/1804.00880.pdf,Weakly Supervised Instance Segmentation using Class Peak Response,2018 +46,VOC,voc,35.9990522,-78.9290629,Duke University,edu,a776acc53591c3eb0b53501d9758d984e2e52a97,citation,https://arxiv.org/pdf/1804.00880.pdf,Weakly Supervised Instance Segmentation using Class Peak Response,2018 +47,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,423b941641728a21e37f41359a691815cdd84ceb,citation,http://arxiv.org/abs/1511.04517,Reversible Recursive Instance-Level Object Segmentation,2016 +48,VOC,voc,47.6423318,-122.1369302,Microsoft,company,666939690c564641b864eed0d60a410b31e49f80,citation,http://pdfs.semanticscholar.org/6669/39690c564641b864eed0d60a410b31e49f80.pdf,What Visual Attributes Characterize an Object Class?,2014 +49,VOC,voc,43.7776426,11.259765,University of Florence,edu,51e8e8c4cac8260ef21c25f9f2a0a68aedbc6d58,citation,https://arxiv.org/pdf/1704.02518.pdf,Deep Generative Adversarial Compression Artifact Removal,2017 +50,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,3b01a839d174dad6f2635cff7ebe7e1aaad701a4,citation,http://pdfs.semanticscholar.org/3b01/a839d174dad6f2635cff7ebe7e1aaad701a4.pdf,Image Co-localization by Mimicking a Good Detector's Confidence Score Distribution,2016 +51,VOC,voc,31.83907195,117.26420748,University of Science and Technology of China,edu,d467035d83fb4e86c4a47b2ca87894388deb8c44,citation,https://pdfs.semanticscholar.org/d467/035d83fb4e86c4a47b2ca87894388deb8c44.pdf,Relief R-CNN : Utilizing Convolutional Feature Interrelationship for Object Detection,2016 +52,VOC,voc,30.284151,-97.73195598,University of Texas at Austin,edu,264a2b946fae4af23c646cc08fc56947b5be82cf,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2015.7301302,Robust object recognition in RGB-D egocentric videos based on Sparse Affine Hull Kernel,2015 +53,VOC,voc,34.0687788,-118.4450094,"University of California, Los Angeles",edu,480888bad59b314236f2d947ebf308ae146c98e4,citation,https://arxiv.org/pdf/1511.06881.pdf,Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net,2016 +54,VOC,voc,25.01682835,121.53846924,National Taiwan University,edu,a1ee55d529e04a80f4eae3b30d0961a985a64fa4,citation,http://www.cs.utexas.edu/~ycsu/publications/mm029-su.pdf,Enabling low bitrate mobile visual recognition: a performance versus bandwidth evaluation,2013 +55,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,0cd736baf31dceea1cc39ac72e00b65587f5fb9e,citation,http://pdfs.semanticscholar.org/4ad0/b6f189718a7287c6e7b90eb05331e56db334.pdf,Learning Hash Functions Using Column Generation,2013 +56,VOC,voc,39.2899685,-76.62196103,University of Maryland,edu,6424574cb92b316928c37232869bfadcb5b4c20f,citation,https://arxiv.org/pdf/1711.05282.pdf,C-WSL: Count-Guided Weakly Supervised Localization,2018 +57,VOC,voc,47.6543238,-122.30800894,University of Washington,edu,51eba481dac6b229a7490f650dff7b17ce05df73,citation,http://grail.cs.washington.edu/wp-content/uploads/2016/09/yatskar2016srv.pdf,Situation Recognition: Visual Semantic Role Labeling for Image Understanding,2016 +58,VOC,voc,47.3764534,8.54770931,ETH Zürich,edu,961a5d5750f18e91e28a767b3cb234a77aac8305,citation,http://pdfs.semanticscholar.org/961a/5d5750f18e91e28a767b3cb234a77aac8305.pdf,Face Detection without Bells and Whistles,2014 +59,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,0c05f60998628884a9ac60116453f1a91bcd9dda,citation,http://pdfs.semanticscholar.org/7b19/80d4ac1730fd0145202a8cb125bf05d96f01.pdf,Optimizing Open-Ended Crowdsourcing: The Next Frontier in Crowdsourced Data Management,2016 +60,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,efa2aacb0fbee857015fad1dba72767f56be6f39,citation,https://pdfs.semanticscholar.org/efa2/aacb0fbee857015fad1dba72767f56be6f39.pdf,Aggregating Crowdsourced Image Segmentations,2018 +61,VOC,voc,37.3936717,-122.0807262,Facebook,company,efa2aacb0fbee857015fad1dba72767f56be6f39,citation,https://pdfs.semanticscholar.org/efa2/aacb0fbee857015fad1dba72767f56be6f39.pdf,Aggregating Crowdsourced Image Segmentations,2018 +62,VOC,voc,34.0687788,-118.4450094,"University of California, Los Angeles",edu,17113b0f647ce05b2e50d1d40c856370f94da7de,citation,http://pdfs.semanticscholar.org/1711/3b0f647ce05b2e50d1d40c856370f94da7de.pdf,Zoom Better to See Clearer: Human Part Segmentation with Auto Zoom Net,2015 +63,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,549d55a06c5402696e063ce36b411f341a64f8a9,citation,http://arxiv.org/pdf/1511.06078v1.pdf,Learning Deep Structure-Preserving Image-Text Embeddings,2016 +64,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,549d55a06c5402696e063ce36b411f341a64f8a9,citation,http://arxiv.org/pdf/1511.06078v1.pdf,Learning Deep Structure-Preserving Image-Text Embeddings,2016 +65,VOC,voc,35.9020448,139.93622009,University of Tokyo,edu,44bfa5311f0921664e9036f63cadd71049a35f35,citation,https://pdfs.semanticscholar.org/44bf/a5311f0921664e9036f63cadd71049a35f35.pdf,Faster R-CNN-Based Glomerular Detection in Multistained Human Whole Slide Images,2018 +66,VOC,voc,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,133f1f2679892d408420d8092283539010723359,citation,http://arxiv.org/pdf/1502.05082v3.pdf,What Makes for Effective Detection Proposals?,2016 +67,VOC,voc,60.18558755,24.8242733,Aalto University,edu,98d04187f091f402a90a6a9a2108393ca5f91563,citation,https://arxiv.org/pdf/1807.09828.pdf,ADVIO: An Authentic Dataset for Visual-Inertial Odometry,2018 +68,VOC,voc,61.44964205,23.85877462,Tampere University of Technology,edu,98d04187f091f402a90a6a9a2108393ca5f91563,citation,https://arxiv.org/pdf/1807.09828.pdf,ADVIO: An Authentic Dataset for Visual-Inertial Odometry,2018 +69,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,f8015e31d1421f6aee5e17fc3907070b8e0a5e59,citation,http://pdfs.semanticscholar.org/f801/5e31d1421f6aee5e17fc3907070b8e0a5e59.pdf,Towards Usable Multimedia Event Detection from Web Videos,2016 +70,VOC,voc,34.0224149,-118.28634407,University of Southern California,edu,6b9e8acef979c13fa9ecc8fe9b635b312fedbcbe,citation,https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Chang_Multiple_Structured-Instance_Learning_2014_CVPR_paper.pdf,Multiple Structured-Instance Learning for Semantic Segmentation with Uncertain Training Data,2014 +71,VOC,voc,51.4584837,-2.6097752,University of Bristol,edu,72fd97d21d6465d4bb407b6f8f3accd4419a2fb4,citation,https://pdfs.semanticscholar.org/384a/ea88ffd79295c99bcb80552f8655dbb87509.pdf,Automated Identification of Individual Great White Sharks from Unrestricted Fin Imagery,2015 +72,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,62b83bf64f200ebb9fa16dfb7108b85e390b2207,citation,https://arxiv.org/pdf/1807.11236.pdf,Semantic Labeling in Very High Resolution Images via a Self-Cascaded Convolutional Neural Network,2018 +73,VOC,voc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,62b83bf64f200ebb9fa16dfb7108b85e390b2207,citation,https://arxiv.org/pdf/1807.11236.pdf,Semantic Labeling in Very High Resolution Images via a Self-Cascaded Convolutional Neural Network,2018 +74,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,2577211aeaaa1f2245ddc379564813bee3d46c06,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Misra_Seeing_Through_the_CVPR_2016_paper.pdf,Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels,2016 +75,VOC,voc,47.6423318,-122.1369302,Microsoft,company,2577211aeaaa1f2245ddc379564813bee3d46c06,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Misra_Seeing_Through_the_CVPR_2016_paper.pdf,Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels,2016 +76,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,3900fb44902396f94fb070be41199b4beecc9081,citation,https://arxiv.org/pdf/1612.02101.pdf,Bottom-Up Top-Down Cues for Weakly-Supervised Semantic Segmentation,2017 +77,VOC,voc,34.0687788,-118.4450094,"University of California, Los Angeles",edu,32c45df9e11e6751bcea1b928f398f6c134d22c6,citation,http://pdfs.semanticscholar.org/32c4/5df9e11e6751bcea1b928f398f6c134d22c6.pdf,Towards Unified Object Detection and Semantic Segmentation,2014 +78,VOC,voc,42.36782045,-71.12666653,Harvard University,edu,2bcd59835528c583bb5b310522a5ba6e99c58b15,citation,http://pdfs.semanticscholar.org/c0ef/596a212d0e40c79c6760673fe122e517b43c.pdf,Multi-class Open Set Recognition Using Probability of Inclusion,2014 +79,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,3920a205990abc7883c70cc96a0410a2d056c2a8,citation,http://groups.inf.ed.ac.uk/calvin/Publications/papazoglouICCV2013-camera-ready.pdf,Fast Object Segmentation in Unconstrained Video,2013 +80,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,b6810adcfd507b2e019ebc8afe4f44f953faf946,citation,https://pdfs.semanticscholar.org/b681/0adcfd507b2e019ebc8afe4f44f953faf946.pdf,ML-LocNet: Improving Object Localization with Multi-view Learning Network,2018 +81,VOC,voc,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,b6810adcfd507b2e019ebc8afe4f44f953faf946,citation,https://pdfs.semanticscholar.org/b681/0adcfd507b2e019ebc8afe4f44f953faf946.pdf,ML-LocNet: Improving Object Localization with Multi-view Learning Network,2018 +82,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,0e08cf0b19f0600dadce0f6694420d643ea9828b,citation,http://openaccess.thecvf.com/content_iccv_2015/papers/Humayun_The_Middle_Child_ICCV_2015_paper.pdf,The Middle Child Problem: Revisiting Parametric Min-Cut and Seeds for Object Proposals,2015 +83,VOC,voc,45.5198289,-122.67797964,Oregon State University,edu,0e08cf0b19f0600dadce0f6694420d643ea9828b,citation,http://openaccess.thecvf.com/content_iccv_2015/papers/Humayun_The_Middle_Child_ICCV_2015_paper.pdf,The Middle Child Problem: Revisiting Parametric Min-Cut and Seeds for Object Proposals,2015 +84,VOC,voc,30.19331415,120.11930822,Zhejiang University,edu,81bf7a4b8b3c21d42cb82f946f762c94031e11b8,citation,https://pdfs.semanticscholar.org/81bf/7a4b8b3c21d42cb82f946f762c94031e11b8.pdf,Segmentation of Nerve on Ultrasound Images Using Deep Adversarial Network,2017 +85,VOC,voc,52.4107358,-4.05295501,Aberystwyth University,edu,30d8fbb9345cdf1096635af7d39a9b04af9b72f9,citation,https://pdfs.semanticscholar.org/30d8/fbb9345cdf1096635af7d39a9b04af9b72f9.pdf,Watching plants grow - a position paper on computer vision and Arabidopsis thaliana,2017 +86,VOC,voc,43.66333345,-79.39769975,University of Toronto,edu,87204e4e1a96b8f59cb91828199dacd192292231,citation,http://pdfs.semanticscholar.org/8720/4e4e1a96b8f59cb91828199dacd192292231.pdf,Towards Real-Time Detection and Tracking of Basketball Players using Deep Neural Networks,2017 +87,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,30a4637cbc461838c151073b265fb08e00492ff4,citation,http://faculty.ucmerced.edu/mhyang/papers/cvpr16_object_localization.pdf,Weakly Supervised Object Localization with Progressive Domain Adaptation,2016 +88,VOC,voc,50.7338124,7.1022465,University of Bonn,edu,606cfdcc43203351dbb944a3bb3719695e557e37,citation,https://pdfs.semanticscholar.org/606c/fdcc43203351dbb944a3bb3719695e557e37.pdf,Ex Paucis Plura : Learning Affordance Segmentation from Very Few Examples,2018 +89,VOC,voc,39.2899685,-76.62196103,University of Maryland,edu,47b6cd69c0746688f6e17b37d73fa12422826dbc,citation,http://pdfs.semanticscholar.org/47b6/cd69c0746688f6e17b37d73fa12422826dbc.pdf,Self corrective Perturbations for Semantic Segmentation and Classification,2017 +90,VOC,voc,38.99203005,-76.9461029,University of Maryland College Park,edu,47b6cd69c0746688f6e17b37d73fa12422826dbc,citation,http://pdfs.semanticscholar.org/47b6/cd69c0746688f6e17b37d73fa12422826dbc.pdf,Self corrective Perturbations for Semantic Segmentation and Classification,2017 +91,VOC,voc,42.8298248,-73.87719385,GE Global Research Center,edu,47b6cd69c0746688f6e17b37d73fa12422826dbc,citation,http://pdfs.semanticscholar.org/47b6/cd69c0746688f6e17b37d73fa12422826dbc.pdf,Self corrective Perturbations for Semantic Segmentation and Classification,2017 +92,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,14421119527aa5882e1552a651fbd2d73bc94637,citation,http://pdfs.semanticscholar.org/9b81/86b6bc1e05d7a473d2afebc8a12698d88691.pdf,Searching for objects driven by context,2012 +93,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,14421119527aa5882e1552a651fbd2d73bc94637,citation,http://pdfs.semanticscholar.org/9b81/86b6bc1e05d7a473d2afebc8a12698d88691.pdf,Searching for objects driven by context,2012 +94,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,3410a1489d04ec6fcfbb3d76d39055117931ccf0,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2013.126,Learning Collections of Part Models for Object Recognition,2013 +95,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,69b647afe6526256a93033eac14ce470204e7bae,citation,http://pdfs.semanticscholar.org/d7dd/4fb9074db71ebf9155d64b439102d4c7b0c5.pdf,Training Deep Neural Networks via Direct Loss Minimization,2016 +96,VOC,voc,43.66333345,-79.39769975,University of Toronto,edu,69b647afe6526256a93033eac14ce470204e7bae,citation,http://pdfs.semanticscholar.org/d7dd/4fb9074db71ebf9155d64b439102d4c7b0c5.pdf,Training Deep Neural Networks via Direct Loss Minimization,2016 +97,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,81825711c2aaa1b9d3ead1a300e71c4353a41382,citation,https://arxiv.org/pdf/1607.03476.pdf,End-to-end training of object class detectors for mean average precision,2016 +98,VOC,voc,39.993008,116.329882,SenseTime,company,2ce073da76e6ed87eda2da08da0e00f4f060f1a6,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.78,Deep Saliency with Encoded Low Level Distance Map and High Level Features,2016 +99,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,2313c827d3cb9a291b6a00d015c29580862bbdcc,citation,https://arxiv.org/pdf/1808.03575.pdf,Weakly- and Semi-supervised Panoptic Segmentation,2018 +100,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,839a2155995acc0a053a326e283be12068b35cb8,citation,http://pdfs.semanticscholar.org/839a/2155995acc0a053a326e283be12068b35cb8.pdf,Handcrafted Local Features are Convolutional Neural Networks,2015 +101,VOC,voc,32.0565957,118.77408833,Nanjing University,edu,634e02d6107529d672cbbdf5b97990966e289829,citation,https://arxiv.org/pdf/1802.05394.pdf,Cost-Effective Training of Deep CNNs with Active Model Adaptation,2018 +102,VOC,voc,56.45796755,-2.98214831,University of Dundee,edu,d0137881f6c791997337b9cc7f1efbd61977270d,citation,http://pdfs.semanticscholar.org/d013/7881f6c791997337b9cc7f1efbd61977270d.pdf,"University of Dundee An automated pattern recognition system for classifying indirect immunofluorescence images for HEp-2 cells and specimens Manivannan,",2016 +103,VOC,voc,42.2942142,-83.71003894,University of Michigan,edu,ed173a39f4cd980eef319116b6ba39cec1b37c42,citation,https://arxiv.org/pdf/1611.05424.pdf,Associative Embedding: End-to-End Learning for Joint Detection and Grouping,2017 +104,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,ed173a39f4cd980eef319116b6ba39cec1b37c42,citation,https://arxiv.org/pdf/1611.05424.pdf,Associative Embedding: End-to-End Learning for Joint Detection and Grouping,2017 +105,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,84cf838be40e2ab05732fbefbb93ccb2afb0cb48,citation,http://pdfs.semanticscholar.org/84cf/838be40e2ab05732fbefbb93ccb2afb0cb48.pdf,Recognizing Handwritten Characters,2016 +106,VOC,voc,37.26728,126.9841151,Seoul National University,edu,b082f440ee91e2751701401919584203b37e1e1a,citation,https://pdfs.semanticscholar.org/303c/28f1ba643a7cd88255cc379e79052fb7e7b1.pdf,SeedNet : Automatic Seed Generation with Deep Reinforcement Learning for Robust Interactive Segmentation,2018 +107,VOC,voc,22.2081469,114.25964115,University of Hong Kong,edu,6008213e4270e88cb414459de759c961469b92dd,citation,https://arxiv.org/pdf/1802.09129.pdf,"Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning",2018 +108,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,90b4470032f2796a347a0080bcd833c2db0e8bf0,citation,https://arxiv.org/pdf/1807.07760.pdf,Improving Image Clustering With Multiple Pretrained CNN Feature Extractors,2018 +109,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,beecaf2d6e9d102b6b2459ea38e15179a4b55ffd,citation,https://arxiv.org/pdf/1611.09587.pdf,Surveillance Video Parsing with Single Frame Supervision,2017 +110,VOC,voc,41.3868913,2.16352385,University of Barcelona,edu,0fb8317a8bf5feaf297af8e9b94c50c5ed0e8277,citation,http://pdfs.semanticscholar.org/0fb8/317a8bf5feaf297af8e9b94c50c5ed0e8277.pdf,Detecting Hands in Egocentric Videos: Towards Action Recognition,2017 +111,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,0e0179eb4b43016691f0f1473a08089dda21f8f0,citation,http://pdfs.semanticscholar.org/0e01/79eb4b43016691f0f1473a08089dda21f8f0.pdf,The Art of Detection,2016 +112,VOC,voc,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,135c957f6a80f250507c7707479e584c288f430f,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2014.498,Image-Based Synthesis and Re-synthesis of Viewpoints Guided by 3D Models,2014 +113,VOC,voc,39.00041165,-77.10327775,National Institutes of Health,edu,c72b063e23b8b45b57a42ebc2f9714297c539a6f,citation,https://arxiv.org/pdf/1801.04334.pdf,TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays,2018 +114,VOC,voc,36.05238585,140.11852361,National Institute of Advanced Industrial Science and Technology,edu,061ffd3967540424ac4e4066f4a605d8318bab90,citation,https://staff.aist.go.jp/takumi.kobayashi/publication/2014/CVPR2014.pdf,Dirichlet-Based Histogram Feature Transform for Image Classification,2014 +115,VOC,voc,42.3583961,-71.09567788,MIT,edu,1a2e9a56e5f71bf95a2f68b6e67e2aaa1c6bf91e,citation,http://pdfs.semanticscholar.org/1a2e/9a56e5f71bf95a2f68b6e67e2aaa1c6bf91e.pdf,FPM: Fine Pose Parts-Based Model with 3D CAD Models,2014 +116,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,c6f58adf4a5ee8499cbc9b9bc1e6f1c39f1f8eae,citation,https://pdfs.semanticscholar.org/c6f5/8adf4a5ee8499cbc9b9bc1e6f1c39f1f8eae.pdf,Earn to P Ay a Ttention,2018 +117,VOC,voc,32.87935255,-117.23110049,"University of California, San Diego",edu,3c8db2ca155ce4e15ec8a2c4c4b979de654fb296,citation,http://pages.ucsd.edu/~ztu/publication/iccv15_hed.pdf,Holistically-Nested Edge Detection,2015 +118,VOC,voc,59.34986645,18.07063213,"KTH Royal Institute of Technology, Stockholm",edu,8ccd6aaf1ee4b66c13fffbf560e3920f9bdf5f10,citation,http://pdfs.semanticscholar.org/8ccd/6aaf1ee4b66c13fffbf560e3920f9bdf5f10.pdf,A multitask deep learning model for real-time deployment in embedded systems,2017 +119,VOC,voc,53.5238572,-113.52282665,University of Alberta,edu,b4f5cf797a1c857f32e5740d53d9990bc925af2b,citation,https://pdfs.semanticscholar.org/b4f5/cf797a1c857f32e5740d53d9990bc925af2b.pdf,Review of Segmentation with Deep Learning and Discover Its Application in Ultrasound Images,2018 +120,VOC,voc,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,3bad18554678ab46bbbf9de41d36423bc8083c83,citation,http://arxiv.org/pdf/1511.07803v1.pdf,Weakly Supervised Object Boundaries,2016 +121,VOC,voc,24.7925484,120.9951183,National Tsing Hua University,edu,07191c2047b5b643dd72a0583c1d537ba59f977a,citation,http://pdfs.semanticscholar.org/0719/1c2047b5b643dd72a0583c1d537ba59f977a.pdf,Interactive Segmentation from 1-Bit Feedback,2016 +122,VOC,voc,37.26728,126.9841151,Seoul National University,edu,ae6e8851dfd9c97e37e1cbd61b21cc54d5e2b9c7,citation,https://arxiv.org/pdf/1802.04977.pdf,Paraphrasing Complex Network: Network Compression via Factor Transfer,2018 +123,VOC,voc,37.26728,126.9841151,Seoul National University,edu,5375a3344017d9502ebb4170325435de3da1fa16,citation,https://doi.org/10.1007/978-3-642-37447-0,Computer Vision – ACCV 2012,2012 +124,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,5375a3344017d9502ebb4170325435de3da1fa16,citation,https://doi.org/10.1007/978-3-642-37447-0,Computer Vision – ACCV 2012,2012 +125,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,5375a3344017d9502ebb4170325435de3da1fa16,citation,https://doi.org/10.1007/978-3-642-37447-0,Computer Vision – ACCV 2012,2012 +126,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,fdfd57d4721174eba288e501c0c120ad076cdca8,citation,https://arxiv.org/pdf/1704.07129.pdf,An Analysis of Action Recognition Datasets for Language and Vision Tasks,2017 +127,VOC,voc,32.0565957,118.77408833,Nanjing University,edu,ec83c63e28ae2a658bc76a6750e078c3a54b9760,citation,https://arxiv.org/pdf/1705.02758.pdf,Deep Descriptor Transforming for Image Co-Localization,2017 +128,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,ec83c63e28ae2a658bc76a6750e078c3a54b9760,citation,https://arxiv.org/pdf/1705.02758.pdf,Deep