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Diffstat (limited to 'site/datasets/verified')
22 files changed, 1592 insertions, 539 deletions
diff --git a/site/datasets/verified/adience.csv b/site/datasets/verified/adience.csv index deadc399..f6e229b6 100644 --- a/site/datasets/verified/adience.csv +++ b/site/datasets/verified/adience.csv @@ -1,2 +1,140 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year 0,,Adience,adience,0.0,0.0,,,,main,,Age and Gender Estimation of Unfiltered Faces,2014 +1,China,Adience,adience,39.993008,116.329882,SenseTime,company,c72a2ea819df9b0e8cd267eebcc6528b8741e03d,citation,https://arxiv.org/pdf/1708.09687.pdf,Quantifying Facial Age by Posterior of Age Comparisons,2017 +2,China,Adience,adience,22.4162632,114.2109318,Chinese University of Hong Kong,edu,c72a2ea819df9b0e8cd267eebcc6528b8741e03d,citation,https://arxiv.org/pdf/1708.09687.pdf,Quantifying Facial Age by Posterior of Age Comparisons,2017 +3,United States,Adience,adience,38.9869183,-76.9425543,"Maryland Univ., College Park, MD, USA",edu,59b6e9320a4e1de9216c6fc49b4b0309211b17e8,citation,https://pdfs.semanticscholar.org/59b6/e9320a4e1de9216c6fc49b4b0309211b17e8.pdf,Robust Representations for unconstrained Face Recognition and its Applications,2016 +4,United Kingdom,Adience,adience,51.49887085,-0.17560797,Imperial College London,edu,d818568838433a6d6831adde49a58cef05e0c89f,citation,http://eprints.mdx.ac.uk/22044/1/agedb_kotsia.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 +5,China,Adience,adience,28.2290209,112.99483204,"National University of Defense Technology, China",mil,4f37f71517420c93c6841beb33ca0926354fa11d,citation,http://www.cs.newpaltz.edu/~lik/publications/Mingxing-Duan-NC-2017.pdf,A hybrid deep learning CNN-ELM for age and gender classification,2018 +6,China,Adience,adience,26.88111275,112.62850666,Hunan University,edu,4f37f71517420c93c6841beb33ca0926354fa11d,citation,http://www.cs.newpaltz.edu/~lik/publications/Mingxing-Duan-NC-2017.pdf,A hybrid deep learning CNN-ELM for age and gender classification,2018 +7,Italy,Adience,adience,46.0658836,11.1159894,University of Trento,edu,cb43519894258b125624dc0df655ab5357b1e42f,citation,https://arxiv.org/pdf/1802.00237.pdf,Face Aging with Contextual Generative Adversarial Nets,2017 +8,China,Adience,adience,39.9041999,116.4073963,"Qihoo 360 AI Institute, Beijing, China",edu,cb43519894258b125624dc0df655ab5357b1e42f,citation,https://arxiv.org/pdf/1802.00237.pdf,Face Aging with Contextual Generative Adversarial Nets,2017 +9,Singapore,Adience,adience,1.2962018,103.77689944,National University of Singapore,edu,cb43519894258b125624dc0df655ab5357b1e42f,citation,https://arxiv.org/pdf/1802.00237.pdf,Face Aging with Contextual Generative Adversarial Nets,2017 +10,United States,Adience,adience,40.51865195,-74.44099801,State University of New Jersey,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://www.rci.rutgers.edu/~vmp93/Conference_pub/btas_age_2016_cameraready.pdf,A cascaded convolutional neural network for age estimation of unconstrained faces,2016 +11,United States,Adience,adience,39.2899685,-76.62196103,University of Maryland,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://www.rci.rutgers.edu/~vmp93/Conference_pub/btas_age_2016_cameraready.pdf,A cascaded convolutional neural network for age estimation of unconstrained faces,2016 +12,Bangladesh,Adience,adience,23.7289899,90.3982682,Institute of Information Technology,edu,6e177341d4412f9c9a639e33e6096344ef930202,citation,https://pdfs.semanticscholar.org/2e58/ec57d71b2b2a3e71086234dd7037559cc17e.pdf,A Gender Recognition System from Facial Image,2018 +13,Bangladesh,Adience,adience,23.7316957,90.3965275,University of Dhaka,edu,6e177341d4412f9c9a639e33e6096344ef930202,citation,https://pdfs.semanticscholar.org/2e58/ec57d71b2b2a3e71086234dd7037559cc17e.pdf,A Gender Recognition System from Facial Image,2018 +14,Canada,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 +15,Egypt,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 +16,Switzerland,Adience,adience,47.376313,8.5476699,ETH Zurich,edu,10195a163ab6348eef37213a46f60a3d87f289c5,citation,http://www.vision.ee.ethz.ch/en/publications/papers/articles/eth_biwi_01299.pdf,Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks,2016 +17,United Kingdom,Adience,adience,51.49887085,-0.17560797,Imperial College London,edu,7f30a36a3faab044c095814d0ce17ea2b6638213,citation,https://arxiv.org/pdf/1802.04636.pdf,Modeling of facial aging and kinship: A survey,2018 +18,United Kingdom,Adience,adience,51.59029705,-0.22963221,Middlesex University,edu,7f30a36a3faab044c095814d0ce17ea2b6638213,citation,https://arxiv.org/pdf/1802.04636.pdf,Modeling of facial aging and kinship: A survey,2018 +19,United States,Adience,adience,40.9153196,-73.1270626,Stony Brook University,edu,25bf288b2d896f3c9dab7e7c3e9f9302e7d6806b,citation,https://arxiv.org/pdf/1608.06557.pdf,Neural Networks with Smooth Adaptive Activation Functions for Regression,2016 +20,United States,Adience,adience,35.93006535,-84.31240032,Oak Ridge National Laboratory,edu,25bf288b2d896f3c9dab7e7c3e9f9302e7d6806b,citation,https://arxiv.org/pdf/1608.06557.pdf,Neural Networks with Smooth Adaptive Activation Functions for Regression,2016 +21,Canada,Adience,adience,49.2767454,-122.91777375,Simon Fraser University,edu,880b4be9afc4d5ef75b5d77f51eadb557acbf251,citation,http://www.cs.umanitoba.ca/~ywang/papers/mmsp18.pdf,Privacy-Preserving Age Estimation for Content Rating,2018 +22,Canada,Adience,adience,49.8091536,-97.13304179,University of Manitoba,edu,880b4be9afc4d5ef75b5d77f51eadb557acbf251,citation,http://www.cs.umanitoba.ca/~ywang/papers/mmsp18.pdf,Privacy-Preserving Age Estimation for Content Rating,2018 +23,United States,Adience,adience,32.9820799,-96.7566278,University of Texas at Dallas,edu,e49d124a3d7eba42b0e3e79c1dd7537e6611602d,citation,https://arxiv.org/pdf/1803.05719.pdf,"SAF- BAGE: Salient Approach for Facial Soft-Biometric Classification - Age, Gender, and Facial Expression",2018 +24,India,Adience,adience,23.0378743,72.55180046,Ahmedabad University,edu,e49d124a3d7eba42b0e3e79c1dd7537e6611602d,citation,https://arxiv.org/pdf/1803.05719.pdf,"SAF- BAGE: Salient Approach for Facial Soft-Biometric Classification - Age, Gender, and Facial Expression",2018 +25,United States,Adience,adience,40.9153196,-73.1270626,Stony Brook University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,https://arxiv.org/pdf/1611.05916.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 +26,United States,Adience,adience,42.36782045,-71.12666653,Harvard University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,https://arxiv.org/pdf/1611.05916.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 +27,United States,Adience,adience,39.2899685,-76.62196103,University of Maryland,edu,31f1e711fcf82c855f27396f181bf5e565a2f58d,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Ranjan_Unconstrained_Age_Estimation_ICCV_2015_paper.pdf,Unconstrained Age Estimation with Deep Convolutional Neural Networks,2015 +28,United States,Adience,adience,40.47913175,-74.43168868,Rutgers University,edu,31f1e711fcf82c855f27396f181bf5e565a2f58d,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Ranjan_Unconstrained_Age_Estimation_ICCV_2015_paper.pdf,Unconstrained Age Estimation with Deep Convolutional Neural Networks,2015 +29,Finland,Adience,adience,65.0592157,25.46632601,University of Oulu,edu,1fe121925668743762ce9f6e157081e087171f4c,citation,http://www.ee.oulu.fi/~jkannala/publications/cvprw2015.pdf,Unsupervised learning of overcomplete face descriptors,2015 +30,United States,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 +31,Italy,Adience,adience,45.47567215,9.23336232,Università degli Studi di Milano,edu,717ffde99c0d6b58675d44b4c66acedce0ca86e8,citation,https://air.unimi.it/retrieve/handle/2434/527428/913482/cisda17.pdf,Age estimation based on face images and pre-trained convolutional neural networks,2017 +32,United States,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 +33,United States,Adience,adience,47.00646895,-120.5367304,Central Washington University,edu,9d6e60d49e92361f8f558013065dfa67043dd337,citation,https://pdfs.semanticscholar.org/9d6e/60d49e92361f8f558013065dfa67043dd337.pdf,Applications of Computational Geometry and Computer Vision,2016 +34,United States,Adience,adience,41.1664858,-73.1920564,University of Bridgeport,edu,0cece7b8989352e16a2fab8c0a0b1911c286906a,citation,https://pdfs.semanticscholar.org/0cec/e7b8989352e16a2fab8c0a0b1911c286906a.pdf,AUTOMATIC AGE ESTIMATION FROM REAL-WORLD AND WILD FACE IMAGES BY USING DEEP NEURAL NETWORKS,2017 +35,United States,Adience,adience,47.00646895,-120.5367304,Central Washington University,edu,56c2fb2438f32529aec604e6fc3b06a595ddbfcc,citation,https://pdfs.semanticscholar.org/60dc/35a42ac758c5372c44f3791c951374658609.pdf,Comparison of Recent Machine Learning Techniques for Gender Recognition from Facial Images,2016 +36,United States,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 +37,United States,Adience,adience,42.357757,-83.06286711,Wayne State University,edu,4f1249369127cc2e2894f6b2f1052d399794919a,citation,http://www.cs.wayne.edu/~mdong/tmm18.pdf,Deep Age Estimation: From Classification to Ranking,2018 +38,United States,Adience,adience,41.1664858,-73.1920564,University of Bridgeport,edu,ac9a331327cceda4e23f9873f387c9fd161fad76,citation,https://arxiv.org/pdf/1709.01664.pdf,Deep Convolutional Neural Network for Age Estimation based on VGG-Face Model,2017 +39,Netherlands,Adience,adience,53.21967825,6.56251482,University of Groningen,edu,4ff4c27e47b0aa80d6383427642bb8ee9d01c0ac,citation,http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_SSCI_2015/data/7560a188.pdf,Deep Convolutional Neural Networks and Support Vector Machines for Gender Recognition,2015 +40,United Kingdom,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 +41,Netherlands,Adience,adience,53.21967825,6.56251482,University of Groningen,edu,361c9ba853c7d69058ddc0f32cdbe94fbc2166d5,citation,https://pdfs.semanticscholar.org/361c/9ba853c7d69058ddc0f32cdbe94fbc2166d5.pdf,Deep Reinforcement Learning of Video Games,2017 +42,South Korea,Adience,adience,37.26728,126.9841151,Seoul National University,edu,282503fa0285240ef42b5b4c74ae0590fe169211,citation,https://arxiv.org/pdf/1801.07848.pdf,Feeding Hand-Crafted Features for Enhancing the Performance of Convolutional Neural Networks,2018 +43,Italy,Adience,adience,45.518383,9.213452,University of Milano-Bicocca,edu,305346d01298edeb5c6dc8b55679e8f60ba97efb,citation,https://pdfs.semanticscholar.org/3053/46d01298edeb5c6dc8b55679e8f60ba97efb.pdf,Fine-Grained Face Annotation Using Deep Multi-Task CNN,2018 +44,Canada,Adience,adience,45.5039761,-73.5749687,McGill University,edu,a760d33a21d2ab338f59d32ac7f96023bbfaa248,citation,https://arxiv.org/pdf/1803.08134.pdf,Fisher Pruning of Deep Nets for Facial Trait Classification,2018 +45,Taiwan,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 +46,China,Adience,adience,25.055125,102.696888,Yunnan Normal University,edu,99c57ec53f2598d63c010f791adbca386b276919,citation,https://pdfs.semanticscholar.org/99c5/7ec53f2598d63c010f791adbca386b276919.pdf,Landmark-Guided Local Deep Neural Networks for Age and Gender Classification,2018 +47,South Korea,Adience,adience,37.5600406,126.9369248,Yonsei University,edu,ba0d9e1e8bc798656429fe7121afee672dddb380,citation,https://arxiv.org/pdf/1809.01990.pdf,Multi-Expert Gender Classification on Age Group by Integrating Deep Neural Networks,2018 +48,Canada,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 +49,United States,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 +50,Singapore,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 +51,United States,Adience,adience,42.357757,-83.06286711,Wayne State University,edu,28d99dc2d673d62118658f8375b414e5192eac6f,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Chen_Using_Ranking-CNN_for_CVPR_2017_paper.pdf,Using Ranking-CNN for Age Estimation,2017 +52,Israel,Adience,adience,32.77824165,34.99565673,Open University of Israel,edu,2cbb4a2f8fd2ddac86f8804fd7ffacd830a66b58,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W08/papers/Levi_Age_and_Gender_2015_CVPR_paper.pdf,Age and gender classification using convolutional neural networks,2015 +53,Australia,Adience,adience,-35.2776999,149.118527,Australian National University,edu,ac2e3a889fc46ca72f9a2cdedbdd6f3d4e9e2627,citation,https://pdfs.semanticscholar.org/ac2e/3a889fc46ca72f9a2cdedbdd6f3d4e9e2627.pdf,Age detection from a single image using multitask neural networks : An overview and design proposal,2016 +54,Australia,Adience,adience,-35.2776999,149.118527,CSIRO,edu,ac2e3a889fc46ca72f9a2cdedbdd6f3d4e9e2627,citation,https://pdfs.semanticscholar.org/ac2e/3a889fc46ca72f9a2cdedbdd6f3d4e9e2627.pdf,Age detection from a single image using multitask neural networks : An overview and design proposal,2016 +55,Australia,Adience,adience,-35.2776999,149.118527,"Data61, CSIRO, Canberra, Australia",edu,ac2e3a889fc46ca72f9a2cdedbdd6f3d4e9e2627,citation,https://pdfs.semanticscholar.org/ac2e/3a889fc46ca72f9a2cdedbdd6f3d4e9e2627.pdf,Age detection from a single image using multitask neural networks : An overview and design proposal,2016 +56,China,Adience,adience,38.8760446,115.4973873,North China Electric Power University,edu,50ff21e595e0ebe51ae808a2da3b7940549f4035,citation,https://arxiv.org/pdf/1710.02985.pdf,Age Group and Gender Estimation in the Wild With Deep RoR Architecture,2017 +57,United States,Adience,adience,38.9403808,-92.3277375,University of Missouri Columbia,edu,50ff21e595e0ebe51ae808a2da3b7940549f4035,citation,https://arxiv.org/pdf/1710.02985.pdf,Age Group and Gender Estimation in the Wild With Deep RoR Architecture,2017 +58,China,Adience,adience,26.88111275,112.62850666,Hunan University,edu,ec0104286c96707f57df26b4f0a4f49b774c486b,citation,http://www.cs.newpaltz.edu/~lik/publications/Mingxing-Duan-IEEE-TIFS-2018.pdf,An Ensemble CNN2ELM for Age Estimation,2018 +59,China,Adience,adience,28.2290209,112.99483204,"National University of Defense Technology, China",mil,ec0104286c96707f57df26b4f0a4f49b774c486b,citation,http://www.cs.newpaltz.edu/~lik/publications/Mingxing-Duan-IEEE-TIFS-2018.pdf,An Ensemble CNN2ELM for Age Estimation,2018 +60,United States,Adience,adience,42.6480516,-73.749576,State University of New York,edu,ec0104286c96707f57df26b4f0a4f49b774c486b,citation,http://www.cs.newpaltz.edu/~lik/publications/Mingxing-Duan-IEEE-TIFS-2018.pdf,An Ensemble CNN2ELM for Age Estimation,2018 +61,United States,Adience,adience,37.3706254,-121.9671894,NVIDIA,company,81e628a23e434762b1208045919af48dceb6c4d2,citation,https://arxiv.org/pdf/1807.07320.pdf,Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery,2018 +62,Spain,Adience,adience,41.5019255,2.1048538,"UAB, Barcelona, Spain",edu,81e628a23e434762b1208045919af48dceb6c4d2,citation,https://arxiv.org/pdf/1807.07320.pdf,Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery,2018 +63,Brazil,Adience,adience,-27.5953995,-48.6154218,University of Campinas,edu,bc749f0e81eafe9e32d56336750782f45d82609d,citation,https://pdfs.semanticscholar.org/bc74/9f0e81eafe9e32d56336750782f45d82609d.pdf,Combination of Texture and Geometric Features for Age Estimation in Face Images,2018 +64,China,Adience,adience,22.4162632,114.2109318,Chinese University of Hong Kong,edu,29db16efc3b378c50511f743e5197a4c0b9e902f,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Kuang_Deeply_Learned_Rich_ICCV_2015_paper.pdf,Deeply Learned Rich Coding for Cross-Dataset Facial Age Estimation,2015 +65,China,Adience,adience,39.993008,116.329882,SenseTime,company,29db16efc3b378c50511f743e5197a4c0b9e902f,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Kuang_Deeply_Learned_Rich_ICCV_2015_paper.pdf,Deeply Learned Rich Coding for Cross-Dataset Facial Age Estimation,2015 +66,Israel,Adience,adience,32.77824165,34.99565673,Open University of Israel,edu,0dccc881cb9b474186a01fd60eb3a3e061fa6546,citation,https://arxiv.org/pdf/1411.7964.pdf,Effective face frontalization in unconstrained images,2015 +67,Russia,Adience,adience,56.3244285,44.0286291,Nizhny Novgorod State Linguistic University,edu,efb56e7488148d52d3b8a2dae9f8880b273f4226,citation,https://arxiv.org/pdf/1807.07718.pdf,"Efficient Facial Representations for Age, Gender and Identity Recognition in Organizing Photo Albums using Multi-output CNN",2018 +68,Russia,Adience,adience,55.694797,37.564332,"Samsung-PDMI Joint AI Center, Steklov Institute of Mathematics",company,efb56e7488148d52d3b8a2dae9f8880b273f4226,citation,https://arxiv.org/pdf/1807.07718.pdf,"Efficient Facial Representations for Age, Gender and Identity Recognition in Organizing Photo Albums using Multi-output CNN",2018 +69,Ireland,Adience,adience,53.308244,-6.2241652,University College Dublin,edu,cc45fb67772898c36519de565c9bd0d1d11f1435,citation,https://forensicsandsecurity.com/papers/EvaluatingFacialAgeEstimation.pdf,Evaluating Automated Facial Age Estimation Techniques for Digital Forensics,2018 +70,China,Adience,adience,37.8956594,114.9042208,"Hebei, China",edu,fca6df7d36f449d48a8d1e48a78c860d52e3baf8,citation,https://arxiv.org/pdf/1805.10445.pdf,Fine-Grained Age Estimation in the wild with Attention LSTM Networks,2018 +71,China,Adience,adience,38.8760446,115.4973873,North China Electric Power University,edu,fca6df7d36f449d48a8d1e48a78c860d52e3baf8,citation,https://arxiv.org/pdf/1805.10445.pdf,Fine-Grained Age Estimation in the wild with Attention LSTM Networks,2018 +72,United States,Adience,adience,38.9403808,-92.3277375,University of Missouri-Columbia,edu,fca6df7d36f449d48a8d1e48a78c860d52e3baf8,citation,https://arxiv.org/pdf/1805.10445.pdf,Fine-Grained Age Estimation in the wild with Attention LSTM Networks,2018 +73,India,Adience,adience,28.5456282,77.2731505,"IIIT Delhi, India",edu,af6e351d58dba0962d6eb1baf4c9a776eb73533f,citation,https://arxiv.org/pdf/1612.07454.pdf,How to Train Your Deep Neural Network with Dictionary Learning,2016 +74,Turkey,Adience,adience,41.10427915,29.02231159,Istanbul Technical University,edu,9755554b13103df634f9b1ef50a147dd02eab02f,citation,https://arxiv.org/pdf/1610.00134.pdf,How Transferable Are CNN-Based Features for Age and Gender Classification?,2016 +75,United States,Adience,adience,42.3619407,-71.0904378,MIT CSAIL,edu,9755554b13103df634f9b1ef50a147dd02eab02f,citation,https://arxiv.org/pdf/1610.00134.pdf,How Transferable Are CNN-Based Features for Age and Gender Classification?,2016 +76,Turkey,Adience,adience,38.029533,32.506051,"Mevlana Universitesi, Konya, Turkey",edu,eb4151eebd0b7451ca990b242cef8357bfa9db92,citation,https://pdfs.semanticscholar.org/eb41/51eebd0b7451ca990b242cef8357bfa9db92.pdf,Human Gender Prediction on Facial Images Taken by Mobile Phone 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+95,China,Adience,adience,26.085573,119.372442,Fujian University of Technology,edu,c5fff7adc5084d69390918daf09e832ec191144b,citation,,Deep learning application based on embedded GPU,2017 +96,China,Adience,adience,25.28164,110.337304,Guilin University of Electronic Technology,edu,c5fff7adc5084d69390918daf09e832ec191144b,citation,,Deep learning application based on embedded GPU,2017 +97,United States,Adience,adience,32.8536333,-117.2035286,Kyung Hee University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +98,Saudi Arabia,Adience,adience,24.7246403,46.62335012,King Saud University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +99,Bangladesh,Adience,adience,23.7289899,90.3982682,Institute of Information Technology,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 +100,Turkey,Adience,adience,38.6747649,39.1866925,"Firat Üniversitesi Elaziğ, Türkiye",edu,ecfb93de88394a244896bfe6ee7bf39fb250b820,citation,,Gender recognition from face images with deep learning,2017 +101,Turkey,Adience,adience,38.6812759,39.196083,"Enerji Sistemleri Müh. 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lightweight deep convolutional network for joint age and gender recognition,2016 +115,China,Adience,adience,23.09461185,113.28788994,Sun Yat-Sen University,edu,dc2f16f967eac710cb9b7553093e9c977e5b761d,citation,,Learning a lightweight deep convolutional network for joint age and gender recognition,2016 +116,South Korea,Adience,adience,36.3721427,127.36039,KAIST,edu,92d051d4680eb41eb172d23cb8c93eed7677af56,citation,,Adversarial Spatial Frequency Domain Critic Learning for Age and Gender Classification,2018 +117,Thailand,Adience,adience,14.0785,100.6140362,"Asian Institute of Technology (AIT), Pathum Thani 12120, Thailand",edu,984edce0b961418d81203ec477b9bfa5a8197ba3,citation,,Customer and target individual face analysis for retail analytics,2018 +118,China,Adience,adience,39.98177,116.330086,National Laboratory of Pattern Recognition,edu,d80159bbe1d576d147ca9adbc9339a05fe3bab28,citation,,"Demographic Analysis from Biometric Data: Achievements, Challenges, and New Frontiers",2018 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States,Adience,adience,40.4319722,-86.92389368,Purdue University,edu,07a1e6d26028b28185b7a3eee86752c240a24261,citation,,MODE: automated neural network model debugging via state differential analysis and input selection,2018 +124,Germany,Adience,adience,-35.4354218,-71.6199998,"Geospatial Laboratory, Universidad Católica del Maule, Talca, Chile",edu,3a05415356bd574cad1a9f1be21214e428bbc81b,citation,,Multinomial Naive Bayes for real-time gender recognition,2016 +125,Cyprus,Adience,adience,34.67567405,33.04577648,Cyprus University of Technology,edu,9f3c9e41f46df9c94d714b1f080dafad6b4de1de,citation,,On the detection of images containing child-pornographic material,2017 +126,United States,Adience,adience,32.8536333,-117.2035286,Kyung Hee University,edu,9d4692e243e25eb465a0480376beb60a5d2f0f13,citation,,Positional Ternary Pattern (PTP): An edge based image descriptor for human age recognition,2016 +127,Italy,Adience,adience,43.7192522,10.4239948,"Istituto di Informatica e Telematica, Consiglio Nazionale delle Ricerche, Pisa, Italy",edu,17de5a9ce09f4834629cd76b8526071a956c9c6d,citation,,Smart Parental Advisory: A Usage Control and Deep Learning-Based Framework for Dynamic Parental Control on Smart TV,2017 +128,Vietnam,Adience,adience,20.8368539,106.6942087,Vietnam Maritime University,edu,d38b32d91d56b01c77ef4dd7d625ce5217c6950b,citation,,Unconstrained gender classification by multi-resolution LPQ and SIFT,2016 +129,Poland,Adience,adience,50.0657033,19.91895867,AGH University of Science and Technology,edu,cca476114c48871d05537abb303061de5ab010d6,citation,,A compact deep convolutional neural network architecture for video based age and gender estimation,2016 +130,Singapore,Adience,adience,1.3483099,103.6831347,"NTU, Singapore",edu,8a917903b0a1d47f24bc7776ab0bd00aa8ec88f3,citation,,A Constrained Deep Neural Network for Ordinal Regression,2018 +131,Spain,Adience,adience,41.5008957,2.111553,Autonomous University of Barcelona,edu,c9c2de3628be7e249722b12911bebad84b567ce6,citation,,Age and gender recognition in the wild with deep attention,2017 +132,China,Adience,adience,38.8637191,115.5148326,"Hebei Information Engineering School, Baoding, China",edu,ea227e47b8a1e8f55983c34a17a81e5d3fa11cfd,citation,,Age group classification in the wild with deep RoR architecture,2017 +133,China,Adience,adience,38.8760446,115.4973873,North China Electric Power University,edu,ea227e47b8a1e8f55983c34a17a81e5d3fa11cfd,citation,,Age group classification in the wild with deep RoR architecture,2017 +134,United States,Adience,adience,38.9403808,-92.3277375,University of Missouri Columbia,edu,ea227e47b8a1e8f55983c34a17a81e5d3fa11cfd,citation,,Age group classification in the wild with deep RoR architecture,2017 +135,India,Adience,adience,29.8542626,77.8880002,"Indian institute of Technology Roorkee, India",edu,f4003cbbff3b3d008aa64c76fed163c10d9c68bd,citation,,Compass local binary patterns for gender recognition of facial photographs and sketches,2016 +136,Malaysia,Adience,adience,3.12267405,101.65356103,"University of Malaya, Kuala Lumpur",edu,d4d1ac1cfb2ca703c4db8cc9a1c7c7531fa940f9,citation,,"Gender estimation based on supervised HOG, Action Units and unsupervised CNN feature extraction",2017 +137,United Kingdom,Adience,adience,51.5247272,-0.03931035,Queen Mary University of London,edu,d7fd3dedb6b260702ed5e4b9175127815286e8da,citation,,Knowledge sharing: From atomic to parametrised context and shallow to deep models,2017 +138,Taiwan,Adience,adience,25.0421852,121.6145477,"Academia Sinica, Taipei, Taiwan",edu,aa6f7c3daed31d331ef626758e990cbc04632852,citation,,Merging Deep Neural Networks for Mobile Devices,2018 diff --git a/site/datasets/verified/brainwash.csv b/site/datasets/verified/brainwash.csv index 628ca090..8b70de6e 100644 --- a/site/datasets/verified/brainwash.csv +++ b/site/datasets/verified/brainwash.csv @@ -3,3 +3,13 @@ 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929b84c1..b85d9458 100644 --- a/site/datasets/verified/duke_mtmc.csv +++ b/site/datasets/verified/duke_mtmc.csv @@ -45,137 +45,181 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,t 43,Australia,Duke MTMC,duke_mtmc,-35.2776999,149.118527,Australian National University,edu,f8f92624c8794d54e08b3a8f94910952ae03cade,citation,,CamStyle: A Novel Data Augmentation Method for Person Re-Identification,2019 44,China,Duke MTMC,duke_mtmc,22.4162632,114.2109318,Chinese University of Hong Kong,edu,08d2a558ea2deb117dd8066e864612bf2899905b,citation,https://arxiv.org/pdf/1807.09975.pdf,Person Re-identification with Deep Similarity-Guided Graph Neural Network,2018 45,China,Duke MTMC,duke_mtmc,39.993008,116.329882,SenseTime,company,08d2a558ea2deb117dd8066e864612bf2899905b,citation,https://arxiv.org/pdf/1807.09975.pdf,Person Re-identification with Deep Similarity-Guided Graph Neural Network,2018 -46,United States,Duke MTMC,duke_mtmc,37.8718992,-122.2585399,University of California,edu,fefa8f07d998f8f4a6c85a7da781b19bf6b78d7d,citation,https://arxiv.org/pdf/1902.00749.pdf,Online Multi-Object Tracking with Dual Matching Attention Networks,2018 -47,China,Duke MTMC,duke_mtmc,39.9808333,116.34101249,Beihang University,edu,7bfc5bbad852f9e6bea3b86c25179d81e2e7fff6,citation,,Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology,2018 -48,China,Duke MTMC,duke_mtmc,40.00229045,116.32098908,Tsinghua University,edu,be79ad118d0524d9b493f4a14a662c8184e6405a,citation,,Attend and Align: Improving Deep Representations with Feature Alignment Layer for Person Retrieval,2018 -49,China,Duke MTMC,duke_mtmc,40.00229045,116.32098908,Tsinghua University,edu,13ea9a2ed134a9e238d33024fba34d3dd6a010e0,citation,https://arxiv.org/pdf/1703.05693.pdf,SVDNet for Pedestrian Retrieval,2017 -50,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,13ea9a2ed134a9e238d33024fba34d3dd6a010e0,citation,https://arxiv.org/pdf/1703.05693.pdf,SVDNet for Pedestrian Retrieval,2017 -51,China,Duke MTMC,duke_mtmc,30.19331415,120.11930822,Zhejiang University,edu,608dede56161fd5f76bcf9228b4dd8c639d65b02,citation,https://arxiv.org/pdf/1807.00537.pdf,SphereReID: Deep Hypersphere Manifold Embedding for Person Re-Identification,2018 -52,United States,Duke MTMC,duke_mtmc,42.7298459,-73.67950216,Rensselaer Polytechnic Institute,edu,24d6d3adf2176516ef0de2e943ce2084e27c4f94,citation,https://arxiv.org/pdf/1811.07487.pdf,Re-Identification with Consistent Attentive Siamese Networks,2018 -53,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,15e1af79939dbf90790b03d8aa02477783fb1d0f,citation,https://arxiv.org/pdf/1701.07717.pdf,Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro,2017 -54,China,Duke MTMC,duke_mtmc,30.778621,103.961236,XiHua University,edu,ec9c20ed6cce15e9b63ac96bb5a6d55e69661e0b,citation,https://pdfs.semanticscholar.org/ec9c/20ed6cce15e9b63ac96bb5a6d55e69661e0b.pdf,Robust Pedestrian Detection for Semi-automatic Construction of a Crowded Person Re-Identification Dataset,2018 -55,United Kingdom,Duke MTMC,duke_mtmc,51.24303255,-0.59001382,University of Surrey,edu,ec9c20ed6cce15e9b63ac96bb5a6d55e69661e0b,citation,https://pdfs.semanticscholar.org/ec9c/20ed6cce15e9b63ac96bb5a6d55e69661e0b.pdf,Robust Pedestrian Detection for Semi-automatic Construction of a Crowded Person Re-Identification Dataset,2018 -56,China,Duke MTMC,duke_mtmc,31.4854255,120.2739581,Jiangnan University,edu,ec9c20ed6cce15e9b63ac96bb5a6d55e69661e0b,citation,https://pdfs.semanticscholar.org/ec9c/20ed6cce15e9b63ac96bb5a6d55e69661e0b.pdf,Robust Pedestrian Detection for Semi-automatic Construction of a Crowded Person Re-Identification Dataset,2018 -57,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,fa3fb32fe0cd392960549b0adb7a535eb3656abd,citation,https://arxiv.org/pdf/1711.08106.pdf,The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching,2017 -58,United Kingdom,Duke MTMC,duke_mtmc,55.94951105,-3.19534913,University of Edinburgh,edu,fa3fb32fe0cd392960549b0adb7a535eb3656abd,citation,https://arxiv.org/pdf/1711.08106.pdf,The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching,2017 -59,United States,Duke MTMC,duke_mtmc,40.1019523,-88.2271615,UIUC,edu,54c28bf64debbdb21c246795182f97d4f7917b74,citation,https://arxiv.org/pdf/1811.04129.pdf,STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification,2018 -60,United States,Duke MTMC,duke_mtmc,34.0803829,-118.3909947,Tencent,company,3b311a1ce30f9c0f3dc1d9c0cf25f13127a5e48c,citation,https://arxiv.org/pdf/1810.12193.pdf,A Coarse-to-fine Pyramidal Model for Person Re-identification via Multi-Loss Dynamic Training,2018 -61,United States,Duke MTMC,duke_mtmc,37.3860784,-121.9877807,Google and Hewlett-Packard Labs,edu,4d799f6e09f442bde583a50a0a9f81131ef707bb,citation,,TAR: Enabling Fine-Grained Targeted Advertising in Retail Stores,2018 -62,United States,Duke MTMC,duke_mtmc,37.3860784,-121.9877807,Hewlett-Packard Labs,edu,4d799f6e09f442bde583a50a0a9f81131ef707bb,citation,,TAR: Enabling Fine-Grained Targeted Advertising in Retail Stores,2018 -63,United States,Duke MTMC,duke_mtmc,39.6321923,-76.3038146,LinkedIn and Hewlett-Packard Labs,edu,4d799f6e09f442bde583a50a0a9f81131ef707bb,citation,,TAR: Enabling Fine-Grained Targeted Advertising in Retail Stores,2018 -64,United States,Duke MTMC,duke_mtmc,34.0224149,-118.28634407,University of Southern California,edu,4d799f6e09f442bde583a50a0a9f81131ef707bb,citation,,TAR: Enabling Fine-Grained Targeted Advertising in Retail Stores,2018 -65,Canada,Duke MTMC,duke_mtmc,49.2767454,-122.91777375,Simon Fraser University,edu,5137ca9f0a7cf4c61f2254d4a252a0c56e5dcfcc,citation,https://arxiv.org/pdf/1811.07130.pdf,Batch Feature Erasing for Person Re-identification and Beyond,2018 -66,China,Duke MTMC,duke_mtmc,32.0565957,118.77408833,Nanjing University,edu,c37c3853ab428725f13906bb0ff4936ffe15d6af,citation,https://arxiv.org/pdf/1809.02874.pdf,Unsupervised Person Re-identification by Deep Learning Tracklet Association,2018 -67,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,c37c3853ab428725f13906bb0ff4936ffe15d6af,citation,https://arxiv.org/pdf/1809.02874.pdf,Unsupervised Person Re-identification by Deep Learning Tracklet Association,2018 -68,United States,Duke MTMC,duke_mtmc,37.8687126,-122.25586815,"University of California, Berkeley",edu,a8d665fa7357f696dcfd188b91fda88da47b964e,citation,https://arxiv.org/pdf/1809.02318.pdf,Scaling Video Analytics Systems to Large Camera Deployments,2018 -69,United States,Duke MTMC,duke_mtmc,47.6423318,-122.1369302,Microsoft,company,a8d665fa7357f696dcfd188b91fda88da47b964e,citation,https://arxiv.org/pdf/1809.02318.pdf,Scaling Video Analytics Systems to Large Camera Deployments,2018 -70,United States,Duke MTMC,duke_mtmc,41.78468745,-87.60074933,University of Chicago,edu,a8d665fa7357f696dcfd188b91fda88da47b964e,citation,https://arxiv.org/pdf/1809.02318.pdf,Scaling