Descriptor Transforming for Image Co-Localization,2017 +129,VOC,voc,59.34986645,18.07063213,"KTH Royal Institute of Technology, Stockholm",edu,b1177aad0db8bd6b605ffe0d68addaf97b1f9a6b,citation,https://pdfs.semanticscholar.org/5035/733022916db7e5965c565327e169da1e2f39.pdf,Visual Representations and Models: From Latent SVM to Deep Learning,2016 +130,VOC,voc,31.83907195,117.26420748,University of Science and Technology of China,edu,a5ae7d662ed086bc5b0c9a2c1dc54fcb23635000,citation,https://pdfs.semanticscholar.org/a5ae/7d662ed086bc5b0c9a2c1dc54fcb23635000.pdf,Relief R-CNN : Utilizing Convolutional Feature Interrelationship for Fast Object Detection Deployment,2016 +131,VOC,voc,22.53521465,113.9315911,Shenzhen University,edu,a5ae7d662ed086bc5b0c9a2c1dc54fcb23635000,citation,https://pdfs.semanticscholar.org/a5ae/7d662ed086bc5b0c9a2c1dc54fcb23635000.pdf,Relief R-CNN : Utilizing Convolutional Feature Interrelationship for Fast Object Detection Deployment,2016 +132,VOC,voc,53.38522185,-6.25740874,Dublin City University,edu,9528e2e8c20517ab916f803c0371abb4f0ed488b,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Pan_Shallow_and_Deep_CVPR_2016_paper.pdf,Shallow and Deep Convolutional Networks for Saliency Prediction,2016 +133,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,e2272f50ffa33b8e41509e4b795ad5a4eb27bb46,citation,https://arxiv.org/pdf/1607.07671.pdf,Region-based semantic segmentation with end-to-end training,2016 +134,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,b8d61dc56a4112e0317c6a7323417ee649476148,citation,https://arxiv.org/pdf/1807.05636.pdf,Cross Pixel Optical Flow Similarity for Self-Supervised Learning,2018 +135,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,db0a4af734dab1854c2e8dfe499fe0e353226e45,citation,https://pdfs.semanticscholar.org/db0a/4af734dab1854c2e8dfe499fe0e353226e45.pdf,Hot Anchors: A Heuristic Anchors Sampling Method in RCNN-Based Object Detection,2018 +136,VOC,voc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,db0a4af734dab1854c2e8dfe499fe0e353226e45,citation,https://pdfs.semanticscholar.org/db0a/4af734dab1854c2e8dfe499fe0e353226e45.pdf,Hot Anchors: A Heuristic Anchors Sampling Method in RCNN-Based Object Detection,2018 +137,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,ffe0f43206169deef3a2bf64cec90fe35bb1a8e5,citation,http://pdfs.semanticscholar.org/ffe0/f43206169deef3a2bf64cec90fe35bb1a8e5.pdf,"Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans +",2015 +138,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,ffe0f43206169deef3a2bf64cec90fe35bb1a8e5,citation,http://pdfs.semanticscholar.org/ffe0/f43206169deef3a2bf64cec90fe35bb1a8e5.pdf,"Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans +",2015 +139,VOC,voc,45.77445695,126.67684917,Harbin Engineering University,edu,479eb6579194d4d944671dfe5e90b122ca4b58fd,citation,https://pdfs.semanticscholar.org/479e/b6579194d4d944671dfe5e90b122ca4b58fd.pdf,Structural inference embedded adversarial networks for scene parsing,2018 +140,VOC,voc,34.2469152,108.91061982,Northwestern Polytechnical University,edu,479eb6579194d4d944671dfe5e90b122ca4b58fd,citation,https://pdfs.semanticscholar.org/479e/b6579194d4d944671dfe5e90b122ca4b58fd.pdf,Structural inference embedded adversarial networks for scene parsing,2018 +141,VOC,voc,1.29500195,103.84909214,Singapore Management University,edu,d289ce63055c10937e5715e940a4bb9d0af7a8c5,citation,http://dl.acm.org/citation.cfm?id=3081360,DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications,2017 +142,VOC,voc,60.18558755,24.8242733,Aalto University,edu,061bba574c7c2ef0ba9de91afc4fcab70feddd4f,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2017.272,Paying Attention to Descriptions Generated by Image Captioning Models,2017 +143,VOC,voc,28.59899755,-81.19712501,University of Central Florida,edu,061bba574c7c2ef0ba9de91afc4fcab70feddd4f,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2017.272,Paying Attention to Descriptions Generated by Image Captioning Models,2017 +144,VOC,voc,34.7275714,135.2371,Kobe University,edu,ee2217f9d22d6a18aaf97f05768035c38305d1fa,citation,https://doi.org/10.1109/APSIPA.2015.7415501,Detection of facial parts via deformable part model using part annotation,2015 +145,VOC,voc,50.7791703,6.06728733,RWTH Aachen University,edu,18219d85bb14f851fc4714df19cc7f38dff8ddc3,citation,http://pdfs.semanticscholar.org/1821/9d85bb14f851fc4714df19cc7f38dff8ddc3.pdf,Online Adaptation of Convolutional Neural Networks for the 2017 DAVIS Challenge on Video Object Segmentation,2017 +146,VOC,voc,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,da44881db32c132eb9cdef524618e3c8ed340b47,citation,https://arxiv.org/pdf/1802.00383.pdf,Annotation-Free and One-Shot Learning for Instance Segmentation of Homogeneous Object Clusters,2018 +147,VOC,voc,50.7338124,7.1022465,University of Bonn,edu,cc94b423c298003f0f164e63e63177d443291a77,citation,https://arxiv.org/pdf/1805.03994.pdf,Multi-View Semantic Labeling of 3D Point Clouds for Automated Plant Phenotyping,2018 +148,VOC,voc,39.9922379,116.30393816,Peking University,edu,83a811fd947415df2413d15386dbc558f07595cb,citation,https://arxiv.org/pdf/1709.08295.pdf,Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN,2017 +149,VOC,voc,-33.8809651,151.20107299,University of Technology Sydney,edu,3a5f5aca6138abcf22ede1af5572e01eb0f761d1,citation,https://pdfs.semanticscholar.org/3a5f/5aca6138abcf22ede1af5572e01eb0f761d1.pdf,Optimizing Multivariate Performance Measures from Multi-View Data,2016 +150,VOC,voc,34.2469152,108.91061982,Northwestern Polytechnical University,edu,ce300b006f42c1b64ca0e53d1cf28d11a98ece8f,citation,https://pdfs.semanticscholar.org/ce30/0b006f42c1b64ca0e53d1cf28d11a98ece8f.pdf,Learning Multi-Instance Enriched Image Representations via Non-Greedy Ratio Maximization of the l 1-Norm Distances,0 +151,VOC,voc,34.0224149,-118.28634407,University of Southern California,edu,71b038958df0b7855fc7b8b8e7dcde8537a7c1ad,citation,http://pdfs.semanticscholar.org/71b0/38958df0b7855fc7b8b8e7dcde8537a7c1ad.pdf,Kernel Methods for Unsupervised Domain Adaptation by Boqing Gong,2015 +152,VOC,voc,34.2469152,108.91061982,Northwestern Polytechnical University,edu,af7cab9b4a2a2a565a3efe0a226c517f47289077,citation,https://arxiv.org/pdf/1803.10910.pdf,Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective,2018 +153,VOC,voc,-35.2776999,149.118527,Australian National University,edu,af7cab9b4a2a2a565a3efe0a226c517f47289077,citation,https://arxiv.org/pdf/1803.10910.pdf,Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective,2018 +154,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,3a6ebdfb6375093885e846153a48139ef1ecfae6,citation,http://arxiv.org/abs/1411.7466,The treasure beneath convolutional layers: Cross-convolutional-layer pooling for image classification,2015 +155,VOC,voc,51.24303255,-0.59001382,University of Surrey,edu,a7e9d230bc44dfbe56757f3025d5b4caa49032f3,citation,http://pdfs.semanticscholar.org/a7e9/d230bc44dfbe56757f3025d5b4caa49032f3.pdf,Unity in Diversity: Discovering Topics from Words - Information Theoretic Co-clustering for Visual Categorization,2012 +156,VOC,voc,37.5557271,127.0436642,Hanyang University,edu,50137d663802224e683951c48970496b38b02141,citation,http://pdfs.semanticscholar.org/5013/7d663802224e683951c48970496b38b02141.pdf,DETRAC: A New Benchmark and Protocol for Multi-Object Tracking,2015 +157,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,07de8371ad4901356145722aa29abaeafd0986b9,citation,http://pdfs.semanticscholar.org/07de/8371ad4901356145722aa29abaeafd0986b9.pdf,Towards Usable Multimedia Event Detection,2017 +158,VOC,voc,41.21002475,-73.80407056,IBM Thomas J. Watson Research Center,company,af386bb1b5e8c9f65b3ae836198a93aa860d6331,citation,https://arxiv.org/pdf/1805.04574.pdf,Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation,2018 +159,VOC,voc,17.4454957,78.34854698,International Institute of Information Technology,edu,d6b1b0e60e1764982ef95d4ade8fcaa10bfb156a,citation,http://pdfs.semanticscholar.org/d6b1/b0e60e1764982ef95d4ade8fcaa10bfb156a.pdf,A Sketch-based Approach for Multimedia Retrieval,2016 +160,VOC,voc,51.49887085,-0.17560797,Imperial College London,edu,37b3637dab65b91a5c91bb6a583e69c448823cc1,citation,https://arxiv.org/pdf/1705.05994.pdf,Learning a Hierarchical Latent-Variable Model of 3D Shapes,2018 +161,VOC,voc,39.9574,-75.19026706,Drexel University,edu,83d16fb8f53156c9e2b28d75abb6532af515440f,citation,http://pdfs.semanticscholar.org/83d1/6fb8f53156c9e2b28d75abb6532af515440f.pdf,Large-scale Document Labeling using Supervised Sequence Embedding,2012 +162,VOC,voc,45.51181205,-122.68492999,Portland State University,edu,05e45f61dc7577c50114a382abc6e952ae24cdac,citation,https://pdfs.semanticscholar.org/05e4/5f61dc7577c50114a382abc6e952ae24cdac.pdf,"Object Detection and Recognition in Natural Settings by George William Dittmar A thesis submitted in partial fulfilment of the requirements of the degree Master of Science in Computer Science Thesis Committee: Melanie Mitchell, Chair",2012 +163,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,192235f5a9e4c9d6a28ec0d333e36f294b32f764,citation,http://www.andrew.cmu.edu/user/sjayasur/iccv.pdf,Reconfiguring the Imaging Pipeline for Computer Vision,2017 +164,VOC,voc,42.4505507,-76.4783513,Cornell University,edu,192235f5a9e4c9d6a28ec0d333e36f294b32f764,citation,http://www.andrew.cmu.edu/user/sjayasur/iccv.pdf,Reconfiguring the Imaging Pipeline for Computer Vision,2017 +165,VOC,voc,50.0764296,14.41802312,Czech Technical University,edu,bd4f2e7a196c0d6033a49390ee8836f4f551b7c8,citation,http://rrc.cvc.uab.es/files/Robust-Reading-Competition-Karatzas.pdf,ICDAR 2015 competition on Robust Reading,2015 +166,VOC,voc,33.59914655,130.22359848,Kyushu University,edu,bd4f2e7a196c0d6033a49390ee8836f4f551b7c8,citation,http://rrc.cvc.uab.es/files/Robust-Reading-Competition-Karatzas.pdf,ICDAR 2015 competition on Robust Reading,2015 +167,VOC,voc,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,3d5575e9ba02128d94c20330f4525fc816411ec2,citation,https://arxiv.org/pdf/1612.02646.pdf,Learning Video Object Segmentation from Static Images,2017 +168,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,78f62042bfb3bb49ba10e142d118a9bb058b2a19,citation,http://pdfs.semanticscholar.org/78f6/2042bfb3bb49ba10e142d118a9bb058b2a19.pdf,WebSeg: Learning Semantic Segmentation from Web Searches,2018 +169,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,0c7aac75ccd17d696cff2e1ce95db0493f5c18a2,citation,https://arxiv.org/pdf/1809.01123.pdf,VideoMatch: Matching Based Video Object Segmentation,2018 +170,VOC,voc,3.12267405,101.65356103,University of Malaya,edu,6c78add400f749c897dc3eb93996eda1c796e91c,citation,https://arxiv.org/pdf/1410.3752.pdf,Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding,2014 +171,VOC,voc,51.49887085,-0.17560797,Imperial College London,edu,6c78add400f749c897dc3eb93996eda1c796e91c,citation,https://arxiv.org/pdf/1410.3752.pdf,Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding,2014 +172,VOC,voc,39.9922379,116.30393816,Peking University,edu,6c78add400f749c897dc3eb93996eda1c796e91c,citation,https://arxiv.org/pdf/1410.3752.pdf,Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding,2014 +173,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,b61c0b11b1c25958d202b4f7ca772e1d95ee1037,citation,http://pdfs.semanticscholar.org/b61c/0b11b1c25958d202b4f7ca772e1d95ee1037.pdf,Bridging Category-level and Instance-level Semantic Image Segmentation,2016 +174,VOC,voc,34.0224149,-118.28634407,University of Southern California,edu,79894ddf290d3c7a768d634eceb7888564b5cf19,citation,https://arxiv.org/pdf/1708.01676.pdf,Query-Guided Regression Network with Context Policy for Phrase Grounding,2017 +175,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,fec2a5a06a3aab5efe923a78d208ec747d5e4894,citation,https://arxiv.org/pdf/1805.12018.pdf,Generalizing to Unseen Domains via Adversarial Data Augmentation,2018 +176,VOC,voc,31.30104395,121.50045497,Fudan University,edu,5ac63895a7d3371a739d066bb1631fc178d8276a,citation,http://doi.acm.org/10.1145/3123266.3123379,Learning Semantic Feature Map for Visual Content Recognition,2017 +177,VOC,voc,39.2899685,-76.62196103,University of Maryland,edu,5ac63895a7d3371a739d066bb1631fc178d8276a,citation,http://doi.acm.org/10.1145/3123266.3123379,Learning Semantic Feature Map for Visual Content Recognition,2017 +178,VOC,voc,-34.40505545,150.87834655,University of Wollongong,edu,4e559f23bcf502c752f2938ad7f0182047b8d1e4,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Wang_A_Fast_Approximate_2013_CVPR_paper.pdf,A Fast Approximate AIB Algorithm for Distributional Word Clustering,2013 +179,VOC,voc,-35.2776999,149.118527,Australian National University,edu,7536b6a9f3cb4ae810e2ef6d0219134b4e546dd0,citation,http://pdfs.semanticscholar.org/7536/b6a9f3cb4ae810e2ef6d0219134b4e546dd0.pdf,Semi-Automatic Image Labelling Using Depth Information,2015 +180,VOC,voc,42.7298459,-73.67950216,Rensselaer Polytechnic Institute,edu,11b89011298e193d9e6a1d99302221c1d8645bda,citation,http://openaccess.thecvf.com/content_iccv_2015/papers/Gao_Structured_Feature_Selection_ICCV_2015_paper.pdf,Structured Feature Selection,2015 +181,VOC,voc,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,de3245c795bc50ebdb5d929c8da664341238264a,citation,https://arxiv.org/pdf/1705.08590.pdf,Generative Model With Coordinate Metric Learning for Object Recognition Based on 3D Models,2018 +182,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,cc2eaa182f33defbb33d69e9547630aab7ed9c9c,citation,http://pdfs.semanticscholar.org/ce2e/e807a63bbdffa530c80915b04d11a7f29a21.pdf,Surpassing Humans and Computers with JELLYBEAN: Crowd-Vision-Hybrid Counting Algorithms,2015 +183,VOC,voc,40.00471095,-83.02859368,Ohio State University,edu,cc2eaa182f33defbb33d69e9547630aab7ed9c9c,citation,http://pdfs.semanticscholar.org/ce2e/e807a63bbdffa530c80915b04d11a7f29a21.pdf,Surpassing Humans and Computers with JELLYBEAN: Crowd-Vision-Hybrid Counting Algorithms,2015 +184,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,cc2eaa182f33defbb33d69e9547630aab7ed9c9c,citation,http://pdfs.semanticscholar.org/ce2e/e807a63bbdffa530c80915b04d11a7f29a21.pdf,Surpassing Humans and Computers with JELLYBEAN: Crowd-Vision-Hybrid Counting Algorithms,2015 +185,VOC,voc,32.0565957,118.77408833,Nanjing University,edu,9c71e6f4e27b3a6f0f872ec683b0f6dfe0966c05,citation,http://pdfs.semanticscholar.org/9c71/e6f4e27b3a6f0f872ec683b0f6dfe0966c05.pdf,"Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey",2017 +186,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,b88b83d2ffd30bf3bc3be3fb7492fd88f633b2fe,citation,http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/4989a827.pdf,Subcategory-Aware Object Classification,2013 +187,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,b6a3802075d460093977f8566c451f950edf7a47,citation,https://pdfs.semanticscholar.org/0999/e5baf505eed0df8e2661c29354f3757b3399.pdf,Facilitating and Exploring Planar Homogeneous Texture for Indoor Scene Understanding,2016 +188,VOC,voc,51.7555205,-1.2261597,Oxford Brookes University,edu,cd6cab9357f333ad9966abc76f830c190a1b7911,citation,https://pdfs.semanticscholar.org/cd6c/ab9357f333ad9966abc76f830c190a1b7911.pdf,"Recognition, reorganisation, reconstruction and reinteraction for scene understanding",2014 +189,VOC,voc,47.3764534,8.54770931,ETH Zürich,edu,0fe8b5503681128da84a8454a4cc94470adc09ea,citation,http://pdfs.semanticscholar.org/b96a/0ccae1d15cffe3b479b2c56d9132b05cd846.pdf,Sparsity Potentials for Detecting Objects with the Hough Transform,2012 +190,VOC,voc,35.7036227,51.35125097,Sharif University of Technology,edu,0fe8b5503681128da84a8454a4cc94470adc09ea,citation,http://pdfs.semanticscholar.org/b96a/0ccae1d15cffe3b479b2c56d9132b05cd846.pdf,Sparsity Potentials for Detecting Objects with the Hough Transform,2012 +191,VOC,voc,47.6423318,-122.1369302,Microsoft,company,9bbc952adb3e3c6091d45d800e806d3373a52bac,citation,https://pdfs.semanticscholar.org/9bbc/952adb3e3c6091d45d800e806d3373a52bac.pdf,Learning Visual Classifiers using Human-centric Annotations,2015 +192,VOC,voc,35.6572957,139.54255868,University of Electro-Communications,edu,6e209d7d33c0be8afae863f4e4e9c3e86826711f,citation,http://img.cs.uec.ac.jp/pub/conf16/161204shimok_1_ppt.pdf,Weakly-supervised segmentation by combining CNN feature maps and object saliency maps,2016 +193,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,46d85e1dc7057bef62647bd9241601e9896a1b02,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/2A_040_ext.pdf,Improving object proposals with multi-thresholding straddling expansion,2015 +194,VOC,voc,35.2742655,137.01327841,Chubu University,edu,67e3fac91c699c085d47774990572d8ccdc36f15,citation,http://pdfs.semanticscholar.org/67e3/fac91c699c085d47774990572d8ccdc36f15.pdf,Multiple Skip Connections and Dilated Convolutions for Semantic Segmentation,2017 +195,VOC,voc,34.0224149,-118.28634407,University of Southern California,edu,a4f29217d2120ed1490aea7e1c5b78c3b76e972f,citation,https://arxiv.org/pdf/1610.06907.pdf,Enhanced object detection via fusion with prior beliefs from image classification,2017 +196,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,f2d07a77711a8d74bbfa48a0436dae18a698b05a,citation,http://pdfs.semanticscholar.org/f2d0/7a77711a8d74bbfa48a0436dae18a698b05a.pdf,Composite Statistical Learning and Inference for Semantic Segmentation,2014 +197,VOC,voc,40.2075951,-8.42566148,University of Coimbra,edu,f2d07a77711a8d74bbfa48a0436dae18a698b05a,citation,http://pdfs.semanticscholar.org/f2d0/7a77711a8d74bbfa48a0436dae18a698b05a.pdf,Composite Statistical Learning and Inference for Semantic Segmentation,2014 +198,VOC,voc,55.7039571,13.1902011,Lund University,edu,f2d07a77711a8d74bbfa48a0436dae18a698b05a,citation,http://pdfs.semanticscholar.org/f2d0/7a77711a8d74bbfa48a0436dae18a698b05a.pdf,Composite Statistical Learning and Inference for Semantic Segmentation,2014 +199,VOC,voc,61.44964205,23.85877462,Tampere University of Technology,edu,ff11cb09e409996020a2dc3a8afc3b535e6b2482,citation,https://arxiv.org/pdf/1807.03142.pdf,Faster Bounding Box Annotation for Object Detection in Indoor Scenes,2018 +200,VOC,voc,35.84658875,127.1350133,Chonbuk National University,edu,e103fa24d7fa297cd206b22b3bf670bfda6c65c4,citation,https://pdfs.semanticscholar.org/e103/fa24d7fa297cd206b22b3bf670bfda6c65c4.pdf,Object Detection in Very High-Resolution Aerial Images Using One-Stage Densely Connected Feature Pyramid Network,2018 +201,VOC,voc,41.8268682,-71.40123146,Brown University,edu,9a781a01b5a9c210dd2d27db8b73b7d62bc64837,citation,http://pdfs.semanticscholar.org/9a78/1a01b5a9c210dd2d27db8b73b7d62bc64837.pdf,An Attempt to Build Object Detection Models by Reusing Parts,2013 +202,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,ac559888f996923c06b1cf90db6b57b12e582289,citation,http://pdfs.semanticscholar.org/ac55/9888f996923c06b1cf90db6b57b12e582289.pdf,Benchmarking neuromorphic vision: lessons learnt from computer vision,2015 +203,VOC,voc,47.3764534,8.54770931,ETH Zürich,edu,ac559888f996923c06b1cf90db6b57b12e582289,citation,http://pdfs.semanticscholar.org/ac55/9888f996923c06b1cf90db6b57b12e582289.pdf,Benchmarking neuromorphic vision: lessons learnt from computer vision,2015 +204,VOC,voc,39.2899685,-76.62196103,University of Maryland,edu,ac559888f996923c06b1cf90db6b57b12e582289,citation,http://pdfs.semanticscholar.org/ac55/9888f996923c06b1cf90db6b57b12e582289.pdf,Benchmarking neuromorphic vision: lessons learnt from computer vision,2015 +205,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,2a4fc35acaf09517e9c63821cadd428a84832416,citation,http://www.vision.ee.ethz.ch/en/publications/papers/proceedings/eth_biwi_00905.pdf,Learning object class detectors from weakly annotated video,2012 +206,VOC,voc,22.053565,113.39913285,Jilin University,edu,cd4850de71e4e858be5f5e6ef7f48d5bf7decea6,citation,http://pdfs.semanticscholar.org/cd48/50de71e4e858be5f5e6ef7f48d5bf7decea6.pdf,Distribution Entropy Boosted VLAD for Image Retrieval,2016 +207,VOC,voc,40.4319722,-86.92389368,Purdue University,edu,34b925a111ba29f73f5c0d1b363f357958d563c1,citation,https://www.microsoft.com/en-us/research/wp-content/uploads/2015/03/Shoaib_DATE_2015.pdf,SAPPHIRE: An always-on context-aware computer vision system for portable devices,2015 +208,VOC,voc,47.6423318,-122.1369302,Microsoft,company,34b925a111ba29f73f5c0d1b363f357958d563c1,citation,https://www.microsoft.com/en-us/research/wp-content/uploads/2015/03/Shoaib_DATE_2015.pdf,SAPPHIRE: An always-on context-aware computer vision system for portable devices,2015 +209,VOC,voc,24.7925484,120.9951183,National Tsing Hua University,edu,c76b611a986a2e09df22603d93b2d9125aaff369,citation,https://arxiv.org/pdf/1810.07050.pdf,Generating Self-Guided Dense Annotations for Weakly Supervised Semantic Segmentation,2018 +210,VOC,voc,22.053565,113.39913285,Jilin University,edu,1927d01b6b9acf865401b544e25b62a7ddbac5fa,citation,https://pdfs.semanticscholar.org/1927/d01b6b9acf865401b544e25b62a7ddbac5fa.pdf,An Enhanced Region Proposal Network for object detection using deep learning method,2018 +211,VOC,voc,-33.8809651,151.20107299,University of Technology Sydney,edu,1ecd20f7fc34344e396825d27bc5a9871ab0d0c2,citation,https://arxiv.org/pdf/1810.09091.pdf,SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation,2018 +212,VOC,voc,42.3583961,-71.09567788,MIT,edu,26aa0aff1ea1baf848a521363cc455044690e090,citation,http://pdfs.semanticscholar.org/26aa/0aff1ea1baf848a521363cc455044690e090.pdf,A 2D + 3D Rich Data Approach to Scene Understanding,2013 +213,VOC,voc,46.0658836,11.1159894,University of Trento,edu,3548cb9ee54bd4c8b3421f1edd393da9038da293,citation,http://www.huppelen.nl/publications/2012cvprUnseenEventCompositionality.pdf,(Unseen) event recognition via semantic compositionality,2012 +214,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,25ee08db14dca641d085584909b551042618b8bf,citation,http://pdfs.semanticscholar.org/25ee/08db14dca641d085584909b551042618b8bf.pdf,Learning to Segment Instances in Videos with Spatial Propagation Network,2017 +215,VOC,voc,37.36566745,-120.42158888,"University of California, Merced",edu,25ee08db14dca641d085584909b551042618b8bf,citation,http://pdfs.semanticscholar.org/25ee/08db14dca641d085584909b551042618b8bf.pdf,Learning to Segment Instances in Videos with Spatial Propagation Network,2017 +216,VOC,voc,48.9095338,9.1831892,University of Stuttgart,edu,d0f81c31e11af1783644704321903a3d2bd83fd6,citation,https://pdfs.semanticscholar.org/d0f8/1c31e11af1783644704321903a3d2bd83fd6.pdf,3D Façade Labeling over Complex Scenarios: A Case Study Using Convolutional Neural Network and Structure-From-Motion,2018 +217,VOC,voc,50.7369302,-3.53647672,University of Exeter,edu,d0f81c31e11af1783644704321903a3d2bd83fd6,citation,https://pdfs.semanticscholar.org/d0f8/1c31e11af1783644704321903a3d2bd83fd6.pdf,3D Façade Labeling over Complex Scenarios: A Case Study Using Convolutional Neural Network and Structure-From-Motion,2018 +218,VOC,voc,38.99203005,-76.9461029,University of Maryland College Park,edu,a996f22a2d0c685f7e4972df9f45e99efc3cbb76,citation,https://arxiv.org/pdf/1708.00079.pdf,Towards the Success Rate of One: Real-Time Unconstrained Salient Object Detection,2018 +219,VOC,voc,47.05821,15.46019568,Graz University of Technology,edu,4da5f0c1d07725a06c6b4a2646e31ea3a5f14435,citation,http://pdfs.semanticscholar.org/4da5/f0c1d07725a06c6b4a2646e31ea3a5f14435.pdf,End-to-End Training of Hybrid CNN-CRF Models for Semantic Segmentation using Structured Learning,2017 +220,VOC,voc,52.3553655,4.9501644,University of Amsterdam,edu,26c58e24687ccbe9737e41837aab74e4a499d259,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Li_Codemaps_-_Segment_2013_ICCV_paper.pdf,"Codemaps - Segment, Classify and Search Objects Locally",2013 +221,VOC,voc,37.4219999,-122.0840575,Google,company,299b65d5d3914dad9aae2f936165dcebcf78db88,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.203,Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation,2015 +222,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,cb5dcd048b0eaa78a887a014be26a8a7b1325d36,citation,https://arxiv.org/pdf/1709.04093.pdf,Joint Learning of Set Cardinality and State Distribution,2018 +223,VOC,voc,34.2469152,108.91061982,Northwestern Polytechnical University,edu,63660c50e2669a5115c2379e622549d8ed79be00,citation,http://porikli.com/mysite/pdfs/porikli%202017%20-%20Deep%20salient%20object%20detection%20by%20integrating%20multi-level%20cues.pdf,Deep Salient Object Detection by Integrating Multi-level Cues,2017 +224,VOC,voc,-35.2776999,149.118527,Australian National University,edu,63660c50e2669a5115c2379e622549d8ed79be00,citation,http://porikli.com/mysite/pdfs/porikli%202017%20-%20Deep%20salient%20object%20detection%20by%20integrating%20multi-level%20cues.pdf,Deep Salient Object Detection by Integrating Multi-level Cues,2017 +225,VOC,voc,48.14955455,11.56775314,Technical University Munich,edu,472541ccd941b9b4c52e1f088cc1152de9b3430f,citation,https://arxiv.org/pdf/1612.00197.pdf,Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses,2017 +226,VOC,voc,47.3764534,8.54770931,ETH Zürich,edu,9184b0c04013bfdfd82f4f271b5f017396c2f085,citation,https://pdfs.semanticscholar.org/9184/b0c04013bfdfd82f4f271b5f017396c2f085.pdf,Semantic Segmentation for Line Drawing Vectorization Using Neural Networks,2018 +227,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,57488aa24092fa7118aa5374c90b282a32473cf9,citation,https://arxiv.org/pdf/1807.01257.pdf,A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities,2018 +228,VOC,voc,39.9492344,-75.19198985,University of Pennsylvania,edu,57488aa24092fa7118aa5374c90b282a32473cf9,citation,https://arxiv.org/pdf/1807.01257.pdf,A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities,2018 +229,VOC,voc,32.0565957,118.77408833,Nanjing University,edu,7771807cd05f78a4591f2d0b094ddd3e0bd5339a,citation,https://arxiv.org/pdf/1707.06399.pdf,Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors,2017 +230,VOC,voc,50.7944026,-1.0971748,Cambridge University,edu,4558338873556d01fd290de6ddc55721c633a1ad,citation,http://pdfs.semanticscholar.org/4558/338873556d01fd290de6ddc55721c633a1ad.pdf,Training Constrained Deconvolutional Networks for Road Scene Semantic Segmentation,2016 +231,VOC,voc,42.3583961,-71.09567788,MIT,edu,85957b49896246bb416c0a182e52b355a8fa40b4,citation,https://arxiv.org/pdf/1806.03510.pdf,Feature Pyramid Network for Multi-Class Land Segmentation,2018 +232,VOC,voc,17.4454957,78.34854698,International Institute of Information Technology,edu,f5eb411217f729ad7ae84bfd4aeb3dedb850206a,citation,https://pdfs.semanticscholar.org/f5eb/411217f729ad7ae84bfd4aeb3dedb850206a.pdf,Tackling Low Resolution for Better Scene Understanding,2018 +233,VOC,voc,53.8338371,10.7035939,Institute of Systems and Robotics,edu,7fb8d9c36c23f274f2dd84945dd32ec2cc143de1,citation,http://pdfs.semanticscholar.org/8e44/ba779d7cdc23d597c2c6e4420129834e7e21.pdf,Semantic Segmentation with Second-Order Pooling,2012 +234,VOC,voc,50.7338124,7.1022465,University of Bonn,edu,7fb8d9c36c23f274f2dd84945dd32ec2cc143de1,citation,http://pdfs.semanticscholar.org/8e44/ba779d7cdc23d597c2c6e4420129834e7e21.pdf,Semantic Segmentation with Second-Order Pooling,2012 +235,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,b5e3beb791cc17cdaf131d5cca6ceb796226d832,citation,http://pdfs.semanticscholar.org/b5e3/beb791cc17cdaf131d5cca6ceb796226d832.pdf,Novel Dataset for Fine-Grained Image Categorization: Stanford Dogs,2012 +236,VOC,voc,39.94976005,116.33629046,Beijing Jiaotong University,edu,b5968e7bb23f5f03213178c22fd2e47af3afa04c,citation,https://arxiv.org/pdf/1705.07206.pdf,Multiple-Human Parsing in the Wild,2017 +237,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,b5968e7bb23f5f03213178c22fd2e47af3afa04c,citation,https://arxiv.org/pdf/1705.07206.pdf,Multiple-Human Parsing in the Wild,2017 +238,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,532c089b43983935e1001c5e35aa35440263beaf,citation,https://arxiv.org/pdf/1804.03166.pdf,G-Distillation: Reducing Overconfident Errors on Novel Samples,2018 +239,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,35fc0b28d0d674b28dd625d170bc641a36b17318,citation,http://pdfs.semanticscholar.org/35fc/0b28d0d674b28dd625d170bc641a36b17318.pdf,CSI: Composite Statistical Inference Techniques for Semantic Segmentation,2013 +240,VOC,voc,55.7039571,13.1902011,Lund University,edu,35fc0b28d0d674b28dd625d170bc641a36b17318,citation,http://pdfs.semanticscholar.org/35fc/0b28d0d674b28dd625d170bc641a36b17318.pdf,CSI: Composite Statistical Inference Techniques for Semantic Segmentation,2013 +241,VOC,voc,58.38131405,26.72078081,University of Tartu,edu,e4cb27d2a3e1153cb517d97d61de48ff0483c988,citation,https://pdfs.semanticscholar.org/e4cb/27d2a3e1153cb517d97d61de48ff0483c988.pdf,Viktoria Plemakova Vehicle Detection Based on Convolutional Neural Networks,2018 +242,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,3d0660e18c17db305b9764bb86b21a429241309e,citation,https://arxiv.org/pdf/1604.03505.pdf,Counting Everyday Objects in Everyday Scenes,2017 +243,VOC,voc,37.2381023,127.1903431,Myongji University,edu,a67da2dd79c01e8cc4029ecc5a05b97967403862,citation,https://pdfs.semanticscholar.org/a67d/a2dd79c01e8cc4029ecc5a05b97967403862.pdf,On Selecting Helpful Unlabeled Data for Improving Semi-Supervised Support Vector Machines,2014 +244,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,4ab69672e1116427d685bf7c1edb5b1fd0573b5e,citation,http://bigml.cs.tsinghua.edu.cn/~lingxi/PDFs/Xie_ACMMM12_EdgeGPP.pdf,Spatial pooling of heterogeneous features for image applications,2012 +245,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,989c7cdafa9b90ab2ea0a9d8fa60634cc698f174,citation,http://pdfs.semanticscholar.org/989c/7cdafa9b90ab2ea0a9d8fa60634cc698f174.pdf,YoloFlow Real - time Object Tracking in Video CS 229 Course Project,2016 +246,VOC,voc,3.12267405,101.65356103,University of Malaya,edu,85af6c005df806b57b306a732dcb98e096d15bfb,citation,https://arxiv.org/pdf/1805.11227.pdf,Getting to Know Low-light Images with The Exclusively Dark Dataset,2018 +247,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,cdb293381ff396d6e9c0f5e9578d411e759347fd,citation,https://pdfs.semanticscholar.org/022e/eae0edc09deb228da26d5390874f781ace0f.pdf,3 DR 2 N 2 : A Unified Approach for Single and Multiview 3 D Object Reconstruction,2016 +248,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,0e67717484684d90ae9d4e1bb9cdceb74b194910,citation,http://pdfs.semanticscholar.org/0e67/717484684d90ae9d4e1bb9cdceb74b194910.pdf,Mining