Video Analytics Systems to Large Camera Deployments,2018 -71,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,dda0b381c162695f21b8d1149aab22188b3c2bc0,citation,https://arxiv.org/pdf/1804.02792.pdf,Occluded Person Re-Identification,2018 -72,China,Duke MTMC,duke_mtmc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,33f358f1d2b54042c524d69b20e80d98dde3dacd,citation,https://arxiv.org/pdf/1811.11405.pdf,Spectral Feature Transformation for Person Re-identification,2018 -73,United States,Duke MTMC,duke_mtmc,32.8734455,-117.2065636,TuSimple,edu,33f358f1d2b54042c524d69b20e80d98dde3dacd,citation,https://arxiv.org/pdf/1811.11405.pdf,Spectral Feature Transformation for Person Re-identification,2018 -74,China,Duke MTMC,duke_mtmc,30.672721,104.098806,University of Electronic Science and Technology of China,edu,8ffc49aead99fdacb0b180468a36984759f2fc1e,citation,https://arxiv.org/pdf/1809.04976.pdf,Sparse Label Smoothing for Semi-supervised Person Re-Identification,2018 -75,Germany,Duke MTMC,duke_mtmc,50.7791703,6.06728733,RWTH Aachen University,edu,10b36c003542545f1e2d73e8897e022c0c260c32,citation,https://arxiv.org/pdf/1705.04608.pdf,Towards a Principled Integration of Multi-camera Re-identification and Tracking Through Optimal Bayes Filters,2017 -76,United Kingdom,Duke MTMC,duke_mtmc,51.7534538,-1.25400997,University of Oxford,edu,94ed6dc44842368b457851b43023c23fd78d5390,citation,https://arxiv.org/pdf/1806.01794.pdf,"Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects",2018 -77,China,Duke MTMC,duke_mtmc,39.9041999,116.4073963,"Beijing, China",edu,280976bbb41d2948a5c0208f86605977397181cd,citation,https://arxiv.org/pdf/1811.08073.pdf,Factorized Distillation: Training Holistic Person Re-identification Model by Distilling an Ensemble of Partial ReID Models,2018 -78,China,Duke MTMC,duke_mtmc,40.00229045,116.32098908,Tsinghua University,edu,280976bbb41d2948a5c0208f86605977397181cd,citation,https://arxiv.org/pdf/1811.08073.pdf,Factorized Distillation: Training Holistic Person Re-identification Model by Distilling an Ensemble of Partial ReID Models,2018 -79,China,Duke MTMC,duke_mtmc,39.9922379,116.30393816,Peking University,edu,014e249422b6bd6ff32b3f7d385b5a0e8c4c9fcf,citation,https://arxiv.org/pdf/1810.05866.pdf,Attention driven person re-identification,2019 -80,Singapore,Duke MTMC,duke_mtmc,1.3484104,103.68297965,Nanyang Technological University,edu,014e249422b6bd6ff32b3f7d385b5a0e8c4c9fcf,citation,https://arxiv.org/pdf/1810.05866.pdf,Attention driven person re-identification,2019 -81,China,Duke MTMC,duke_mtmc,39.9808333,116.34101249,Beihang University,edu,e9d549989926f36abfa5dc7348ae3d79a567bf30,citation,,Orientation-Guided Similarity Learning for Person Re-identification,2018 -82,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,95bdd45fed0392418e0e5d3e51d34714917e3c87,citation,https://arxiv.org/pdf/1812.03282.pdf,Spatial-Temporal Person Re-identification,2019 -83,China,Duke MTMC,duke_mtmc,31.30104395,121.50045497,Fudan University,edu,00e3957212517a252258baef833833921dd308d4,citation,,Adaptively Weighted Multi-task Deep Network for Person Attribute Classification,2017 -84,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,705073015bb8ae97212532a30488c05d50894bec,citation,https://arxiv.org/pdf/1803.09786.pdf,Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-identification,2018 -85,United States,Duke MTMC,duke_mtmc,35.9990522,-78.9290629,Duke University,edu,9e644b1e33dd9367be167eb9d832174004840400,citation,https://users.cs.duke.edu/~tomasi/papers/ristani/ristaniTCAS16.pdf,Tracking Social Groups Within and Across Cameras,2017 -86,Italy,Duke MTMC,duke_mtmc,44.6451046,10.9279268,University of Modena,edu,9e644b1e33dd9367be167eb9d832174004840400,citation,https://users.cs.duke.edu/~tomasi/papers/ristani/ristaniTCAS16.pdf,Tracking Social Groups Within and Across Cameras,2017 -87,United States,Duke MTMC,duke_mtmc,35.9990522,-78.9290629,Duke University,edu,27a2fad58dd8727e280f97036e0d2bc55ef5424c,citation,https://arxiv.org/pdf/1609.01775.pdf,"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking",2016 -88,Switzerland,Duke MTMC,duke_mtmc,46.5190557,6.5667576,EPFL,edu,4e4e3ddb55607e127a4abdef45d92adf1ff78de2,citation,http://openaccess.thecvf.com/content_ICCV_2017/papers/Maksai_Non-Markovian_Globally_Consistent_ICCV_2017_paper.pdf,Non-Markovian Globally Consistent Multi-object Tracking,2017 -89,Switzerland,Duke MTMC,duke_mtmc,46.109237,7.08453549,IDIAP Research Institute,edu,4e4e3ddb55607e127a4abdef45d92adf1ff78de2,citation,http://openaccess.thecvf.com/content_ICCV_2017/papers/Maksai_Non-Markovian_Globally_Consistent_ICCV_2017_paper.pdf,Non-Markovian Globally Consistent Multi-object Tracking,2017 -90,United States,Duke MTMC,duke_mtmc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,4e4e3ddb55607e127a4abdef45d92adf1ff78de2,citation,http://openaccess.thecvf.com/content_ICCV_2017/papers/Maksai_Non-Markovian_Globally_Consistent_ICCV_2017_paper.pdf,Non-Markovian Globally Consistent Multi-object Tracking,2017 -91,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,fc26fc2340a863d6da0b427cd924fb4cb101051b,citation,http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w37/Chen_Person_Re-Identification_by_ICCV_2017_paper.pdf,Person Re-identification by Deep Learning Multi-scale Representations,2017 -92,United Kingdom,Duke MTMC,duke_mtmc,55.378051,-3.435973,"Vision Semantics Ltd, UK",edu,fc26fc2340a863d6da0b427cd924fb4cb101051b,citation,http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w37/Chen_Person_Re-Identification_by_ICCV_2017_paper.pdf,Person Re-identification by Deep Learning Multi-scale Representations,2017 -93,Canada,Duke MTMC,duke_mtmc,43.4983503,-80.5478382,"Senstar Corporation, Waterloo, Canada",company,8e42568c2b3feaafd1e442e1e861ec50a4ac144f,citation,https://arxiv.org/pdf/1805.06086.pdf,An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-identification,2018 -94,Italy,Duke MTMC,duke_mtmc,45.4377672,12.321807,University Iuav of Venice,edu,eddb1a126eafecad2cead01c6c3bb4b88120d78a,citation,https://arxiv.org/pdf/1802.02181.pdf,Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition,2018 -95,China,Duke MTMC,duke_mtmc,40.00229045,116.32098908,Tsinghua University,edu,fc068f7f8a3b2921ec4f3246e9b6c6015165df9a,citation,https://arxiv.org/pdf/1711.09349.pdf,Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline),2018 -96,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,fc068f7f8a3b2921ec4f3246e9b6c6015165df9a,citation,https://arxiv.org/pdf/1711.09349.pdf,Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline),2018 -97,United States,Duke MTMC,duke_mtmc,29.58333105,-98.61944505,University of Texas at San Antonio,edu,fc068f7f8a3b2921ec4f3246e9b6c6015165df9a,citation,https://arxiv.org/pdf/1711.09349.pdf,Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline),2018 -98,United States,Duke MTMC,duke_mtmc,43.0008093,-78.7889697,University at Buffalo,edu,fdd1bde7066c7e9c7515f330546e0b3a8de8a4a6,citation,https://arxiv.org/pdf/1811.06582.pdf,CAN: Composite Appearance Network and a Novel Evaluation Metric for Person Tracking,2018 -99,United States,Duke MTMC,duke_mtmc,43.0008093,-78.7889697,University at Buffalo,edu,3144c9b3bedb6e3895dcd36998bcb0903271841d,citation,https://arxiv.org/pdf/1811.06582.pdf,CAN: Composite Appearance Network and a Novel Evaluation Metric for Person Tracking,2018 -100,China,Duke MTMC,duke_mtmc,29.1416432,119.7889248,"Alibaba Group, Zhejiang, People’s Republic of China",edu,f4e65ab81a0f4ffa50d0c9bc308d7365e012cc75,citation,https://arxiv.org/pdf/1812.05785.pdf,Deep Active Learning for Video-based Person Re-identification,2018 -101,China,Duke MTMC,duke_mtmc,30.19331415,120.11930822,Zhejiang University,edu,f4e65ab81a0f4ffa50d0c9bc308d7365e012cc75,citation,https://arxiv.org/pdf/1812.05785.pdf,Deep Active Learning for Video-based Person Re-identification,2018 -102,China,Duke MTMC,duke_mtmc,38.88140235,121.52281098,Dalian University of Technology,edu,5be74c6fa7f890ea530e427685dadf0d0a371fc1,citation,https://arxiv.org/pdf/1804.11027.pdf,Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification,2018 -103,Australia,Duke MTMC,duke_mtmc,-27.49741805,153.01316956,University of Queensland,edu,5be74c6fa7f890ea530e427685dadf0d0a371fc1,citation,https://arxiv.org/pdf/1804.11027.pdf,Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification,2018 -104,Australia,Duke MTMC,duke_mtmc,-33.88890695,151.18943366,University of Sydney,edu,5be74c6fa7f890ea530e427685dadf0d0a371fc1,citation,https://arxiv.org/pdf/1804.11027.pdf,Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification,2018 -105,Switzerland,Duke MTMC,duke_mtmc,46.5184121,6.5684654,École Polytechnique Fédérale de Lausanne,edu,0f3eb3719b6f6f544b766e0bfeb8f962c9bd59f4,citation,https://arxiv.org/pdf/1811.10984.pdf,Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking,2018 -106,Italy,Duke MTMC,duke_mtmc,45.434532,12.326197,"DAIS, Università Ca’ Foscari, Venice, Italy",edu,6dce5866ebc46355a35b8667c1e04a4790c2289b,citation,https://pdfs.semanticscholar.org/6dce/5866ebc46355a35b8667c1e04a4790c2289b.pdf,Extensions of dominant sets and their applications in computer vision,2018 -107,United States,Duke MTMC,duke_mtmc,42.3383668,-71.08793524,Northeastern University,edu,8abe89ab85250fd7a8117da32bc339a71c67dc21,citation,https://arxiv.org/pdf/1709.07065.pdf,Multi-camera Multi-Object Tracking,2017 -108,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,b856c0eb039effce7da9ff45c3f5987f18928bef,citation,https://arxiv.org/pdf/1707.00408.pdf,Pedestrian Alignment Network for Large-scale Person Re-identification,2017 -109,Germany,Duke MTMC,duke_mtmc,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,bab66082d01b393e6b9e841e5e06782a6c61ec88,citation,https://arxiv.org/pdf/1803.08709.pdf,Pose-Driven Deep Models for Person Re-Identification,2018 -110,China,Duke MTMC,duke_mtmc,31.30104395,121.50045497,Fudan University,edu,e6d8f332ae26e9983d5b42af4466ff95b55f2341,citation,https://arxiv.org/pdf/1712.02225.pdf,Pose-Normalized Image Generation for Person Re-identification,2018 -111,Japan,Duke MTMC,duke_mtmc,34.7321121,135.7328585,Nara Institute of Science and Technology,edu,e6d8f332ae26e9983d5b42af4466ff95b55f2341,citation,https://arxiv.org/pdf/1712.02225.pdf,Pose-Normalized Image Generation for Person Re-identification,2018 -112,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,e6d8f332ae26e9983d5b42af4466ff95b55f2341,citation,https://arxiv.org/pdf/1712.02225.pdf,Pose-Normalized Image Generation for Person Re-identification,2018 -113,China,Duke MTMC,duke_mtmc,22.8376,108.289839,Guangxi University,edu,4a91be40e6b382c3ddf3385ac44062b2399336a8,citation,https://arxiv.org/pdf/1809.09970.pdf,Random Occlusion-recovery for Person Re-identification,2018 -114,China,Duke MTMC,duke_mtmc,31.28473925,121.49694909,Tongji University,edu,4a91be40e6b382c3ddf3385ac44062b2399336a8,citation,https://arxiv.org/pdf/1809.09970.pdf,Random Occlusion-recovery for Person Re-identification,2018 -115,France,Duke MTMC,duke_mtmc,45.2173989,5.7921349,"Naver Labs Europe, Meylan, France",edu,4d8347a69e77cc02c1e1aba3a8b6646eac1a0b3d,citation,https://arxiv.org/pdf/1801.05339.pdf,Re-ID done right: towards good practices for person re-identification.,2018 -116,United States,Duke MTMC,duke_mtmc,28.59899755,-81.19712501,University of Central Florida,edu,a1e97c4043d5cc9896dc60ae7ca135782d89e5fc,citation,https://arxiv.org/pdf/1612.02155.pdf,"Re-identification of Humans in Crowds using Personal, Social and Environmental Constraints",2016 -117,China,Duke MTMC,duke_mtmc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,0e36bf238d2db6c970ade0b5f68811ed6debc4e8,citation,https://arxiv.org/pdf/1810.07399.pdf,Recognizing Partial Biometric Patterns,2018 -118,United States,Duke MTMC,duke_mtmc,42.4505507,-76.4783513,Cornell University,edu,6d76eefecdcaa130a000d1d6c93cf57166ebd18e,citation,https://arxiv.org/pdf/1805.08805.pdf,Resource Aware Person Re-identification Across Multiple Resolutions,2018 -119,China,Duke MTMC,duke_mtmc,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,6d76eefecdcaa130a000d1d6c93cf57166ebd18e,citation,https://arxiv.org/pdf/1805.08805.pdf,Resource Aware Person Re-identification Across Multiple Resolutions,2018 -120,China,Duke MTMC,duke_mtmc,40.00229045,116.32098908,Tsinghua University,edu,6d76eefecdcaa130a000d1d6c93cf57166ebd18e,citation,https://arxiv.org/pdf/1805.08805.pdf,Resource Aware Person Re-identification Across Multiple Resolutions,2018 -121,China,Duke MTMC,duke_mtmc,31.846918,117.29053367,Hefei University of Technology,edu,42dc432f58adfaa7bf6af07e5faf9e75fea29122,citation,https://arxiv.org/pdf/1811.08115.pdf,Sequence-based Person Attribute Recognition with Joint CTC-Attention Model,2018 -122,China,Duke MTMC,duke_mtmc,31.1675446,121.3974873,"Tencent, Shanghai, China",company,42dc432f58adfaa7bf6af07e5faf9e75fea29122,citation,https://arxiv.org/pdf/1811.08115.pdf,Sequence-based Person Attribute Recognition with Joint CTC-Attention Model,2018 -123,United States,Duke MTMC,duke_mtmc,47.6423318,-122.1369302,Microsoft,company,8a77025bde5479a1366bb93c6f2366b5a6293720,citation,https://arxiv.org/pdf/1805.02336.pdf,Sharp Attention Network via Adaptive Sampling for Person Re-identification,2018 -124,United States,Duke MTMC,duke_mtmc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,8a77025bde5479a1366bb93c6f2366b5a6293720,citation,https://arxiv.org/pdf/1805.02336.pdf,Sharp Attention Network via Adaptive Sampling for Person Re-identification,2018 -125,China,Duke 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of Sciences,edu,1bfe59be5b42d6b7257da4b35a408239c01ab79d,citation,,Adversarially Occluded Samples for Person Re-identification,2018 -141,China,Duke MTMC,duke_mtmc,22.543096,114.057865,"SenseNets Corporation, Shenzhen, China",company,14ce502bc19b225466126b256511f9c05cadcb6e,citation,,Attention-Aware Compositional Network for Person Re-identification,2018 -142,China,Duke MTMC,duke_mtmc,39.993008,116.329882,SenseTime,company,14ce502bc19b225466126b256511f9c05cadcb6e,citation,,Attention-Aware Compositional Network for Person Re-identification,2018 -143,Australia,Duke MTMC,duke_mtmc,-33.88890695,151.18943366,University of Sydney,edu,14ce502bc19b225466126b256511f9c05cadcb6e,citation,,Attention-Aware Compositional Network for Person Re-identification,2018 -144,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,1822ca8db58b0382b0c64f310840f0f875ea02c0,citation,,Camera Style Adaptation for Person Re-identification,2018 -145,China,Duke MTMC,duke_mtmc,24.4399419,118.09301781,Xiamen University,edu,1822ca8db58b0382b0c64f310840f0f875ea02c0,citation,,Camera Style Adaptation for Person Re-identification,2018 -146,China,Duke MTMC,duke_mtmc,36.16161795,120.49355276,Ocean University of China,edu,38259235a1c7b2c68ca09f3bc0930987ae99cf00,citation,,Deep Feature Ranking for Person Re-Identification,2019 -147,South Korea,Duke MTMC,duke_mtmc,35.84658875,127.1350133,Chonbuk National University,edu,c635564fe2f7d91b578bd6959904982aaa61234d,citation,,Deep Multi-Task Network for Learning Person Identity and Attributes,2018 -148,China,Duke MTMC,duke_mtmc,22.4162632,114.2109318,Chinese University of Hong Kong,edu,947954cafdefd471b75da8c3bb4c21b9e6d57838,citation,,End-to-End Deep Kronecker-Product Matching for Person Re-identification,2018 -149,China,Duke MTMC,duke_mtmc,39.993008,116.329882,SenseTime,company,947954cafdefd471b75da8c3bb4c21b9e6d57838,citation,,End-to-End Deep Kronecker-Product Matching for Person Re-identification,2018 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MTMC,duke_mtmc,22.4162632,114.2109318,Chinese University of Hong Kong,edu,308a13fd1d2847d98930a8e5542f773a9651a0ae,citation,,Group Consistent Similarity Learning via Deep CRF for Person Re-identification,2018 -155,Italy,Duke MTMC,duke_mtmc,46.0658836,11.1159894,University of Trento,edu,308a13fd1d2847d98930a8e5542f773a9651a0ae,citation,,Group Consistent Similarity Learning via Deep CRF for Person Re-identification,2018 -156,China,Duke MTMC,duke_mtmc,34.250803,108.983693,Xi’an Jiaotong University,edu,308a13fd1d2847d98930a8e5542f773a9651a0ae,citation,,Group Consistent Similarity Learning via Deep CRF for Person Re-identification,2018 -157,Turkey,Duke MTMC,duke_mtmc,41.10427915,29.02231159,Istanbul Technical University,edu,7ba225a614d77efd9bdf66bf74c80dd2da09229a,citation,,Human Semantic Parsing for Person Re-identification,2018 -158,United States,Duke MTMC,duke_mtmc,28.59899755,-81.19712501,University of Central Florida,edu,7ba225a614d77efd9bdf66bf74c80dd2da09229a,citation,,Human Semantic Parsing for Person Re-identification,2018 -159,Australia,Duke MTMC,duke_mtmc,-32.00686365,115.89691775,Curtin University,edu,292286c0024d6625fe606fb5b8a0df54ea3ffe91,citation,,Identity Adaptation for Person Re-Identification,2018 -160,United Kingdom,Duke MTMC,duke_mtmc,54.00975365,-2.78757491,Lancaster University,edu,292286c0024d6625fe606fb5b8a0df54ea3ffe91,citation,,Identity Adaptation for Person Re-Identification,2018 -161,Australia,Duke MTMC,duke_mtmc,-31.95040445,115.79790037,University of Western Australia,edu,292286c0024d6625fe606fb5b8a0df54ea3ffe91,citation,,Identity Adaptation for Person Re-Identification,2018 -162,China,Duke MTMC,duke_mtmc,40.0044795,116.370238,Chinese Academy of Sciences,edu,6cde93a5288e84671a7bee98cf6c94037f42da42,citation,,Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification,2018 -163,Singapore,Duke MTMC,duke_mtmc,1.340216,103.965089,Singapore University of Technology and Design,edu,6cde93a5288e84671a7bee98cf6c94037f42da42,citation,,Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification,2018 -164,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,6cde93a5288e84671a7bee98cf6c94037f42da42,citation,,Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification,2018 -165,China,Duke MTMC,duke_mtmc,39.0607286,117.1256421,Tianjin Normal University,edu,67289bd3b7c9406429c6012eb7292305e50dff0b,citation,,Integration Convolutional Neural Network for Person Re-Identification in Camera Networks,2018 -166,China,Duke MTMC,duke_mtmc,32.05765485,118.7550004,HoHai University,edu,fedb656c45aa332cfc373b413f3000b6228eee08,citation,,Joint Learning of Body and Part Representation for Person Re-Identification,2018 -167,China,Duke MTMC,duke_mtmc,33.5491006,119.035706,"Huaiyin Institute of Technology, Huaian, China",edu,fedb656c45aa332cfc373b413f3000b6228eee08,citation,,Joint Learning of Body and Part Representation for Person Re-Identification,2018 -168,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,fedb656c45aa332cfc373b413f3000b6228eee08,citation,,Joint Learning of Body and Part Representation for Person Re-Identification,2018 -169,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,b37538f9364252eec4182bdbb80ef1e4614c3acd,citation,,Learning a Semantically Discriminative Joint Space for Attribute Based Person Re-identification,2017 -170,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,004acfec16c36649408c561faa102dd9de76f085,citation,,Multi-level Factorisation Net for Person Re-identification,2018 -171,United Kingdom,Duke MTMC,duke_mtmc,55.94951105,-3.19534913,University of Edinburgh,edu,004acfec16c36649408c561faa102dd9de76f085,citation,,Multi-level Factorisation Net for Person Re-identification,2018 -172,China,Duke MTMC,duke_mtmc,39.0607286,117.1256421,Tianjin Normal University,edu,a80d8506fa28334c947989ca153b70aafc63ac7f,citation,,Pedestrian Retrieval via Part-Based Gradation Regularization in Sensor Networks,2018 -173,United States,Duke MTMC,duke_mtmc,35.9990522,-78.9290629,Duke University,edu,96e77135e745385e87fdd0f7ced951bf1fe9a756,citation,,People Tracking and Re-Identification from Multiple Cameras,2018 -174,China,Duke MTMC,duke_mtmc,30.274084,120.15507,Alibaba,company,90c18409b7a3be2cd6da599d02accba4c769e94e,citation,,Person Re-identification with Cascaded Pairwise Convolutions,2018 -175,China,Duke MTMC,duke_mtmc,31.83907195,117.26420748,University of Science and Technology of China,edu,90c18409b7a3be2cd6da599d02accba4c769e94e,citation,,Person Re-identification with Cascaded Pairwise Convolutions,2018 -176,China,Duke MTMC,duke_mtmc,30.5360485,114.3643219,"Wuhan Univeristy, Wuhan, 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Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification,2018 +58,United States,Duke MTMC,duke_mtmc,22.5447154,113.9357164,Tencent,company,3b311a1ce30f9c0f3dc1d9c0cf25f13127a5e48c,citation,https://arxiv.org/pdf/1810.12193.pdf,A Coarse-to-fine Pyramidal Model for Person Re-identification via Multi-Loss Dynamic Training,2018 +59,United States,Duke MTMC,duke_mtmc,37.3860784,-121.9877807,Google and Hewlett-Packard Labs,company,4d799f6e09f442bde583a50a0a9f81131ef707bb,citation,,TAR: Enabling Fine-Grained Targeted Advertising in Retail Stores,2018 +60,United States,Duke MTMC,duke_mtmc,37.3860784,-121.9877807,Hewlett-Packard Labs,edu,4d799f6e09f442bde583a50a0a9f81131ef707bb,citation,,TAR: Enabling Fine-Grained Targeted Advertising in Retail Stores,2018 +61,United States,Duke MTMC,duke_mtmc,39.6321923,-76.3038146,LinkedIn and Hewlett-Packard Labs,edu,4d799f6e09f442bde583a50a0a9f81131ef707bb,citation,,TAR: Enabling Fine-Grained Targeted Advertising in Retail Stores,2018 +62,United States,Duke MTMC,duke_mtmc,34.0224149,-118.28634407,University of Southern California,edu,4d799f6e09f442bde583a50a0a9f81131ef707bb,citation,,TAR: Enabling Fine-Grained Targeted Advertising in Retail Stores,2018 +63,Canada,Duke MTMC,duke_mtmc,49.2767454,-122.91777375,Simon Fraser University,edu,5137ca9f0a7cf4c61f2254d4a252a0c56e5dcfcc,citation,https://arxiv.org/pdf/1811.07130.pdf,Batch Feature Erasing for Person Re-identification and Beyond,2018 +64,China,Duke MTMC,duke_mtmc,32.0565957,118.77408833,Nanjing University,edu,c37c3853ab428725f13906bb0ff4936ffe15d6af,citation,https://arxiv.org/pdf/1809.02874.pdf,Unsupervised Person Re-identification by Deep Learning Tracklet Association,2018 +65,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,c37c3853ab428725f13906bb0ff4936ffe15d6af,citation,https://arxiv.org/pdf/1809.02874.pdf,Unsupervised Person Re-identification by Deep Learning Tracklet Association,2018 +66,United States,Duke MTMC,duke_mtmc,37.8687126,-122.25586815,"University of California, Berkeley",edu,a8d665fa7357f696dcfd188b91fda88da47b964e,citation,https://arxiv.org/pdf/1809.02318.pdf,Scaling Video Analytics Systems to Large Camera Deployments,2018 +67,United States,Duke MTMC,duke_mtmc,47.6423318,-122.1369302,Microsoft,company,a8d665fa7357f696dcfd188b91fda88da47b964e,citation,https://arxiv.org/pdf/1809.02318.pdf,Scaling Video Analytics Systems to Large Camera Deployments,2018 +68,United States,Duke MTMC,duke_mtmc,41.78468745,-87.60074933,University of Chicago,edu,a8d665fa7357f696dcfd188b91fda88da47b964e,citation,https://arxiv.org/pdf/1809.02318.pdf,Scaling Video Analytics Systems to Large Camera Deployments,2018 +69,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,dda0b381c162695f21b8d1149aab22188b3c2bc0,citation,https://arxiv.org/pdf/1804.02792.pdf,Occluded Person Re-Identification,2018 +70,China,Duke MTMC,duke_mtmc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,33f358f1d2b54042c524d69b20e80d98dde3dacd,citation,https://arxiv.org/pdf/1811.11405.pdf,Spectral Feature Transformation for Person Re-identification,2018 +71,United States,Duke MTMC,duke_mtmc,32.8734455,-117.2065636,TuSimple,edu,33f358f1d2b54042c524d69b20e80d98dde3dacd,citation,https://arxiv.org/pdf/1811.11405.pdf,Spectral Feature Transformation for Person Re-identification,2018 +72,China,Duke MTMC,duke_mtmc,30.672721,104.098806,University of Electronic Science and Technology of China,edu,8ffc49aead99fdacb0b180468a36984759f2fc1e,citation,https://arxiv.org/pdf/1809.04976.pdf,Sparse Label Smoothing for Semi-supervised Person Re-Identification,2018 +73,Germany,Duke MTMC,duke_mtmc,50.7791703,6.06728733,RWTH Aachen University,edu,10b36c003542545f1e2d73e8897e022c0c260c32,citation,https://arxiv.org/pdf/1705.04608.pdf,Towards a Principled Integration of Multi-camera Re-identification and Tracking Through Optimal Bayes Filters,2017 +74,United Kingdom,Duke MTMC,duke_mtmc,51.7534538,-1.25400997,University of Oxford,edu,94ed6dc44842368b457851b43023c23fd78d5390,citation,https://arxiv.org/pdf/1806.01794.pdf,"Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects",2018 +75,China,Duke MTMC,duke_mtmc,39.9041999,116.4073963,"Beijing, China",edu,280976bbb41d2948a5c0208f86605977397181cd,citation,https://arxiv.org/pdf/1811.08073.pdf,Factorized Distillation: Training Holistic Person Re-identification Model by Distilling an Ensemble of Partial ReID Models,2018 +76,China,Duke MTMC,duke_mtmc,40.00229045,116.32098908,Tsinghua University,edu,280976bbb41d2948a5c0208f86605977397181cd,citation,https://arxiv.org/pdf/1811.08073.pdf,Factorized Distillation: Training Holistic Person Re-identification Model by Distilling an Ensemble of Partial ReID Models,2018 +77,China,Duke MTMC,duke_mtmc,39.9922379,116.30393816,Peking University,edu,014e249422b6bd6ff32b3f7d385b5a0e8c4c9fcf,citation,https://arxiv.org/pdf/1810.05866.pdf,Attention driven person re-identification,2019 +78,Singapore,Duke MTMC,duke_mtmc,1.3484104,103.68297965,Nanyang Technological University,edu,014e249422b6bd6ff32b3f7d385b5a0e8c4c9fcf,citation,https://arxiv.org/pdf/1810.05866.pdf,Attention driven person re-identification,2019 +79,China,Duke MTMC,duke_mtmc,39.9808333,116.34101249,Beihang University,edu,e9d549989926f36abfa5dc7348ae3d79a567bf30,citation,,Orientation-Guided Similarity Learning for Person Re-identification,2018 +80,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,95bdd45fed0392418e0e5d3e51d34714917e3c87,citation,https://arxiv.org/pdf/1812.03282.pdf,Spatial-Temporal Person Re-identification,2019 +81,China,Duke MTMC,duke_mtmc,31.30104395,121.50045497,Fudan University,edu,00e3957212517a252258baef833833921dd308d4,citation,,Adaptively Weighted Multi-task Deep Network for Person Attribute Classification,2017 +82,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,705073015bb8ae97212532a30488c05d50894bec,citation,https://arxiv.org/pdf/1803.09786.pdf,Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-identification,2018 +83,United States,Duke MTMC,duke_mtmc,35.9990522,-78.9290629,Duke University,edu,9e644b1e33dd9367be167eb9d832174004840400,citation,https://users.cs.duke.edu/~tomasi/papers/ristani/ristaniTCAS16.pdf,Tracking Social Groups Within and Across Cameras,2017 +84,Italy,Duke MTMC,duke_mtmc,44.6451046,10.9279268,University of Modena,edu,9e644b1e33dd9367be167eb9d832174004840400,citation,https://users.cs.duke.edu/~tomasi/papers/ristani/ristaniTCAS16.pdf,Tracking Social Groups Within and Across Cameras,2017 +85,United States,Duke MTMC,duke_mtmc,35.9990522,-78.9290629,Duke University,edu,27a2fad58dd8727e280f97036e0d2bc55ef5424c,citation,https://arxiv.org/pdf/1609.01775.pdf,"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking",2016 +86,Switzerland,Duke MTMC,duke_mtmc,46.5190557,6.5667576,EPFL,edu,4e4e3ddb55607e127a4abdef45d92adf1ff78de2,citation,http://openaccess.thecvf.com/content_ICCV_2017/papers/Maksai_Non-Markovian_Globally_Consistent_ICCV_2017_paper.pdf,Non-Markovian Globally Consistent Multi-object Tracking,2017 +87,Switzerland,Duke MTMC,duke_mtmc,46.109237,7.08453549,IDIAP Research Institute,edu,4e4e3ddb55607e127a4abdef45d92adf1ff78de2,citation,http://openaccess.thecvf.com/content_ICCV_2017/papers/Maksai_Non-Markovian_Globally_Consistent_ICCV_2017_paper.pdf,Non-Markovian Globally Consistent Multi-object Tracking,2017 +88,United States,Duke MTMC,duke_mtmc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,4e4e3ddb55607e127a4abdef45d92adf1ff78de2,citation,http://openaccess.thecvf.com/content_ICCV_2017/papers/Maksai_Non-Markovian_Globally_Consistent_ICCV_2017_paper.pdf,Non-Markovian Globally Consistent Multi-object Tracking,2017 +89,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,fc26fc2340a863d6da0b427cd924fb4cb101051b,citation,http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w37/Chen_Person_Re-Identification_by_ICCV_2017_paper.pdf,Person Re-identification by Deep Learning Multi-scale Representations,2017 +90,United Kingdom,Duke MTMC,duke_mtmc,55.378051,-3.435973,"Vision Semantics Ltd, UK",edu,fc26fc2340a863d6da0b427cd924fb4cb101051b,citation,http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w37/Chen_Person_Re-Identification_by_ICCV_2017_paper.pdf,Person Re-identification by Deep Learning Multi-scale Representations,2017 +91,Canada,Duke MTMC,duke_mtmc,43.4983503,-80.5478382,"Senstar Corporation, Waterloo, Canada",company,8e42568c2b3feaafd1e442e1e861ec50a4ac144f,citation,https://arxiv.org/pdf/1805.06086.pdf,An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-identification,2018 +92,Italy,Duke MTMC,duke_mtmc,45.4377672,12.321807,University Iuav of Venice,edu,eddb1a126eafecad2cead01c6c3bb4b88120d78a,citation,https://arxiv.org/pdf/1802.02181.pdf,Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition,2018 +93,China,Duke MTMC,duke_mtmc,40.00229045,116.32098908,Tsinghua University,edu,fc068f7f8a3b2921ec4f3246e9b6c6015165df9a,citation,https://arxiv.org/pdf/1711.09349.pdf,Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline),2018 +94,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,fc068f7f8a3b2921ec4f3246e9b6c6015165df9a,citation,https://arxiv.org/pdf/1711.09349.pdf,Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline),2018 +95,United States,Duke MTMC,duke_mtmc,29.58333105,-98.61944505,University of Texas at San Antonio,edu,fc068f7f8a3b2921ec4f3246e9b6c6015165df9a,citation,https://arxiv.org/pdf/1711.09349.pdf,Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline),2018 +96,United States,Duke MTMC,duke_mtmc,43.0008093,-78.7889697,University at Buffalo,edu,fdd1bde7066c7e9c7515f330546e0b3a8de8a4a6,citation,https://arxiv.org/pdf/1811.06582.pdf,CAN: Composite Appearance Network and a Novel Evaluation Metric for Person Tracking,2018 +97,United States,Duke MTMC,duke_mtmc,43.0008093,-78.7889697,University at Buffalo,edu,3144c9b3bedb6e3895dcd36998bcb0903271841d,citation,https://arxiv.org/pdf/1811.06582.pdf,CAN: Composite Appearance Network and a Novel Evaluation Metric for Person Tracking,2018 +98,China,Duke MTMC,duke_mtmc,29.1416432,119.7889248,"Alibaba Group, Zhejiang, People’s Republic of China",edu,f4e65ab81a0f4ffa50d0c9bc308d7365e012cc75,citation,https://arxiv.org/pdf/1812.05785.pdf,Deep Active Learning for Video-based Person Re-identification,2018 +99,China,Duke MTMC,duke_mtmc,30.19331415,120.11930822,Zhejiang University,edu,f4e65ab81a0f4ffa50d0c9bc308d7365e012cc75,citation,https://arxiv.org/pdf/1812.05785.pdf,Deep Active Learning for Video-based Person Re-identification,2018 +100,China,Duke MTMC,duke_mtmc,38.88140235,121.52281098,Dalian University of Technology,edu,5be74c6fa7f890ea530e427685dadf0d0a371fc1,citation,https://arxiv.org/pdf/1804.11027.pdf,Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification,2018 +101,Australia,Duke MTMC,duke_mtmc,-27.49741805,153.01316956,University of Queensland,edu,5be74c6fa7f890ea530e427685dadf0d0a371fc1,citation,https://arxiv.org/pdf/1804.11027.pdf,Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification,2018 +102,Australia,Duke MTMC,duke_mtmc,-33.88890695,151.18943366,University of Sydney,edu,5be74c6fa7f890ea530e427685dadf0d0a371fc1,citation,https://arxiv.org/pdf/1804.11027.pdf,Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification,2018 +103,Switzerland,Duke MTMC,duke_mtmc,46.5184121,6.5684654,École Polytechnique Fédérale de Lausanne,edu,0f3eb3719b6f6f544b766e0bfeb8f962c9bd59f4,citation,https://arxiv.org/pdf/1811.10984.pdf,Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking,2018 +104,Italy,Duke MTMC,duke_mtmc,45.434532,12.326197,"DAIS, Università Ca’ Foscari, Venice, Italy",edu,6dce5866ebc46355a35b8667c1e04a4790c2289b,citation,https://pdfs.semanticscholar.org/6dce/5866ebc46355a35b8667c1e04a4790c2289b.pdf,Extensions of dominant sets and their applications in computer vision,2018 +105,United States,Duke MTMC,duke_mtmc,42.3383668,-71.08793524,Northeastern University,edu,8abe89ab85250fd7a8117da32bc339a71c67dc21,citation,https://arxiv.org/pdf/1709.07065.pdf,Multi-camera Multi-Object Tracking,2017 +106,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,b856c0eb039effce7da9ff45c3f5987f18928bef,citation,https://arxiv.org/pdf/1707.00408.pdf,Pedestrian Alignment Network for Large-scale Person Re-identification,2017 +107,Germany,Duke MTMC,duke_mtmc,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,bab66082d01b393e6b9e841e5e06782a6c61ec88,citation,https://arxiv.org/pdf/1803.08709.pdf,Pose-Driven Deep Models for Person Re-Identification,2018 +108,China,Duke MTMC,duke_mtmc,31.30104395,121.50045497,Fudan University,edu,e6d8f332ae26e9983d5b42af4466ff95b55f2341,citation,https://arxiv.org/pdf/1712.02225.pdf,Pose-Normalized Image Generation for Person Re-identification,2018 +109,Japan,Duke MTMC,duke_mtmc,34.7321121,135.7328585,Nara Institute of Science and Technology,edu,e6d8f332ae26e9983d5b42af4466ff95b55f2341,citation,https://arxiv.org/pdf/1712.02225.pdf,Pose-Normalized Image Generation for Person Re-identification,2018 +110,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,e6d8f332ae26e9983d5b42af4466ff95b55f2341,citation,https://arxiv.org/pdf/1712.02225.pdf,Pose-Normalized Image Generation for Person Re-identification,2018 +111,China,Duke MTMC,duke_mtmc,22.8376,108.289839,Guangxi University,edu,4a91be40e6b382c3ddf3385ac44062b2399336a8,citation,https://arxiv.org/pdf/1809.09970.pdf,Random Occlusion-recovery for Person Re-identification,2018 +112,China,Duke MTMC,duke_mtmc,31.28473925,121.49694909,Tongji University,edu,4a91be40e6b382c3ddf3385ac44062b2399336a8,citation,https://arxiv.org/pdf/1809.09970.pdf,Random Occlusion-recovery for Person Re-identification,2018 +113,France,Duke MTMC,duke_mtmc,45.2173989,5.7921349,"Naver Labs Europe, Meylan, France",edu,4d8347a69e77cc02c1e1aba3a8b6646eac1a0b3d,citation,https://arxiv.org/pdf/1801.05339.pdf,Re-ID done right: towards good practices for person re-identification.,2018 +114,China,Duke MTMC,duke_mtmc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,0e36bf238d2db6c970ade0b5f68811ed6debc4e8,citation,https://arxiv.org/pdf/1810.07399.pdf,Recognizing Partial Biometric Patterns,2018 +115,United States,Duke MTMC,duke_mtmc,42.4505507,-76.4783513,Cornell University,edu,6d76eefecdcaa130a000d1d6c93cf57166ebd18e,citation,https://arxiv.org/pdf/1805.08805.pdf,Resource Aware Person Re-identification Across Multiple Resolutions,2018 +116,China,Duke MTMC,duke_mtmc,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,6d76eefecdcaa130a000d1d6c93cf57166ebd18e,citation,https://arxiv.org/pdf/1805.08805.pdf,Resource Aware Person Re-identification Across Multiple Resolutions,2018 +117,China,Duke MTMC,duke_mtmc,40.00229045,116.32098908,Tsinghua University,edu,6d76eefecdcaa130a000d1d6c93cf57166ebd18e,citation,https://arxiv.org/pdf/1805.08805.pdf,Resource Aware Person Re-identification Across Multiple Resolutions,2018 +118,China,Duke MTMC,duke_mtmc,31.846918,117.29053367,Hefei University of Technology,edu,42dc432f58adfaa7bf6af07e5faf9e75fea29122,citation,https://arxiv.org/pdf/1811.08115.pdf,Sequence-based Person Attribute Recognition with Joint CTC-Attention Model,2018 +119,China,Duke MTMC,duke_mtmc,22.5447154,113.9357164,"Tencent, Shanghai, China",company,42dc432f58adfaa7bf6af07e5faf9e75fea29122,citation,https://arxiv.org/pdf/1811.08115.pdf,Sequence-based Person Attribute Recognition with Joint CTC-Attention Model,2018 +120,United States,Duke MTMC,duke_mtmc,47.6423318,-122.1369302,Microsoft,company,8a77025bde5479a1366bb93c6f2366b5a6293720,citation,https://arxiv.org/pdf/1805.02336.pdf,Sharp Attention Network via Adaptive Sampling for Person Re-identification,2018 +121,United States,Duke MTMC,duke_mtmc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,8a77025bde5479a1366bb93c6f2366b5a6293720,citation,https://arxiv.org/pdf/1805.02336.pdf,Sharp Attention Network via Adaptive Sampling for Person Re-identification,2018 +122,China,Duke MTMC,duke_mtmc,30.19331415,120.11930822,Zhejiang University,edu,8a77025bde5479a1366bb93c6f2366b5a6293720,citation,https://arxiv.org/pdf/1805.02336.pdf,Sharp Attention Network via Adaptive Sampling for Person Re-identification,2018 +123,Australia,Duke MTMC,duke_mtmc,-35.2776999,149.118527,Australian National University,edu,304196021200067a838c06002d9e96d6a12a1e46,citation,https://arxiv.org/pdf/1811.10551.pdf,Similarity-preserving Image-image Domain Adaptation for Person Re-identification,2018 +124,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,304196021200067a838c06002d9e96d6a12a1e46,citation,https://arxiv.org/pdf/1811.10551.pdf,Similarity-preserving Image-image Domain Adaptation for Person Re-identification,2018 +125,China,Duke MTMC,duke_mtmc,28.2290209,112.99483204,"National University of Defense Technology, China",mil,e90816e1a0e14ea1e7039e0b2782260999aef786,citation,https://arxiv.org/pdf/1809.03137.pdf,Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers,2018 +126,United Kingdom,Duke MTMC,duke_mtmc,51.5231607,-0.1282037,University College London,edu,e90816e1a0e14ea1e7039e0b2782260999aef786,citation,https://arxiv.org/pdf/1809.03137.pdf,Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers,2018 +127,United States,Duke MTMC,duke_mtmc,37.2283843,-80.4234167,Virginia Tech,edu,e278218ba1ff1b85d06680e99b08e817d0962dab,citation,https://arxiv.org/pdf/1710.02139.pdf,Tracking Persons-of-Interest via Unsupervised Representation Adaptation,2017 +128,China,Duke MTMC,duke_mtmc,34.250803,108.983693,Xi’an Jiaotong University,edu,e278218ba1ff1b85d06680e99b08e817d0962dab,citation,https://arxiv.org/pdf/1710.02139.pdf,Tracking Persons-of-Interest via Unsupervised Representation Adaptation,2017 +129,China,Duke MTMC,duke_mtmc,30.508964,114.410577,"Huazhong Univ. of Science and Technology, China",edu,42656cf2b75dccc7f8f224f7a86c2ea4de1ae671,citation,https://arxiv.org/pdf/1807.11334.pdf,Unsupervised Domain Adaptive Re-Identification: Theory and Practice,2018 +130,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,788ab52d4f7fedb4b79347bb81822c4f3c430d80,citation,https://arxiv.org/pdf/1901.10177.pdf,Unsupervised Person Re-identification by Deep Asymmetric Metric Embedding,2018 +131,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,31da1da2d4e7254dd8f2a4578d887c57e0678438,citation,https://arxiv.org/pdf/1705.10444.pdf,Unsupervised Person Re-identification: Clustering and Fine-tuning,2018 +132,United Kingdom,Duke MTMC,duke_mtmc,54.6141723,-5.9002151,Queen's University Belfast,edu,1e146982a7b088e7a3790d2683484944c3b9dcf7,citation,https://pdfs.semanticscholar.org/1e14/6982a7b088e7a3790d2683484944c3b9dcf7.pdf,Video Person Re-Identification for Wide Area Tracking based on Recurrent Neural Networks,2017 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of Sciences,edu,1bfe59be5b42d6b7257da4b35a408239c01ab79d,citation,,Adversarially Occluded Samples for Person Re-identification,2018 +138,China,Duke MTMC,duke_mtmc,22.543096,114.057865,"SenseNets Corporation, Shenzhen, China",company,14ce502bc19b225466126b256511f9c05cadcb6e,citation,,Attention-Aware Compositional Network for Person Re-identification,2018 +139,China,Duke MTMC,duke_mtmc,39.993008,116.329882,SenseTime,company,14ce502bc19b225466126b256511f9c05cadcb6e,citation,,Attention-Aware Compositional Network for Person Re-identification,2018 +140,Australia,Duke MTMC,duke_mtmc,-33.88890695,151.18943366,University of Sydney,edu,14ce502bc19b225466126b256511f9c05cadcb6e,citation,,Attention-Aware Compositional Network for Person Re-identification,2018 +141,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,1822ca8db58b0382b0c64f310840f0f875ea02c0,citation,,Camera Style Adaptation for Person Re-identification,2018 +142,China,Duke 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University,edu,0353fe24ecd237f4d9ae4dbc277a6a67a69ce8ed,citation,https://pdfs.semanticscholar.org/0353/fe24ecd237f4d9ae4dbc277a6a67a69ce8ed.pdf,Discriminative Feature Representation for Person Re-identification by Batch-contrastive Loss,2018 +222,China,Duke MTMC,duke_mtmc,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,fd2bc4833c19a60d3646368952dcf35dbda007f3,citation,,Improving Person Re-Identification by Adaptive Hard Sample Mining,2018 +223,China,Duke MTMC,duke_mtmc,30.60903415,114.3514284,Wuhan University of Technology,edu,fd2bc4833c19a60d3646368952dcf35dbda007f3,citation,,Improving Person Re-Identification by Adaptive Hard Sample Mining,2018 diff --git a/site/datasets/verified/fiw_300.csv b/site/datasets/verified/fiw_300.csv index afcd74c1..c87a054d 100644 --- a/site/datasets/verified/fiw_300.csv +++ b/site/datasets/verified/fiw_300.csv @@ -1,2 +1,11 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year 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States,300-W,fiw_300,37.3936717,-122.0807262,Facebook,company,dcd2ac544a8336d73e4d3d80b158477c783e1e50,citation,https://arxiv.org/pdf/1709.01591.pdf,Improving Landmark Localization with Semi-Supervised Learning,2018 +5,United States,300-W,fiw_300,37.3706254,-121.9671894,NVIDIA,company,dcd2ac544a8336d73e4d3d80b158477c783e1e50,citation,https://arxiv.org/pdf/1709.01591.pdf,Improving Landmark Localization with Semi-Supervised Learning,2018 +6,Canada,300-W,fiw_300,45.5010087,-73.6157778,University of Montreal,edu,dcd2ac544a8336d73e4d3d80b158477c783e1e50,citation,https://arxiv.org/pdf/1709.01591.pdf,Improving Landmark Localization with Semi-Supervised Learning,2018 +7,United States,300-W,fiw_300,38.7768106,-94.9442982,Amazon,company,e7265c560b3f10013bf70aacbbf0eb4631b7e2aa,citation,https://arxiv.org/pdf/1805.10483.pdf,Look at Boundary: A Boundary-Aware Face Alignment Algorithm,2018 +8,China,300-W,fiw_300,39.993008,116.329882,SenseTime,company,e7265c560b3f10013bf70aacbbf0eb4631b7e2aa,citation,https://arxiv.org/pdf/1805.10483.pdf,Look at Boundary: A Boundary-Aware Face Alignment Algorithm,2018 +9,China,300-W,fiw_300,40.00229045,116.32098908,Tsinghua University,edu,e7265c560b3f10013bf70aacbbf0eb4631b7e2aa,citation,https://arxiv.org/pdf/1805.10483.pdf,Look at Boundary: A Boundary-Aware Face Alignment Algorithm,2018 diff --git a/site/datasets/verified/geofaces.csv b/site/datasets/verified/geofaces.csv index 9331c186..02570e4c 100644 --- a/site/datasets/verified/geofaces.csv +++ b/site/datasets/verified/geofaces.csv @@ -1,2 +1,5 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year 0,,GeoFaces,geofaces,0.0,0.0,,,,main,,Exploring the geo-dependence of human face appearance,2014 +1,United States,GeoFaces,geofaces,38.0333742,-84.5017758,University of Kentucky,edu,68eb46d2920d2e7568d543de9fa2fc42cb8f5cbb,citation,http://cs.uky.edu/~jacobs/papers/face2gps.pdf,FACE2GPS: Estimating geographic location from facial features,2015 +2,United States,GeoFaces,geofaces,38.0333742,-84.5017758,University of Kentucky,edu,17b46e2dad927836c689d6787ddb3387c6159ece,citation,,GeoFaceExplorer: exploring the geo-dependence of facial attributes,2014 +3,United States,GeoFaces,geofaces,38.0333742,-84.5017758,University of Kentucky,edu,9b9bf5e623cb8af7407d2d2d857bc3f1b531c182,citation,,Who goes there?: approaches to mapping facial appearance diversity,2016 diff --git a/site/datasets/verified/helen.csv b/site/datasets/verified/helen.csv index a9f9a846..19fb12fb 100644 --- a/site/datasets/verified/helen.csv +++ b/site/datasets/verified/helen.csv @@ -1,2 +1,324 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year 0,,Helen,helen,0.0,0.0,,,,main,,Interactive Facial Feature Localization,2012 +1,United States,Helen,helen,40.4441619,-79.94272826,Carnegie Mellon University,edu,bae86526b3b0197210b64cdd95cb5aca4209c98a,citation,https://arxiv.org/pdf/1802.01777.pdf,"Brute-Force Facial Landmark Analysis With a 140, 000-Way Classifier",2018 +2,China,Helen,helen,28.2290209,112.99483204,"National University of Defense Technology, China",mil,1b8541ec28564db66a08185510c8b300fa4dc793,citation,,Affine-Transformation Parameters Regression for Face Alignment,2016 +3,China,Helen,helen,31.83907195,117.26420748,University of Science and Technology of China,edu,084bd02d171e36458f108f07265386f22b34a1ae,citation,http://7xrqgw.com1.z0.glb.clouddn.com/3000fps.pdf,Face Alignment at 3000 FPS via Regressing Local Binary Features,2014 +4,United States,Helen,helen,47.6423318,-122.1369302,Microsoft,company,084bd02d171e36458f108f07265386f22b34a1ae,citation,http://7xrqgw.com1.z0.glb.clouddn.com/3000fps.pdf,Face Alignment at 3000 FPS via Regressing Local Binary Features,2014 +5,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,5bd3d08335bb4e444a86200c5e9f57fd9d719e14,citation,https://pdfs.semanticscholar.org/5bd3/d08335bb4e444a86200c5e9f57fd9d719e14.pdf,3 D Face Morphable Models “ Inthe-Wild ”,0 +6,United States,Helen,helen,38.7768106,-94.9442982,Amazon,company,5bd3d08335bb4e444a86200c5e9f57fd9d719e14,citation,https://pdfs.semanticscholar.org/5bd3/d08335bb4e444a86200c5e9f57fd9d719e14.pdf,3 D Face Morphable Models “ Inthe-Wild ”,0 +7,Finland,Helen,helen,65.0592157,25.46632601,University of Oulu,edu,5bd3d08335bb4e444a86200c5e9f57fd9d719e14,citation,https://pdfs.semanticscholar.org/5bd3/d08335bb4e444a86200c5e9f57fd9d719e14.pdf,3 D Face Morphable Models “ Inthe-Wild ”,0 +8,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,12095f9b35ee88272dd5abc2d942a4f55804b31e,citation,https://pdfs.semanticscholar.org/1209/5f9b35ee88272dd5abc2d942a4f55804b31e.pdf,DenseReg : Fully Convolutional Dense Shape Regression Inthe-Wild Rıza,0 +9,United States,Helen,helen,38.7768106,-94.9442982,Amazon,company,12095f9b35ee88272dd5abc2d942a4f55804b31e,citation,https://pdfs.semanticscholar.org/1209/5f9b35ee88272dd5abc2d942a4f55804b31e.pdf,DenseReg : Fully Convolutional Dense Shape Regression Inthe-Wild Rıza,0 +10,United Kingdom,Helen,helen,51.5231607,-0.1282037,University College London,edu,12095f9b35ee88272dd5abc2d942a4f55804b31e,citation,https://pdfs.semanticscholar.org/1209/5f9b35ee88272dd5abc2d942a4f55804b31e.pdf,DenseReg : Fully Convolutional Dense Shape Regression Inthe-Wild Rıza,0 +11,United Kingdom,Helen,helen,51.24303255,-0.59001382,University of Surrey,edu,2d2e1d1f50645fe20c051339e9a0fca7b176422a,citation,https://arxiv.org/pdf/1803.05536.pdf,Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild,2018 +12,United Kingdom,Helen,helen,56.1454119,-3.9205713,University of Stirling,edu,2d2e1d1f50645fe20c051339e9a0fca7b176422a,citation,https://arxiv.org/pdf/1803.05536.pdf,Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild,2018 +13,China,Helen,helen,31.4854255,120.2739581,Jiangnan University,edu,2d2e1d1f50645fe20c051339e9a0fca7b176422a,citation,https://arxiv.org/pdf/1803.05536.pdf,Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild,2018 +14,China,Helen,helen,30.642769,104.06751175,"Sichuan University, Chengdu",edu,2d2e1d1f50645fe20c051339e9a0fca7b176422a,citation,https://arxiv.org/pdf/1803.05536.pdf,Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild,2018 +15,Germany,Helen,helen,48.48187645,9.18682404,Reutlingen University,edu,2d2e1d1f50645fe20c051339e9a0fca7b176422a,citation,https://arxiv.org/pdf/1803.05536.pdf,Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild,2018 +16,United States,Helen,helen,45.57022705,-122.63709346,Concordia University,edu,266ed43dcea2e7db9f968b164ca08897539ca8dd,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/3B_037.pdf,Beyond Principal Components: Deep Boltzmann Machines for face modeling,2015 +17,United States,Helen,helen,40.4441619,-79.94272826,Carnegie Mellon University,edu,266ed43dcea2e7db9f968b164ca08897539ca8dd,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/3B_037.pdf,Beyond Principal Components: Deep Boltzmann Machines for face modeling,2015 +18,Germany,Helen,helen,52.5098686,13.3984513,"Amazon Research, Berlin",company,ba1c0600d3bdb8ed9d439e8aa736a96214156284,citation,http://www.eurasip.org/Proceedings/Eusipco/Eusipco2017/papers/1570347043.pdf,Complex representations for learning statistical shape priors,2017 +19,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,ba1c0600d3bdb8ed9d439e8aa736a96214156284,citation,http://www.eurasip.org/Proceedings/Eusipco/Eusipco2017/papers/1570347043.pdf,Complex representations for learning statistical shape priors,2017 +20,United States,Helen,helen,40.47913175,-74.43168868,Rutgers 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Barcelona,edu,cc4fc9a309f300e711e09712701b1509045a8e04,citation,https://pdfs.semanticscholar.org/cea6/9010a2f75f7a057d56770e776dec206ed705.pdf,Continuous Supervised Descent Method for Facial Landmark Localisation,2016 +24,Thailand,Helen,helen,13.65450525,100.49423171,Robotics Institute,edu,cc4fc9a309f300e711e09712701b1509045a8e04,citation,https://pdfs.semanticscholar.org/cea6/9010a2f75f7a057d56770e776dec206ed705.pdf,Continuous Supervised Descent Method for Facial Landmark Localisation,2016 +25,United States,Helen,helen,40.44415295,-79.96243993,University of Pittsburgh,edu,cc4fc9a309f300e711e09712701b1509045a8e04,citation,https://pdfs.semanticscholar.org/cea6/9010a2f75f7a057d56770e776dec206ed705.pdf,Continuous Supervised Descent Method for Facial Landmark Localisation,2016 +26,Canada,Helen,helen,43.0095971,-81.2737336,University of Western Ontario,edu,f7ae38a073be7c9cd1b92359131b9c8374579b13,citation,http://www.digitalimaginggroup.ca/members/Shuo/07487053.pdf,Descriptor Learning via 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Based Landmarks-Attention Network,2018 +34,United Kingdom,Helen,helen,53.22853665,-0.54873472,University of Lincoln,edu,232b6e2391c064d483546b9ee3aafe0ba48ca519,citation,http://doc.utwente.nl/89696/1/Pantic_Optimization_problems_for_fast_AAM_fitting.pdf,Optimization Problems for Fast AAM Fitting in-the-Wild,2013 +35,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,232b6e2391c064d483546b9ee3aafe0ba48ca519,citation,http://doc.utwente.nl/89696/1/Pantic_Optimization_problems_for_fast_AAM_fitting.pdf,Optimization Problems for Fast AAM Fitting in-the-Wild,2013 +36,United Kingdom,Helen,helen,52.9387428,-1.20029569,University of Nottingham,edu,75fd9acf5e5b7ed17c658cc84090c4659e5de01d,citation,http://eprints.nottingham.ac.uk/31442/1/tzimiro_CVPR15.pdf,Project-Out Cascaded Regression with an application to face alignment,2015 +37,Denmark,Helen,helen,57.01590275,9.97532827,Aalborg University,edu,087002ab569e35432cdeb8e63b2c94f1abc53ea9,citation,http://openaccess.thecvf.com/content_cvpr_workshops_2015/W09/papers/Irani_Spatiotemporal_Analysis_of_2015_CVPR_paper.pdf,Spatiotemporal analysis of RGB-D-T facial images for multimodal pain level recognition,2015 +38,Spain,Helen,helen,41.5008957,2.111553,"Computer Vision Center, UAB, Barcelona, Spain",edu,087002ab569e35432cdeb8e63b2c94f1abc53ea9,citation,http://openaccess.thecvf.com/content_cvpr_workshops_2015/W09/papers/Irani_Spatiotemporal_Analysis_of_2015_CVPR_paper.pdf,Spatiotemporal analysis of RGB-D-T facial images for multimodal pain level recognition,2015 +39,China,Helen,helen,39.9041999,116.4073963,Key Lab of Intelligent Information Processing of Chinese Academy of Sciences,edu,090ff8f992dc71a1125636c1adffc0634155b450,citation,https://pdfs.semanticscholar.org/090f/f8f992dc71a1125636c1adffc0634155b450.pdf,Topic-Aware Deep Auto-Encoders (TDA) for Face Alignment,2014 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Detection with Tweaked Convolutional Neural Networks,2018 +44,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,034b3f3bac663fb814336a69a9fd3514ca0082b9,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/alabort_cvpr2015.pdf,Unifying holistic and Parts-Based Deformable Model fitting,2015 +45,China,Helen,helen,39.9808333,116.34101249,Beihang University,edu,86b6afc667bb14ff4d69e7a5e8bb2454a6bbd2cd,citation,https://pdfs.semanticscholar.org/86b6/afc667bb14ff4d69e7a5e8bb2454a6bbd2cd.pdf,Attentional Alignment Networks,2018 +46,United States,Helen,helen,32.7283683,-97.11201835,University of Texas at Arlington,edu,86b6afc667bb14ff4d69e7a5e8bb2454a6bbd2cd,citation,https://pdfs.semanticscholar.org/86b6/afc667bb14ff4d69e7a5e8bb2454a6bbd2cd.pdf,Attentional Alignment Networks,2018 +47,China,Helen,helen,31.20081505,121.42840681,Shanghai Jiao Tong 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--git a/site/datasets/verified/ijb_c.csv b/site/datasets/verified/ijb_c.csv index 4b8c251d..a728f73d 100644 --- a/site/datasets/verified/ijb_c.csv +++ b/site/datasets/verified/ijb_c.csv @@ -1,6 +1,5 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year 0,,IJB-C,ijb_c,0.0,0.0,,,,main,,IARPA Janus Benchmark - C: Face Dataset and Protocol,2018 -1,United Kingdom,IJB-C,ijb_c,51.7520849,-1.2516646,Oxford University,edu,9286eab328444401a848cd2e13186840be8f0409,citation,https://arxiv.org/pdf/1807.09192.pdf,Multicolumn Networks for Face Recognition,2018 -2,United Kingdom,IJB-C,ijb_c,51.7520849,-1.2516646,Oxford University,edu,ac5ab8f71edde6d1a2129da12d051ed03a8446a1,citation,https://arxiv.org/pdf/1807.11440.pdf,Comparator Networks,2018 -3,United States,IJB-C,ijb_c,29.7207902,-95.34406271,University of Houston,edu,3b3941524d97e7f778367a1250ba1efb9205d5fc,citation,https://arxiv.org/pdf/1901.09447.pdf,Open Source Face Recognition Performance Evaluation 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a/site/datasets/verified/imdb_wiki.csv +++ b/site/datasets/verified/imdb_wiki.csv @@ -1,2 +1,7 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year -0,,IMDB,imdb_wiki,0.0,0.0,,,,main,,Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks,2016 +0,,IMDB-Wiki,imdb_wiki,0.0,0.0,,,,main,,Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks,2016 +1,Denmark,IMDB-Wiki,imdb_wiki,56.1681384,10.2030118,Aarhus University,edu,1277b1b8b609a18b94e4907d76a117c9783a5373,citation,https://arxiv.org/pdf/1808.10151.pdf,VirtualIdentity: Privacy preserving user profiling,2016 +2,United States,IMDB-Wiki,imdb_wiki,36.9915847,-122.0582771,University of California Santa Cruz,edu,1277b1b8b609a18b94e4907d76a117c9783a5373,citation,https://arxiv.org/pdf/1808.10151.pdf,VirtualIdentity: Privacy preserving user profiling,2016 +3,United States,IMDB-Wiki,imdb_wiki,47.6543238,-122.30800894,University of Washington,edu,1277b1b8b609a18b94e4907d76a117c9783a5373,citation,https://arxiv.org/pdf/1808.10151.pdf,VirtualIdentity: Privacy preserving user profiling,2016 +4,Germany,IMDB-Wiki,imdb_wiki,49.4295181,7.7513891,"German Institute of Artificial Intelligence (DFKI), Kaiserslautern, Germany",edu,775c15a5dfca426d53c634668e58dd5d3314ea89,citation,https://pdfs.semanticscholar.org/775c/15a5dfca426d53c634668e58dd5d3314ea89.pdf,Image Quality-aware Deep Networks Ensemble for Efficient Gender Recognition in the Wild,2018 +5,Germany,IMDB-Wiki,imdb_wiki,49.4253891,7.7553196,"TU Kaiserslautern, Germany",edu,775c15a5dfca426d53c634668e58dd5d3314ea89,citation,https://pdfs.semanticscholar.org/775c/15a5dfca426d53c634668e58dd5d3314ea89.pdf,Image Quality-aware Deep Networks Ensemble for Efficient Gender Recognition in the Wild,2018 diff --git a/site/datasets/verified/lfpw.csv b/site/datasets/verified/lfpw.csv index a2b6a265..ac34778e 100644 --- a/site/datasets/verified/lfpw.csv +++ b/site/datasets/verified/lfpw.csv @@ -1,2 +1,232 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year -0,,LFWP,lfpw,0.0,0.0,,,,main,,Localizing Parts of Faces Using a Consensus of Exemplars,2011 +0,,LFPW,lfpw,0.0,0.0,,,,main,,Localizing Parts of Faces Using a Consensus of Exemplars,2011 +1,China,LFPW,lfpw,28.2290209,112.99483204,"National University of Defense Technology, China",mil,ac51d9ddbd462d023ec60818bac6cdae83b66992,citation,http://downloads.hindawi.com/journals/cin/2015/709072.pdf,An Efficient Robust Eye Localization by Learning the Convolution Distribution Using Eye Template,2015 +2,United Kingdom,LFPW,lfpw,52.9387428,-1.20029569,University of Nottingham,edu,529b1f33aed49dbe025a99ac1d211c777ad881ec,citation,http://eprints.eemcs.utwente.nl/26776/01/Pantic_Fast_and_Exact_Bi-Directional_Fitting.pdf,Fast and exact bi-directional fitting of active appearance models,2015 +3,Netherlands,LFPW,lfpw,52.2380139,6.8566761,University of Twente,edu,529b1f33aed49dbe025a99ac1d211c777ad881ec,citation,http://eprints.eemcs.utwente.nl/26776/01/Pantic_Fast_and_Exact_Bi-Directional_Fitting.pdf,Fast and exact bi-directional fitting of active appearance models,2015 +4,China,LFPW,lfpw,31.32235655,121.38400941,Shanghai University,edu,63fd7a159e58add133b9c71c4b1b37b899dd646f,citation,http://wei-shen.weebly.com/uploads/2/3/8/2/23825939/posecorrection.pdf,Exemplar-Based Human Action Pose Correction,2014 +5,China,LFPW,lfpw,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,63fd7a159e58add133b9c71c4b1b37b899dd646f,citation,http://wei-shen.weebly.com/uploads/2/3/8/2/23825939/posecorrection.pdf,Exemplar-Based Human Action Pose Correction,2014 +6,United States,LFPW,lfpw,47.6423318,-122.1369302,Microsoft,company,63fd7a159e58add133b9c71c4b1b37b899dd646f,citation,http://wei-shen.weebly.com/uploads/2/3/8/2/23825939/posecorrection.pdf,Exemplar-Based Human Action Pose Correction,2014 +7,United States,LFPW,lfpw,42.3614256,-71.0812092,Microsoft Research Asia,company,63fd7a159e58add133b9c71c4b1b37b899dd646f,citation,http://wei-shen.weebly.com/uploads/2/3/8/2/23825939/posecorrection.pdf,Exemplar-Based Human Action Pose Correction,2014 +8,China,LFPW,lfpw,22.4162632,114.2109318,Chinese University of Hong Kong,edu,57ebeff9273dea933e2a75c306849baf43081a8c,citation,http://mmlab.ie.cuhk.edu.hk/archive/CNN/data/CNN_FacePoint.pdf,Deep Convolutional Network Cascade for Facial Point Detection,2013 +9,China,LFPW,lfpw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,57ebeff9273dea933e2a75c306849baf43081a8c,citation,http://mmlab.ie.cuhk.edu.hk/archive/CNN/data/CNN_FacePoint.pdf,Deep Convolutional Network Cascade for Facial Point Detection,2013 +10,Canada,LFPW,lfpw,43.0095971,-81.2737336,University of Western Ontario,edu,f7ae38a073be7c9cd1b92359131b9c8374579b13,citation,http://www.digitalimaginggroup.ca/members/Shuo/07487053.pdf,Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression,2017 +11,Canada,LFPW,lfpw,42.960348,-81.226628,"London Healthcare Sciences Centre, Ontario, Canada",edu,f7ae38a073be7c9cd1b92359131b9c8374579b13,citation,http://www.digitalimaginggroup.ca/members/Shuo/07487053.pdf,Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression,2017 +12,United Kingdom,LFPW,lfpw,55.0030632,-1.57463231,Northumbria University,edu,f7ae38a073be7c9cd1b92359131b9c8374579b13,citation,http://www.digitalimaginggroup.ca/members/Shuo/07487053.pdf,Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression,2017 +13,Canada,LFPW,lfpw,43.0012953,-81.2550455,"St. Joseph's Health Care, Ontario, Canada",edu,f7ae38a073be7c9cd1b92359131b9c8374579b13,citation,http://www.digitalimaginggroup.ca/members/Shuo/07487053.pdf,Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression,2017 +14,United States,LFPW,lfpw,37.3936717,-122.0807262,Facebook,company,dcd2ac544a8336d73e4d3d80b158477c783e1e50,citation,https://arxiv.org/pdf/1709.01591.pdf,Improving Landmark Localization with Semi-Supervised Learning,2018 +15,United States,LFPW,lfpw,37.3706254,-121.9671894,NVIDIA,company,dcd2ac544a8336d73e4d3d80b158477c783e1e50,citation,https://arxiv.org/pdf/1709.01591.pdf,Improving Landmark Localization with Semi-Supervised Learning,2018 +16,Canada,LFPW,lfpw,45.5010087,-73.6157778,University of Montreal,edu,dcd2ac544a8336d73e4d3d80b158477c783e1e50,citation,https://arxiv.org/pdf/1709.01591.pdf,Improving Landmark Localization with Semi-Supervised Learning,2018 +17,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,034b3f3bac663fb814336a69a9fd3514ca0082b9,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/alabort_cvpr2015.pdf,Unifying holistic and Parts-Based Deformable Model fitting,2015 +18,China,LFPW,lfpw,31.83907195,117.26420748,University of Science and Technology of China,edu,084bd02d171e36458f108f07265386f22b34a1ae,citation,http://7xrqgw.com1.z0.glb.clouddn.com/3000fps.pdf,Face Alignment at 3000 FPS via Regressing Local Binary Features,2014 +19,United States,LFPW,lfpw,47.6423318,-122.1369302,Microsoft,company,084bd02d171e36458f108f07265386f22b34a1ae,citation,http://7xrqgw.com1.z0.glb.clouddn.com/3000fps.pdf,Face Alignment at 3000 FPS via Regressing Local Binary Features,2014 +20,United Kingdom,LFPW,lfpw,51.24303255,-0.59001382,University of Surrey,edu,2d2e1d1f50645fe20c051339e9a0fca7b176422a,citation,https://arxiv.org/pdf/1803.05536.pdf,Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild,2018 +21,United Kingdom,LFPW,lfpw,56.1454119,-3.9205713,University of Stirling,edu,2d2e1d1f50645fe20c051339e9a0fca7b176422a,citation,https://arxiv.org/pdf/1803.05536.pdf,Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild,2018 +22,China,LFPW,lfpw,31.4854255,120.2739581,Jiangnan University,edu,2d2e1d1f50645fe20c051339e9a0fca7b176422a,citation,https://arxiv.org/pdf/1803.05536.pdf,Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild,2018 +23,China,LFPW,lfpw,30.642769,104.06751175,"Sichuan University, Chengdu",edu,2d2e1d1f50645fe20c051339e9a0fca7b176422a,citation,https://arxiv.org/pdf/1803.05536.pdf,Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild,2018 +24,Germany,LFPW,lfpw,48.48187645,9.18682404,Reutlingen University,edu,2d2e1d1f50645fe20c051339e9a0fca7b176422a,citation,https://arxiv.org/pdf/1803.05536.pdf,Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild,2018 +25,United States,LFPW,lfpw,45.57022705,-122.63709346,Concordia University,edu,266ed43dcea2e7db9f968b164ca08897539ca8dd,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/3B_037.pdf,Beyond Principal Components: Deep Boltzmann Machines for face modeling,2015 +26,United States,LFPW,lfpw,40.4441619,-79.94272826,Carnegie Mellon University,edu,266ed43dcea2e7db9f968b164ca08897539ca8dd,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/3B_037.pdf,Beyond Principal Components: Deep Boltzmann Machines for face modeling,2015 +27,United States,LFPW,lfpw,40.47913175,-74.43168868,Rutgers University,edu,3b470b76045745c0ef5321e0f1e0e6a4b1821339,citation,https://pdfs.semanticscholar.org/8e72/fa02f2d90ba31f31e0a7aa96a6d3e10a66fc.pdf,Consensus of Regression for Occlusion-Robust Facial Feature Localization,2014 +28,United States,LFPW,lfpw,37.3309307,-121.8940485,"Adobe Research, San Jose, CA",company,3b470b76045745c0ef5321e0f1e0e6a4b1821339,citation,https://pdfs.semanticscholar.org/8e72/fa02f2d90ba31f31e0a7aa96a6d3e10a66fc.pdf,Consensus of Regression for Occlusion-Robust Facial Feature Localization,2014 +29,China,LFPW,lfpw,40.0044795,116.370238,Chinese Academy of Sciences,edu,2a4153655ad1169d482e22c468d67f3bc2c49f12,citation,http://cseweb.ucsd.edu/~mkchandraker/classes/CSE291/Winter2018/Lectures/FaceAlignment.pdf,Face Alignment Across Large Poses: A 3D Solution,2016 +30,United States,LFPW,lfpw,42.718568,-84.47791571,Michigan State University,edu,2a4153655ad1169d482e22c468d67f3bc2c49f12,citation,http://cseweb.ucsd.edu/~mkchandraker/classes/CSE291/Winter2018/Lectures/FaceAlignment.pdf,Face Alignment Across Large Poses: A 3D Solution,2016 +31,United Kingdom,LFPW,lfpw,53.22853665,-0.54873472,University of Lincoln,edu,232b6e2391c064d483546b9ee3aafe0ba48ca519,citation,http://doc.utwente.nl/89696/1/Pantic_Optimization_problems_for_fast_AAM_fitting.pdf,Optimization Problems for Fast AAM Fitting in-the-Wild,2013 +32,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,232b6e2391c064d483546b9ee3aafe0ba48ca519,citation,http://doc.utwente.nl/89696/1/Pantic_Optimization_problems_for_fast_AAM_fitting.pdf,Optimization Problems for Fast AAM