Pixels: Weakly Supervised Semantic Segmentation Using Image Labels,2016 +249,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,5b4b84ce3518c8a14f57f5f95a1d07fb60e58223,citation,https://pdfs.semanticscholar.org/9f92/05a60ddf1135929e0747db34363b3a8c6bc8.pdf,Diagnosing Error in Object Detectors,2012 +250,VOC,voc,42.718568,-84.47791571,Michigan State University,edu,47203943c86e4d9355ffd99cd3d75f37211fd805,citation,http://pdfs.semanticscholar.org/be18/9c7066c4d99d617d137c975139c594ad09af.pdf,Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning,2012 +251,VOC,voc,42.8298248,-73.87719385,GE Global Research Center,edu,47203943c86e4d9355ffd99cd3d75f37211fd805,citation,http://pdfs.semanticscholar.org/be18/9c7066c4d99d617d137c975139c594ad09af.pdf,Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning,2012 +252,VOC,voc,39.95472495,-75.15346905,Temple University,edu,45ff38add61df32a027048624f58952a67a7c5f5,citation,http://pdfs.semanticscholar.org/45ff/38add61df32a027048624f58952a67a7c5f5.pdf,Deep Context Convolutional Neural Networks for Semantic Segmentation,2017 +253,VOC,voc,43.08250655,-77.67121663,Rochester Institute of Technology,edu,0a789733ccb300d0dd9df6174faaa7e8c64e0409,citation,http://pdfs.semanticscholar.org/0a78/9733ccb300d0dd9df6174faaa7e8c64e0409.pdf,High-Resolution Multispectral Dataset for Semantic Segmentation,2017 +254,VOC,voc,47.05821,15.46019568,Graz University of Technology,edu,9d3a6e459e0cecda20a8afd69d182877ff0224cf,citation,http://pdfs.semanticscholar.org/9d3a/6e459e0cecda20a8afd69d182877ff0224cf.pdf,A Framework for Articulated Hand Pose Estimation and Evaluation,2015 +255,VOC,voc,52.3553655,4.9501644,University of Amsterdam,edu,943a1e218b917172199e524944006aa349f58968,citation,https://arxiv.org/pdf/1807.11857.pdf,Joint Learning of Intrinsic Images and Semantic Segmentation,2018 +256,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,5f68e2131d9275d56092e9fca05bcfc65abea0d8,citation,http://doi.acm.org/10.1145/2806416.2806469,Cross-Modal Similarity Learning: A Low Rank Bilinear Formulation,2015 +257,VOC,voc,40.9153196,-73.1270626,Stony Brook University,edu,f989a20fbcc2d576c0c4514a0e5085c741580778,citation,https://arxiv.org/pdf/1612.03236.pdf,Co-localization with Category-Consistent Features and Geodesic Distance Propagation,2017 +258,VOC,voc,42.36782045,-71.12666653,Harvard University,edu,f989a20fbcc2d576c0c4514a0e5085c741580778,citation,https://arxiv.org/pdf/1612.03236.pdf,Co-localization with Category-Consistent Features and Geodesic Distance Propagation,2017 +259,VOC,voc,24.7925484,120.9951183,National Tsing Hua University,edu,cf94200a476dc15d6da95db809349db4cfd8e92c,citation,https://arxiv.org/pdf/1807.11436.pdf,Leveraging Motion Priors in Videos for Improving Human Segmentation,2018 +260,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,25dba68e4db0ce361032126b91f734f9252cae7c,citation,https://arxiv.org/pdf/1611.08998.pdf,DeepSetNet: Predicting Sets with Deep Neural Networks,2017 +261,VOC,voc,59.34986645,18.07063213,"KTH Royal Institute of Technology, Stockholm",edu,883767948f535ea2bf8a0c03047ca9064e1b078f,citation,https://pdfs.semanticscholar.org/8837/67948f535ea2bf8a0c03047ca9064e1b078f.pdf,A Combination of Object Recognition and Localisation for an Autonomous Racecar,0 +262,VOC,voc,23.09461185,113.28788994,Sun Yat-Sen University,edu,18095a530b532a70f3b615fef2f59e6fdacb2d84,citation,https://arxiv.org/pdf/1604.02271v3.pdf,Deep Structured Scene Parsing by Learning with Image Descriptions,2016 +263,VOC,voc,45.7413921,126.62552755,Harbin Institute of Technology,edu,18095a530b532a70f3b615fef2f59e6fdacb2d84,citation,https://arxiv.org/pdf/1604.02271v3.pdf,Deep Structured Scene Parsing by Learning with Image Descriptions,2016 +264,VOC,voc,-27.47715625,153.02841004,Queensland University of Technology,edu,9397e7acd062245d37350f5c05faf56e9cfae0d6,citation,http://pdfs.semanticscholar.org/9397/e7acd062245d37350f5c05faf56e9cfae0d6.pdf,DeepFruits: A Fruit Detection System Using Deep Neural Networks,2016 +265,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,03a24d15533dae78de78fd9d5f6c9050fb97f186,citation,https://doi.org/10.1109/SSCI.2016.7850112,Pedestrian detection aided by scale-discriminative network,2016 +266,VOC,voc,-33.88890695,151.18943366,University of Sydney,edu,17d4fd92352baf6f0039ec64d43ca572c8252384,citation,https://arxiv.org/pdf/1806.07049.pdf,MoE-SPNet: A mixture-of-experts scene parsing network,2018 +267,VOC,voc,47.05821,15.46019568,Graz University of Technology,edu,30a29f6c407749e97bc7c2db5674a62773af9d27,citation,http://pdfs.semanticscholar.org/30a2/9f6c407749e97bc7c2db5674a62773af9d27.pdf,Tracking and Visual Quality Inspection in Harsh Environments (print-version),2012 +268,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,280d632ef3234c5ab06018c6eaccead75bc173b3,citation,http://pdfs.semanticscholar.org/6b1a/c8e438041ac02cc8fab5762ca069c386f473.pdf,Efficient Image and Video Co-localization with Frank-Wolfe Algorithm,2014 +269,VOC,voc,31.83907195,117.26420748,University of Science and Technology of China,edu,0f945f796a9343b51a3dc69941c0fa1a98c0f448,citation,http://pdfs.semanticscholar.org/a7ef/979ce52b9e4bcbd6ee5524dfd4e92baf6292.pdf,Local Hypersphere Coding Based on Edges between Visual Words,2012 +270,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,0db6a58927a671c01089c53248b0e1c36bdc3231,citation,http://openaccess.thecvf.com/content_cvpr_2016/papers/Pham_Efficient_Point_Process_CVPR_2016_paper.pdf,Efficient Point Process Inference for Large-Scale Object Detection,2016 +271,VOC,voc,42.2942142,-83.71003894,University of Michigan,edu,14d0afea52c4e9b7a488f6398e4a92bd4f4b93c7,citation,https://arxiv.org/pdf/1804.07667.pdf,Rethinking the Faster R-CNN Architecture for Temporal Action Localization,2018 +272,VOC,voc,42.2942142,-83.71003894,University of Michigan,edu,8da1b0834688edb311a803532e33939e9ecf8292,citation,https://arxiv.org/pdf/1808.01244.pdf,CornerNet: Detecting Objects as Paired Keypoints,2018 +273,VOC,voc,39.2899685,-76.62196103,University of Maryland,edu,f42d3225afd9e463ddb7a355f64b54af8bd14227,citation,https://arxiv.org/pdf/1804.10343.pdf,Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation,2018 +274,VOC,voc,31.83907195,117.26420748,University of Science and Technology of China,edu,a1dd88f44d045b360569a9a8721f728afbd951c3,citation,https://pdfs.semanticscholar.org/a1dd/88f44d045b360569a9a8721f728afbd951c3.pdf,Relief Impression Image Detection : Unsupervised Extracting Objects Directly From Feature Arrangements of Deep CNN,2016 +275,VOC,voc,34.0687788,-118.4450094,"University of California, Los Angeles",edu,fc027fccb19512a439fc17181c34ee1c3aad51b5,citation,https://arxiv.org/pdf/1708.03383.pdf,Joint Multi-person Pose Estimation and Semantic Part Segmentation,2017 +276,VOC,voc,39.329053,-76.619425,Johns Hopkins University,edu,377f2b65e6a9300448bdccf678cde59449ecd337,citation,https://arxiv.org/pdf/1804.10275.pdf,Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results,2018 +277,VOC,voc,40.47913175,-74.43168868,Rutgers University,edu,377f2b65e6a9300448bdccf678cde59449ecd337,citation,https://arxiv.org/pdf/1804.10275.pdf,Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results,2018 +278,VOC,voc,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,451eed7fd8ae281d1cc76ca8cdecbaf47816e55a,citation,http://pdfs.semanticscholar.org/451e/ed7fd8ae281d1cc76ca8cdecbaf47816e55a.pdf,Close Yet Distinctive Domain Adaptation,2017 +279,VOC,voc,35.9990522,-78.9290629,Duke University,edu,992b93ab9d016640551a8cebcaf4757288154f32,citation,http://pdfs.semanticscholar.org/e38c/f96363aaf1f17c487c484ad27d3175ca4b31.pdf,Nested Pictorial Structures,2012 +280,VOC,voc,43.08250655,-77.67121663,Rochester Institute of Technology,edu,7489990ea3d6ab4c1c86c9ed9f049399961dfaef,citation,https://people.rit.edu/ndcsma/pubs/WNYISPW_Nov_2014_Chew.pdf,Normalized cutswith soft must-link constraints for image segmentation and clustering,2014 +281,VOC,voc,59.34986645,18.07063213,"KTH Royal Institute of Technology, Stockholm",edu,41199678ad9370ff8ca7e9e3c2617b62a297fac3,citation,http://pdfs.semanticscholar.org/4119/9678ad9370ff8ca7e9e3c2617b62a297fac3.pdf,Multitask Deep Learning models for real-time deployment in embedded systems,2017 +282,VOC,voc,39.7487516,30.47653071,Eskisehir Osmangazi University,edu,7fb74f5abab4830e3cdaf477230e5571d9e3ca57,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Cevikalp_Polyhedral_Conic_Classifiers_CVPR_2017_paper.pdf,Polyhedral Conic Classifiers for Visual Object Detection and Classification,2017 +283,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,10793d1475607929fedc6d9a677911ad16843e58,citation,http://openaccess.thecvf.com/content_cvpr_2016/papers/Li_Unsupervised_Learning_of_CVPR_2016_paper.pdf,Unsupervised Learning of Edges,2016 +284,VOC,voc,31.30104395,121.50045497,Fudan University,edu,c94fd258a8f1e8f4033a7fe491f1372dcf7d3cd6,citation,https://arxiv.org/pdf/1807.04897.pdf,TS ^2 2 C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection,2018 +285,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,c94fd258a8f1e8f4033a7fe491f1372dcf7d3cd6,citation,https://arxiv.org/pdf/1807.04897.pdf,TS ^2 2 C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection,2018 +286,VOC,voc,52.5180641,13.3250425,TU Berlin,edu,2581a12189eb1a0b5b27a7fd1c2cbe44c88fcc20,citation,http://arxiv.org/pdf/1512.00172v1.pdf,Analyzing Classifiers: Fisher Vectors and Deep Neural Networks,2016 +287,VOC,voc,32.0565957,118.77408833,Nanjing University,edu,96416b1b44fb05302c6e9a8ab1b74d9204995e73,citation,http://pdfs.semanticscholar.org/9641/6b1b44fb05302c6e9a8ab1b74d9204995e73.pdf,Learning Effective Binary Visual Representations with Deep Networks,2018 +288,VOC,voc,42.3619407,-71.0904378,MIT CSAIL,edu,aa2ddae22760249729ac2c2c4e24c8b665bcd40e,citation,https://pdfs.semanticscholar.org/8c47/635ae7f1641c2bdd45026ad7dbff70c24398.pdf,Interpretable Basis Decomposition for Visual Explanation,2018 +289,VOC,voc,42.2942142,-83.71003894,University of Michigan,edu,60542b1a857024c79db8b5b03db6e79f74ec8f9f,citation,https://arxiv.org/pdf/1702.05448.pdf,Learning to Detect Human-Object Interactions,2018 +290,VOC,voc,36.3693473,120.673818,Shandong University,edu,bd8a85acaa45d4068fca584e8d9e3bd3bb4eea4d,citation,http://pdfs.semanticscholar.org/bd8a/85acaa45d4068fca584e8d9e3bd3bb4eea4d.pdf,Toward Scene Recognition by Discovering Semantic Structures and Parts,2015 +291,VOC,voc,49.2767454,-122.91777375,Simon Fraser University,edu,bd8a85acaa45d4068fca584e8d9e3bd3bb4eea4d,citation,http://pdfs.semanticscholar.org/bd8a/85acaa45d4068fca584e8d9e3bd3bb4eea4d.pdf,Toward Scene Recognition by Discovering Semantic Structures and Parts,2015 +292,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,456abee9c8d31f004b2f0a3b47222043e20f5042,citation,https://arxiv.org/pdf/1603.09188.pdf,Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings,2016 +293,VOC,voc,31.83907195,117.26420748,University of Science and Technology of China,edu,7c2f6424b0bb2c28f282fbc0b4e98bf85d5584eb,citation,http://pdfs.semanticscholar.org/a5ae/7d662ed086bc5b0c9a2c1dc54fcb23635000.pdf,Relief R-CNN: Utilizing Convolutional Feature Interrelationship for Fast Object Detection Deployment,2016 +294,VOC,voc,22.53521465,113.9315911,Shenzhen University,edu,7c2f6424b0bb2c28f282fbc0b4e98bf85d5584eb,citation,http://pdfs.semanticscholar.org/a5ae/7d662ed086bc5b0c9a2c1dc54fcb23635000.pdf,Relief R-CNN: Utilizing Convolutional Feature Interrelationship for Fast Object Detection Deployment,2016 +295,VOC,voc,37.5557271,127.0436642,Hanyang University,edu,59e9934720baf3c5df3a0e1e988202856e1f83ce,citation,https://arxiv.org/pdf/1511.04136.pdf,UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking,2015 +296,VOC,voc,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,d58c44bd9b464d9ac1db1344445c31364925f75a,citation,https://pdfs.semanticscholar.org/d58c/44bd9b464d9ac1db1344445c31364925f75a.pdf,TBN: Convolutional Neural Network with Ternary Inputs and Binary Weights,2018 +297,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,81ba5202424906f64b77f68afca063658139fbb2,citation,https://arxiv.org/pdf/1611.09078.pdf,Social Scene Understanding: End-to-End Multi-person Action Localization and Collective Activity Recognition,2017 +298,VOC,voc,46.109237,7.08453549,IDIAP Research Institute,edu,81ba5202424906f64b77f68afca063658139fbb2,citation,https://arxiv.org/pdf/1611.09078.pdf,Social Scene Understanding: End-to-End Multi-person Action Localization and Collective Activity Recognition,2017 +299,VOC,voc,50.7338124,7.1022465,University of Bonn,edu,0b6f64c78c44dc043e2972fa7bfe2a5753768609,citation,https://doi.org/10.1109/ICPR.2016.7900008,A future for learning semantic models of man-made environments,2016 +300,VOC,voc,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,016eb7b32d1fdec0899151fb03799378bf59bbe5,citation,http://pdfs.semanticscholar.org/016e/b7b32d1fdec0899151fb03799378bf59bbe5.pdf,Point Linking Network for Object Detection,2017 +301,VOC,voc,33.9928298,-81.02685168,University of South Carolina,edu,cd9d654c6a4250e0cf8bcfddc2afab9e70ee6cae,citation,http://pdfs.semanticscholar.org/cd9d/654c6a4250e0cf8bcfddc2afab9e70ee6cae.pdf,Object Detection with Mask-based Feature Encoding,2018 +302,VOC,voc,36.20304395,117.05842113,Tianjin University,edu,cd9d654c6a4250e0cf8bcfddc2afab9e70ee6cae,citation,http://pdfs.semanticscholar.org/cd9d/654c6a4250e0cf8bcfddc2afab9e70ee6cae.pdf,Object Detection with Mask-based Feature Encoding,2018 +303,VOC,voc,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,28737575297a20d431dd2b777a79a8be2c9c2bbd,citation,http://pdfs.semanticscholar.org/2873/7575297a20d431dd2b777a79a8be2c9c2bbd.pdf,Object Ranking on Deformable Part Models with Bagged LambdaMART,2014 +304,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,46702e0127e16a4d6a1feda3ffc5f0f123957e87,citation,https://arxiv.org/pdf/1809.06131.pdf,Revisit Multinomial Logistic Regression in Deep Learning: Data Dependent Model Initialization for Image Recognition,2018 +305,VOC,voc,34.0687788,-118.4450094,"University of California, Los Angeles",edu,d2b2cb1d5cc1aa30cf5be7bcb0494198934caabb,citation,http://pdfs.semanticscholar.org/d2b2/cb1d5cc1aa30cf5be7bcb0494198934caabb.pdf,A Restricted Visual Turing Test for Deep Scene and Event Understanding,2015 +306,VOC,voc,37.8687126,-122.25586815,"University of California, Berkeley",edu,446fbff6a2a7c9989b0a0465f960e236d9a5e886,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Pathak_Context_Encoders_Feature_CVPR_2016_paper.pdf,Context Encoders: Feature Learning by Inpainting,2016 +307,VOC,voc,51.49887085,-0.17560797,Imperial College London,edu,291e5377df2eec4835b5c6889896941831a11c69,citation,http://pdfs.semanticscholar.org/291e/5377df2eec4835b5c6889896941831a11c69.pdf,Recovering 6D Object Pose: Multi-modal Analyses on Challenges,2017 +308,VOC,voc,40.9153196,-73.1270626,Stony Brook University,edu,b69fbf046faf685655b5fa52fef07fb77e75eff4,citation,http://pdfs.semanticscholar.org/b69f/bf046faf685655b5fa52fef07fb77e75eff4.pdf,Modeling guidance and recognition in categorical search: bridging human and computer object detection.,2013 +309,VOC,voc,39.7487516,30.47653071,Eskisehir Osmangazi University,edu,13bda03fc8984d5943ed8d02e49a779d27c84114,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6248047,Efficient object detection using cascades of nearest convex model classifiers,2012 +310,VOC,voc,50.7338124,7.1022465,University of Bonn,edu,87a66ccc68374ffb704ee6fb9fa7df369718095c,citation,http://pdfs.semanticscholar.org/ea90/16fb585ba6449d3d6f98bf85fa0bcd1f4621.pdf,Multi-person Pose Estimation with Local Joint-to-Person Associations,2016 +311,VOC,voc,39.9922379,116.30393816,Peking University,edu,4960ab1cef23e5ccd60173725ea280f462164a0e,citation,https://pdfs.semanticscholar.org/4960/ab1cef23e5ccd60173725ea280f462164a0e.pdf,Video Object Segmentation by Learning Location-Sensitive Embeddings,2018 +312,VOC,voc,39.977217,116.337632,Microsoft Research Asia,company,4960ab1cef23e5ccd60173725ea280f462164a0e,citation,https://pdfs.semanticscholar.org/4960/ab1cef23e5ccd60173725ea280f462164a0e.pdf,Video Object Segmentation by Learning Location-Sensitive Embeddings,2018 +313,VOC,voc,35.9990522,-78.9290629,Duke University,edu,8856fbf333b2aba7b9f1f746e16a2b7f083ee5b8,citation,http://pdfs.semanticscholar.org/8856/fbf333b2aba7b9f1f746e16a2b7f083ee5b8.pdf,Analyzing animal behavior via classifying each video frame using convolutional neural networks,2015 +314,VOC,voc,34.1235825,108.83546,Xidian University,edu,f9f01af981f8d25f0c96ea06d88be62dabb79256,citation,https://pdfs.semanticscholar.org/f9f0/1af981f8d25f0c96ea06d88be62dabb79256.pdf,Terahertz Image Detection with the Improved Faster Region-Based Convolutional Neural Network,2018 +315,VOC,voc,37.5600406,126.9369248,Yonsei University,edu,09066d7d0bb6273bf996c8538d7b34c38ea6a500,citation,https://arxiv.org/pdf/1809.01845.pdf,"Yes, IoU loss is submodular - as a function of the mispredictions",2018 +316,VOC,voc,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,4aeebd1c9b4b936ed2e4d988d8d28e27f129e6f1,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Chiu_See_the_Difference_ICCV_2015_paper.pdf,See the Difference: Direct Pre-Image Reconstruction and Pose Estimation by Differentiating HOG,2015 +317,VOC,voc,34.0687788,-118.4450094,"University of California, Los Angeles",edu,232ff2dab49cb5a1dae1012fd7ba53382909ec18,citation,http://pdfs.semanticscholar.org/232f/f2dab49cb5a1dae1012fd7ba53382909ec18.pdf,Semantic Video Segmentation from Occlusion Relations within a Convex Optimization Framework,2013 +318,VOC,voc,50.13053055,8.69234224,University of Frankfurt,edu,465c34c3334f29de28f973b7702a235509649429,citation,http://pdfs.semanticscholar.org/465c/34c3334f29de28f973b7702a235509649429.pdf,Stereopsis via deep learning,2013 +319,VOC,voc,47.6543238,-122.30800894,University of Washington,edu,caa2ded6d8d5de97c824d29b0c7a18d220c596c8,citation,https://arxiv.org/pdf/1709.02554.pdf,Learning to Segment Breast Biopsy Whole Slide Images,2018 +320,VOC,voc,44.48116865,-73.2002179,University of Vermont,edu,caa2ded6d8d5de97c824d29b0c7a18d220c596c8,citation,https://arxiv.org/pdf/1709.02554.pdf,Learning to Segment Breast Biopsy Whole Slide Images,2018 +321,VOC,voc,42.2942142,-83.71003894,University of Michigan,edu,289d833a35c2156b7e332e67d1cb099fd0683025,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Chao_HICO_A_Benchmark_ICCV_2015_paper.pdf,HICO: A Benchmark for Recognizing Human-Object Interactions in Images,2015 +322,VOC,voc,37.8687126,-122.25586815,"University of California, Berkeley",edu,0fbdd4b8eb9e4c4cfbe5b76ab29ab8b0219fbdc0,citation,https://people.eecs.berkeley.edu/~pathak/papers/iccv15.pdf,Constrained Convolutional Neural Networks for Weakly Supervised Segmentation,2015 +323,VOC,voc,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,f94f79168c1cfaebb8eab5151e01d56478ab0b73,citation,http://pdfs.semanticscholar.org/f94f/79168c1cfaebb8eab5151e01d56478ab0b73.pdf,Optimizing Region Selection for Weakly Supervised Object Detection,2017 +324,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,6bb51f431f348b2b3e1db859827e80f97a576c30,citation,http://pdfs.semanticscholar.org/6bb5/1f431f348b2b3e1db859827e80f97a576c30.pdf,Irregular Convolutional Neural Networks,2017 +325,VOC,voc,22.42031295,114.20788644,Chinese University of Hong Kong,edu,b78e611c32dc0daf762cfa93044558cdb545d857,citation,http://pdfs.semanticscholar.org/b78e/611c32dc0daf762cfa93044558cdb545d857.pdf,Temporal Action Detection with Structured Segment Networks Supplementary Materials,2017 +326,VOC,voc,48.14955455,11.56775314,Technical University Munich,edu,bc12715a1ddf1a540dab06bf3ac4f3a32a26b135,citation,http://pdfs.semanticscholar.org/bc12/715a1ddf1a540dab06bf3ac4f3a32a26b135.pdf,Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking,2017 +327,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,bc12715a1ddf1a540dab06bf3ac4f3a32a26b135,citation,http://pdfs.semanticscholar.org/bc12/715a1ddf1a540dab06bf3ac4f3a32a26b135.pdf,Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking,2017 +328,VOC,voc,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,4d1757aacbc49c74a5d4e53259c92ab0e47544da,citation,https://arxiv.org/pdf/1805.04310.pdf,Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer,2018 +329,VOC,voc,36.1112058,140.1055176,University of Tsukuba,edu,d392098688a999c70589c995bd4427c212eff69d,citation,http://pdfs.semanticscholar.org/d392/098688a999c70589c995bd4427c212eff69d.pdf,Object