Fitting in-the-Wild,2013 +33,United Kingdom,LFPW,lfpw,52.9387428,-1.20029569,University of Nottingham,edu,75fd9acf5e5b7ed17c658cc84090c4659e5de01d,citation,http://eprints.nottingham.ac.uk/31442/1/tzimiro_CVPR15.pdf,Project-Out Cascaded Regression with an application to face alignment,2015 +34,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,788a7b59ea72e23ef4f86dc9abb4450efefeca41,citation,http://eprints.eemcs.utwente.nl/26840/01/Pantic_Robust_Statistical_Face_Frontalization.pdf,Robust Statistical Face Frontalization,2015 +35,Netherlands,LFPW,lfpw,52.2380139,6.8566761,University of Twente,edu,788a7b59ea72e23ef4f86dc9abb4450efefeca41,citation,http://eprints.eemcs.utwente.nl/26840/01/Pantic_Robust_Statistical_Face_Frontalization.pdf,Robust Statistical Face Frontalization,2015 +36,China,LFPW,lfpw,39.9041999,116.4073963,Key Lab of Intelligent Information Processing of Chinese Academy of Sciences,edu,090ff8f992dc71a1125636c1adffc0634155b450,citation,https://pdfs.semanticscholar.org/090f/f8f992dc71a1125636c1adffc0634155b450.pdf,Topic-Aware Deep Auto-Encoders (TDA) for Face Alignment,2014 +37,China,LFPW,lfpw,40.0044795,116.370238,Chinese Academy of Sciences,edu,090ff8f992dc71a1125636c1adffc0634155b450,citation,https://pdfs.semanticscholar.org/090f/f8f992dc71a1125636c1adffc0634155b450.pdf,Topic-Aware Deep Auto-Encoders (TDA) for Face Alignment,2014 +38,China,LFPW,lfpw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,090ff8f992dc71a1125636c1adffc0634155b450,citation,https://pdfs.semanticscholar.org/090f/f8f992dc71a1125636c1adffc0634155b450.pdf,Topic-Aware Deep Auto-Encoders (TDA) for Face Alignment,2014 +39,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,090ff8f992dc71a1125636c1adffc0634155b450,citation,https://pdfs.semanticscholar.org/090f/f8f992dc71a1125636c1adffc0634155b450.pdf,Topic-Aware Deep Auto-Encoders (TDA) for Face Alignment,2014 +40,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,e4754afaa15b1b53e70743880484b8d0736990ff,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/1-s2.0-s0262885616000147-main.pdf,300 Faces In-The-Wild Challenge: database and results,2016 +41,United Kingdom,LFPW,lfpw,52.9387428,-1.20029569,University of Nottingham,edu,e4754afaa15b1b53e70743880484b8d0736990ff,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/1-s2.0-s0262885616000147-main.pdf,300 Faces In-The-Wild Challenge: database and results,2016 +42,Netherlands,LFPW,lfpw,52.2380139,6.8566761,University of Twente,edu,e4754afaa15b1b53e70743880484b8d0736990ff,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/1-s2.0-s0262885616000147-main.pdf,300 Faces In-The-Wild Challenge: database and results,2016 +43,United States,LFPW,lfpw,38.2167565,-85.75725023,University of Louisville,edu,9a4c45e5c6e4f616771a7325629d167a38508691,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W02/papers/Mostafa_A_Facial_Features_2015_CVPR_paper.pdf,A facial features detector integrating holistic facial information and part-based model,2015 +44,Egypt,LFPW,lfpw,31.21051105,29.91314562,Alexandria University,edu,9a4c45e5c6e4f616771a7325629d167a38508691,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W02/papers/Mostafa_A_Facial_Features_2015_CVPR_paper.pdf,A facial features detector integrating holistic facial information and part-based model,2015 +45,Egypt,LFPW,lfpw,27.18794105,31.17009498,Assiut University,edu,9a4c45e5c6e4f616771a7325629d167a38508691,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W02/papers/Mostafa_A_Facial_Features_2015_CVPR_paper.pdf,A facial features detector integrating holistic facial information and part-based model,2015 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University,edu,ebd5df2b4105ba04cef4ca334fcb9bfd6ea0430c,citation,https://arxiv.org/pdf/1403.6888.pdf,Fast Localization of Facial Landmark Points,2014 +214,Croatia,LFPW,lfpw,45.801121,15.9708409,University of Zagreb,edu,ebd5df2b4105ba04cef4ca334fcb9bfd6ea0430c,citation,https://arxiv.org/pdf/1403.6888.pdf,Fast Localization of Facial Landmark Points,2014 +215,United States,LFPW,lfpw,29.736724,-95.3931825,Houston University,edu,5b2cfee6e81ef36507ebf3c305e84e9e0473575a,citation,https://arxiv.org/pdf/1704.02402.pdf,GoDP: Globally Optimized Dual Pathway deep network architecture for facial landmark localization in-the-wild,2018 +216,United States,LFPW,lfpw,43.07982815,-89.43066425,University of Wisconsin Madison,edu,fd615118fb290a8e3883e1f75390de8a6c68bfde,citation,https://pdfs.semanticscholar.org/fd61/5118fb290a8e3883e1f75390de8a6c68bfde.pdf,Joint Face Alignment with Non-parametric Shape Models,2012 +217,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College 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Re-identification by Mid-level Attribute and Part-based Identity Learning,2018 -23,Australia,Market 1501,market_1501,-35.2776999,149.118527,Australian National University,edu,c66350cbdee8c6873cc99807d342e932594aa0b9,citation,https://arxiv.org/pdf/1812.02162.pdf,Dissecting Person Re-identification from the Viewpoint of Viewpoint,2018 -24,Brazil,Market 1501,market_1501,-27.5953995,-48.6154218,University of Campinas,edu,b986a535e45751cef684a30631a74476e911a749,citation,https://arxiv.org/pdf/1807.05618.pdf,Improved Person Re-Identification Based on Saliency and Semantic Parsing with Deep Neural Network Models,2018 -25,South Korea,Market 1501,market_1501,37.26728,126.9841151,Seoul National University,edu,315df9b7dd354ae78ddf1049fb428b086eee632c,citation,https://arxiv.org/pdf/1804.07094.pdf,Part-Aligned Bilinear Representations for Person Re-identification,2018 -26,Germany,Market 1501,market_1501,48.7468939,9.0805141,Max Planck Institute for Intelligent Systems,edu,315df9b7dd354ae78ddf1049fb428b086eee632c,citation,https://arxiv.org/pdf/1804.07094.pdf,Part-Aligned Bilinear Representations for Person Re-identification,2018 -27,United States,Market 1501,market_1501,47.6423318,-122.1369302,Microsoft,company,315df9b7dd354ae78ddf1049fb428b086eee632c,citation,https://arxiv.org/pdf/1804.07094.pdf,Part-Aligned Bilinear Representations for Person Re-identification,2018 -28,Australia,Market 1501,market_1501,-33.8809651,151.20107299,University of Technology Sydney,edu,7f23a4bb0c777dd72cca7665a5f370ac7980217e,citation,https://arxiv.org/pdf/1703.07220.pdf,Improving Person Re-identification by Attribute and Identity Learning,2017 -29,United States,Market 1501,market_1501,40.1019523,-88.2271615,UIUC,edu,cc78e3f1e531342f639e4a1fc8107a7a778ae1cf,citation,https://arxiv.org/pdf/1811.10144.pdf,One Shot Domain Adaptation for Person Re-Identification,2018 -30,China,Market 1501,market_1501,22.053565,113.39913285,Jilin University,edu,4abf902cefca527f707e4f76dd4e14fcd5d47361,citation,https://arxiv.org/pdf/1811.11510.pdf,Identity Preserving Generative Adversarial Network for Cross-Domain Person Re-identification,2018 -31,China,Market 1501,market_1501,32.0565957,118.77408833,Nanjing University,edu,088e7b24bd1cf6e5922ae6c80d37439e05fadce9,citation,https://arxiv.org/pdf/1711.07155.pdf,Let Features Decide for Themselves: Feature Mask Network for Person Re-identification,2017 -32,China,Market 1501,market_1501,22.4162632,114.2109318,Chinese University of Hong Kong,edu,4f8e06ac894e9cc1eb1617a293e43448930c7d4f,citation,https://arxiv.org/pdf/1810.02936.pdf,FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification,2018 -33,China,Market 1501,market_1501,39.993008,116.329882,SenseTime,company,4f8e06ac894e9cc1eb1617a293e43448930c7d4f,citation,https://arxiv.org/pdf/1810.02936.pdf,FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification,2018 -34,United States,Market 1501,market_1501,39.3299013,-76.6205177,Johns Hopkins University,edu,4f8e06ac894e9cc1eb1617a293e43448930c7d4f,citation,https://arxiv.org/pdf/1810.02936.pdf,FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification,2018 -35,China,Market 1501,market_1501,31.83907195,117.26420748,University of Science and Technology of China,edu,4f8e06ac894e9cc1eb1617a293e43448930c7d4f,citation,https://arxiv.org/pdf/1810.02936.pdf,FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification,2018 -36,China,Market 1501,market_1501,30.19331415,120.11930822,Zhejiang University,edu,84984c7201a7e5bc8ef4c01f0a7cfbe08c2c523b,citation,https://arxiv.org/pdf/1804.06964.pdf,GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning,2018 -37,China,Market 1501,market_1501,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,c753521ba6fb06c12369d6fff814bb704c682ef5,citation,https://pdfs.semanticscholar.org/c753/521ba6fb06c12369d6fff814bb704c682ef5.pdf,Mancs: A Multi-task Attentional Network with Curriculum Sampling for Person Re-Identification,2018 -38,China,Market 1501,market_1501,22.4162632,114.2109318,Chinese University of Hong Kong,edu,0a808a17f5c86413bd552a324ee6ba180a12f46d,citation,https://arxiv.org/pdf/1808.01571.pdf,Improving Deep Visual Representation for Person Re-identification by Global and Local Image-language Association,2018 -39,China,Market 1501,market_1501,39.993008,116.329882,SenseTime,company,0a808a17f5c86413bd552a324ee6ba180a12f46d,citation,https://arxiv.org/pdf/1808.01571.pdf,Improving Deep Visual Representation for Person Re-identification by Global and Local Image-language Association,2018 -40,China,Market 1501,market_1501,34.250803,108.983693,Xi’an Jiaotong University,edu,0a808a17f5c86413bd552a324ee6ba180a12f46d,citation,https://arxiv.org/pdf/1808.01571.pdf,Improving Deep Visual Representation for Person Re-identification by Global and Local Image-language Association,2018 -41,Germany,Market 1501,market_1501,48.7468939,9.0805141,"Max Planck Instutite for Intelligent Systems, Tüebingen",edu,9db841848aa96f60e765299de4cce7abe5ccb47d,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Tang_Multiple_People_Tracking_CVPR_2017_paper.pdf,Multiple People Tracking by Lifted Multicut and Person Re-identification,2017 -42,Germany,Market 1501,market_1501,49.2578657,7.0457956,"Max-Planck-Institut für Informatik, Saarbrücken, Germany",edu,9db841848aa96f60e765299de4cce7abe5ccb47d,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Tang_Multiple_People_Tracking_CVPR_2017_paper.pdf,Multiple People Tracking by Lifted Multicut and Person Re-identification,2017 -43,France,Market 1501,market_1501,48.8457981,2.3567236,Pierre and Marie Curie University,edu,231a12de5dedddf1184ae9caafbc4a954ce584c3,citation,https://pdfs.semanticscholar.org/231a/12de5dedddf1184ae9caafbc4a954ce584c3.pdf,Closed and Open World Multi-shot Person Re-identification. (Ré-identification de personnes à partir de multiples images dans le cadre de bases d'identités fermées et ouvertes),2017 -44,China,Market 1501,market_1501,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,07dead6b98379faac1cf0b2cb34a5db842ab9de9,citation,https://arxiv.org/pdf/1711.10658.pdf,Deep-Person: Learning Discriminative Deep Features for Person Re-Identification,2017 -45,Canada,Market 1501,market_1501,46.7817463,-71.2747424,Université Laval,edu,a743127b44397b7a017a65a7ad52d0d7ccb4db93,citation,https://arxiv.org/pdf/1804.10094.pdf,Domain Adaptation Through Synthesis for Unsupervised Person Re-identification,2018 -46,Australia,Market 1501,market_1501,-35.2776999,149.118527,Australian National University,edu,12d62f1360587fdecee728e6c509acc378f38dc9,citation,https://arxiv.org/pdf/1805.06118.pdf,Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification,2018 -47,China,Market 1501,market_1501,32.20541,118.726956,Nanjing University of Information Science & Technology,edu,12d62f1360587fdecee728e6c509acc378f38dc9,citation,https://arxiv.org/pdf/1805.06118.pdf,Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification,2018 -48,Australia,Market 1501,market_1501,-33.8809651,151.20107299,University of Technology Sydney,edu,12d62f1360587fdecee728e6c509acc378f38dc9,citation,https://arxiv.org/pdf/1805.06118.pdf,Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification,2018 -49,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,14b3a7aa61c15fd9cab0a4d8bc2a205a89fb572e,citation,https://arxiv.org/pdf/1807.11206.pdf,Hard-Aware Point-to-Set Deep Metric for Person Re-identification,2018 -50,China,Market 1501,market_1501,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,14b3a7aa61c15fd9cab0a4d8bc2a205a89fb572e,citation,https://arxiv.org/pdf/1807.11206.pdf,Hard-Aware Point-to-Set Deep Metric for Person Re-identification,2018 -51,China,Market 1501,market_1501,22.304572,114.17976285,Hong Kong Polytechnic University,edu,fea0895326b663bf72be89151a751362db8ae881,citation,https://arxiv.org/pdf/1804.08866.pdf,Homocentric Hypersphere Feature Embedding for Person Re-identification,2018 -52,China,Market 1501,market_1501,22.4162632,114.2109318,Chinese University of Hong Kong,edu,0c769c19d894e0dbd6eb314781dc1db3c626df57,citation,https://arxiv.org/pdf/1604.01850.pdf,Joint Detection and Identification Feature Learning for Person Search,2017 -53,China,Market 1501,market_1501,39.993008,116.329882,SenseTime,company,0c769c19d894e0dbd6eb314781dc1db3c626df57,citation,https://arxiv.org/pdf/1604.01850.pdf,Joint Detection and Identification Feature Learning for Person Search,2017 -54,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,0c769c19d894e0dbd6eb314781dc1db3c626df57,citation,https://arxiv.org/pdf/1604.01850.pdf,Joint Detection and Identification Feature Learning for Person Search,2017 -55,China,Market 1501,market_1501,30.209484,120.220912,"Hikvision Digital Technology Co., Ltd.",company,ed3991046e6dfba0c5cebdbbe914cc3aa06d0235,citation,https://arxiv.org/pdf/1812.06576.pdf,Learning Incremental Triplet Margin for Person Re-identification,2019 -56,China,Market 1501,market_1501,24.4399419,118.09301781,Xiamen University,edu,e746447afc4898713a0bcf2bb560286eb4d20019,citation,https://arxiv.org/pdf/1811.02074.pdf,Leveraging Virtual and Real Person for Unsupervised Person Re-identification,2018 -57,United States,Market 1501,market_1501,40.4441619,-79.94272826,Carnegie Mellon University,edu,76fb9e2963928bf8e940944d45c13d52db947702,citation,https://arxiv.org/pdf/1710.00478.pdf,Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification,2017 -58,China,Market 1501,market_1501,30.19331415,120.11930822,Zhejiang University,edu,76fb9e2963928bf8e940944d45c13d52db947702,citation,https://arxiv.org/pdf/1710.00478.pdf,Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification,2017 -59,Italy,Market 1501,market_1501,45.434532,12.326197,"DAIS, Università Ca’ Foscari, Venice, Italy",edu,bee609ea6e71aba9b449731242efdb136d556222,citation,https://arxiv.org/pdf/1706.06196.pdf,Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets,2017 -60,Italy,Market 1501,market_1501,45.4377672,12.321807,University Iuav of Venice,edu,bee609ea6e71aba9b449731242efdb136d556222,citation,https://arxiv.org/pdf/1706.06196.pdf,Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets,2017 -61,India,Market 1501,market_1501,13.0222347,77.56718325,Indian Institute of Science Bangalore,edu,317f5a56519df95884cce81cfba180ee3adaf5a5,citation,https://arxiv.org/pdf/1807.07295.pdf,Operator-In-The-Loop Deep Sequential Multi-camera Feature Fusion for Person Re-identification,2018 -62,Spain,Market 1501,market_1501,41.5007811,2.11143663,Universitat Autònoma de Barcelona,edu,388b03244e7cdf28c750d7f6d4b4eb64219c3e7a,citation,https://arxiv.org/pdf/1812.02937.pdf,Optimizing Speed/Accuracy Trade-Off for Person Re-identification via Knowledge Distillation,2018 -63,China,Market 1501,market_1501,39.10041,121.821932,Dalian University,edu,ae5983048e59a339c77fee89e9279a4a787ba985,citation,https://arxiv.org/pdf/1705.02145.pdf,Part-Based Deep Hashing for Large-Scale Person Re-Identification,2017 -64,Australia,Market 1501,market_1501,-33.8809651,151.20107299,University of Technology Sydney,edu,ae5983048e59a339c77fee89e9279a4a787ba985,citation,https://arxiv.org/pdf/1705.02145.pdf,Part-Based Deep Hashing for Large-Scale Person Re-Identification,2017 -65,United States,Market 1501,market_1501,29.58333105,-98.61944505,University of Texas at San Antonio,edu,ae5983048e59a339c77fee89e9279a4a787ba985,citation,https://arxiv.org/pdf/1705.02145.pdf,Part-Based Deep Hashing for Large-Scale Person Re-Identification,2017 -66,Germany,Market 1501,market_1501,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,9812542cae5a470ea601e7c3a871331694105093,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w17/papers/Schumann_Person_Re-Identification_by_CVPR_2017_paper.pdf,Person Re-identification by Deep Learning Attribute-Complementary Information,2017 -67,China,Market 1501,market_1501,34.250803,108.983693,Xi’an Jiaotong University,edu,e1dcc3946fa750da4bc05b1154b6321db163ad62,citation,http://gr.xjtu.edu.cn/c/document_library/get_file?folderId=1540809&name=DLFE-80365.pdf,Similarity Learning with Spatial Constraints for Person Re-identification,2016 -68,United States,Market 1501,market_1501,42.366183,-71.092455,Mitsubishi Electric Research Laboratories,company,bb4f83458976755e9310b241a689c8d21b481238,citation,http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w23/Jones_Improving_Face_Verification_ICCV_2017_paper.pdf,Improving Face Verification and Person Re-Identification Accuracy Using Hyperplane Similarity,2017 -69,United States,Market 1501,market_1501,42.3383668,-71.08793524,Northeastern University,edu,32dc3e04dea2306ec34ca3f39db27a2b0a49e0a1,citation,http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w21/Gou_moM_Mean_of_ICCV_2017_paper.pdf,moM: Mean of Moments Feature for Person Re-identification,2017 -70,United States,Market 1501,market_1501,42.3383668,-71.08793524,Northeastern University,edu,0deca8c53adcc13d8da72050d9a4b638da52264b,citation,https://pdfs.semanticscholar.org/0dec/a8c53adcc13d8da72050d9a4b638da52264b.pdf,"A Comprehensive Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets",2016 -71,Australia,Market 1501,market_1501,-33.8809651,151.20107299,University of Technology Sydney,edu,193089d56758ab88391d846edd08d359b1f9a863,citation,https://arxiv.org/pdf/1611.05666.pdf,A Discriminatively Learned CNN Embedding for Person Reidentification,2017 -72,China,Market 1501,market_1501,31.821994,117.28059,"USTC, Hefei, China",edu,83c19722450e8f7dcb89dabb38265f19efafba27,citation,https://arxiv.org/pdf/1803.02983.pdf,A framework with updateable joint images re-ranking for Person Re-identification.,2018 -73,Singapore,Market 1501,market_1501,1.3484104,103.68297965,Nanyang Technological University,edu,6bb8a5f9e2ddf1bdcd42aa7212eb0499992c1e9e,citation,https://arxiv.org/pdf/1607.08381.pdf,A Siamese Long Short-Term Memory Architecture for Human Re-Identification,2016 -74,China,Market 1501,market_1501,40.00229045,116.32098908,Tsinghua University,edu,6bb8a5f9e2ddf1bdcd42aa7212eb0499992c1e9e,citation,https://arxiv.org/pdf/1607.08381.pdf,A Siamese Long Short-Term Memory Architecture for Human Re-Identification,2016 -75,Australia,Market 1501,market_1501,-33.88890695,151.18943366,University of Sydney,edu,6bb8a5f9e2ddf1bdcd42aa7212eb0499992c1e9e,citation,https://arxiv.org/pdf/1607.08381.pdf,A Siamese Long Short-Term Memory Architecture for Human Re-Identification,2016 -76,Germany,Market 1501,market_1501,49.4109266,8.6979529,Heidelberg University,edu,5fdb3533152f9862e3e4c2282cd5f1400af18956,citation,https://arxiv.org/pdf/1804.04694.pdf,A Variational U-Net for Conditional Appearance and Shape Generation,2018 -77,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,635efc8bddec1cf94b1ee4951e4d216331758422,citation,https://arxiv.org/pdf/1812.00914.pdf,Accelerating Large Scale Knowledge Distillation via Dynamic Importance Sampling,2018 -78,Canada,Market 1501,market_1501,53.5238572,-113.52282665,University of Alberta,edu,635efc8bddec1cf94b1ee4951e4d216331758422,citation,https://arxiv.org/pdf/1812.00914.pdf,Accelerating Large Scale Knowledge Distillation via Dynamic Importance Sampling,2018 -79,China,Market 1501,market_1501,39.9808333,116.34101249,Beihang University,edu,19be4580df2e76b70a39af6e749bf189e1ca3975,citation,https://arxiv.org/pdf/1803.10914.pdf,Adversarial Binary Coding for Efficient Person Re-identification,2018 -80,United Kingdom,Market 1501,market_1501,51.7534538,-1.25400997,University of Oxford,edu,47f4dec5f733e933c8b9a8fdcda9419741f2bf62,citation,https://arxiv.org/pdf/1901.10650.pdf,Adversarial Metric Attack for Person Re-identification,2019 -81,United States,Market 1501,market_1501,39.3299013,-76.6205177,Johns Hopkins University,edu,47f4dec5f733e933c8b9a8fdcda9419741f2bf62,citation,https://arxiv.org/pdf/1901.10650.pdf,Adversarial Metric Attack for Person Re-identification,2019 -82,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,eee4cc389ca85d23700cba9627fa11e5ee65d740,citation,https://arxiv.org/pdf/1807.10482.pdf,Adversarial Open-World Person Re-Identification,2018 -83,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,7969cc315bbafcd38a637eb8cd5d45ba897be319,citation,https://arxiv.org/pdf/1604.07807.pdf,An enhanced deep feature representation for person re-identification,2016 -84,China,Market 1501,market_1501,22.3874201,114.2082222,Hong Kong Baptist University,edu,c0e9d06383442d89426808d723ca04586db91747,citation,https://pdfs.semanticscholar.org/c0e9/d06383442d89426808d723ca04586db91747.pdf,Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification,2018 -85,China,Market 1501,market_1501,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,c0e9d06383442d89426808d723ca04586db91747,citation,https://pdfs.semanticscholar.org/c0e9/d06383442d89426808d723ca04586db91747.pdf,Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification,2018 -86,Japan,Market 1501,market_1501,35.6924853,139.7582533,"National Institute of Informatics, Japan",edu,c0e9d06383442d89426808d723ca04586db91747,citation,https://pdfs.semanticscholar.org/c0e9/d06383442d89426808d723ca04586db91747.pdf,Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification,2018 -87,China,Market 1501,market_1501,40.00229045,116.32098908,Tsinghua University,edu,5e1514de6d20d3b1d148d6925edc89a6c891ce47,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Lin_Consistent-Aware_Deep_Learning_CVPR_2017_paper.pdf,Consistent-Aware Deep Learning for Person Re-identification in a Camera Network,2017 -88,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,bff1e1ecf00c37ec91edc7c5c85c1390726c3687,citation,https://arxiv.org/pdf/1511.07545.pdf,Constrained Deep Metric Learning for Person Re-identification,2015 -89,China,Market 1501,market_1501,40.00229045,116.32098908,Tsinghua University,edu,6ce6da7a6b2d55fac604d986595ba6979580393b,citation,https://arxiv.org/pdf/1611.06026.pdf,Cross Domain Knowledge Transfer for Person Re-identification,2016 -90,China,Market 1501,market_1501,23.0502042,113.39880323,South China University of Technology,edu,c249f0aa1416c51bf82be5bb47cbeb8aac6dee35,citation,https://arxiv.org/pdf/1806.04533.pdf,Cross-Dataset Person Re-identification Using Similarity Preserved Generative Adversarial Networks,2018 -91,China,Market 1501,market_1501,40.00229045,116.32098908,Tsinghua University,edu,4f83ef534c164bd7fbd1e71fe6a3d09a30326b26,citation,https://arxiv.org/pdf/1810.10221.pdf,Cross-Resolution Person Re-identification with Deep Antithetical Learning,2018 -92,China,Market 1501,market_1501,28.16437,112.93251,Central South University,edu,a6bc69831dea3efc5804b8ab65cf5a06688ddae0,citation,https://arxiv.org/pdf/1801.01760.pdf,Crossing Generative Adversarial Networks for Cross-View Person Re-identification,2018 -93,Australia,Market 1501,market_1501,-27.49741805,153.01316956,University of Queensland,edu,a6bc69831dea3efc5804b8ab65cf5a06688ddae0,citation,https://arxiv.org/pdf/1801.01760.pdf,Crossing Generative Adversarial Networks for Cross-View Person Re-identification,2018 -94,Australia,Market 1501,market_1501,-33.91758275,151.23124025,University of New South Wales,edu,a6bc69831dea3efc5804b8ab65cf5a06688ddae0,citation,https://arxiv.org/pdf/1801.01760.pdf,Crossing Generative Adversarial Networks for Cross-View Person Re-identification,2018 -95,China,Market 1501,market_1501,39.98177,116.330086,National Laboratory of Pattern Recognition,edu,34b8e675d4651db45e484da34f3c415c60ef3ea2,citation,https://arxiv.org/pdf/1707.01220.pdf,DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer,2018 -96,China,Market 1501,market_1501,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,34b8e675d4651db45e484da34f3c415c60ef3ea2,citation,https://arxiv.org/pdf/1707.01220.pdf,DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer,2018 -97,Australia,Market 1501,market_1501,-27.49741805,153.01316956,University of Queensland,edu,d1ba33106567c880bf99daba2bd31fe88df4ecba,citation,https://arxiv.org/pdf/1706.03160.pdf,Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification,2018 -98,Australia,Market 1501,market_1501,-33.91758275,151.23124025,University of New South Wales,edu,d1ba33106567c880bf99daba2bd31fe88df4ecba,citation,https://arxiv.org/pdf/1706.03160.pdf,Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification,2018 -99,Australia,Market 1501,market_1501,-33.88890695,151.18943366,University of Sydney,edu,d1ba33106567c880bf99daba2bd31fe88df4ecba,citation,https://arxiv.org/pdf/1706.03160.pdf,Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification,2018 -100,China,Market 1501,market_1501,39.9922379,116.30393816,Peking University,edu,2788f382e4396290acfc8b21df45cc811586e66e,citation,https://arxiv.org/pdf/1605.03259.pdf,Deep Attributes Driven Multi-Camera Person Re-identification,2016 -101,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,2788f382e4396290acfc8b21df45cc811586e66e,citation,https://arxiv.org/pdf/1605.03259.pdf,Deep Attributes Driven Multi-Camera Person Re-identification,2016 -102,United States,Market 1501,market_1501,29.58333105,-98.61944505,University of Texas at San Antonio,edu,2788f382e4396290acfc8b21df45cc811586e66e,citation,https://arxiv.org/pdf/1605.03259.pdf,Deep Attributes Driven Multi-Camera Person Re-identification,2016 -103,United States,Market 1501,market_1501,40.4441619,-79.94272826,Carnegie Mellon University,edu,63e1ce7de0fdbce6e03d25b5001c670c30139aa8,citation,https://arxiv.org/pdf/1707.07791.pdf,Deep Feature Learning via Structured Graph Laplacian Embedding for Person Re-Identification,2018 -104,China,Market 1501,market_1501,34.250803,108.983693,Xi’an Jiaotong University,edu,63e1ce7de0fdbce6e03d25b5001c670c30139aa8,citation,https://arxiv.org/pdf/1707.07791.pdf,Deep Feature Learning via Structured Graph Laplacian Embedding for Person Re-Identification,2018 -105,United Kingdom,Market 1501,market_1501,51.5247272,-0.03931035,Queen Mary University of London,edu,e3e36ccd836458d51676789fb133b092d42dac16,citation,https://arxiv.org/pdf/1610.05047.pdf,Deep learning prototype domains for person re-identification,2017 -106,Australia,Market 1501,market_1501,-34.9189226,138.60423668,University of Adelaide,edu,63ac85ec1bff6009bb36f0b24ef189438836bc91,citation,https://arxiv.org/pdf/1606.01595.pdf,Deep linear discriminant analysis on fisher networks: A hybrid architecture for person re-identification,2017 -107,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,9a81f46fcf8c6c0efbe34649552b5056ce419a3d,citation,https://arxiv.org/pdf/1705.03332.pdf,Deep person re-identification with improved embedding and efficient training,2017 -108,China,Market 1501,market_1501,34.250803,108.983693,Xi’an Jiaotong University,edu,6562c40932ea734f46e5068555fbf3a185a345de,citation,https://arxiv.org/pdf/1707.00409.pdf,Deep Ranking Model by Large Adaptive Margin Learning for Person Re-identification,2018 -109,United Kingdom,Market 1501,market_1501,51.5247272,-0.03931035,Queen Mary University of London,edu,35b9af6057801fb2f28881840c8427c9cf648757,citation,https://arxiv.org/pdf/1707.02785.pdf,Deep Reinforcement Learning Attention Selection For Person Re-Identification,2017 -110,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,8961677300a9ee30ca51e1a3cf9815b4a162265b,citation,https://arxiv.org/pdf/1707.00798.pdf,Deep Representation Learning with Part Loss for Person Re-Identification,2017 -111,China,Market 1501,market_1501,39.9922379,116.30393816,Peking University,edu,8961677300a9ee30ca51e1a3cf9815b4a162265b,citation,https://arxiv.org/pdf/1707.00798.pdf,Deep Representation Learning with Part Loss for Person Re-Identification,2017 -112,United States,Market 1501,market_1501,29.58333105,-98.61944505,University of Texas at San Antonio,edu,8961677300a9ee30ca51e1a3cf9815b4a162265b,citation,https://arxiv.org/pdf/1707.00798.pdf,Deep Representation Learning with Part Loss for Person Re-Identification,2017 -113,China,Market 1501,market_1501,34.250803,108.983693,Xi’an Jiaotong University,edu,123286df95d93600f4281c60a60c69121c6440c7,citation,https://arxiv.org/pdf/1710.05711.pdf,Deep Self-Paced Learning for Person Re-Identification,2018 -114,China,Market 1501,market_1501,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,d8949f4f4085b15978e20ed7c5c34a080dd637f2,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w17/papers/Chen_Deep_Spatial-Temporal_Fusion_CVPR_2017_paper.pdf,Deep Spatial-Temporal Fusion Network for Video-Based Person Re-identification,2017 -115,China,Market 1501,market_1501,39.9922379,116.30393816,Peking University,edu,31c0968fb5f587918f1c49bf7fa51453b3e89cf7,citation,https://arxiv.org/pdf/1611.05244.pdf,Deep Transfer Learning for Person Re-Identification,2018 -116,China,Market 1501,market_1501,30.19331415,120.11930822,Zhejiang University,edu,50bf4f77d8b66ec838ad59a869630eace7e0e4a7,citation,https://arxiv.org/pdf/1707.07256.pdf,Deeply-Learned Part-Aligned Representations for Person Re-identification,2017 -117,United States,Market 1501,market_1501,47.6423318,-122.1369302,Microsoft,company,50bf4f77d8b66ec838ad59a869630eace7e0e4a7,citation,https://arxiv.org/pdf/1707.07256.pdf,Deeply-Learned Part-Aligned Representations for Person Re-identification,2017 -118,China,Market 1501,market_1501,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,d497543834f23f72f4092252b613bf3adaefc606,citation,https://arxiv.org/pdf/1805.07698.pdf,Density-Adaptive Kernel based Re-Ranking for Person Re-Identification,2018 -119,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,19a0f34440c25323544b90d9d822a212bfed0eb5,citation,https://arxiv.org/pdf/1901.10100.pdf,Discovering Underlying Person Structure Pattern with Relative Local Distance for Person Re-identification,2019 -120,China,Market 1501,market_1501,34.250803,108.983693,Xi’an Jiaotong University,edu,7b2e0c87aece7ff1404ef2034d4c5674770301b2,citation,https://arxiv.org/pdf/1807.01455.pdf,Discriminative Feature Learning with Foreground Attention for Person Re-Identification,2018 -121,China,Market 1501,market_1501,31.2284923,121.40211389,East China Normal University,edu,0353fe24ecd237f4d9ae4dbc277a6a67a69ce8ed,citation,https://pdfs.semanticscholar.org/0353/fe24ecd237f4d9ae4dbc277a6a67a69ce8ed.pdf,Discriminative Feature Representation for Person Re-identification by Batch-contrastive Loss,2018 -122,United Kingdom,Market 1501,market_1501,55.94951105,-3.19534913,University of Edinburgh,edu,68621721705e3115355268450b4b447362e455c6,citation,https://arxiv.org/pdf/1812.02605.pdf,Disjoint Label Space Transfer Learning with Common Factorised Space,2019 -123,China,Market 1501,market_1501,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,d950af49c44bc5d9f4a5cc1634e606004790b1e5,citation,https://arxiv.org/pdf/1708.04169.pdf,Divide and Fuse: A Re-ranking Approach for Person Re-identification,2017 -124,United Arab Emirates,Market 1501,market_1501,24.453884,54.3773438,New York University Abu Dhabi,edu,a94b832facb57ea37b18927b13d2dd4c5fa3a9ea,citation,https://arxiv.org/pdf/1803.09733.pdf,Domain transfer convolutional attribute embedding,2018 -125,China,Market 1501,market_1501,39.9106327,116.3356321,Chinese Academy of Science,edu,7f8d4494aba2a2b11a88bf7de4b8879b047dd69b,citation,http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_Easy_Identification_From_CVPR_2018_paper.pdf,Easy