Repositioning Based on the Perspective in a Single Image,2014 +330,VOC,voc,22.42031295,114.20788644,Chinese University of Hong Kong,edu,1c1f21bf136fe2eec412e5f70fd918c27c5ccb0a,citation,http://pdfs.semanticscholar.org/1c1f/21bf136fe2eec412e5f70fd918c27c5ccb0a.pdf,Object Detection and Viewpoint Estimation with Auto-masking Neural Network,2014 +331,VOC,voc,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,1c1f21bf136fe2eec412e5f70fd918c27c5ccb0a,citation,http://pdfs.semanticscholar.org/1c1f/21bf136fe2eec412e5f70fd918c27c5ccb0a.pdf,Object Detection and Viewpoint Estimation with Auto-masking Neural Network,2014 +332,VOC,voc,51.49887085,-0.17560797,Imperial College London,edu,72e9acdd64e71fc2084acaf177aafaa2e075bd8c,citation,http://pdfs.semanticscholar.org/72e9/acdd64e71fc2084acaf177aafaa2e075bd8c.pdf,The 2017 Hands in the Million Challenge on 3D Hand Pose Estimation,2017 +333,VOC,voc,51.49887085,-0.17560797,Imperial College London,edu,0209389b8369aaa2a08830ac3b2036d4901ba1f1,citation,https://arxiv.org/pdf/1612.01202v2.pdf,DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild,2017 +334,VOC,voc,51.5231607,-0.1282037,University College London,edu,0209389b8369aaa2a08830ac3b2036d4901ba1f1,citation,https://arxiv.org/pdf/1612.01202v2.pdf,DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild,2017 +335,VOC,voc,50.7338124,7.1022465,University of Bonn,edu,07b8a9a225b738c4074a50cf80ee5fe516878421,citation,https://arxiv.org/pdf/1807.09169.pdf,Convolutional Simplex Projection Network for Weakly Supervised Semantic Segmentation,2018 +336,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,1bd1645a629f1b612960ab9bba276afd4cf7c666,citation,http://arxiv.org/pdf/1506.04878.pdf,End-to-End People Detection in Crowded Scenes,2016 +337,VOC,voc,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,1bd1645a629f1b612960ab9bba276afd4cf7c666,citation,http://arxiv.org/pdf/1506.04878.pdf,End-to-End People Detection in Crowded Scenes,2016 +338,VOC,voc,43.7776426,11.259765,University of Florence,edu,1bbe0371ca22c2fdb6e0d098049bbf6430324bdb,citation,http://doi.acm.org/10.1145/2906152,"Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval",2016 +339,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,1bbe0371ca22c2fdb6e0d098049bbf6430324bdb,citation,http://doi.acm.org/10.1145/2906152,"Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval",2016 +340,VOC,voc,34.7275714,135.2371,Kobe University,edu,9954f7ee5288724184f9420e39cca9165efa6822,citation,http://www.me.cs.scitec.kobe-u.ac.jp/~takigu/pdf/2015/Th5_4.pdf,Estimation of object functions using deformable part model,2015 +341,VOC,voc,48.14955455,11.56775314,Technical University Munich,edu,e212b2bc41645fe467a73d004067fcf1ca77d87f,citation,http://pdfs.semanticscholar.org/e212/b2bc41645fe467a73d004067fcf1ca77d87f.pdf,Deep Active Contours,2016 +342,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,51c4ecf4539f56c4b1035b890f743b3a91dd758b,citation,http://arxiv.org/abs/1504.06434,Situational object boundary detection,2015 +343,VOC,voc,37.8687126,-122.25586815,"University of California, 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Toronto,edu,acdc333f7b32d987e65ce15f21db64e850ca9471,citation,https://pdfs.semanticscholar.org/acdc/333f7b32d987e65ce15f21db64e850ca9471.pdf,Direct Loss Minimization for Training Deep Neural Nets,2015 +370,VOC,voc,28.2290209,112.99483204,"National University of Defense Technology, China",edu,da4137396f26bf3e76d04eeed0c94e11b7824aa6,citation,https://arxiv.org/pdf/1711.06828.pdf,Transferable Semi-Supervised Semantic Segmentation,2018 +371,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,da4137396f26bf3e76d04eeed0c94e11b7824aa6,citation,https://arxiv.org/pdf/1711.06828.pdf,Transferable Semi-Supervised Semantic Segmentation,2018 +372,VOC,voc,40.11571585,-88.22750772,Beckman Institute,edu,da4137396f26bf3e76d04eeed0c94e11b7824aa6,citation,https://arxiv.org/pdf/1711.06828.pdf,Transferable Semi-Supervised Semantic Segmentation,2018 +373,VOC,voc,40.9153196,-73.1270626,Stony Brook 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Adelaide,edu,247ca98c5a46616044cf6ae32b0d5b4140a7a161,citation,http://pdfs.semanticscholar.org/247c/a98c5a46616044cf6ae32b0d5b4140a7a161.pdf,High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks,2016 +381,VOC,voc,28.2290209,112.99483204,"National University of Defense Technology, China",edu,5f771fed91c8e4b666489ba2384d0705bcf75030,citation,https://arxiv.org/pdf/1804.03287.pdf,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018 +382,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,5f771fed91c8e4b666489ba2384d0705bcf75030,citation,https://arxiv.org/pdf/1804.03287.pdf,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018 +383,VOC,voc,51.6091578,-3.97934429,Swansea University,edu,d115c4a66d765fef596b0b171febca334cea15b5,citation,http://pdfs.semanticscholar.org/d115/c4a66d765fef596b0b171febca334cea15b5.pdf,Combining Stacked Denoising Autoencoders and Random Forests for Face Detection,2016 +384,VOC,voc,39.2899685,-76.62196103,University of Maryland,edu,e20ab84ac7fa0a5d36d4cf2266b7065c60e1c804,citation,https://pdfs.semanticscholar.org/e20a/b84ac7fa0a5d36d4cf2266b7065c60e1c804.pdf,Stacked U-Nets for Ground Material Segmentation in Remote Sensing Imagery,0 +385,VOC,voc,22.3386304,114.2620337,Hong Kong University of Science and Technology,edu,a1fdf45e6649b0020eb533c70d6062b9183561ff,citation,https://arxiv.org/pdf/1802.07931.pdf,Where's YOUR focus: Personalized Attention,2017 +386,VOC,voc,36.05238585,140.11852361,National Institute of Advanced Industrial Science and Technology,edu,775c51b965e8ff37646a265aab64136b4a620526,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/2A_059_ext.pdf,Three viewpoints toward exemplar SVM,2015 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University,edu,e771661fa441f008c111ea786eb275153919da6e,citation,http://pdfs.semanticscholar.org/e771/661fa441f008c111ea786eb275153919da6e.pdf,Globally Optimal Object Tracking with Fully Convolutional Networks,2016 +396,VOC,voc,41.5007811,2.11143663,Universitat Autònoma de Barcelona,edu,5feacd9dd73827fb438a6bf6c8b406f4f11aa2fa,citation,http://pdfs.semanticscholar.org/5fea/cd9dd73827fb438a6bf6c8b406f4f11aa2fa.pdf,Slanted Stixels: Representing San Francisco's Steepest Streets,2017 +397,VOC,voc,47.3764534,8.54770931,ETH Zürich,edu,5feacd9dd73827fb438a6bf6c8b406f4f11aa2fa,citation,http://pdfs.semanticscholar.org/5fea/cd9dd73827fb438a6bf6c8b406f4f11aa2fa.pdf,Slanted Stixels: Representing San Francisco's Steepest Streets,2017 diff --git a/site/datasets/final/yfcc_100m.csv b/site/datasets/final/yfcc_100m.csv new file mode 100644 index 00000000..daee2cf4 --- /dev/null +++ b/site/datasets/final/yfcc_100m.csv @@ -0,0 +1,69 @@ 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Estimation Using Hierarchical Mixture Density Network,2018 +6,YouTube Pose,youtube_poses,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,0ca2f48fad7f69fb415ecbb99945250cbf8f011c,citation,https://pdfs.semanticscholar.org/0ca2/f48fad7f69fb415ecbb99945250cbf8f011c.pdf,Outliers Cleaning in Dynamic Systems,2017 +7,YouTube Pose,youtube_poses,42.3383668,-71.08793524,Northeastern University,edu,0ca2f48fad7f69fb415ecbb99945250cbf8f011c,citation,https://pdfs.semanticscholar.org/0ca2/f48fad7f69fb415ecbb99945250cbf8f011c.pdf,Outliers Cleaning in Dynamic Systems,2017 +8,YouTube Pose,youtube_poses,37.43131385,-122.16936535,Stanford University,edu,815e77b8f2e8f17205e46162b3addd02b2ea8ff0,citation,http://pdfs.semanticscholar.org/815e/77b8f2e8f17205e46162b3addd02b2ea8ff0.pdf,Marker-less Pose Estimation,2017 +9,YouTube Pose,youtube_poses,39.9492344,-75.19198985,University of Pennsylvania,edu,bbd9b5e4d4761d923d21a060513e826bf5bfc620,citation,https://arxiv.org/pdf/1704.04793.pdf,Harvesting Multiple Views for Marker-Less 3D Human Pose Annotations,2017 +10,YouTube Pose,youtube_poses,43.65815275,-79.3790801,Ryerson University,edu,bbd9b5e4d4761d923d21a060513e826bf5bfc620,citation,https://arxiv.org/pdf/1704.04793.pdf,Harvesting Multiple Views for Marker-Less 3D Human Pose Annotations,2017 +11,YouTube Pose,youtube_poses,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,10d255fb0bb651b6e9cc69855a970c44f121f2c9,citation,https://arxiv.org/pdf/1710.06513.pdf,Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation,2018 +12,YouTube Pose,youtube_poses,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,3e682d368422ff31632760611039372a07eeabc6,citation,https://pdfs.semanticscholar.org/a254/e59f6fd1f8c51e3a5398c01cc1b45aebc66e.pdf,Articulated Multi-person Tracking in the Wild,2016 +13,YouTube Pose,youtube_poses,-35.2776999,149.118527,Australian National University,edu,ce2fd44a8c43642b76f219fe32291c1b2644cb73,citation,https://arxiv.org/pdf/1707.09240.pdf,Human Pose Forecasting via Deep Markov Models,2017 +14,YouTube Pose,youtube_poses,52.17638955,0.14308882,University of Cambridge,edu,4065d038ecbda579a0791aaf46fc62bbcba5b1f3,citation,http://pdfs.semanticscholar.org/4065/d038ecbda579a0791aaf46fc62bbcba5b1f3.pdf,Real-time Factored ConvNets: Extracting the X Factor in Human Parsing,2017 +15,YouTube Pose,youtube_poses,50.7338124,7.1022465,University of Bonn,edu,7a0cd36d02ad962f628d9d504d02a850e27d5bfb,citation,https://arxiv.org/pdf/1710.10000.pdf,PoseTrack: A Benchmark for Human Pose Estimation and Tracking,2017 +16,YouTube Pose,youtube_poses,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,8e74244e220a1c9e89417caa1ad22f649884d311,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.142,ArtTrack: Articulated Multi-Person Tracking in the Wild,2017 +17,YouTube Pose,youtube_poses,65.0592157,25.46632601,University of Oulu,edu,a287643d3eddca3dcc09b3532f2b070a28d4a022,citation,http://pdfs.semanticscholar.org/a287/643d3eddca3dcc09b3532f2b070a28d4a022.pdf,Real-time Human Pose Estimation from Video with Convolutional Neural Networks,2016 +18,YouTube Pose,youtube_poses,60.18558755,24.8242733,Aalto University,edu,a287643d3eddca3dcc09b3532f2b070a28d4a022,citation,http://pdfs.semanticscholar.org/a287/643d3eddca3dcc09b3532f2b070a28d4a022.pdf,Real-time Human Pose Estimation from Video with Convolutional Neural Networks,2016 |