Identification from Better Constraints: Multi-shot Person Re-identification from Reference Constraints,2018 -126,United States,Market 1501,market_1501,42.0551164,-87.67581113,Northwestern University,edu,7f8d4494aba2a2b11a88bf7de4b8879b047dd69b,citation,http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_Easy_Identification_From_CVPR_2018_paper.pdf,Easy Identification from Better Constraints: Multi-shot Person Re-identification from Reference Constraints,2018 -127,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,ca1db9dc493a045e3fadf8d8209eaa4311bbdc70,citation,https://arxiv.org/pdf/1709.09304.pdf,Effective Image Retrieval via Multilinear Multi-index Fusion,2017 -128,United States,Market 1501,market_1501,29.58333105,-98.61944505,University of Texas at San Antonio,edu,ca1db9dc493a045e3fadf8d8209eaa4311bbdc70,citation,https://arxiv.org/pdf/1709.09304.pdf,Effective Image Retrieval via Multilinear Multi-index Fusion,2017 -129,United States,Market 1501,market_1501,42.0551164,-87.67581113,Northwestern University,edu,00bf7bcf31ee71f5f325ca5307883157ba3d580f,citation,http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhou_Efficient_Online_Local_ICCV_2017_paper.pdf,Efficient Online Local Metric Adaptation via Negative Samples for Person Re-identification,2017 -130,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,febff0f6faa8dde77848845e4b3e6f6c91180d33,citation,https://arxiv.org/pdf/1611.00137.pdf,Embedding Deep Metric for Person Re-identication A Study Against Large Variations,2016 -131,China,Market 1501,market_1501,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,febff0f6faa8dde77848845e4b3e6f6c91180d33,citation,https://arxiv.org/pdf/1611.00137.pdf,Embedding Deep Metric for Person Re-identication A Study Against Large Variations,2016 -132,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,febff0f6faa8dde77848845e4b3e6f6c91180d33,citation,https://arxiv.org/pdf/1611.00137.pdf,Embedding Deep Metric for Person Re-identication A Study Against Large Variations,2016 -133,China,Market 1501,market_1501,31.846918,117.29053367,Hefei University of Technology,edu,fd0e1fecf7e72318a4c53463fd5650720df40281,citation,https://arxiv.org/pdf/1606.04404.pdf,End-to-End Comparative Attention Networks for Person Re-Identification,2017 -134,China,Market 1501,market_1501,39.9041999,116.4073963,"Qihoo 360 AI Institute, Beijing, China",edu,fd0e1fecf7e72318a4c53463fd5650720df40281,citation,https://arxiv.org/pdf/1606.04404.pdf,End-to-End Comparative Attention Networks for Person Re-Identification,2017 -135,Singapore,Market 1501,market_1501,1.2966426,103.7763939,Singapore / National University of Singapore,edu,fd0e1fecf7e72318a4c53463fd5650720df40281,citation,https://arxiv.org/pdf/1606.04404.pdf,End-to-End Comparative Attention Networks for Person Re-Identification,2017 -136,China,Market 1501,market_1501,31.970907,118.8128989,PLA Army Engineering University,edu,c8ac121e9c4eb9964be9c5713f22a95c1c3b57e9,citation,https://arxiv.org/pdf/1901.05798.pdf,Ensemble Feature for Person Re-Identification,2019 -137,Spain,Market 1501,market_1501,41.5008957,2.111553,Autonomous University of Barcelona,edu,fe54a5a10288648f3bd0a71b053cdb896716b552,citation,https://arxiv.org/pdf/1804.04419.pdf,"Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification",2018 -138,Spain,Market 1501,market_1501,41.40657415,2.1945341,Universitat Oberta de Catalunya,edu,fe54a5a10288648f3bd0a71b053cdb896716b552,citation,https://arxiv.org/pdf/1804.04419.pdf,"Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification",2018 -139,Spain,Market 1501,market_1501,41.3868913,2.16352385,University of Barcelona,edu,fe54a5a10288648f3bd0a71b053cdb896716b552,citation,https://arxiv.org/pdf/1804.04419.pdf,"Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification",2018 -140,United States,Market 1501,market_1501,33.2416008,-111.8839083,Intel,company,6a9c3011b5092daa1d0cacda23f20ca4ae74b902,citation,https://arxiv.org/pdf/1812.02465.pdf,Fast and Accurate Person Re-Identification with RMNet.,2018 -141,China,Market 1501,market_1501,39.9808333,116.34101249,Beihang University,edu,91cc3981c304227e13ae151a43fbb124419bc0ce,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Chen_Fast_Person_Re-Identification_CVPR_2017_paper.pdf,Fast Person Re-identification via Cross-Camera Semantic Binary Transformation,2017 -142,United Kingdom,Market 1501,market_1501,52.6221571,1.2409136,University of East Anglia,edu,91cc3981c304227e13ae151a43fbb124419bc0ce,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Chen_Fast_Person_Re-Identification_CVPR_2017_paper.pdf,Fast Person Re-identification via Cross-Camera Semantic Binary Transformation,2017 -143,Singapore,Market 1501,market_1501,1.3484104,103.68297965,Nanyang Technological University,edu,6123e52c1a560c88817d8720e05fbff8565271fb,citation,https://arxiv.org/pdf/1607.08378.pdf,Gated Siamese Convolutional Neural Network Architecture for Human Re-Identification,2016 -144,United States,Market 1501,market_1501,38.5336349,-121.79077264,"University of California, Davis",edu,79c959833ff49f860e20b6654dbf4d6acdee0230,citation,https://arxiv.org/pdf/1811.02545.pdf,Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond,2018 -145,China,Market 1501,market_1501,30.19331415,120.11930822,Zhejiang University,edu,79c959833ff49f860e20b6654dbf4d6acdee0230,citation,https://arxiv.org/pdf/1811.02545.pdf,Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond,2018 -146,Taiwan,Market 1501,market_1501,25.0410728,121.6147562,Institute of Information Science,edu,3cbb4cf942ee95d14505c0f83a48ba224abdd00b,citation,https://arxiv.org/pdf/1712.06820.pdf,Hierarchical Cross Network for Person Re-identification,2017 -147,Japan,Market 1501,market_1501,33.8941968,130.8394083,Kyushu Institute of Technology,edu,7da961cb039b1a01cad9b78d93bdfe2a69ed3ccf,citation,https://arxiv.org/pdf/1706.04318.pdf,Hierarchical Gaussian Descriptors with Application to Person Re-Identification,2017 -148,Japan,Market 1501,market_1501,33.59914655,130.22359848,Kyushu University,edu,7da961cb039b1a01cad9b78d93bdfe2a69ed3ccf,citation,https://arxiv.org/pdf/1706.04318.pdf,Hierarchical Gaussian Descriptors with Application to Person Re-Identification,2017 -149,Japan,Market 1501,market_1501,35.9020448,139.93622009,University of Tokyo,edu,7da961cb039b1a01cad9b78d93bdfe2a69ed3ccf,citation,https://arxiv.org/pdf/1706.04318.pdf,Hierarchical Gaussian Descriptors with Application to Person Re-Identification,2017 -150,United States,Market 1501,market_1501,42.3504253,-71.10056114,Boston University,edu,7c25ed788da1f5f61d8d1da23dd319dfb4e5ac2d,citation,https://arxiv.org/pdf/1612.01345.pdf,Human-In-The-Loop Person Re-Identification,2016 -151,United Kingdom,Market 1501,market_1501,51.5247272,-0.03931035,Queen Mary University of London,edu,7c25ed788da1f5f61d8d1da23dd319dfb4e5ac2d,citation,https://arxiv.org/pdf/1612.01345.pdf,Human-In-The-Loop Person Re-Identification,2016 -152,United Kingdom,Market 1501,market_1501,55.378051,-3.435973,"Vision Semantics Ltd, UK",edu,7c25ed788da1f5f61d8d1da23dd319dfb4e5ac2d,citation,https://arxiv.org/pdf/1612.01345.pdf,Human-In-The-Loop Person Re-Identification,2016 -153,Australia,Market 1501,market_1501,-37.9062737,145.1319449,"CSIRO, Australia",edu,53492cb14b33a26b10c91102daa2d5a2a3ed069d,citation,https://arxiv.org/pdf/1806.07592.pdf,Improving Online Multiple Object tracking with Deep Metric Learning,2018 -154,Germany,Market 1501,market_1501,50.7791703,6.06728733,RWTH Aachen University,edu,a3d11e98794896849ab2304a42bf83e2979e5fb5,citation,https://arxiv.org/pdf/1703.07737.pdf,In Defense of the Triplet Loss for Person Re-Identification,2017 -155,China,Market 1501,market_1501,34.250803,108.983693,Xi’an Jiaotong University,edu,cb8567f074573a0d66d50e75b5a91df283ccd503,citation,https://arxiv.org/pdf/1708.05512.pdf,Large Margin Learning in Set-to-Set Similarity Comparison for Person Reidentification,2018 -156,United Kingdom,Market 1501,market_1501,51.5247272,-0.03931035,Queen Mary University of London,edu,207e0ac5301a3c79af862951b70632ed650f74f7,citation,https://arxiv.org/pdf/1603.02139.pdf,Learning a Discriminative Null Space for Person Re-identification,2016 -157,China,Market 1501,market_1501,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,34cf90fcbf83025666c5c86ec30ac58b632b27b0,citation,https://arxiv.org/pdf/1710.06555.pdf,Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification,2017 -158,United States,Market 1501,market_1501,40.007581,-105.2659417,University of Colorado,edu,ad3be20fe0106d80c567def71fef01146564df4b,citation,https://arxiv.org/pdf/1802.05312.pdf,Learning Deep Disentangled Embeddings With the F-Statistic Loss,2018 -159,Russia,Market 1501,market_1501,55.6846566,37.3407539,"Skolkovo Institute of Science and Technology, Skolkovo, Moscow",edu,218603147709344d4ff66625d83603deee2854bf,citation,https://arxiv.org/pdf/1611.00822.pdf,Learning Deep Embeddings with Histogram Loss,2016 -160,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,489decd84645b77d31001d17a66abb92bb96c731,citation,https://arxiv.org/pdf/1803.11333.pdf,Learning View-Specific Deep Networks for Person Re-Identification,2018 -161,Norway,Market 1501,market_1501,63.419499,10.4020771,Norwegian University of Science and Technology,edu,2102915d0c51cfda4d85133bd593ecb9508fa4bb,citation,https://arxiv.org/pdf/1701.03153.pdf,Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification,2018 -162,Italy,Market 1501,market_1501,41.9037626,12.5144384,Sapienza University of Rome,edu,2102915d0c51cfda4d85133bd593ecb9508fa4bb,citation,https://arxiv.org/pdf/1701.03153.pdf,Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification,2018 -163,Italy,Market 1501,market_1501,45.437398,11.003376,University of Verona,edu,2102915d0c51cfda4d85133bd593ecb9508fa4bb,citation,https://arxiv.org/pdf/1701.03153.pdf,Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification,2018 -164,China,Market 1501,market_1501,40.00229045,116.32098908,Tsinghua University,edu,c0387e788a52f10bf35d4d50659cfa515d89fbec,citation,https://pdfs.semanticscholar.org/c038/7e788a52f10bf35d4d50659cfa515d89fbec.pdf,MARS: A Video Benchmark for Large-Scale Person Re-Identification,2016 -165,China,Market 1501,market_1501,40.00229045,116.32098908,Tsinghua University,edu,1e83e2abcb258cd62b160e3f31a490a6bc042e83,citation,https://arxiv.org/pdf/1704.02492.pdf,Metric Learning in Codebook Generation of Bag-of-Words for Person Re-identification,2017 -166,China,Market 1501,market_1501,31.8405068,117.2638057,Hefei University,edu,7c9d8593cdf2f8ba9f27906b2b5827b145631a0b,citation,https://arxiv.org/pdf/1810.08534.pdf,MsCGAN: Multi-scale Conditional Generative Adversarial Networks for Person Image Generation,2018 -167,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,1565bf91f8fdfe5f5168a5050b1418debc662151,citation,https://arxiv.org/pdf/1711.03368.pdf,One-pass Person Re-identification by Sketch Online Discriminant Analysis,2017 -168,Australia,Market 1501,market_1501,-33.8809651,151.20107299,University of Technology Sydney,edu,592e555ebe4bd2d821230e7074d7e9626af716b0,citation,https://arxiv.org/pdf/1809.02681.pdf,Open Set Adversarial Examples,2018 -169,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,fcaa88dcb1a440ef09c4e5d724ed209bfc5d3367,citation,https://arxiv.org/pdf/1811.09928.pdf,PCGAN: Partition-Controlled Human Image Generation,2019 -170,China,Market 1501,market_1501,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,fcaa88dcb1a440ef09c4e5d724ed209bfc5d3367,citation,https://arxiv.org/pdf/1811.09928.pdf,PCGAN: Partition-Controlled Human Image Generation,2019 -171,China,Market 1501,market_1501,22.4162632,114.2109318,Chinese University of Hong Kong,edu,2fad06ed34169a5b1f736112364c58140577a6b4,citation,https://pdfs.semanticscholar.org/2fad/06ed34169a5b1f736112364c58140577a6b4.pdf,Pedestrian Color Naming via Convolutional Neural Network,2016 -172,China,Market 1501,market_1501,22.4162632,114.2109318,Chinese University of Hong Kong,edu,25bb4212af72d64ec20cac533f58f7af1472e057,citation,https://arxiv.org/pdf/1703.08837.pdf,Person Re-Identification by Camera Correlation Aware Feature Augmentation,2018 -173,China,Market 1501,market_1501,28.2290209,112.99483204,"National University of Defense Technology, China",mil,25bb4212af72d64ec20cac533f58f7af1472e057,citation,https://arxiv.org/pdf/1703.08837.pdf,Person Re-Identification by Camera Correlation Aware Feature Augmentation,2018 -174,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,25bb4212af72d64ec20cac533f58f7af1472e057,citation,https://arxiv.org/pdf/1703.08837.pdf,Person Re-Identification by Camera Correlation Aware Feature Augmentation,2018 -175,United Kingdom,Market 1501,market_1501,51.5247272,-0.03931035,Queen Mary University of London,edu,744cc8c69255cbe9d992315e456b9efb06f42e20,citation,https://arxiv.org/pdf/1705.04724.pdf,Person Re-Identification by Deep Joint Learning of Multi-Loss Classification,2017 +1,Germany,Market 1501,market_1501,48.7468939,9.0805141,"Max Planck Instutite for Intelligent Systems, Tüebingen",edu,9db841848aa96f60e765299de4cce7abe5ccb47d,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Tang_Multiple_People_Tracking_CVPR_2017_paper.pdf,Multiple People Tracking by Lifted Multicut and Person Re-identification,2017 +2,Germany,Market 1501,market_1501,49.2578657,7.0457956,"Max-Planck-Institut für Informatik, Saarbrücken, Germany",edu,9db841848aa96f60e765299de4cce7abe5ccb47d,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Tang_Multiple_People_Tracking_CVPR_2017_paper.pdf,Multiple People Tracking by Lifted Multicut and Person Re-identification,2017 +3,France,Market 1501,market_1501,48.8457981,2.3567236,Pierre and Marie Curie University,edu,231a12de5dedddf1184ae9caafbc4a954ce584c3,citation,https://pdfs.semanticscholar.org/231a/12de5dedddf1184ae9caafbc4a954ce584c3.pdf,Closed and Open World Multi-shot Person Re-identification. (Ré-identification de personnes à partir de multiples images dans le cadre de bases d'identités fermées et ouvertes),2017 +4,China,Market 1501,market_1501,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,07dead6b98379faac1cf0b2cb34a5db842ab9de9,citation,https://arxiv.org/pdf/1711.10658.pdf,Deep-Person: Learning Discriminative Deep Features for Person Re-Identification,2017 +5,China,Market 1501,market_1501,34.250803,108.983693,Xi’an Jiaotong University,edu,e1dcc3946fa750da4bc05b1154b6321db163ad62,citation,http://gr.xjtu.edu.cn/c/document_library/get_file?folderId=1540809&name=DLFE-80365.pdf,Similarity Learning with Spatial Constraints for Person Re-identification,2016 +6,United States,Market 1501,market_1501,42.366183,-71.092455,Mitsubishi Electric Research Laboratories,company,bb4f83458976755e9310b241a689c8d21b481238,citation,http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w23/Jones_Improving_Face_Verification_ICCV_2017_paper.pdf,Improving Face Verification and Person Re-Identification Accuracy Using Hyperplane Similarity,2017 +7,United States,Market 1501,market_1501,42.3383668,-71.08793524,Northeastern University,edu,32dc3e04dea2306ec34ca3f39db27a2b0a49e0a1,citation,http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w21/Gou_moM_Mean_of_ICCV_2017_paper.pdf,moM: Mean of Moments Feature for Person Re-identification,2017 +8,United States,Market 1501,market_1501,42.3383668,-71.08793524,Northeastern University,edu,0deca8c53adcc13d8da72050d9a4b638da52264b,citation,https://pdfs.semanticscholar.org/0dec/a8c53adcc13d8da72050d9a4b638da52264b.pdf,"A Comprehensive Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets",2016 +9,Australia,Market 1501,market_1501,-33.8809651,151.20107299,University of Technology Sydney,edu,193089d56758ab88391d846edd08d359b1f9a863,citation,https://arxiv.org/pdf/1611.05666.pdf,A Discriminatively Learned CNN Embedding for Person Reidentification,2017 +10,China,Market 1501,market_1501,31.821994,117.28059,"USTC, Hefei, China",edu,83c19722450e8f7dcb89dabb38265f19efafba27,citation,https://arxiv.org/pdf/1803.02983.pdf,A framework with updateable joint images re-ranking for Person Re-identification.,2018 +11,Singapore,Market 1501,market_1501,1.3484104,103.68297965,Nanyang Technological University,edu,6bb8a5f9e2ddf1bdcd42aa7212eb0499992c1e9e,citation,https://arxiv.org/pdf/1607.08381.pdf,A Siamese Long Short-Term Memory Architecture for Human Re-Identification,2016 +12,China,Market 1501,market_1501,40.00229045,116.32098908,Tsinghua University,edu,6bb8a5f9e2ddf1bdcd42aa7212eb0499992c1e9e,citation,https://arxiv.org/pdf/1607.08381.pdf,A Siamese Long Short-Term Memory Architecture for Human Re-Identification,2016 +13,Australia,Market 1501,market_1501,-33.88890695,151.18943366,University of Sydney,edu,6bb8a5f9e2ddf1bdcd42aa7212eb0499992c1e9e,citation,https://arxiv.org/pdf/1607.08381.pdf,A Siamese Long Short-Term Memory Architecture for Human Re-Identification,2016 +14,Germany,Market 1501,market_1501,49.4109266,8.6979529,Heidelberg University,edu,5fdb3533152f9862e3e4c2282cd5f1400af18956,citation,https://arxiv.org/pdf/1804.04694.pdf,A Variational U-Net for Conditional Appearance and Shape Generation,2018 +15,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,635efc8bddec1cf94b1ee4951e4d216331758422,citation,https://arxiv.org/pdf/1812.00914.pdf,Accelerating Large Scale Knowledge Distillation via Dynamic Importance Sampling,2018 +16,Canada,Market 1501,market_1501,53.5238572,-113.52282665,University of Alberta,edu,635efc8bddec1cf94b1ee4951e4d216331758422,citation,https://arxiv.org/pdf/1812.00914.pdf,Accelerating Large Scale Knowledge Distillation via Dynamic Importance Sampling,2018 +17,China,Market 1501,market_1501,39.9808333,116.34101249,Beihang University,edu,19be4580df2e76b70a39af6e749bf189e1ca3975,citation,https://arxiv.org/pdf/1803.10914.pdf,Adversarial Binary Coding for Efficient Person Re-identification,2018 +18,United Kingdom,Market 1501,market_1501,51.7534538,-1.25400997,University of Oxford,edu,47f4dec5f733e933c8b9a8fdcda9419741f2bf62,citation,https://arxiv.org/pdf/1901.10650.pdf,Adversarial Metric Attack for Person Re-identification,2019 +19,United States,Market 1501,market_1501,39.3299013,-76.6205177,Johns Hopkins University,edu,47f4dec5f733e933c8b9a8fdcda9419741f2bf62,citation,https://arxiv.org/pdf/1901.10650.pdf,Adversarial Metric Attack for Person Re-identification,2019 +20,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,eee4cc389ca85d23700cba9627fa11e5ee65d740,citation,https://arxiv.org/pdf/1807.10482.pdf,Adversarial Open-World Person Re-Identification,2018 +21,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,7969cc315bbafcd38a637eb8cd5d45ba897be319,citation,https://arxiv.org/pdf/1604.07807.pdf,An enhanced deep feature representation for person re-identification,2016 +22,China,Market 1501,market_1501,22.3874201,114.2082222,Hong Kong Baptist University,edu,c0e9d06383442d89426808d723ca04586db91747,citation,https://pdfs.semanticscholar.org/c0e9/d06383442d89426808d723ca04586db91747.pdf,Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification,2018 +23,China,Market 1501,market_1501,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,c0e9d06383442d89426808d723ca04586db91747,citation,https://pdfs.semanticscholar.org/c0e9/d06383442d89426808d723ca04586db91747.pdf,Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification,2018 +24,Japan,Market 1501,market_1501,35.6924853,139.7582533,"National Institute of Informatics, Japan",edu,c0e9d06383442d89426808d723ca04586db91747,citation,https://pdfs.semanticscholar.org/c0e9/d06383442d89426808d723ca04586db91747.pdf,Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification,2018 +25,China,Market 1501,market_1501,40.00229045,116.32098908,Tsinghua University,edu,5e1514de6d20d3b1d148d6925edc89a6c891ce47,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Lin_Consistent-Aware_Deep_Learning_CVPR_2017_paper.pdf,Consistent-Aware Deep Learning for Person Re-identification in a Camera Network,2017 +26,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,bff1e1ecf00c37ec91edc7c5c85c1390726c3687,citation,https://arxiv.org/pdf/1511.07545.pdf,Constrained Deep Metric Learning for Person Re-identification,2015 +27,China,Market 1501,market_1501,40.00229045,116.32098908,Tsinghua University,edu,6ce6da7a6b2d55fac604d986595ba6979580393b,citation,https://arxiv.org/pdf/1611.06026.pdf,Cross Domain Knowledge Transfer for Person Re-identification,2016 +28,China,Market 1501,market_1501,23.0502042,113.39880323,South China University of Technology,edu,c249f0aa1416c51bf82be5bb47cbeb8aac6dee35,citation,https://arxiv.org/pdf/1806.04533.pdf,Cross-Dataset Person Re-identification Using Similarity Preserved Generative Adversarial Networks,2018 +29,China,Market 1501,market_1501,40.00229045,116.32098908,Tsinghua University,edu,4f83ef534c164bd7fbd1e71fe6a3d09a30326b26,citation,https://arxiv.org/pdf/1810.10221.pdf,Cross-Resolution Person Re-identification with Deep Antithetical Learning,2018 +30,China,Market 1501,market_1501,28.16437,112.93251,Central South University,edu,a6bc69831dea3efc5804b8ab65cf5a06688ddae0,citation,https://arxiv.org/pdf/1801.01760.pdf,Crossing Generative Adversarial Networks for Cross-View Person Re-identification,2018 +31,Australia,Market 1501,market_1501,-27.49741805,153.01316956,University of Queensland,edu,a6bc69831dea3efc5804b8ab65cf5a06688ddae0,citation,https://arxiv.org/pdf/1801.01760.pdf,Crossing Generative Adversarial Networks for Cross-View Person Re-identification,2018 +32,Australia,Market 1501,market_1501,-33.91758275,151.23124025,University of New South Wales,edu,a6bc69831dea3efc5804b8ab65cf5a06688ddae0,citation,https://arxiv.org/pdf/1801.01760.pdf,Crossing Generative Adversarial Networks for Cross-View Person Re-identification,2018 +33,China,Market 1501,market_1501,39.98177,116.330086,National Laboratory of Pattern Recognition,edu,34b8e675d4651db45e484da34f3c415c60ef3ea2,citation,https://arxiv.org/pdf/1707.01220.pdf,DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer,2018 +34,China,Market 1501,market_1501,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,34b8e675d4651db45e484da34f3c415c60ef3ea2,citation,https://arxiv.org/pdf/1707.01220.pdf,DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer,2018 +35,Australia,Market 1501,market_1501,-27.49741805,153.01316956,University of Queensland,edu,d1ba33106567c880bf99daba2bd31fe88df4ecba,citation,https://arxiv.org/pdf/1706.03160.pdf,Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification,2018 +36,Australia,Market 1501,market_1501,-33.91758275,151.23124025,University of New South Wales,edu,d1ba33106567c880bf99daba2bd31fe88df4ecba,citation,https://arxiv.org/pdf/1706.03160.pdf,Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification,2018 +37,Australia,Market 1501,market_1501,-33.88890695,151.18943366,University of Sydney,edu,d1ba33106567c880bf99daba2bd31fe88df4ecba,citation,https://arxiv.org/pdf/1706.03160.pdf,Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification,2018 +38,China,Market 1501,market_1501,39.9922379,116.30393816,Peking University,edu,2788f382e4396290acfc8b21df45cc811586e66e,citation,https://arxiv.org/pdf/1605.03259.pdf,Deep Attributes Driven Multi-Camera Person Re-identification,2016 +39,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,2788f382e4396290acfc8b21df45cc811586e66e,citation,https://arxiv.org/pdf/1605.03259.pdf,Deep Attributes Driven Multi-Camera Person Re-identification,2016 +40,United States,Market 1501,market_1501,29.58333105,-98.61944505,University of Texas at San Antonio,edu,2788f382e4396290acfc8b21df45cc811586e66e,citation,https://arxiv.org/pdf/1605.03259.pdf,Deep Attributes Driven Multi-Camera Person Re-identification,2016 +41,United States,Market 1501,market_1501,40.4441619,-79.94272826,Carnegie Mellon University,edu,63e1ce7de0fdbce6e03d25b5001c670c30139aa8,citation,https://arxiv.org/pdf/1707.07791.pdf,Deep Feature Learning via Structured Graph Laplacian Embedding for Person Re-Identification,2018 +42,China,Market 1501,market_1501,34.250803,108.983693,Xi’an Jiaotong University,edu,63e1ce7de0fdbce6e03d25b5001c670c30139aa8,citation,https://arxiv.org/pdf/1707.07791.pdf,Deep Feature Learning via Structured Graph Laplacian Embedding for Person Re-Identification,2018 +43,United Kingdom,Market 1501,market_1501,51.5247272,-0.03931035,Queen Mary University of London,edu,e3e36ccd836458d51676789fb133b092d42dac16,citation,https://arxiv.org/pdf/1610.05047.pdf,Deep learning prototype domains for person re-identification,2017 +44,Australia,Market 1501,market_1501,-34.9189226,138.60423668,University of Adelaide,edu,63ac85ec1bff6009bb36f0b24ef189438836bc91,citation,https://arxiv.org/pdf/1606.01595.pdf,Deep linear discriminant analysis on fisher networks: A hybrid architecture for person re-identification,2017 +45,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,9a81f46fcf8c6c0efbe34649552b5056ce419a3d,citation,https://arxiv.org/pdf/1705.03332.pdf,Deep person re-identification with improved embedding and efficient training,2017 +46,China,Market 1501,market_1501,34.250803,108.983693,Xi’an Jiaotong University,edu,6562c40932ea734f46e5068555fbf3a185a345de,citation,https://arxiv.org/pdf/1707.00409.pdf,Deep Ranking Model by Large Adaptive Margin Learning for Person Re-identification,2018 +47,United Kingdom,Market 1501,market_1501,51.5247272,-0.03931035,Queen Mary University of London,edu,35b9af6057801fb2f28881840c8427c9cf648757,citation,https://arxiv.org/pdf/1707.02785.pdf,Deep Reinforcement Learning Attention Selection For Person Re-Identification,2017 +48,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,8961677300a9ee30ca51e1a3cf9815b4a162265b,citation,https://arxiv.org/pdf/1707.00798.pdf,Deep Representation Learning with Part Loss for Person Re-Identification,2017 +49,China,Market 1501,market_1501,39.9922379,116.30393816,Peking University,edu,8961677300a9ee30ca51e1a3cf9815b4a162265b,citation,https://arxiv.org/pdf/1707.00798.pdf,Deep Representation Learning with Part Loss for Person Re-Identification,2017 +50,United States,Market 1501,market_1501,29.58333105,-98.61944505,University of Texas at San Antonio,edu,8961677300a9ee30ca51e1a3cf9815b4a162265b,citation,https://arxiv.org/pdf/1707.00798.pdf,Deep Representation Learning with Part Loss for Person Re-Identification,2017 +51,China,Market 1501,market_1501,34.250803,108.983693,Xi’an Jiaotong University,edu,123286df95d93600f4281c60a60c69121c6440c7,citation,https://arxiv.org/pdf/1710.05711.pdf,Deep Self-Paced Learning for Person Re-Identification,2018 +52,China,Market 1501,market_1501,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,d8949f4f4085b15978e20ed7c5c34a080dd637f2,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w17/papers/Chen_Deep_Spatial-Temporal_Fusion_CVPR_2017_paper.pdf,Deep Spatial-Temporal Fusion Network for Video-Based Person Re-identification,2017 +53,China,Market 1501,market_1501,39.9922379,116.30393816,Peking University,edu,31c0968fb5f587918f1c49bf7fa51453b3e89cf7,citation,https://arxiv.org/pdf/1611.05244.pdf,Deep Transfer Learning for Person Re-Identification,2018 +54,China,Market 1501,market_1501,30.19331415,120.11930822,Zhejiang University,edu,50bf4f77d8b66ec838ad59a869630eace7e0e4a7,citation,https://arxiv.org/pdf/1707.07256.pdf,Deeply-Learned Part-Aligned Representations for Person Re-identification,2017 +55,United States,Market 1501,market_1501,47.6423318,-122.1369302,Microsoft,company,50bf4f77d8b66ec838ad59a869630eace7e0e4a7,citation,https://arxiv.org/pdf/1707.07256.pdf,Deeply-Learned Part-Aligned Representations for Person Re-identification,2017 +56,China,Market 1501,market_1501,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,d497543834f23f72f4092252b613bf3adaefc606,citation,https://arxiv.org/pdf/1805.07698.pdf,Density-Adaptive Kernel based Re-Ranking for Person Re-Identification,2018 +57,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,19a0f34440c25323544b90d9d822a212bfed0eb5,citation,https://arxiv.org/pdf/1901.10100.pdf,Discovering Underlying Person Structure Pattern with Relative Local Distance for Person Re-identification,2019 +58,China,Market 1501,market_1501,34.250803,108.983693,Xi’an Jiaotong University,edu,7b2e0c87aece7ff1404ef2034d4c5674770301b2,citation,https://arxiv.org/pdf/1807.01455.pdf,Discriminative Feature Learning with Foreground Attention for Person Re-Identification,2018 +59,United Kingdom,Market 1501,market_1501,55.94951105,-3.19534913,University of Edinburgh,edu,68621721705e3115355268450b4b447362e455c6,citation,https://arxiv.org/pdf/1812.02605.pdf,Disjoint Label Space Transfer Learning with Common Factorised Space,2019 +60,China,Market 1501,market_1501,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,d950af49c44bc5d9f4a5cc1634e606004790b1e5,citation,https://arxiv.org/pdf/1708.04169.pdf,Divide and Fuse: A Re-ranking Approach for Person Re-identification,2017 +61,United Arab Emirates,Market 1501,market_1501,24.453884,54.3773438,New York University Abu Dhabi,edu,a94b832facb57ea37b18927b13d2dd4c5fa3a9ea,citation,https://arxiv.org/pdf/1803.09733.pdf,Domain transfer convolutional attribute embedding,2018 +62,China,Market 1501,market_1501,39.9106327,116.3356321,Chinese Academy of Science,edu,7f8d4494aba2a2b11a88bf7de4b8879b047dd69b,citation,http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_Easy_Identification_From_CVPR_2018_paper.pdf,Easy Identification from Better Constraints: Multi-shot Person Re-identification from Reference Constraints,2018 +63,United States,Market 1501,market_1501,42.0551164,-87.67581113,Northwestern University,edu,7f8d4494aba2a2b11a88bf7de4b8879b047dd69b,citation,http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_Easy_Identification_From_CVPR_2018_paper.pdf,Easy Identification from Better Constraints: Multi-shot Person Re-identification from Reference Constraints,2018 +64,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,ca1db9dc493a045e3fadf8d8209eaa4311bbdc70,citation,https://arxiv.org/pdf/1709.09304.pdf,Effective Image Retrieval via Multilinear Multi-index Fusion,2017 +65,United States,Market 1501,market_1501,29.58333105,-98.61944505,University of Texas at San Antonio,edu,ca1db9dc493a045e3fadf8d8209eaa4311bbdc70,citation,https://arxiv.org/pdf/1709.09304.pdf,Effective Image Retrieval via Multilinear Multi-index Fusion,2017 +66,United States,Market 1501,market_1501,42.0551164,-87.67581113,Northwestern University,edu,00bf7bcf31ee71f5f325ca5307883157ba3d580f,citation,http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhou_Efficient_Online_Local_ICCV_2017_paper.pdf,Efficient Online Local Metric Adaptation via Negative Samples for Person Re-identification,2017 +67,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,febff0f6faa8dde77848845e4b3e6f6c91180d33,citation,https://arxiv.org/pdf/1611.00137.pdf,Embedding Deep Metric for Person Re-identication A Study Against Large Variations,2016 +68,China,Market 1501,market_1501,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,febff0f6faa8dde77848845e4b3e6f6c91180d33,citation,https://arxiv.org/pdf/1611.00137.pdf,Embedding Deep Metric for Person Re-identication A Study Against Large Variations,2016 +69,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,febff0f6faa8dde77848845e4b3e6f6c91180d33,citation,https://arxiv.org/pdf/1611.00137.pdf,Embedding Deep Metric for Person Re-identication A Study Against Large Variations,2016 +70,China,Market 1501,market_1501,31.846918,117.29053367,Hefei University of Technology,edu,fd0e1fecf7e72318a4c53463fd5650720df40281,citation,https://arxiv.org/pdf/1606.04404.pdf,End-to-End Comparative Attention Networks for Person Re-Identification,2017 +71,China,Market 1501,market_1501,39.9041999,116.4073963,"Qihoo 360 AI Institute, Beijing, China",edu,fd0e1fecf7e72318a4c53463fd5650720df40281,citation,https://arxiv.org/pdf/1606.04404.pdf,End-to-End Comparative Attention Networks for Person Re-Identification,2017 +72,Singapore,Market 1501,market_1501,1.2966426,103.7763939,Singapore / National University of Singapore,edu,fd0e1fecf7e72318a4c53463fd5650720df40281,citation,https://arxiv.org/pdf/1606.04404.pdf,End-to-End Comparative Attention Networks for Person Re-Identification,2017 +73,China,Market 1501,market_1501,32.035225,118.855317,PLA Army Engineering University,mil,c8ac121e9c4eb9964be9c5713f22a95c1c3b57e9,citation,https://arxiv.org/pdf/1901.05798.pdf,Ensemble Feature for Person Re-Identification,2019 +74,Spain,Market 1501,market_1501,41.5008957,2.111553,Autonomous University of Barcelona,edu,fe54a5a10288648f3bd0a71b053cdb896716b552,citation,https://arxiv.org/pdf/1804.04419.pdf,"Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification",2018 +75,Spain,Market 1501,market_1501,41.40657415,2.1945341,Universitat Oberta de Catalunya,edu,fe54a5a10288648f3bd0a71b053cdb896716b552,citation,https://arxiv.org/pdf/1804.04419.pdf,"Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification",2018 +76,Spain,Market 1501,market_1501,41.3868913,2.16352385,University of Barcelona,edu,fe54a5a10288648f3bd0a71b053cdb896716b552,citation,https://arxiv.org/pdf/1804.04419.pdf,"Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification",2018 +77,United States,Market 1501,market_1501,33.2416008,-111.8839083,Intel,company,6a9c3011b5092daa1d0cacda23f20ca4ae74b902,citation,https://arxiv.org/pdf/1812.02465.pdf,Fast and Accurate Person Re-Identification with RMNet.,2018 +78,China,Market 1501,market_1501,39.9808333,116.34101249,Beihang University,edu,91cc3981c304227e13ae151a43fbb124419bc0ce,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Chen_Fast_Person_Re-Identification_CVPR_2017_paper.pdf,Fast Person Re-identification via Cross-Camera Semantic Binary Transformation,2017 +79,United Kingdom,Market 1501,market_1501,52.6221571,1.2409136,University of East Anglia,edu,91cc3981c304227e13ae151a43fbb124419bc0ce,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Chen_Fast_Person_Re-Identification_CVPR_2017_paper.pdf,Fast Person Re-identification via Cross-Camera Semantic Binary Transformation,2017 +80,Singapore,Market 1501,market_1501,1.3484104,103.68297965,Nanyang Technological University,edu,6123e52c1a560c88817d8720e05fbff8565271fb,citation,https://arxiv.org/pdf/1607.08378.pdf,Gated Siamese Convolutional Neural Network Architecture for Human Re-Identification,2016 +81,United States,Market 1501,market_1501,38.5336349,-121.79077264,"University of California, Davis",edu,79c959833ff49f860e20b6654dbf4d6acdee0230,citation,https://arxiv.org/pdf/1811.02545.pdf,Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond,2018 +82,China,Market 1501,market_1501,30.19331415,120.11930822,Zhejiang University,edu,79c959833ff49f860e20b6654dbf4d6acdee0230,citation,https://arxiv.org/pdf/1811.02545.pdf,Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond,2018 +83,Taiwan,Market 1501,market_1501,25.0410728,121.6147562,Institute of Information Science,edu,3cbb4cf942ee95d14505c0f83a48ba224abdd00b,citation,https://arxiv.org/pdf/1712.06820.pdf,Hierarchical Cross Network for Person Re-identification,2017 +84,Japan,Market 1501,market_1501,33.8941968,130.8394083,Kyushu Institute of Technology,edu,7da961cb039b1a01cad9b78d93bdfe2a69ed3ccf,citation,https://arxiv.org/pdf/1706.04318.pdf,Hierarchical Gaussian Descriptors with Application to Person Re-Identification,2017 +85,Japan,Market 1501,market_1501,33.59914655,130.22359848,Kyushu University,edu,7da961cb039b1a01cad9b78d93bdfe2a69ed3ccf,citation,https://arxiv.org/pdf/1706.04318.pdf,Hierarchical Gaussian Descriptors with Application to Person Re-Identification,2017 +86,Japan,Market 1501,market_1501,35.9020448,139.93622009,University of Tokyo,edu,7da961cb039b1a01cad9b78d93bdfe2a69ed3ccf,citation,https://arxiv.org/pdf/1706.04318.pdf,Hierarchical Gaussian Descriptors with Application to Person Re-Identification,2017 +87,United States,Market 1501,market_1501,42.3504253,-71.10056114,Boston University,edu,7c25ed788da1f5f61d8d1da23dd319dfb4e5ac2d,citation,https://arxiv.org/pdf/1612.01345.pdf,Human-In-The-Loop Person Re-Identification,2016 +88,United Kingdom,Market 1501,market_1501,51.5247272,-0.03931035,Queen Mary University of London,edu,7c25ed788da1f5f61d8d1da23dd319dfb4e5ac2d,citation,https://arxiv.org/pdf/1612.01345.pdf,Human-In-The-Loop Person Re-Identification,2016 +89,United Kingdom,Market 1501,market_1501,55.378051,-3.435973,"Vision Semantics Ltd, UK",edu,7c25ed788da1f5f61d8d1da23dd319dfb4e5ac2d,citation,https://arxiv.org/pdf/1612.01345.pdf,Human-In-The-Loop Person Re-Identification,2016 +90,Australia,Market 1501,market_1501,-37.9062737,145.1319449,"CSIRO, Australia",edu,53492cb14b33a26b10c91102daa2d5a2a3ed069d,citation,https://arxiv.org/pdf/1806.07592.pdf,Improving Online Multiple Object tracking with Deep Metric Learning,2018 +91,Germany,Market 1501,market_1501,50.7791703,6.06728733,RWTH Aachen University,edu,a3d11e98794896849ab2304a42bf83e2979e5fb5,citation,https://arxiv.org/pdf/1703.07737.pdf,In Defense of the Triplet Loss for Person Re-Identification,2017 +92,China,Market 1501,market_1501,34.250803,108.983693,Xi’an Jiaotong University,edu,cb8567f074573a0d66d50e75b5a91df283ccd503,citation,https://arxiv.org/pdf/1708.05512.pdf,Large Margin Learning in Set-to-Set Similarity Comparison for Person Reidentification,2018 +93,United Kingdom,Market 1501,market_1501,51.5247272,-0.03931035,Queen Mary University of London,edu,207e0ac5301a3c79af862951b70632ed650f74f7,citation,https://arxiv.org/pdf/1603.02139.pdf,Learning a Discriminative Null Space for Person Re-identification,2016 +94,China,Market 1501,market_1501,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,34cf90fcbf83025666c5c86ec30ac58b632b27b0,citation,https://arxiv.org/pdf/1710.06555.pdf,Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification,2017 +95,United States,Market 1501,market_1501,40.007581,-105.2659417,University of Colorado,edu,ad3be20fe0106d80c567def71fef01146564df4b,citation,https://arxiv.org/pdf/1802.05312.pdf,Learning Deep Disentangled Embeddings With the F-Statistic Loss,2018 +96,Russia,Market 1501,market_1501,55.6846566,37.3407539,"Skolkovo Institute of Science and Technology, Skolkovo, Moscow",edu,218603147709344d4ff66625d83603deee2854bf,citation,https://arxiv.org/pdf/1611.00822.pdf,Learning Deep Embeddings with Histogram Loss,2016 +97,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,489decd84645b77d31001d17a66abb92bb96c731,citation,https://arxiv.org/pdf/1803.11333.pdf,Learning View-Specific Deep Networks for Person Re-Identification,2018 +98,Norway,Market 1501,market_1501,63.419499,10.4020771,Norwegian University of Science and Technology,edu,2102915d0c51cfda4d85133bd593ecb9508fa4bb,citation,https://arxiv.org/pdf/1701.03153.pdf,Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification,2018 +99,Italy,Market 1501,market_1501,41.9037626,12.5144384,Sapienza University of Rome,edu,2102915d0c51cfda4d85133bd593ecb9508fa4bb,citation,https://arxiv.org/pdf/1701.03153.pdf,Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification,2018 +100,Italy,Market 1501,market_1501,45.437398,11.003376,University of Verona,edu,2102915d0c51cfda4d85133bd593ecb9508fa4bb,citation,https://arxiv.org/pdf/1701.03153.pdf,Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification,2018 +101,China,Market 1501,market_1501,40.00229045,116.32098908,Tsinghua University,edu,c0387e788a52f10bf35d4d50659cfa515d89fbec,citation,https://pdfs.semanticscholar.org/c038/7e788a52f10bf35d4d50659cfa515d89fbec.pdf,MARS: A Video Benchmark for Large-Scale Person Re-Identification,2016 +102,China,Market 1501,market_1501,40.00229045,116.32098908,Tsinghua University,edu,1e83e2abcb258cd62b160e3f31a490a6bc042e83,citation,https://arxiv.org/pdf/1704.02492.pdf,Metric Learning in Codebook Generation of Bag-of-Words for Person Re-identification,2017 +103,China,Market 1501,market_1501,31.8405068,117.2638057,Hefei University,edu,7c9d8593cdf2f8ba9f27906b2b5827b145631a0b,citation,https://arxiv.org/pdf/1810.08534.pdf,MsCGAN: Multi-scale Conditional Generative Adversarial Networks for Person Image Generation,2018 +104,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,1565bf91f8fdfe5f5168a5050b1418debc662151,citation,https://arxiv.org/pdf/1711.03368.pdf,One-pass Person Re-identification by Sketch Online Discriminant Analysis,2017 +105,Australia,Market 1501,market_1501,-33.8809651,151.20107299,University of Technology Sydney,edu,592e555ebe4bd2d821230e7074d7e9626af716b0,citation,https://arxiv.org/pdf/1809.02681.pdf,Open Set Adversarial Examples,2018 +106,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,fcaa88dcb1a440ef09c4e5d724ed209bfc5d3367,citation,https://arxiv.org/pdf/1811.09928.pdf,PCGAN: Partition-Controlled Human Image Generation,2019 +107,China,Market 1501,market_1501,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,fcaa88dcb1a440ef09c4e5d724ed209bfc5d3367,citation,https://arxiv.org/pdf/1811.09928.pdf,PCGAN: Partition-Controlled Human Image Generation,2019 +108,China,Market 1501,market_1501,22.4162632,114.2109318,Chinese University of Hong Kong,edu,2fad06ed34169a5b1f736112364c58140577a6b4,citation,https://pdfs.semanticscholar.org/2fad/06ed34169a5b1f736112364c58140577a6b4.pdf,Pedestrian Color Naming via Convolutional Neural Network,2016 +109,China,Market 1501,market_1501,22.4162632,114.2109318,Chinese University of Hong Kong,edu,25bb4212af72d64ec20cac533f58f7af1472e057,citation,https://arxiv.org/pdf/1703.08837.pdf,Person Re-Identification by Camera Correlation Aware Feature Augmentation,2018 +110,China,Market 1501,market_1501,28.2290209,112.99483204,"National University of Defense Technology, China",mil,25bb4212af72d64ec20cac533f58f7af1472e057,citation,https://arxiv.org/pdf/1703.08837.pdf,Person Re-Identification by Camera Correlation Aware Feature Augmentation,2018 +111,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,25bb4212af72d64ec20cac533f58f7af1472e057,citation,https://arxiv.org/pdf/1703.08837.pdf,Person Re-Identification by Camera Correlation Aware Feature Augmentation,2018 +112,United Kingdom,Market 1501,market_1501,51.5247272,-0.03931035,Queen Mary University of London,edu,744cc8c69255cbe9d992315e456b9efb06f42e20,citation,https://arxiv.org/pdf/1705.04724.pdf,Person Re-Identification by Deep Joint Learning of Multi-Loss Classification,2017 diff --git a/site/datasets/verified/megaage.csv b/site/datasets/verified/megaage.csv index 04702674..baad68b9 100644 --- a/site/datasets/verified/megaage.csv +++ b/site/datasets/verified/megaage.csv @@ -1,2 +1,8 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year 0,,MegaAge,megaage,0.0,0.0,,,,main,,Quantifying Facial Age by Posterior of Age Comparisons,2017 +1,China,MegaAge,megaage,39.98177,116.330086,National Laboratory of Pattern Recognition,edu,f3ec43a7b22f6e5414fec473acda8ffd843e7baf,citation,https://arxiv.org/pdf/1809.07447.pdf,A Coupled Evolutionary Network for Age Estimation,2018 +2,China,MegaAge,megaage,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 +3,China,MegaAge,megaage,22.4162632,114.2109318,Chinese University of Hong Kong,edu,aaa2b45153051e23d5a35ccf9af8ecabc0fe24cd,citation,https://pdfs.semanticscholar.org/aaa2/b45153051e23d5a35ccf9af8ecabc0fe24cd.pdf,1 How Good can Human Predict Facial Age ?,2017 +4,China,MegaAge,megaage,39.993008,116.329882,SenseTime,company,aaa2b45153051e23d5a35ccf9af8ecabc0fe24cd,citation,https://pdfs.semanticscholar.org/aaa2/b45153051e23d5a35ccf9af8ecabc0fe24cd.pdf,1 How Good can Human Predict Facial Age ?,2017 +5,Taiwan,MegaAge,megaage,25.0421852,121.6145477,"Academia Sinica, Taiwan",edu,c62c07de196e95eaaf614fb150a4fa4ce49588b4,citation,https://pdfs.semanticscholar.org/c62c/07de196e95eaaf614fb150a4fa4ce49588b4.pdf,SSR-Net: A Compact Soft Stagewise Regression Network for Age Estimation,2018 +6,Taiwan,MegaAge,megaage,25.01682835,121.53846924,National Taiwan University,edu,c62c07de196e95eaaf614fb150a4fa4ce49588b4,citation,https://pdfs.semanticscholar.org/c62c/07de196e95eaaf614fb150a4fa4ce49588b4.pdf,SSR-Net: A Compact Soft Stagewise Regression Network for Age Estimation,2018 diff --git a/site/datasets/verified/megaface.csv b/site/datasets/verified/megaface.csv index d9f78ec3..4c38af0b 100644 --- a/site/datasets/verified/megaface.csv +++ b/site/datasets/verified/megaface.csv @@ -2,3 +2,65 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,t 0,,MegaFace,megaface,0.0,0.0,,,,main,,Level Playing Field for Million Scale Face Recognition,2017 1,Netherlands,MegaFace,megaface,53.21967825,6.56251482,University of Groningen,edu,8efda5708bbcf658d4f567e3866e3549fe045bbb,citation,https://pdfs.semanticscholar.org/8efd/a5708bbcf658d4f567e3866e3549fe045bbb.pdf,Pre-trained Deep Convolutional Neural Networks for Face Recognition,2018 2,United States,MegaFace,megaface,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 +3,China,MegaFace,megaface,22.4162632,114.2109318,Chinese University of Hong Kong,edu,2401cd5606c6bc5390acc352d00c1685f0c8af60,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018 +4,China,MegaFace,megaface,39.993008,116.329882,SenseTime,company,2401cd5606c6bc5390acc352d00c1685f0c8af60,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018 +5,Singapore,MegaFace,megaface,1.3484104,103.68297965,Nanyang Technological University,edu,2401cd5606c6bc5390acc352d00c1685f0c8af60,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018 +6,United Kingdom,MegaFace,megaface,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 +7,China,MegaFace,megaface,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 +8,China,MegaFace,megaface,40.0044795,116.370238,Chinese Academy of Sciences,edu,1345fb7700389f9d02f203b3cb25ac3594855054,citation,,Hierarchical Training for Large Scale Face Recognition with Few Samples Per Subject,2018 +9,United States,MegaFace,megaface,45.57022705,-122.63709346,Concordia University,edu,db374308655256da1479c272582d7c7139c97173,citation,https://arxiv.org/pdf/1811.11080.pdf,MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices,2018 +10,United States,MegaFace,megaface,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,db374308655256da1479c272582d7c7139c97173,citation,https://arxiv.org/pdf/1811.11080.pdf,MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices,2018 +11,United States,MegaFace,megaface,36.0678324,-94.1736551,University of Arkansas,edu,db374308655256da1479c272582d7c7139c97173,citation,https://arxiv.org/pdf/1811.11080.pdf,MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices,2018 +12,United Kingdom,MegaFace,megaface,51.49887085,-0.17560797,Imperial College London,edu,51992fa881541ca3a4520c1ff9100b83e2f1ad87,citation,https://arxiv.org/pdf/1801.07698.pdf,ArcFace: Additive Angular Margin Loss for Deep Face Recognition,2018 +13,China,MegaFace,megaface,40.0044795,116.370238,Chinese Academy of Sciences,edu,94f74c6314ffd02db581e8e887b5fd81ce288dbf,citation,https://arxiv.org/pdf/1511.02683.pdf,A Light CNN for Deep Face Representation With Noisy Labels,2018 +14,China,MegaFace,megaface,22.4162632,114.2109318,Chinese University of Hong Kong,edu,53840c83f7b6ae78d4310c5b84ab3fde1a33bc4f,citation,https://arxiv.org/pdf/1801.01687.pdf,Accelerated Training for Massive Classification via Dynamic Class Selection,2018 +15,China,MegaFace,megaface,39.993008,116.329882,SenseTime,company,53840c83f7b6ae78d4310c5b84ab3fde1a33bc4f,citation,https://arxiv.org/pdf/1801.01687.pdf,Accelerated Training for Massive Classification via Dynamic Class Selection,2018 +16,United States,MegaFace,megaface,38.99203005,-76.9461029,University of Maryland College Park,edu,7323b594d3a8508f809e276aa2d224c4e7ec5a80,citation,https://arxiv.org/pdf/1808.05508.pdf,An Experimental Evaluation of Covariates Effects on Unconstrained Face Verification,2018 +17,China,MegaFace,megaface,22.304572,114.17976285,Hong Kong Polytechnic University,edu,f60070d3a4d333aa1436e4c372b1feb5b316a7ba,citation,https://arxiv.org/pdf/1801.05678.pdf,Face Recognition via Centralized Coordinate Learning,2018 +18,United Kingdom,MegaFace,megaface,54.687254,-5.882736,Ulster University,edu,ddfde808af8dc8b737d115869d6cca780d050884,citation,https://arxiv.org/pdf/1805.06741.pdf,Minimum Margin Loss for Deep Face Recognition,2018 +19,China,MegaFace,megaface,39.9922379,116.30393816,Peking University,edu,4f0b641860d90dfa4c185670bf636149a2b2b717,citation,,Improve Cross-Domain Face Recognition with IBN-block,2018 +20,United States,MegaFace,megaface,40.4441619,-79.94272826,Carnegie Mellon University,edu,67a9659de0bf671fafccd7f39b7587f85fb6dfbd,citation,,Ring Loss: Convex Feature Normalization for Face Recognition,2018 +21,United States,MegaFace,megaface,41.70456775,-86.23822026,University of Notre Dame,edu,841855205818d3a6d6f85ec17a22515f4f062882,citation,https://arxiv.org/pdf/1805.11529.pdf,Low Resolution Face Recognition in the Wild,2018 +22,United Kingdom,MegaFace,megaface,51.5247272,-0.03931035,Queen Mary University of London,edu,2306b2a8fba28539306052764a77a0d0f5d1236a,citation,https://arxiv.org/pdf/1804.09691.pdf,Surveillance Face Recognition Challenge,2018 +23,United Kingdom,MegaFace,megaface,55.378051,-3.435973,"Vision Semantics Ltd, UK",edu,2306b2a8fba28539306052764a77a0d0f5d1236a,citation,https://arxiv.org/pdf/1804.09691.pdf,Surveillance Face Recognition Challenge,2018 +24,United States,MegaFace,megaface,42.366183,-71.092455,Mitsubishi Electric Research Laboratories,company,57246142814d7010d3592e3a39a1ed819dd01f3b,citation,https://pdfs.semanticscholar.org/5724/6142814d7010d3592e3a39a1ed819dd01f3b.pdf,Verification of Very Low-Resolution Faces Using An Identity-Preserving Deep Face Super-resolution Network,0 +25,China,MegaFace,megaface,22.4162632,114.2109318,Chinese University of Hong Kong,edu,f3a59d85b7458394e3c043d8277aa1ffe3cdac91,citation,https://arxiv.org/pdf/1802.09900.pdf,Query-Free Attacks on Industry-Grade Face Recognition Systems under Resource Constraints,2018 +26,United States,MegaFace,megaface,39.86948105,-84.87956905,Indiana University,edu,f3a59d85b7458394e3c043d8277aa1ffe3cdac91,citation,https://arxiv.org/pdf/1802.09900.pdf,Query-Free Attacks on Industry-Grade Face Recognition Systems under Resource Constraints,2018 +27,Singapore,MegaFace,megaface,1.3484104,103.68297965,Nanyang Technological University,edu,9e31e77f9543ab42474ba4e9330676e18c242e72,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018 +28,China,MegaFace,megaface,39.993008,116.329882,SenseTime,company,9e31e77f9543ab42474ba4e9330676e18c242e72,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018 +29,United States,MegaFace,megaface,32.87935255,-117.23110049,"University of California, San Diego",edu,9e31e77f9543ab42474ba4e9330676e18c242e72,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018 +30,United States,MegaFace,megaface,22.5447154,113.9357164,Tencent,company,7a7fddb3020e0c2dd4e3fe275329eb10f1cfbb8a,citation,https://arxiv.org/pdf/1810.07599.pdf,Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition,2018 +31,United States,MegaFace,megaface,47.6423318,-122.1369302,Microsoft,company,6cacda04a541d251e8221d70ac61fda88fb61a70,citation,https://arxiv.org/pdf/1707.05574.pdf,One-shot Face Recognition by Promoting Underrepresented Classes,2017 +32,Czech Republic,MegaFace,megaface,49.20172,16.6033168,Brno University of Technology,edu,b55e70df03d9b80c91446a97957bc95772dcc45b,citation,,MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,2018 +33,Germany,MegaFace,megaface,48.5670466,13.4517835,University of Passau,edu,b55e70df03d9b80c91446a97957bc95772dcc45b,citation,,MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,2018 +34,Germany,MegaFace,megaface,50.7171497,7.12825184,"Deutsche Welle, Bonn, 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Multimodal Emotion Analysis,2018 +39,Czech Republic,MegaFace,megaface,49.2238302,16.5982602,"Phonexia, Brno-Krlovo Pole, Czech Republic",edu,b55e70df03d9b80c91446a97957bc95772dcc45b,citation,,MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,2018 +40,Ireland,MegaFace,megaface,53.3498053,-6.2603097,"Siren Solutions, Dublin, Ireland",company,b55e70df03d9b80c91446a97957bc95772dcc45b,citation,,MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,2018 +41,China,MegaFace,megaface,22.5447154,113.9357164,"Tencent AI Lab, Shenzhen, China",company,1174b869c325222c3446d616975842e8d2989cf2,citation,https://arxiv.org/pdf/1801.09414.pdf,CosFace: Large Margin Cosine Loss for Deep Face Recognition,2018 +42,United States,MegaFace,megaface,33.776033,-84.39884086,Georgia Institute of Technology,edu,bd8f77b7d3b9d272f7a68defc1412f73e5ac3135,citation,https://arxiv.org/pdf/1704.08063.pdf,SphereFace: Deep Hypersphere Embedding for Face Recognition,2017 +43,United States,MegaFace,megaface,40.4441619,-79.94272826,Carnegie Mellon University,edu,bd8f77b7d3b9d272f7a68defc1412f73e5ac3135,citation,https://arxiv.org/pdf/1704.08063.pdf,SphereFace: Deep Hypersphere Embedding for Face Recognition,2017 +44,China,MegaFace,megaface,23.09461185,113.28788994,Sun Yat-Sen University,edu,bd8f77b7d3b9d272f7a68defc1412f73e5ac3135,citation,https://arxiv.org/pdf/1704.08063.pdf,SphereFace: Deep Hypersphere Embedding for Face Recognition,2017 +45,China,MegaFace,megaface,30.672721,104.098806,University of Electronic Science and Technology of China,edu,93af36da08bf99e68c9b0d36e141ed8154455ac2,citation,https://pdfs.semanticscholar.org/93af/36da08bf99e68c9b0d36e141ed8154455ac2.pdf,A Dditive M Argin S Oftmax for F Ace V Erification,2018 +46,United States,MegaFace,megaface,33.776033,-84.39884086,Georgia Institute of Technology,edu,93af36da08bf99e68c9b0d36e141ed8154455ac2,citation,https://pdfs.semanticscholar.org/93af/36da08bf99e68c9b0d36e141ed8154455ac2.pdf,A Dditive M Argin 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University,edu,672fae3da801b2a0d2bad65afdbbbf1b2320623e,citation,https://arxiv.org/pdf/1609.07042.pdf,Pose-Selective Max Pooling for Measuring Similarity,2016 +51,China,MegaFace,megaface,22.53521465,113.9315911,Shenzhen University,edu,a32878e85941b5392d58d28e5248f94e16e25d78,citation,https://arxiv.org/pdf/1801.06445.pdf,Quality Classified Image Analysis with Application to Face Detection and Recognition,2018 +52,China,MegaFace,megaface,22.4162632,114.2109318,Chinese University of Hong Kong,edu,380d5138cadccc9b5b91c707ba0a9220b0f39271,citation,https://arxiv.org/pdf/1806.00194.pdf,Deep Imbalanced Learning for Face Recognition and Attribute Prediction,2018 +53,United States,MegaFace,megaface,40.4432741,-79.9456995,Robotics Institute at Carnegie Mellon University,edu,380d5138cadccc9b5b91c707ba0a9220b0f39271,citation,https://arxiv.org/pdf/1806.00194.pdf,Deep Imbalanced Learning for Face Recognition and Attribute Prediction,2018 +54,Israel,MegaFace,megaface,32.7767783,35.0231271,Technion-Israel Institute of Technology,edu,d00787e215bd74d32d80a6c115c4789214da5edb,citation,https://pdfs.semanticscholar.org/d007/87e215bd74d32d80a6c115c4789214da5edb.pdf,Faster and Lighter Online Sparse Dictionary Learning Project report,0 +55,China,MegaFace,megaface,39.9808333,116.34101249,Beihang University,edu,0a23bdc55fb0d04acdac4d3ea0a9994623133562,citation,https://arxiv.org/pdf/1806.03018.pdf,Large-scale Bisample Learning on ID vs. Spot Face Recognition,2018 +56,United States,MegaFace,megaface,45.57022705,-122.63709346,Concordia University,edu,8e0becfc5fe3ecdd2ac93fabe34634827b21ef2b,citation,https://arxiv.org/pdf/1711.10520.pdf,Learning from Longitudinal Face Demonstration - Where Tractable Deep Modeling Meets Inverse Reinforcement Learning,2017 +57,United States,MegaFace,megaface,40.4437954,-79.9465522,"CyLab, Carnegie Mellon, Pittsburgh, USA",edu,8e0becfc5fe3ecdd2ac93fabe34634827b21ef2b,citation,https://arxiv.org/pdf/1711.10520.pdf,Learning from Longitudinal Face Demonstration - Where Tractable Deep Modeling Meets Inverse Reinforcement Learning,2017 +58,United States,MegaFace,megaface,33.776033,-84.39884086,Georgia Institute of Technology,edu,9fc17fa5708584fa848164461f82a69e97f6ed69,citation,,Additive Margin Softmax for Face Verification,2018 +59,China,MegaFace,megaface,30.672721,104.098806,University of Electronic Science and Technology of China,edu,9fc17fa5708584fa848164461f82a69e97f6ed69,citation,,Additive Margin Softmax for Face Verification,2018 +60,Italy,MegaFace,megaface,45.1867156,9.1561041,University of Pavia,edu,746c0205fdf191a737df7af000eaec9409ede73f,citation,,Investigating Nuisances in DCNN-Based Face Recognition,2018 +61,Italy,MegaFace,megaface,43.7776426,11.259765,University of Florence,edu,746c0205fdf191a737df7af000eaec9409ede73f,citation,,Investigating Nuisances in DCNN-Based Face Recognition,2018 +62,United States,MegaFace,megaface,47.6543238,-122.30800894,University of Washington,edu,28d4e027c7e90b51b7d8908fce68128d1964668a,citation,,Level Playing Field for Million Scale Face Recognition,2017 +63,China,MegaFace,megaface,31.30104395,121.50045497,Fudan University,edu,c5e37630d0672e4d44f7dee83ac2c1528be41c2e,citation,,Multi-task Deep Neural Network for Joint Face Recognition and Facial Attribute Prediction,2017 +64,United States,MegaFace,megaface,39.65404635,-79.96475355,West Virginia University,edu,b1b7603a70860cbe5ff7b963976b5e6f780c88fc,citation,,A Deep Face Identification Network Enhanced by Facial Attributes Prediction,2018 diff --git a/site/datasets/verified/msceleb.csv b/site/datasets/verified/msceleb.csv index d1a7ec8c..be5b063c 100644 --- a/site/datasets/verified/msceleb.csv +++ b/site/datasets/verified/msceleb.csv @@ -3,125 +3,93 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,t 1,China,MsCeleb,msceleb,22.4162632,114.2109318,Chinese University of Hong Kong,edu,2011d4da646f794456bebb617d1500ddf71989ed,citation,https://pdfs.semanticscholar.org/2011/d4da646f794456bebb617d1500ddf71989ed.pdf,Transductive Centroid Projection for Semi-supervised Large-Scale Recognition,2018 2,China,MsCeleb,msceleb,39.993008,116.329882,SenseTime,company,2011d4da646f794456bebb617d1500ddf71989ed,citation,https://pdfs.semanticscholar.org/2011/d4da646f794456bebb617d1500ddf71989ed.pdf,Transductive Centroid Projection for Semi-supervised Large-Scale Recognition,2018 3,United States,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 -4,United 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China,edu,98518fc368d7e1478cef40f5f8fd4468763645ad,citation,http://downloads.hindawi.com/journals/cin/2018/4512473.pdf,A Community Detection Approach to Cleaning Extremely Large Face Database,2018 -8,China,MsCeleb,msceleb,28.2290209,112.99483204,"National University of Defense Technology, China",mil,98518fc368d7e1478cef40f5f8fd4468763645ad,citation,http://downloads.hindawi.com/journals/cin/2018/4512473.pdf,A Community Detection Approach to Cleaning Extremely Large Face Database,2018 -9,China,MsCeleb,msceleb,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,6cdbbced12bff53bcbdde3cdb6d20b4bd02a9d6c,citation,https://arxiv.org/pdf/1811.12026.pdf,Attacks on State-of-the-Art Face Recognition using Attentional Adversarial Attack Generative Network,2018 -10,China,MsCeleb,msceleb,39.98177,116.330086,National Laboratory of Pattern Recognition,edu,e47f4a127f41c055fb7893ddc295932ead783c63,citation,https://arxiv.org/pdf/1709.03675.pdf,Adversarial Discriminative Heterogeneous Face Recognition,2018 -11,China,MsCeleb,msceleb,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,e47f4a127f41c055fb7893ddc295932ead783c63,citation,https://arxiv.org/pdf/1709.03675.pdf,Adversarial Discriminative Heterogeneous Face Recognition,2018 -12,China,MsCeleb,msceleb,22.4162632,114.2109318,Chinese University of Hong Kong,edu,2401cd5606c6bc5390acc352d00c1685f0c8af60,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018 -13,China,MsCeleb,msceleb,39.993008,116.329882,SenseTime,company,2401cd5606c6bc5390acc352d00c1685f0c8af60,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018 -14,Singapore,MsCeleb,msceleb,1.3484104,103.68297965,Nanyang Technological University,edu,2401cd5606c6bc5390acc352d00c1685f0c8af60,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data 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Sciences,edu,4cdb6144d56098b819076a8572a664a2c2d27f72,citation,https://arxiv.org/pdf/1806.01196.pdf,Face Synthesis for Eyeglass-Robust Face Recognition,2018 -19,China,MsCeleb,msceleb,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,4cdb6144d56098b819076a8572a664a2c2d27f72,citation,https://arxiv.org/pdf/1806.01196.pdf,Face Synthesis for Eyeglass-Robust Face Recognition,2018 -20,United States,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 -21,United States,MsCeleb,msceleb,42.718568,-84.47791571,Michigan State University,edu,3011b5fce49112228711a9e5f92d6f191687c1ea,citation,https://arxiv.org/pdf/1803.09014.pdf,Feature Transfer Learning for Deep Face Recognition with Long-Tail Data,2018 -22,United Kingdom,MsCeleb,msceleb,51.49887085,-0.17560797,Imperial College 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Recognition",edu,3ac09c2589178dac0b6a2ea2edf04b7629672d81,citation,https://arxiv.org/pdf/1708.02412.pdf,Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition,2018 -117,China,MsCeleb,msceleb,40.0044795,116.370238,Chinese Academy of Sciences,edu,3ac09c2589178dac0b6a2ea2edf04b7629672d81,citation,https://arxiv.org/pdf/1708.02412.pdf,Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition,2018 -118,United States,MsCeleb,msceleb,38.99203005,-76.9461029,University of Maryland College Park,edu,b35ff9985aaee9371588330bcef0dfc88d1401d7,citation,,Deep Density Clustering of Unconstrained Faces,2018 -119,United States,MsCeleb,msceleb,30.6108365,-96.352128,Texas A&M University,edu,e36fdb50844132fc7925550398e68e7ae95467de,citation,,Face Verification with Disguise Variations via Deep Disguise Recognizer,2018 -120,United States,MsCeleb,msceleb,39.65404635,-79.96475355,West Virginia University,edu,e36fdb50844132fc7925550398e68e7ae95467de,citation,,Face Verification with Disguise Variations via Deep Disguise Recognizer,2018 -121,United States,MsCeleb,msceleb,42.4505507,-76.4783513,Cornell University,edu,9ccf528ef8df99372ce6286ffbb0bf6f9a505cca,citation,,Learning Compositional Visual Concepts with Mutual Consistency,2018 -122,United States,MsCeleb,msceleb,40.3442079,-74.5924599,"Siemens Corporate Research, Princeton, NJ",edu,9ccf528ef8df99372ce6286ffbb0bf6f9a505cca,citation,,Learning Compositional Visual Concepts with Mutual Consistency,2018 -123,United States,MsCeleb,msceleb,42.3383668,-71.08793524,Northeastern University,edu,3827f1cab643a57e3cd22fbffbf19dd5e8a298a8,citation,,One-Shot Face Recognition via Generative Learning,2018 -124,China,MsCeleb,msceleb,39.9106327,116.3356321,Chinese Academy of Science,edu,20f87ed94a423b5d8599d85d1f2f80bab8902107,citation,,Pose-Guided Photorealistic Face Rotation,2018 -125,United States,MsCeleb,msceleb,40.4441619,-79.94272826,Carnegie Mellon University,edu,67a9659de0bf671fafccd7f39b7587f85fb6dfbd,citation,,Ring Loss: Convex Feature Normalization for Face Recognition,2018 +4,France,MsCeleb,msceleb,46.1476461,-1.1549415,University of La Rochelle,edu,153fbae25efd061f9046970071d0cfe739a35a0e,citation,,FaceLiveNet: End-to-End Networks Combining Face Verification with Interactive Facial Expression-Based Liveness Detection,2018 +5,China,MsCeleb,msceleb,26.89887,112.590435,University of South China,edu,98518fc368d7e1478cef40f5f8fd4468763645ad,citation,http://downloads.hindawi.com/journals/cin/2018/4512473.pdf,A Community Detection Approach to Cleaning Extremely Large Face Database,2018 +6,China,MsCeleb,msceleb,28.2290209,112.99483204,"National University of Defense Technology, China",mil,98518fc368d7e1478cef40f5f8fd4468763645ad,citation,http://downloads.hindawi.com/journals/cin/2018/4512473.pdf,A Community Detection Approach to Cleaning Extremely Large Face Database,2018 +7,China,MsCeleb,msceleb,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,6cdbbced12bff53bcbdde3cdb6d20b4bd02a9d6c,citation,https://arxiv.org/pdf/1811.12026.pdf,Attacks on State-of-the-Art Face Recognition using Attentional Adversarial Attack Generative Network,2018 +8,China,MsCeleb,msceleb,39.98177,116.330086,National Laboratory of Pattern Recognition,edu,e47f4a127f41c055fb7893ddc295932ead783c63,citation,https://arxiv.org/pdf/1709.03675.pdf,Adversarial Discriminative Heterogeneous Face Recognition,2018 +9,China,MsCeleb,msceleb,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,e47f4a127f41c055fb7893ddc295932ead783c63,citation,https://arxiv.org/pdf/1709.03675.pdf,Adversarial Discriminative Heterogeneous Face Recognition,2018 +10,United States,MsCeleb,msceleb,42.718568,-84.47791571,Michigan State University,edu,b446bcd7fb78adfe346cf7a01a38e4f43760f363,citation,https://pdfs.semanticscholar.org/b446/bcd7fb78adfe346cf7a01a38e4f43760f363.pdf,To appear in ICB 2018 Longitudinal Study of Child Face Recognition,2017 +11,United Kingdom,MsCeleb,msceleb,51.3791442,-2.3252332,University of Bath,edu,26567da544239cc6628c5696b0b10539144cbd57,citation,https://arxiv.org/pdf/1811.12784.pdf,The GAN that Warped: Semantic Attribute Editing with Unpaired Data,2018 +12,United States,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 +13,United States,MsCeleb,msceleb,42.718568,-84.47791571,Michigan State University,edu,3011b5fce49112228711a9e5f92d6f191687c1ea,citation,https://arxiv.org/pdf/1803.09014.pdf,Feature Transfer Learning for Deep Face Recognition with Long-Tail Data,2018 +14,United Kingdom,MsCeleb,msceleb,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 +15,United States,MsCeleb,msceleb,39.2899685,-76.62196103,University of Maryland,edu,b6f758be954d34817d4ebaa22b30c63a4b8ddb35,citation,https://arxiv.org/pdf/1703.04835.pdf,A Proximity-Aware Hierarchical Clustering of Faces,2017 +16,United States,MsCeleb,msceleb,42.718568,-84.47791571,Michigan State University,edu,19fa871626df604639550c6445d2f76cd369dd13,citation,https://arxiv.org/pdf/1805.02283.pdf,DocFace: Matching ID Document Photos to Selfies,2018 +17,United States,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 +18,United States,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 +19,United States,MsCeleb,msceleb,40.4441619,-79.94272826,Carnegie Mellon University,edu,d35534f3f59631951011539da2fe83f2844ca245,citation,https://arxiv.org/pdf/1705.07904.pdf,Semantically Decomposing the Latent Spaces of Generative Adversarial Networks,2017 +20,Canada,MsCeleb,msceleb,49.2767454,-122.91777375,Simon Fraser University,edu,b301fd2fc33f24d6f75224e7c0991f4f04b64a65,citation,https://arxiv.org/pdf/1803.06340.pdf,Faces as Lighting Probes via Unsupervised Deep Highlight Extraction,2018 +21,China,MsCeleb,msceleb,28.2290209,112.99483204,"National University of Defense Technology, China",mil,b301fd2fc33f24d6f75224e7c0991f4f04b64a65,citation,https://arxiv.org/pdf/1803.06340.pdf,Faces as Lighting Probes via Unsupervised Deep Highlight Extraction,2018 +22,United States,MsCeleb,msceleb,42.3614256,-71.0812092,Microsoft Research Asia,company,b301fd2fc33f24d6f75224e7c0991f4f04b64a65,citation,https://arxiv.org/pdf/1803.06340.pdf,Faces as Lighting Probes via Unsupervised Deep Highlight Extraction,2018 +23,China,MsCeleb,msceleb,39.9041999,116.4073963,"Beijing, China",edu,7fa4e972da46735971aad52413d17c4014c49e6e,citation,https://arxiv.org/pdf/1709.02940.pdf,How to Train Triplet Networks with 100K Identities?,2017 +24,Singapore,MsCeleb,msceleb,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 +25,China,MsCeleb,msceleb,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 +26,Japan,MsCeleb,msceleb,35.6992503,139.7721568,"Hitachi, Ltd., Tokyo, Japan",company,3b4da93fbdf7ae520fa00d39ffa694e850b85162,citation,,Face-Voice Matching using Cross-modal Embeddings,2018 +27,China,MsCeleb,msceleb,30.19331415,120.11930822,Zhejiang University,edu,85860d38c66a5cf2e6ffd6475a3a2ba096ea2920,citation,,Celeb-500K: A Large Training Dataset for Face Recognition,2018 +28,China,MsCeleb,msceleb,40.0044795,116.370238,Chinese Academy of Sciences,edu,6f5309d8cc76d3d300b72745887addd2a2480ba8,citation,,KinNet: Fine-to-Coarse Deep Metric Learning for Kinship Verification,2017 +29,China,MsCeleb,msceleb,40.00229045,116.32098908,Tsinghua University,edu,09ad80c4e80e1e02afb8fa4cb6dab260fb66df53,citation,,Feature Learning for One-Shot Face Recognition,2018 +30,United States,MsCeleb,msceleb,40.4441619,-79.94272826,Carnegie Mellon University,edu,c71217b2b111a51a31cf1107c71d250348d1ff68,citation,https://arxiv.org/pdf/1703.09912.pdf,One Network to Solve Them All — Solving Linear Inverse Problems Using Deep Projection Models,2017 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UALITY I NDEX ( GQI ) BY GAN-INDUCED C LASSIFIER,2018 +39,China,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 +40,China,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 +41,United States,MsCeleb,msceleb,42.4505507,-76.4783513,Cornell University,edu,dec0c26855da90876c405e9fd42830c3051c2f5f,citation,https://pdfs.semanticscholar.org/dec0/c26855da90876c405e9fd42830c3051c2f5f.pdf,Supplementary Material : Learning Compositional Visual Concepts with Mutual Consistency,2018 +42,France,MsCeleb,msceleb,48.8476037,2.2639934,"Université Paris-Saclay, France",edu,96e318f8ff91ba0b10348d4de4cb7c2142eb8ba9,citation,,State-of-the-art face recognition performance using publicly available software and datasets,2018 +43,United States,MsCeleb,msceleb,29.7207902,-95.34406271,University of Houston,edu,38d8ff137ff753f04689e6b76119a44588e143f3,citation,https://arxiv.org/pdf/1709.06532.pdf,When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition,2017 +44,United States,MsCeleb,msceleb,38.0333742,-84.5017758,University of Kentucky,edu,455a7e03a0c5ab618d0e86a06c9910ac179f0479,citation,https://arxiv.org/pdf/1807.08772.pdf,Identity Preserving Face Completion for Large Ocular Region Occlusion,2018 +45,United States,MsCeleb,msceleb,34.0224149,-118.28634407,University of Southern California,edu,455a7e03a0c5ab618d0e86a06c9910ac179f0479,citation,https://arxiv.org/pdf/1807.08772.pdf,Identity Preserving Face Completion for Large Ocular Region Occlusion,2018 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Kingdom,MsCeleb,msceleb,51.59029705,-0.22963221,Middlesex University,edu,9b0489f2d5739213ef8c3e2e18739c4353c3a3b7,citation,https://arxiv.org/pdf/1801.06665.pdf,Visual Data Augmentation through Learning,2018 +51,United States,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 +52,China,MsCeleb,msceleb,22.4162632,114.2109318,Chinese University of Hong Kong,edu,9e182e0cd9d70f876f1be7652c69373bcdf37fb4,citation,https://arxiv.org/pdf/1807.07860.pdf,Talking Face Generation by Adversarially Disentangled Audio-Visual Representation,2018 +53,United States,MsCeleb,msceleb,38.99203005,-76.9461029,University of Maryland College Park,edu,83447d47bb2837b831b982ebf9e63616742bfdec,citation,https://arxiv.org/pdf/1812.04058.pdf,An Automatic System for Unconstrained Video-Based Face Recognition,2018 +54,United States,MsCeleb,msceleb,43.7192587,10.4207947,"CNR ISTI-Institute of Information Science and Technologies, Pisa, Italy",edu,266766818dbc5a4ca1161ae2bc14c9e269ddc490,citation,https://pdfs.semanticscholar.org/2667/66818dbc5a4ca1161ae2bc14c9e269ddc490.pdf,Boosting a Low-Cost Smart Home Environment with Usage and Access Control Rules,2018 +55,United States,MsCeleb,msceleb,38.99203005,-76.9461029,University of Maryland College Park,edu,944ea33211d67663e04d0181843db634e42cb2ca,citation,https://arxiv.org/pdf/1804.01159.pdf,Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition.,2018 +56,Taiwan,MsCeleb,msceleb,25.01682835,121.53846924,National Taiwan University,edu,f15b7c317f106816bf444ac4ffb6c280cd6392c7,citation,http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w1/Zhang_Deep_Disguised_Faces_CVPR_2018_paper.pdf,Deep Disguised Faces Recognition,2018 +57,China,MsCeleb,msceleb,40.00229045,116.32098908,Tsinghua University,edu,19d53bb35baf6ab02368756412800c218a2df71c,citation,https://arxiv.org/pdf/1711.09515.pdf,DeepDeblur: Fast one-step blurry face images restoration.,2017 +58,United States,MsCeleb,msceleb,42.718568,-84.47791571,Michigan State University,edu,12ba7c6f559a69fbfaacf61bfb2f8431505b09a0,citation,https://arxiv.org/pdf/1809.05620.pdf,DocFace+: ID Document to Selfie Matching,2018 +59,South Korea,MsCeleb,msceleb,37.5600406,126.9369248,Yonsei University,edu,d8526863f35b29cbf8ac2ae756eaae0d2930ffb1,citation,http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w27/Choe_Face_Generation_for_ICCV_2017_paper.pdf,Face Generation for Low-Shot Learning Using Generative Adversarial Networks,2017 +60,Germany,MsCeleb,msceleb,52.381515,9.720171,"Leibniz Information Centre for Science and Technology, Hannover, Germany",edu,5209758096819efee15751c8875121bd27f2ee78,citation,https://arxiv.org/pdf/1806.08246.pdf,Finding Person Relations in Image Data of the Internet Archive,2018 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University,edu,5c54e0f46330787c4fac48aecced9a8f8e37658a,citation,http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w23/Ming_Simple_Triplet_Loss_ICCV_2017_paper.pdf,Simple Triplet Loss Based on Intra/Inter-Class Metric Learning for Face Verification,2017 +80,China,MsCeleb,msceleb,31.83907195,117.26420748,University of Science and Technology of China,edu,c5b324f7f9abdffc1be83f640674beda81b74315,citation,,Towards Open-Set Identity Preserving Face Synthesis,2018 +81,Italy,MsCeleb,msceleb,44.6451046,10.9279268,University of Modena and Reggio Emilia,edu,ff44d8938c52cfdca48c80f8e1618bbcbf91cb2a,citation,https://pdfs.semanticscholar.org/ff44/d8938c52cfdca48c80f8e1618bbcbf91cb2a.pdf,Towards Video Captioning with Naming: A Novel Dataset and a Multi-modal Approach,2017 +82,France,MsCeleb,msceleb,45.7833631,4.76877036,Ecole Centrale de Lyon,edu,727d03100d4a8e12620acd7b1d1972bbee54f0e6,citation,https://arxiv.org/pdf/1706.04264.pdf,von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification,2017 +83,France,MsCeleb,msceleb,48.832493,2.267474,Safran Identity and Security,company,727d03100d4a8e12620acd7b1d1972bbee54f0e6,citation,https://arxiv.org/pdf/1706.04264.pdf,von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification,2017 +84,China,MsCeleb,msceleb,39.980196,116.333305,"CASIA, Center for Research on Intelligent Perception and Computing, Beijing, 100190, China",edu,3ac09c2589178dac0b6a2ea2edf04b7629672d81,citation,https://arxiv.org/pdf/1708.02412.pdf,Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition,2018 +85,China,MsCeleb,msceleb,39.979203,116.33287,"CASIA, National Laboratory of Pattern Recognition",edu,3ac09c2589178dac0b6a2ea2edf04b7629672d81,citation,https://arxiv.org/pdf/1708.02412.pdf,Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition,2018 +86,China,MsCeleb,msceleb,40.0044795,116.370238,Chinese Academy of Sciences,edu,3ac09c2589178dac0b6a2ea2edf04b7629672d81,citation,https://arxiv.org/pdf/1708.02412.pdf,Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition,2018 +87,United States,MsCeleb,msceleb,38.99203005,-76.9461029,University of Maryland College Park,edu,b35ff9985aaee9371588330bcef0dfc88d1401d7,citation,,Deep Density Clustering of Unconstrained Faces,2018 +88,United States,MsCeleb,msceleb,30.6108365,-96.352128,Texas A&M University,edu,e36fdb50844132fc7925550398e68e7ae95467de,citation,,Face Verification with Disguise Variations via Deep Disguise Recognizer,2018 +89,United States,MsCeleb,msceleb,39.65404635,-79.96475355,West Virginia University,edu,e36fdb50844132fc7925550398e68e7ae95467de,citation,,Face Verification with Disguise Variations via Deep Disguise Recognizer,2018 +90,China,MsCeleb,msceleb,39.9106327,116.3356321,Chinese Academy of Science,edu,20f87ed94a423b5d8599d85d1f2f80bab8902107,citation,,Pose-Guided Photorealistic Face Rotation,2018 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California,edu,b53289f3f3b17dad91fa4fd25d09fdbc14f8c8cc,citation,http://faculty.ucmerced.edu/mhyang/papers/cviu16_MOT.pdf,Online multi-object tracking via robust collaborative model and sample selection,2017 -19,United States,TownCentre,oxford_town_centre,28.59899755,-81.19712501,University of Central Florida,edu,920246280e7e70900762ddfa7c41a79ec4517350,citation,http://crcv-web.eecs.ucf.edu/papers/eccv2012/MPMPT-ECCV12.pdf,(MP) 2 T: multiple people multiple parts tracker,2012 -20,United States,TownCentre,oxford_town_centre,37.8718992,-122.2585399,University of California,edu,14d5bd23667db4413a7f362565be21d462d3fc93,citation,http://alumni.cs.ucr.edu/~zqin001/cvpr2014.pdf,An Online Learned Elementary Grouping Model for Multi-target Tracking,2014 -21,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,9070045c1a9564a5f25b42f3facc7edf4c302483,citation,http://virtualhumans.mpi-inf.mpg.de/papers/lealPonsmollICCVW2011/lealPonsmollICCVW2011.pdf,Everybody 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Firenze",edu,2914a20df10f3bb55c5d4764ece85101c1a3e5a8,citation,http://www.micc.unifi.it/seidenari/wp-content/papercite-data/pdf/icpr_16.pdf,User interest profiling using tracking-free coarse gaze estimation,2016 -29,United States,TownCentre,oxford_town_centre,40.4441619,-79.94272826,Carnegie Mellon University,edu,1f4fed0183048d9014e22a72fd50e1e5fbe0777c,citation,https://pdfs.semanticscholar.org/6b7b/1760ed23ef15ec210b2d6795fdf9ad36d0e2.pdf,A Game-Theoretic Approach to Multi-Pedestrian Activity Forecasting,2016 -30,United States,TownCentre,oxford_town_centre,37.43131385,-122.16936535,Stanford University,edu,1f4fed0183048d9014e22a72fd50e1e5fbe0777c,citation,https://pdfs.semanticscholar.org/6b7b/1760ed23ef15ec210b2d6795fdf9ad36d0e2.pdf,A Game-Theoretic Approach to Multi-Pedestrian Activity Forecasting,2016 -31,United States,TownCentre,oxford_town_centre,42.3354481,-71.16813864,Boston College,edu,869df5e8221129850e81e77d4dc36e6c0f854fe6,citation,https://arxiv.org/pdf/1601.03094.pdf,A 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Institute for Intelligent Systems,edu,b6d0e461535116a675a0354e7da65b2c1d2958d4,citation,https://arxiv.org/pdf/1805.03430.pdf,Deep Directional Statistics: Pose Estimation with Uncertainty Quantification,2018 -39,United States,TownCentre,oxford_town_centre,38.7768106,-94.9442982,Amazon,company,b6d0e461535116a675a0354e7da65b2c1d2958d4,citation,https://arxiv.org/pdf/1805.03430.pdf,Deep Directional Statistics: Pose Estimation with Uncertainty Quantification,2018 -40,United States,TownCentre,oxford_town_centre,47.6423318,-122.1369302,Microsoft,company,b6d0e461535116a675a0354e7da65b2c1d2958d4,citation,https://arxiv.org/pdf/1805.03430.pdf,Deep Directional Statistics: Pose Estimation with Uncertainty Quantification,2018 -41,United Kingdom,TownCentre,oxford_town_centre,55.91029135,-3.32345777,Heriot-Watt University,edu,70be5432677c0fbe000ac0c28dda351a950e0536,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2014/W14/papers/Leach_Detecting_Social_Groups_2014_CVPR_paper.pdf,Detecting Social Groups in Crowded Surveillance Videos Using Visual Attention,2014 -42,Switzerland,TownCentre,oxford_town_centre,47.376313,8.5476699,ETH Zurich,edu,9458642e7645bfd865911140ee8413e2f5f9fcd6,citation,https://pdfs.semanticscholar.org/9458/642e7645bfd865911140ee8413e2f5f9fcd6.pdf,Efficient Multiple People Tracking Using Minimum Cost Arborescences,2014 -43,United Kingdom,TownCentre,oxford_town_centre,54.6141723,-5.9002151,Queen's University Belfast,edu,2a7935706d43c01789d43a81a1d391418f220a0a,citation,https://pure.qub.ac.uk/portal/files/31960902/285.pdf,Enhancing Linear Programming with Motion Modeling for Multi-target Tracking,2015 -44,Sri Lanka,TownCentre,oxford_town_centre,6.7970862,79.9019094,University of Moratuwa,edu,b183914d0b16647a41f0bfd4af64bf94a83a2b14,citation,http://iwinlab.eng.usf.edu/papers/Extensible%20video%20surveillance%20software%20with%20simultaneous%20event%20detection%20for%20low%20and%20high%20density%20crowd%20analysis.pdf,Extensible video surveillance software with simultaneous event detection for low and high density crowd analysis,2014 -45,United States,TownCentre,oxford_town_centre,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,fa5aca45965e312362d2d75a69312a0678fdf5d7,citation,https://pdfs.semanticscholar.org/fa5a/ca45965e312362d2d75a69312a0678fdf5d7.pdf,Fast and Accurate Head Pose Estimation via Random Projection Forests : Supplementary Material,2015 -46,United States,TownCentre,oxford_town_centre,37.3641651,-120.4254615,University of California at Merced,edu,fa5aca45965e312362d2d75a69312a0678fdf5d7,citation,https://pdfs.semanticscholar.org/fa5a/ca45965e312362d2d75a69312a0678fdf5d7.pdf,Fast and Accurate Head Pose Estimation via Random Projection Forests : Supplementary Material,2015 -47,Australia,TownCentre,oxford_town_centre,-32.8892352,151.6998983,"University of Newcastle, Australia",edu,2feb7c57d51df998aafa6f3017662263a91625b4,citation,https://pdfs.semanticscholar.org/d344/9eaaf392fd07b676e744410049f4095b4b5c.pdf,Feature Selection for Intelligent Transportation Systems,2014 -48,Germany,TownCentre,oxford_town_centre,49.01546,8.4257999,Fraunhofer,company,1f82eebadc3ffa41820ad1a0f53770247fc96dcd,citation,https://pdfs.semanticscholar.org/c5ac/81b17b8fcc028f375fbbd090b558ba9a437a.pdf,Using Trajectories derived by Dense Optical Flows as a Spatial Component in Background Subtraction,2016 -49,United States,TownCentre,oxford_town_centre,42.3583961,-71.09567788,MIT,edu,b18f94c5296a9cebe9e779d50d193fd180f78ed9,citation,https://arxiv.org/pdf/1604.01431.pdf,Forecasting Interactive Dynamics of Pedestrians with Fictitious Play,2017 -50,United Kingdom,TownCentre,oxford_town_centre,51.7520849,-1.2516646,Oxford University,edu,b18f94c5296a9cebe9e779d50d193fd180f78ed9,citation,https://arxiv.org/pdf/1604.01431.pdf,Forecasting Interactive Dynamics of Pedestrians with Fictitious Play,2017 -51,United States,TownCentre,oxford_town_centre,37.43131385,-122.16936535,Stanford University,edu,b18f94c5296a9cebe9e779d50d193fd180f78ed9,citation,https://arxiv.org/pdf/1604.01431.pdf,Forecasting Interactive Dynamics of Pedestrians with Fictitious Play,2017 -52,Netherlands,TownCentre,oxford_town_centre,52.3553655,4.9501644,University of Amsterdam,edu,687ec23addf5a1279e49cc46b78e3245af94ac7b,citation,https://pdfs.semanticscholar.org/687e/c23addf5a1279e49cc46b78e3245af94ac7b.pdf,UvA-DARE ( Digital Academic Repository ) Visual Tracking : An Experimental Survey Smeulders,2013 -53,Italy,TownCentre,oxford_town_centre,45.1847248,9.1582069,"Italian Institute of Technology, Genova, Italy",edu,5ab9f00a707a55f4955b378981ad425aa1cb8ea3,citation,https://arxiv.org/pdf/1901.02000.pdf,Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets,2019 -54,Germany,TownCentre,oxford_town_centre,48.1820038,11.5978282,"OSRAM GmbH, Germany",company,5ab9f00a707a55f4955b378981ad425aa1cb8ea3,citation,https://arxiv.org/pdf/1901.02000.pdf,Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets,2019 -55,Italy,TownCentre,oxford_town_centre,45.437398,11.003376,University of Verona,edu,5ab9f00a707a55f4955b378981ad425aa1cb8ea3,citation,https://arxiv.org/pdf/1901.02000.pdf,Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets,2019 -56,United Kingdom,TownCentre,oxford_town_centre,51.7534538,-1.25400997,University of Oxford,edu,3ed9730e5ec8716e8cdf55f207ef973a9c854574,citation,https://arxiv.org/pdf/1612.05234.pdf,Visual Compiler: Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator,2016 -57,United States,TownCentre,oxford_town_centre,29.7207902,-95.34406271,University of Houston,edu,58eba9930b63cc14715368acf40017293b8dc94f,citation,https://pdfs.semanticscholar.org/7508/ac08dd7b9694bcfe71a617df7fcf3df80952.pdf,What Do I See? Modeling Human Visual Perception for Multi-person Tracking,2014 -58,United States,TownCentre,oxford_town_centre,29.7207902,-95.34406271,University of Houston,edu,a0b489eeb4f7fd2249da756d829e179a6718d9d1,citation,,"""Seeing is Believing"": Pedestrian Trajectory Forecasting Using Visual Frustum of Attention",2018 -59,Belgium,TownCentre,oxford_town_centre,50.8779545,4.7002953,"KULeuven, EAVISE",edu,4ec4392246a7760d189cd6ea48a81664cd2fe4bf,citation,https://pdfs.semanticscholar.org/4ec4/392246a7760d189cd6ea48a81664cd2fe4bf.pdf,GPU Accelerated ACF Detector,2018 -60,United States,TownCentre,oxford_town_centre,40.7982133,-77.8599084,The Pennsylvania State University,edu,6e32c368a6157fb911c9363dc3e967a7fb2ad9f7,citation,https://pdfs.semanticscholar.org/8268/d68f6aa510a765466b2c7f2ba2ea34a48c51.pdf,Hybrid Stochastic / Deterministic Optimization for Tracking Sports Players and Pedestrians,2014 -61,United States,TownCentre,oxford_town_centre,40.4439789,-79.9464634,Disney Research Pittsburgh,edu,6e32c368a6157fb911c9363dc3e967a7fb2ad9f7,citation,https://pdfs.semanticscholar.org/8268/d68f6aa510a765466b2c7f2ba2ea34a48c51.pdf,Hybrid Stochastic / Deterministic Optimization for Tracking Sports Players and Pedestrians,2014 -62,India,TownCentre,oxford_town_centre,13.0304619,77.5646862,"M.S. Ramaiah Institute of Technology, Bangalore, India",edu,6f089f9959cc711e16f1ebe0c6251aaf8a65959a,citation,https://pdfs.semanticscholar.org/6f08/9f9959cc711e16f1ebe0c6251aaf8a65959a.pdf,Improvement in object detection using Super Pixels,2016 -63,United States,TownCentre,oxford_town_centre,38.99203005,-76.9461029,University of Maryland College Park,edu,4e82908e6482d973c280deb79c254631a60f1631,citation,https://pdfs.semanticscholar.org/4e82/908e6482d973c280deb79c254631a60f1631.pdf,Improving Efficiency and Scalability in Visual Surveillance Applications,2013 -64,United States,TownCentre,oxford_town_centre,37.8718992,-122.2585399,University of California,edu,38b5a83f7941fea5fd82466f8ce1ce4ed7749f59,citation,http://rlair.cs.ucr.edu/papers/docs/grouptracking.pdf,Improving multi-target tracking via social grouping,2012 -65,Singapore,TownCentre,oxford_town_centre,1.3484104,103.68297965,Nanyang Technological University,edu,13caf4d2e0a4b6fcfcd4b9e8e2341b8ebd38258d,citation,https://arxiv.org/pdf/1605.04502.pdf,Joint Learning of Siamese CNNs and Temporally Constrained Metrics for Tracklet Association,2016 -66,United States,TownCentre,oxford_town_centre,35.9049122,-79.0469134,The University of North Carolina at Chapel Hill,edu,45e459462a80af03e1bb51a178648c10c4250925,citation,https://arxiv.org/pdf/1606.08998.pdf,LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning,2016 -67,China,TownCentre,oxford_town_centre,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,c0262e24324a6a4e6af5bd99fc79e2eb802519b3,citation,https://arxiv.org/pdf/1611.03968.pdf,Learning Scene-specific Object Detectors Based on a Generative-Discriminative Model with Minimal Supervision,2016 -68,China,TownCentre,oxford_town_centre,30.527151,114.400762,China University of Geosciences,edu,c0262e24324a6a4e6af5bd99fc79e2eb802519b3,citation,https://arxiv.org/pdf/1611.03968.pdf,Learning Scene-specific Object Detectors Based on a Generative-Discriminative Model with Minimal Supervision,2016 -69,China,TownCentre,oxford_town_centre,32.0565957,118.77408833,Nanjing University,edu,c0262e24324a6a4e6af5bd99fc79e2eb802519b3,citation,https://arxiv.org/pdf/1611.03968.pdf,Learning Scene-specific Object Detectors Based on a Generative-Discriminative Model with Minimal Supervision,2016 -70,United Kingdom,TownCentre,oxford_town_centre,51.5247272,-0.03931035,Queen Mary University of London,edu,1883387726897d94b663cc4de4df88e5c31df285,citation,http://www.eecs.qmul.ac.uk/~andrea/papers/2014_TIP_MultiTargetTrackingEvaluation_Tahir_Poiesi_Cavallaro.pdf,Measures of Effective Video Tracking,2014 -71,United States,TownCentre,oxford_town_centre,35.9113971,-79.0504529,University of North Carolina at Chapel Hill,edu,8d2bf6ecbfda94f57000b84509bf77f4c47c1c66,citation,https://arxiv.org/pdf/1707.09100.pdf,MixedPeds: Pedestrian Detection in Unannotated Videos Using Synthetically Generated Human-Agents for Training,2018 -72,United States,TownCentre,oxford_town_centre,37.8718992,-122.2585399,University of California,edu,b506aa23949b6d1f0c868ad03aaaeb5e5f7f6b57,citation,http://rlair.cs.ucr.edu/papers/docs/zqin-phd.pdf,Modeling Social and Temporal Context for Video Analysis,2015 -73,Australia,TownCentre,oxford_town_centre,-34.920603,138.6062277,Adelaide University,edu,5bae9822d703c585a61575dced83fa2f4dea1c6d,citation,https://arxiv.org/pdf/1504.01942.pdf,MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking,2015 -74,Switzerland,TownCentre,oxford_town_centre,47.376313,8.5476699,ETH Zurich,edu,5bae9822d703c585a61575dced83fa2f4dea1c6d,citation,https://arxiv.org/pdf/1504.01942.pdf,MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking,2015 -75,Germany,TownCentre,oxford_town_centre,49.8748277,8.6563281,TU Darmstadt,edu,5bae9822d703c585a61575dced83fa2f4dea1c6d,citation,https://arxiv.org/pdf/1504.01942.pdf,MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking,2015 -76,United States,TownCentre,oxford_town_centre,37.8718992,-122.2585399,University of California,edu,e6d48d23308a9e0a215f7b5ba6ae30ee5d2f0ef5,citation,https://pdfs.semanticscholar.org/e6d4/8d23308a9e0a215f7b5ba6ae30ee5d2f0ef5.pdf,Multi-person Tracking by Online Learned Grouping Model with Non-linear Motion Context,2015 -77,France,TownCentre,oxford_town_centre,45.217886,5.807369,INRIA,edu,fc30d7dbf4c3cdd377d8cd4e7eeabd5d73814b8f,citation,https://pdfs.semanticscholar.org/fc30/d7dbf4c3cdd377d8cd4e7eeabd5d73814b8f.pdf,Multiple Object Tracking by Efficient Graph Partitioning,2014 -78,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,290eda31bc13cbd5933acec8b6a25b3e3761c788,citation,https://arxiv.org/pdf/1411.7935.pdf,Multiple object tracking with context awareness,2014 -79,Czech Republic,TownCentre,oxford_town_centre,49.20172,16.6033168,Brno University of Technology,edu,dc53c4bb04e787a0d45dd761ba2101cc51c17b82,citation,https://pdfs.semanticscholar.org/dc53/c4bb04e787a0d45dd761ba2101cc51c17b82.pdf,Multiple-Person Tracking by Detection,2016 -80,Germany,TownCentre,oxford_town_centre,48.1820038,11.5978282,"OSRAM GmbH, Germany",company,943b1b92b5bdee0b5770418c645a4a17bded1ccf,citation,https://arxiv.org/pdf/1805.00652.pdf,MX-LSTM: Mixing Tracklets and Vislets to Jointly Forecast Trajectories and Head Poses,2018 -81,Italy,TownCentre,oxford_town_centre,45.437398,11.003376,University of Verona,edu,943b1b92b5bdee0b5770418c645a4a17bded1ccf,citation,https://arxiv.org/pdf/1805.00652.pdf,MX-LSTM: Mixing Tracklets and Vislets to Jointly Forecast Trajectories and Head Poses,2018 -82,France,TownCentre,oxford_town_centre,48.8422058,2.3451689,"INRIA / Ecole Normale Supérieure, France",edu,47119c99f5aa1e47bbeb86de0f955e7c500e6a93,citation,https://arxiv.org/pdf/1408.3304.pdf,On pairwise costs for network flow multi-object tracking,2015 -83,United States,TownCentre,oxford_town_centre,42.3504253,-71.10056114,Boston University,edu,1ae3dd081b93c46cda4d72100d8b1d59eb585157,citation,https://pdfs.semanticscholar.org/fea1/0f39b0a77035fb549fc580fd951384b79f9b.pdf,Online Motion Agreement Tracking,2013 -84,Malaysia,TownCentre,oxford_town_centre,4.3400673,101.1429799,Universiti Tunku Abdul Rahman,edu,e1f815c50a6c0c6d790c60a1348393264f829e60,citation,https://pdfs.semanticscholar.org/e1f8/15c50a6c0c6d790c60a1348393264f829e60.pdf,PEDESTRIAN DETECTION AND TRACKING IN SURVEILLANCE VIDEO By PENNY CHONG,2016 -85,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,422d352a7d26fef692a3cd24466bfb5b4526efea,citation,https://pdfs.semanticscholar.org/422d/352a7d26fef692a3cd24466bfb5b4526efea.pdf,Pedestrian interaction in tracking : the social force model and global optimization methods,2012 -86,Sweden,TownCentre,oxford_town_centre,57.6897063,11.9741654,Chalmers University of Technology,edu,367b5b814aa991329c2ae7f8793909ad8c0a56f1,citation,https://arxiv.org/pdf/1211.0191.pdf,Performance evaluation of random set based pedestrian tracking algorithms,2013 -87,Japan,TownCentre,oxford_town_centre,35.5152072,134.1733553,Tottori University,edu,9d89f1bc88fd65e90b31a2129719384796bed17a,citation,http://vision.unipv.it/CV/materiale2016-17/2nd%20Choice/0225.pdf,Person re-identification using co-occurrence attributes of physical and adhered human characteristics,2016 -88,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,48705017d91a157949cfaaeb19b826014899a36b,citation,https://pdfs.semanticscholar.org/4870/5017d91a157949cfaaeb19b826014899a36b.pdf,PROBABILISTIC MULTI-PERSON TRACKING USING DYNAMIC BAYES NETWORKS,2015 -89,Italy,TownCentre,oxford_town_centre,39.2173657,9.1149218,"Università degli Studi di Cagliari, Italy",edu,7c1f47ca50a8a55f93bf69791d9df2f994019758,citation,http://veprints.unica.it/1295/1/PhD_ThesisPalaF.pdf,Re-identification and semantic retrieval of pedestrians in video surveillance scenarios,2016 -90,United Kingdom,TownCentre,oxford_town_centre,51.5247272,-0.03931035,Queen Mary University of London,edu,3a28059df29b74775f77fd20a15dc6b5fe857556,citation,https://pdfs.semanticscholar.org/3a28/059df29b74775f77fd20a15dc6b5fe857556.pdf,Riccardo Mazzon PhD Thesis 2013,2013 -91,Brazil,TownCentre,oxford_town_centre,-30.0338248,-51.218828,Federal University of Rio Grande do Sul,edu,057517452369751bd63d83902ea91558d58161da,citation,http://inf.ufrgs.br/~gfuhr/papers/102095_3.pdf,Robust Patch-Based Pedestrian Tracking Using Monocular Calibrated Cameras,2012 -92,China,TownCentre,oxford_town_centre,28.727339,115.816633,Jiangxi University of Finance and Economics,edu,1642358cd9410abe9ee512d34ba68296b308770e,citation,https://arxiv.org/pdf/1807.04562.pdf,Robustness Analysis of Pedestrian Detectors for Surveillance,2018 -93,Singapore,TownCentre,oxford_town_centre,1.3484104,103.68297965,Nanyang Technological University,edu,1642358cd9410abe9ee512d34ba68296b308770e,citation,https://arxiv.org/pdf/1807.04562.pdf,Robustness Analysis of Pedestrian Detectors for Surveillance,2018 -94,China,TownCentre,oxford_town_centre,34.250803,108.983693,Xi’an Jiaotong University,edu,1642358cd9410abe9ee512d34ba68296b308770e,citation,https://arxiv.org/pdf/1807.04562.pdf,Robustness Analysis of Pedestrian Detectors for Surveillance,2018 -95,Singapore,TownCentre,oxford_town_centre,1.3484104,103.68297965,Nanyang Technological University,edu,7c132e0a2b7e13c78784287af38ad74378da31e5,citation,https://pdfs.semanticscholar.org/7c13/2e0a2b7e13c78784287af38ad74378da31e5.pdf,Salient Parts based Multi-people Tracking,2015 -96,China,TownCentre,oxford_town_centre,40.0044795,116.370238,Chinese Academy of Sciences,edu,679136c2844eeddca34e98e483aca1ff6ef5e902,citation,https://arxiv.org/pdf/1712.08745.pdf,Scene-Specific Pedestrian Detection Based on Parallel Vision,2017 -97,China,TownCentre,oxford_town_centre,34.250803,108.983693,Xi’an Jiaotong University,edu,679136c2844eeddca34e98e483aca1ff6ef5e902,citation,https://arxiv.org/pdf/1712.08745.pdf,Scene-Specific Pedestrian Detection Based on Parallel Vision,2017 -98,China,TownCentre,oxford_town_centre,40.0044795,116.370238,Chinese Academy of Sciences,edu,57e9b0d3ab6295e914d5a30cfaa3b2c81189abc1,citation,https://arxiv.org/pdf/1611.07544.pdf,Self-Learning Scene-Specific Pedestrian Detectors Using a Progressive Latent Model,2017 -99,United States,TownCentre,oxford_town_centre,35.9990522,-78.9290629,Duke University,edu,57e9b0d3ab6295e914d5a30cfaa3b2c81189abc1,citation,https://arxiv.org/pdf/1611.07544.pdf,Self-Learning Scene-Specific Pedestrian Detectors Using a Progressive Latent Model,2017 -100,Switzerland,TownCentre,oxford_town_centre,47.3764534,8.54770931,ETH Zürich,edu,70b42bbd76e6312d39ea06b8a0c24beb4a93e022,citation,http://www.tnt.uni-hannover.de/papers/data/1075/WACV2015_Abstract.pdf,Solving Multiple People Tracking in a Minimum Cost Arborescence,2015 -101,United States,TownCentre,oxford_town_centre,42.718568,-84.47791571,Michigan State University,edu,acf0db156406ddad1ace2ff2696cb60d0a04cf7c,citation,http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf,Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,2018 -102,United Kingdom,TownCentre,oxford_town_centre,51.7534538,-1.25400997,University of Oxford,edu,acf0db156406ddad1ace2ff2696cb60d0a04cf7c,citation,http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf,Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,2018 -103,Japan,TownCentre,oxford_town_centre,36.05238585,140.11852361,Institute of Industrial Science,edu,acf0db156406ddad1ace2ff2696cb60d0a04cf7c,citation,http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf,Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,2018 -104,United States,TownCentre,oxford_town_centre,40.4441619,-79.94272826,Carnegie Mellon University,edu,acf0db156406ddad1ace2ff2696cb60d0a04cf7c,citation,http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf,Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,2018 -105,Sweden,TownCentre,oxford_town_centre,57.7172004,11.9218558,"Volvo Construction Equipment, Göthenburg, Sweden",company,acf0db156406ddad1ace2ff2696cb60d0a04cf7c,citation,http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf,Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,2018 -106,United States,TownCentre,oxford_town_centre,35.9990522,-78.9290629,Duke University,edu,64e0690dd176a93de9d4328f6e31fc4afe1e7536,citation,https://pdfs.semanticscholar.org/64e0/690dd176a93de9d4328f6e31fc4afe1e7536.pdf,Tracking Multiple People Online and in Real Time,2014 -107,Switzerland,TownCentre,oxford_town_centre,47.3764534,8.54770931,ETH Zürich,edu,64c78c8bf779a27e819fd9d5dba91247ab5a902b,citation,https://arxiv.org/pdf/1607.07304.pdf,Tracking with multi-level features.,2016 -108,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,64c78c8bf779a27e819fd9d5dba91247ab5a902b,citation,https://arxiv.org/pdf/1607.07304.pdf,Tracking with multi-level features.,2016 -109,Germany,TownCentre,oxford_town_centre,48.14955455,11.56775314,Technical University Munich,edu,64c78c8bf779a27e819fd9d5dba91247ab5a902b,citation,https://arxiv.org/pdf/1607.07304.pdf,Tracking with multi-level features.,2016 -110,Singapore,TownCentre,oxford_town_centre,1.3484104,103.68297965,Nanyang Technological University,edu,7d3698c0e828d05f147682b0f5bfcd3b681ff205,citation,https://arxiv.org/pdf/1511.06654.pdf,Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation,2017 -111,Australia,TownCentre,oxford_town_centre,-35.2809368,149.1300092,"NICTA, Canberra",edu,f0cc615b14c97482faa9c47eb855303c71ff03a7,citation,https://pdfs.semanticscholar.org/f0cc/615b14c97482faa9c47eb855303c71ff03a7.pdf,Tracklet clustering for robust multiple object tracking using distance dependent Chinese restaurant processes,2016 -112,Germany,TownCentre,oxford_town_centre,52.5180641,13.3250425,TU Berlin,edu,c4cd19cf41a2f5cd543d81b94afe6cc42785920a,citation,http://elvera.nue.tu-berlin.de/files/1491Bochinski2016.pdf,Training a convolutional neural network for multi-class object detection using solely virtual world data,2016 +18,United States,TownCentre,oxford_town_centre,28.59899755,-81.19712501,University of Central Florida,edu,920246280e7e70900762ddfa7c41a79ec4517350,citation,http://crcv-web.eecs.ucf.edu/papers/eccv2012/MPMPT-ECCV12.pdf,(MP) 2 T: multiple people multiple parts tracker,2012 +19,United States,TownCentre,oxford_town_centre,33.98071305,-117.33261035,"University of California, Riverside",edu,14d5bd23667db4413a7f362565be21d462d3fc93,citation,http://alumni.cs.ucr.edu/~zqin001/cvpr2014.pdf,An Online Learned Elementary Grouping Model for Multi-target Tracking,2014 +20,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,9070045c1a9564a5f25b42f3facc7edf4c302483,citation,http://virtualhumans.mpi-inf.mpg.de/papers/lealPonsmollICCVW2011/lealPonsmollICCVW2011.pdf,Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker,2011 +21,Singapore,TownCentre,oxford_town_centre,1.3484104,103.68297965,Nanyang Technological University,edu,2323cb559c9e18673db836ffc283c27e4a002ed9,citation,http://arxiv.org/pdf/1605.04502v1.pdf,Joint Learning of Convolutional Neural Networks and Temporally Constrained Metrics for Tracklet Association,2016 +22,China,TownCentre,oxford_town_centre,39.905838,116.375516,"Huawei Technologies, Beijing, China",company,434627a03d4433b0df03058724524c3ac1c07478,citation,http://jianghz.com/pubs/mtt_tip_final.pdf,Online Multi-Target Tracking With Unified Handling of Complex Scenarios,2015 +23,China,TownCentre,oxford_town_centre,34.250803,108.983693,Xi’an Jiaotong University,edu,434627a03d4433b0df03058724524c3ac1c07478,citation,http://jianghz.com/pubs/mtt_tip_final.pdf,Online Multi-Target Tracking With Unified Handling of Complex Scenarios,2015 +24,United States,TownCentre,oxford_town_centre,28.59899755,-81.19712501,University of Central Florida,edu,084352b63e98d3b3310521fb3bda8cb4a77a0254,citation,http://crcv.ucf.edu/papers/1439.pdf,Part-based multiple-person tracking with partial occlusion handling,2012 +25,United States,TownCentre,oxford_town_centre,39.5469449,-119.81346566,University of Nevada,edu,084352b63e98d3b3310521fb3bda8cb4a77a0254,citation,http://crcv.ucf.edu/papers/1439.pdf,Part-based multiple-person tracking with partial occlusion handling,2012 +26,United Kingdom,TownCentre,oxford_town_centre,55.7782474,-4.1040988,University of the West of Scotland,edu,32b9be86de4f82c5a43da2a1a0a892515da8910d,citation,http://users.informatik.haw-hamburg.de/~ubicomp/arbeiten/papers/ICISP2014.pdf,Robust False Positive Detection for Real-Time Multi-target Tracking,2014 +27,Italy,TownCentre,oxford_town_centre,43.7776426,11.259765,"Università degli Studi di Firenze, Firenze",edu,2914a20df10f3bb55c5d4764ece85101c1a3e5a8,citation,http://www.micc.unifi.it/seidenari/wp-content/papercite-data/pdf/icpr_16.pdf,User interest profiling using tracking-free coarse gaze estimation,2016 +28,United States,TownCentre,oxford_town_centre,40.4441619,-79.94272826,Carnegie Mellon University,edu,1f4fed0183048d9014e22a72fd50e1e5fbe0777c,citation,https://pdfs.semanticscholar.org/6b7b/1760ed23ef15ec210b2d6795fdf9ad36d0e2.pdf,A Game-Theoretic Approach to Multi-Pedestrian Activity Forecasting,2016 +29,United States,TownCentre,oxford_town_centre,37.43131385,-122.16936535,Stanford University,edu,1f4fed0183048d9014e22a72fd50e1e5fbe0777c,citation,https://pdfs.semanticscholar.org/6b7b/1760ed23ef15ec210b2d6795fdf9ad36d0e2.pdf,A Game-Theoretic Approach to Multi-Pedestrian Activity Forecasting,2016 +30,United States,TownCentre,oxford_town_centre,42.3354481,-71.16813864,Boston College,edu,869df5e8221129850e81e77d4dc36e6c0f854fe6,citation,https://arxiv.org/pdf/1601.03094.pdf,A metric for sets of trajectories that is practical and mathematically consistent,2016 +31,United States,TownCentre,oxford_town_centre,34.1579742,-118.2894729,Disney Research,company,d8bc2e2537cecbe6e751d4791837251a249cd06d,citation,http://www.cse.psu.edu/~rtc12/Papers/wacv2016CarrCollins.pdf,Assessing tracking performance in complex scenarios using mean time between failures,2016 +32,United States,TownCentre,oxford_town_centre,40.7982133,-77.8599084,The Pennsylvania State University,edu,d8bc2e2537cecbe6e751d4791837251a249cd06d,citation,http://www.cse.psu.edu/~rtc12/Papers/wacv2016CarrCollins.pdf,Assessing tracking performance in complex scenarios using mean time between failures,2016 +33,United States,TownCentre,oxford_town_centre,28.59899755,-81.19712501,University of Central Florida,edu,2dfba157e0b5db5becb99b3c412ac729cf3bb32d,citation,https://pdfs.semanticscholar.org/7fb2/f6ce372db950f26f9395721651d6c6aa7b76.pdf,Automatic Detection and Tracking of Pedestrians in Videos with Various Crowd Densities,2012 +34,India,TownCentre,oxford_town_centre,12.9914929,80.2336907,"IIT Madras, India",edu,37f2e03c7cbec9ffc35eac51578e7e8fdfee3d4e,citation,http://www.cse.iitm.ac.in/~amittal/wacv2015_review.pdf,Co-operative Pedestrians Group Tracking in Crowded Scenes Using an MST Approach,2015 +35,United Kingdom,TownCentre,oxford_town_centre,55.91029135,-3.32345777,Heriot-Watt University,edu,b8af24279c58a718091817236f878c805a7843e1,citation,https://pdfs.semanticscholar.org/b8af/24279c58a718091817236f878c805a7843e1.pdf,Context Aware Anomalous Behaviour Detection in Crowded Surveillance,2013 +36,Russia,TownCentre,oxford_town_centre,55.8067104,37.5416381,"Faculty of Computer Science, Moscow, Russia",edu,224547337e1ace6411a69c2e06ce538bc67923f7,citation,https://pdfs.semanticscholar.org/2245/47337e1ace6411a69c2e06ce538bc67923f7.pdf,Convolutional Neural Network for Camera Pose Estimation from Object Detections,2017 +37,Germany,TownCentre,oxford_town_centre,48.7468939,9.0805141,Max Planck Institute for Intelligent Systems,edu,b6d0e461535116a675a0354e7da65b2c1d2958d4,citation,https://arxiv.org/pdf/1805.03430.pdf,Deep Directional Statistics: Pose Estimation with Uncertainty Quantification,2018 +38,United States,TownCentre,oxford_town_centre,38.7768106,-94.9442982,Amazon,company,b6d0e461535116a675a0354e7da65b2c1d2958d4,citation,https://arxiv.org/pdf/1805.03430.pdf,Deep Directional Statistics: Pose Estimation with Uncertainty Quantification,2018 +39,United States,TownCentre,oxford_town_centre,47.6423318,-122.1369302,Microsoft,company,b6d0e461535116a675a0354e7da65b2c1d2958d4,citation,https://arxiv.org/pdf/1805.03430.pdf,Deep Directional Statistics: Pose Estimation with Uncertainty Quantification,2018 +40,United Kingdom,TownCentre,oxford_town_centre,55.91029135,-3.32345777,Heriot-Watt University,edu,70be5432677c0fbe000ac0c28dda351a950e0536,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2014/W14/papers/Leach_Detecting_Social_Groups_2014_CVPR_paper.pdf,Detecting Social Groups in Crowded Surveillance Videos Using Visual Attention,2014 +41,Switzerland,TownCentre,oxford_town_centre,47.376313,8.5476699,ETH Zurich,edu,9458642e7645bfd865911140ee8413e2f5f9fcd6,citation,https://pdfs.semanticscholar.org/9458/642e7645bfd865911140ee8413e2f5f9fcd6.pdf,Efficient Multiple People Tracking Using Minimum Cost Arborescences,2014 +42,United Kingdom,TownCentre,oxford_town_centre,54.6141723,-5.9002151,Queen's University Belfast,edu,2a7935706d43c01789d43a81a1d391418f220a0a,citation,https://pure.qub.ac.uk/portal/files/31960902/285.pdf,Enhancing Linear Programming with Motion Modeling for Multi-target Tracking,2015 +43,Sri Lanka,TownCentre,oxford_town_centre,6.7970862,79.9019094,University of Moratuwa,edu,b183914d0b16647a41f0bfd4af64bf94a83a2b14,citation,http://iwinlab.eng.usf.edu/papers/Extensible%20video%20surveillance%20software%20with%20simultaneous%20event%20detection%20for%20low%20and%20high%20density%20crowd%20analysis.pdf,Extensible video surveillance software with simultaneous event detection for low and high density crowd analysis,2014 +44,United States,TownCentre,oxford_town_centre,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,fa5aca45965e312362d2d75a69312a0678fdf5d7,citation,https://pdfs.semanticscholar.org/fa5a/ca45965e312362d2d75a69312a0678fdf5d7.pdf,Fast and Accurate Head Pose Estimation via Random Projection Forests : Supplementary Material,2015 +45,United States,TownCentre,oxford_town_centre,37.3641651,-120.4254615,University of California at Merced,edu,fa5aca45965e312362d2d75a69312a0678fdf5d7,citation,https://pdfs.semanticscholar.org/fa5a/ca45965e312362d2d75a69312a0678fdf5d7.pdf,Fast and Accurate Head Pose Estimation via Random Projection Forests : Supplementary Material,2015 +46,Australia,TownCentre,oxford_town_centre,-32.8892352,151.6998983,"University of Newcastle, Australia",edu,2feb7c57d51df998aafa6f3017662263a91625b4,citation,https://pdfs.semanticscholar.org/d344/9eaaf392fd07b676e744410049f4095b4b5c.pdf,Feature Selection for Intelligent Transportation Systems,2014 +47,Germany,TownCentre,oxford_town_centre,49.01546,8.4257999,Fraunhofer,company,1f82eebadc3ffa41820ad1a0f53770247fc96dcd,citation,https://pdfs.semanticscholar.org/c5ac/81b17b8fcc028f375fbbd090b558ba9a437a.pdf,Using Trajectories derived by Dense Optical Flows as a Spatial Component in Background Subtraction,2016 +48,United States,TownCentre,oxford_town_centre,42.3583961,-71.09567788,MIT,edu,b18f94c5296a9cebe9e779d50d193fd180f78ed9,citation,https://arxiv.org/pdf/1604.01431.pdf,Forecasting Interactive Dynamics of Pedestrians with Fictitious Play,2017 +49,United Kingdom,TownCentre,oxford_town_centre,51.7520849,-1.2516646,Oxford University,edu,b18f94c5296a9cebe9e779d50d193fd180f78ed9,citation,https://arxiv.org/pdf/1604.01431.pdf,Forecasting Interactive Dynamics of Pedestrians with Fictitious Play,2017 +50,United States,TownCentre,oxford_town_centre,37.43131385,-122.16936535,Stanford University,edu,b18f94c5296a9cebe9e779d50d193fd180f78ed9,citation,https://arxiv.org/pdf/1604.01431.pdf,Forecasting Interactive Dynamics of Pedestrians with Fictitious Play,2017 +51,Netherlands,TownCentre,oxford_town_centre,52.3553655,4.9501644,University of Amsterdam,edu,687ec23addf5a1279e49cc46b78e3245af94ac7b,citation,https://pdfs.semanticscholar.org/687e/c23addf5a1279e49cc46b78e3245af94ac7b.pdf,UvA-DARE ( Digital Academic Repository ) Visual Tracking : An Experimental Survey Smeulders,2013 +52,Italy,TownCentre,oxford_town_centre,45.1847248,9.1582069,"Italian Institute of Technology, Genova, Italy",edu,5ab9f00a707a55f4955b378981ad425aa1cb8ea3,citation,https://arxiv.org/pdf/1901.02000.pdf,Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets,2019 +53,Germany,TownCentre,oxford_town_centre,48.1820038,11.5978282,"OSRAM GmbH, Germany",company,5ab9f00a707a55f4955b378981ad425aa1cb8ea3,citation,https://arxiv.org/pdf/1901.02000.pdf,Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets,2019 +54,Italy,TownCentre,oxford_town_centre,45.437398,11.003376,University of Verona,edu,5ab9f00a707a55f4955b378981ad425aa1cb8ea3,citation,https://arxiv.org/pdf/1901.02000.pdf,Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets,2019 +55,United Kingdom,TownCentre,oxford_town_centre,51.7534538,-1.25400997,University of Oxford,edu,3ed9730e5ec8716e8cdf55f207ef973a9c854574,citation,https://arxiv.org/pdf/1612.05234.pdf,Visual Compiler: Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator,2016 +56,United States,TownCentre,oxford_town_centre,29.7207902,-95.34406271,University of Houston,edu,58eba9930b63cc14715368acf40017293b8dc94f,citation,https://pdfs.semanticscholar.org/7508/ac08dd7b9694bcfe71a617df7fcf3df80952.pdf,What Do I See? Modeling Human Visual Perception for Multi-person Tracking,2014 +57,United States,TownCentre,oxford_town_centre,29.7207902,-95.34406271,University of Houston,edu,a0b489eeb4f7fd2249da756d829e179a6718d9d1,citation,,"""Seeing is Believing"": Pedestrian Trajectory Forecasting Using Visual Frustum of Attention",2018 +58,Belgium,TownCentre,oxford_town_centre,50.8779545,4.7002953,"KULeuven, EAVISE",edu,4ec4392246a7760d189cd6ea48a81664cd2fe4bf,citation,https://pdfs.semanticscholar.org/4ec4/392246a7760d189cd6ea48a81664cd2fe4bf.pdf,GPU Accelerated ACF Detector,2018 +59,United States,TownCentre,oxford_town_centre,40.7982133,-77.8599084,The Pennsylvania State University,edu,6e32c368a6157fb911c9363dc3e967a7fb2ad9f7,citation,https://pdfs.semanticscholar.org/8268/d68f6aa510a765466b2c7f2ba2ea34a48c51.pdf,Hybrid Stochastic / Deterministic Optimization for Tracking Sports Players and Pedestrians,2014 +60,United States,TownCentre,oxford_town_centre,40.4439789,-79.9464634,Disney Research Pittsburgh,edu,6e32c368a6157fb911c9363dc3e967a7fb2ad9f7,citation,https://pdfs.semanticscholar.org/8268/d68f6aa510a765466b2c7f2ba2ea34a48c51.pdf,Hybrid Stochastic / Deterministic Optimization for Tracking Sports Players and Pedestrians,2014 +61,India,TownCentre,oxford_town_centre,13.0304619,77.5646862,"M.S. Ramaiah Institute of Technology, Bangalore, India",edu,6f089f9959cc711e16f1ebe0c6251aaf8a65959a,citation,https://pdfs.semanticscholar.org/6f08/9f9959cc711e16f1ebe0c6251aaf8a65959a.pdf,Improvement in object detection using Super Pixels,2016 +62,United States,TownCentre,oxford_town_centre,38.99203005,-76.9461029,University of Maryland College Park,edu,4e82908e6482d973c280deb79c254631a60f1631,citation,https://pdfs.semanticscholar.org/4e82/908e6482d973c280deb79c254631a60f1631.pdf,Improving Efficiency and Scalability in Visual Surveillance Applications,2013 +63,United States,TownCentre,oxford_town_centre,33.98071305,-117.33261035,"University of California, Riverside",edu,38b5a83f7941fea5fd82466f8ce1ce4ed7749f59,citation,http://rlair.cs.ucr.edu/papers/docs/grouptracking.pdf,Improving multi-target tracking via social grouping,2012 +64,Singapore,TownCentre,oxford_town_centre,1.3484104,103.68297965,Nanyang Technological University,edu,13caf4d2e0a4b6fcfcd4b9e8e2341b8ebd38258d,citation,https://arxiv.org/pdf/1605.04502.pdf,Joint Learning of Siamese CNNs and Temporally Constrained Metrics for Tracklet Association,2016 +65,United States,TownCentre,oxford_town_centre,35.9049122,-79.0469134,The University of North Carolina at Chapel Hill,edu,45e459462a80af03e1bb51a178648c10c4250925,citation,https://arxiv.org/pdf/1606.08998.pdf,LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning,2016 +66,China,TownCentre,oxford_town_centre,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,c0262e24324a6a4e6af5bd99fc79e2eb802519b3,citation,https://arxiv.org/pdf/1611.03968.pdf,Learning Scene-specific Object Detectors Based on a Generative-Discriminative Model with Minimal Supervision,2016 +67,China,TownCentre,oxford_town_centre,30.527151,114.400762,China University of Geosciences,edu,c0262e24324a6a4e6af5bd99fc79e2eb802519b3,citation,https://arxiv.org/pdf/1611.03968.pdf,Learning Scene-specific Object Detectors Based on a Generative-Discriminative Model with Minimal Supervision,2016 +68,China,TownCentre,oxford_town_centre,32.0565957,118.77408833,Nanjing University,edu,c0262e24324a6a4e6af5bd99fc79e2eb802519b3,citation,https://arxiv.org/pdf/1611.03968.pdf,Learning Scene-specific Object Detectors Based on a Generative-Discriminative Model with Minimal Supervision,2016 +69,United Kingdom,TownCentre,oxford_town_centre,51.5247272,-0.03931035,Queen Mary University of London,edu,1883387726897d94b663cc4de4df88e5c31df285,citation,http://www.eecs.qmul.ac.uk/~andrea/papers/2014_TIP_MultiTargetTrackingEvaluation_Tahir_Poiesi_Cavallaro.pdf,Measures of Effective Video Tracking,2014 +70,United States,TownCentre,oxford_town_centre,35.9113971,-79.0504529,University of North Carolina at Chapel Hill,edu,8d2bf6ecbfda94f57000b84509bf77f4c47c1c66,citation,https://arxiv.org/pdf/1707.09100.pdf,MixedPeds: Pedestrian Detection in Unannotated Videos Using Synthetically Generated Human-Agents for Training,2018 +71,Australia,TownCentre,oxford_town_centre,-34.920603,138.6062277,Adelaide University,edu,5bae9822d703c585a61575dced83fa2f4dea1c6d,citation,https://arxiv.org/pdf/1504.01942.pdf,MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking,2015 +72,Switzerland,TownCentre,oxford_town_centre,47.376313,8.5476699,ETH Zurich,edu,5bae9822d703c585a61575dced83fa2f4dea1c6d,citation,https://arxiv.org/pdf/1504.01942.pdf,MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking,2015 +73,Germany,TownCentre,oxford_town_centre,49.8748277,8.6563281,TU Darmstadt,edu,5bae9822d703c585a61575dced83fa2f4dea1c6d,citation,https://arxiv.org/pdf/1504.01942.pdf,MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking,2015 +74,United States,TownCentre,oxford_town_centre,33.98071305,-117.33261035,"University of California, Riverside",edu,e6d48d23308a9e0a215f7b5ba6ae30ee5d2f0ef5,citation,https://pdfs.semanticscholar.org/e6d4/8d23308a9e0a215f7b5ba6ae30ee5d2f0ef5.pdf,Multi-person Tracking by Online Learned Grouping Model with Non-linear Motion Context,2015 +75,France,TownCentre,oxford_town_centre,45.217886,5.807369,INRIA,edu,fc30d7dbf4c3cdd377d8cd4e7eeabd5d73814b8f,citation,https://pdfs.semanticscholar.org/fc30/d7dbf4c3cdd377d8cd4e7eeabd5d73814b8f.pdf,Multiple Object Tracking by Efficient Graph Partitioning,2014 +76,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,290eda31bc13cbd5933acec8b6a25b3e3761c788,citation,https://arxiv.org/pdf/1411.7935.pdf,Multiple object tracking with context awareness,2014 +77,Czech Republic,TownCentre,oxford_town_centre,49.20172,16.6033168,Brno University of Technology,edu,dc53c4bb04e787a0d45dd761ba2101cc51c17b82,citation,https://pdfs.semanticscholar.org/dc53/c4bb04e787a0d45dd761ba2101cc51c17b82.pdf,Multiple-Person Tracking by Detection,2016 +78,Germany,TownCentre,oxford_town_centre,48.1820038,11.5978282,"OSRAM GmbH, Germany",company,943b1b92b5bdee0b5770418c645a4a17bded1ccf,citation,https://arxiv.org/pdf/1805.00652.pdf,MX-LSTM: Mixing Tracklets and Vislets to Jointly Forecast Trajectories and Head Poses,2018 +79,Italy,TownCentre,oxford_town_centre,45.437398,11.003376,University of Verona,edu,943b1b92b5bdee0b5770418c645a4a17bded1ccf,citation,https://arxiv.org/pdf/1805.00652.pdf,MX-LSTM: Mixing Tracklets and Vislets to Jointly Forecast Trajectories and Head Poses,2018 +80,France,TownCentre,oxford_town_centre,48.8422058,2.3451689,"INRIA / Ecole Normale Supérieure, France",edu,47119c99f5aa1e47bbeb86de0f955e7c500e6a93,citation,https://arxiv.org/pdf/1408.3304.pdf,On pairwise costs for network flow multi-object tracking,2015 +81,United States,TownCentre,oxford_town_centre,42.3504253,-71.10056114,Boston University,edu,1ae3dd081b93c46cda4d72100d8b1d59eb585157,citation,https://pdfs.semanticscholar.org/fea1/0f39b0a77035fb549fc580fd951384b79f9b.pdf,Online Motion Agreement Tracking,2013 +82,Malaysia,TownCentre,oxford_town_centre,4.3400673,101.1429799,Universiti Tunku Abdul Rahman,edu,e1f815c50a6c0c6d790c60a1348393264f829e60,citation,https://pdfs.semanticscholar.org/e1f8/15c50a6c0c6d790c60a1348393264f829e60.pdf,PEDESTRIAN DETECTION AND TRACKING IN SURVEILLANCE VIDEO By PENNY CHONG,2016 +83,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,422d352a7d26fef692a3cd24466bfb5b4526efea,citation,https://pdfs.semanticscholar.org/422d/352a7d26fef692a3cd24466bfb5b4526efea.pdf,Pedestrian interaction in tracking : the social force model and global optimization methods,2012 +84,Sweden,TownCentre,oxford_town_centre,57.6897063,11.9741654,Chalmers University of Technology,edu,367b5b814aa991329c2ae7f8793909ad8c0a56f1,citation,https://arxiv.org/pdf/1211.0191.pdf,Performance evaluation of random set based pedestrian tracking algorithms,2013 +85,Japan,TownCentre,oxford_town_centre,35.5152072,134.1733553,Tottori University,edu,9d89f1bc88fd65e90b31a2129719384796bed17a,citation,http://vision.unipv.it/CV/materiale2016-17/2nd%20Choice/0225.pdf,Person re-identification using co-occurrence attributes of physical and adhered human characteristics,2016 +86,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,48705017d91a157949cfaaeb19b826014899a36b,citation,https://pdfs.semanticscholar.org/4870/5017d91a157949cfaaeb19b826014899a36b.pdf,PROBABILISTIC MULTI-PERSON TRACKING USING DYNAMIC BAYES NETWORKS,2015 +87,Italy,TownCentre,oxford_town_centre,39.2173657,9.1149218,"Università degli Studi di Cagliari, Italy",edu,7c1f47ca50a8a55f93bf69791d9df2f994019758,citation,http://veprints.unica.it/1295/1/PhD_ThesisPalaF.pdf,Re-identification and semantic retrieval of pedestrians in video surveillance scenarios,2016 +88,United Kingdom,TownCentre,oxford_town_centre,51.5247272,-0.03931035,Queen Mary University of London,edu,3a28059df29b74775f77fd20a15dc6b5fe857556,citation,https://pdfs.semanticscholar.org/3a28/059df29b74775f77fd20a15dc6b5fe857556.pdf,Riccardo Mazzon PhD Thesis 2013,2013 +89,Brazil,TownCentre,oxford_town_centre,-30.0338248,-51.218828,Federal University of Rio Grande do Sul,edu,057517452369751bd63d83902ea91558d58161da,citation,http://inf.ufrgs.br/~gfuhr/papers/102095_3.pdf,Robust Patch-Based Pedestrian Tracking Using Monocular Calibrated Cameras,2012 +90,China,TownCentre,oxford_town_centre,28.727339,115.816633,Jiangxi University of Finance and Economics,edu,1642358cd9410abe9ee512d34ba68296b308770e,citation,https://arxiv.org/pdf/1807.04562.pdf,Robustness Analysis of Pedestrian Detectors for Surveillance,2018 +91,Singapore,TownCentre,oxford_town_centre,1.3484104,103.68297965,Nanyang Technological University,edu,1642358cd9410abe9ee512d34ba68296b308770e,citation,https://arxiv.org/pdf/1807.04562.pdf,Robustness Analysis of Pedestrian Detectors for Surveillance,2018 +92,China,TownCentre,oxford_town_centre,34.250803,108.983693,Xi’an Jiaotong University,edu,1642358cd9410abe9ee512d34ba68296b308770e,citation,https://arxiv.org/pdf/1807.04562.pdf,Robustness Analysis of Pedestrian Detectors for Surveillance,2018 +93,Singapore,TownCentre,oxford_town_centre,1.3484104,103.68297965,Nanyang Technological University,edu,7c132e0a2b7e13c78784287af38ad74378da31e5,citation,https://pdfs.semanticscholar.org/7c13/2e0a2b7e13c78784287af38ad74378da31e5.pdf,Salient Parts based Multi-people Tracking,2015 +94,China,TownCentre,oxford_town_centre,40.0044795,116.370238,Chinese Academy of Sciences,edu,679136c2844eeddca34e98e483aca1ff6ef5e902,citation,https://arxiv.org/pdf/1712.08745.pdf,Scene-Specific Pedestrian Detection Based on Parallel Vision,2017 +95,China,TownCentre,oxford_town_centre,34.250803,108.983693,Xi’an Jiaotong University,edu,679136c2844eeddca34e98e483aca1ff6ef5e902,citation,https://arxiv.org/pdf/1712.08745.pdf,Scene-Specific Pedestrian Detection Based on Parallel Vision,2017 +96,China,TownCentre,oxford_town_centre,40.0044795,116.370238,Chinese Academy of Sciences,edu,57e9b0d3ab6295e914d5a30cfaa3b2c81189abc1,citation,https://arxiv.org/pdf/1611.07544.pdf,Self-Learning Scene-Specific Pedestrian Detectors Using a Progressive Latent Model,2017 +97,United States,TownCentre,oxford_town_centre,35.9990522,-78.9290629,Duke University,edu,57e9b0d3ab6295e914d5a30cfaa3b2c81189abc1,citation,https://arxiv.org/pdf/1611.07544.pdf,Self-Learning Scene-Specific Pedestrian Detectors Using a Progressive Latent Model,2017 +98,Switzerland,TownCentre,oxford_town_centre,47.3764534,8.54770931,ETH Zürich,edu,70b42bbd76e6312d39ea06b8a0c24beb4a93e022,citation,http://www.tnt.uni-hannover.de/papers/data/1075/WACV2015_Abstract.pdf,Solving Multiple People Tracking in a Minimum Cost Arborescence,2015 +99,United States,TownCentre,oxford_town_centre,42.718568,-84.47791571,Michigan State University,edu,acf0db156406ddad1ace2ff2696cb60d0a04cf7c,citation,http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf,Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,2018 +100,United Kingdom,TownCentre,oxford_town_centre,51.7534538,-1.25400997,University of Oxford,edu,acf0db156406ddad1ace2ff2696cb60d0a04cf7c,citation,http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf,Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,2018 +101,Japan,TownCentre,oxford_town_centre,36.05238585,140.11852361,Institute of Industrial Science,edu,acf0db156406ddad1ace2ff2696cb60d0a04cf7c,citation,http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf,Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,2018 +102,United States,TownCentre,oxford_town_centre,40.4441619,-79.94272826,Carnegie Mellon University,edu,acf0db156406ddad1ace2ff2696cb60d0a04cf7c,citation,http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf,Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,2018 +103,Sweden,TownCentre,oxford_town_centre,57.7172004,11.9218558,"Volvo Construction Equipment, Göthenburg, Sweden",company,acf0db156406ddad1ace2ff2696cb60d0a04cf7c,citation,http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf,Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,2018 +104,United States,TownCentre,oxford_town_centre,35.9990522,-78.9290629,Duke University,edu,64e0690dd176a93de9d4328f6e31fc4afe1e7536,citation,https://pdfs.semanticscholar.org/64e0/690dd176a93de9d4328f6e31fc4afe1e7536.pdf,Tracking Multiple People Online and in Real Time,2014 +105,Switzerland,TownCentre,oxford_town_centre,47.3764534,8.54770931,ETH Zürich,edu,64c78c8bf779a27e819fd9d5dba91247ab5a902b,citation,https://arxiv.org/pdf/1607.07304.pdf,Tracking with multi-level features.,2016 +106,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,64c78c8bf779a27e819fd9d5dba91247ab5a902b,citation,https://arxiv.org/pdf/1607.07304.pdf,Tracking with multi-level features.,2016 +107,Germany,TownCentre,oxford_town_centre,48.14955455,11.56775314,Technical University Munich,edu,64c78c8bf779a27e819fd9d5dba91247ab5a902b,citation,https://arxiv.org/pdf/1607.07304.pdf,Tracking with multi-level features.,2016 +108,Singapore,TownCentre,oxford_town_centre,1.3484104,103.68297965,Nanyang Technological University,edu,7d3698c0e828d05f147682b0f5bfcd3b681ff205,citation,https://arxiv.org/pdf/1511.06654.pdf,Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation,2017 +109,Australia,TownCentre,oxford_town_centre,-35.2809368,149.1300092,"NICTA, Canberra",edu,f0cc615b14c97482faa9c47eb855303c71ff03a7,citation,https://pdfs.semanticscholar.org/f0cc/615b14c97482faa9c47eb855303c71ff03a7.pdf,Tracklet clustering for robust multiple object tracking using distance dependent Chinese restaurant processes,2016 +110,Germany,TownCentre,oxford_town_centre,52.5180641,13.3250425,TU Berlin,edu,c4cd19cf41a2f5cd543d81b94afe6cc42785920a,citation,http://elvera.nue.tu-berlin.de/files/1491Bochinski2016.pdf,Training a convolutional neural network for multi-class object detection using solely virtual world data,2016 +111,Egypt,TownCentre,oxford_town_centre,29.956063,31.255471,AvidBeam,company,2d81cf3214281af85eb1d9d270a897d62302e88e,citation,,High density people estimation in video surveillance,2017 +112,Egypt,TownCentre,oxford_town_centre,29.9866381,31.4414218,Faculty of Media Engineering & Technology German University in Cairo,edu,2d81cf3214281af85eb1d9d270a897d62302e88e,citation,,High density people estimation in video surveillance,2017 +113,Egypt,TownCentre,oxford_town_centre,29.9866381,31.4414218,German University in Cairo,edu,2d81cf3214281af85eb1d9d270a897d62302e88e,citation,,High density people estimation in video surveillance,2017 diff --git a/site/datasets/verified/penn_fudan.csv b/site/datasets/verified/penn_fudan.csv index 10427ed0..d63535a2 100644 --- a/site/datasets/verified/penn_fudan.csv +++ b/site/datasets/verified/penn_fudan.csv @@ -1,2 +1,4 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year 0,,Penn Fudan,penn_fudan,0.0,0.0,,,,main,,Object Detection Combining Recognition and Segmentation,2007 +1,Turkey,Penn Fudan,penn_fudan,41.0082376,28.9783589,"Elektronik ve Haberleşme Mühendisliği Bölümü, NETAŞ Telekomünikasyon A.Ş, İstanbul, Türkiye",edu,92b2386e11164738d9285117ae647b4788da2c31,citation,,Pedestrian detection with multiple classifiers on still images,2018 +2,Turkey,Penn Fudan,penn_fudan,41.0288022,28.8900143,"Elektronik ve Haberleşme Mühendisliği Bölümü, Yıldız Teknik Üniversitesi, İstanbul, Türkiye",edu,92b2386e11164738d9285117ae647b4788da2c31,citation,,Pedestrian detection with multiple classifiers on still images,2018 diff --git a/site/datasets/verified/pipa.csv b/site/datasets/verified/pipa.csv index 3acdccff..1124eebc 100644 --- a/site/datasets/verified/pipa.csv +++ b/site/datasets/verified/pipa.csv @@ -1,2 +1,47 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year 0,,PIPA,pipa,0.0,0.0,,,,main,,Beyond frontal faces: Improving Person Recognition using multiple cues,2015 +1,Australia,PIPA,pipa,-35.2776999,149.118527,Australian National University,edu,9ce12c9f1d1661f56908edc8ef3848e91b24d557,citation,https://arxiv.org/pdf/1810.13103.pdf,Query Adaptive Late Fusion for Image Retrieval,2018 +2,China,PIPA,pipa,40.00229045,116.32098908,Tsinghua University,edu,9ce12c9f1d1661f56908edc8ef3848e91b24d557,citation,https://arxiv.org/pdf/1810.13103.pdf,Query Adaptive Late Fusion for Image Retrieval,2018 +3,Singapore,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 +4,China,PIPA,pipa,28.2290209,112.99483204,"National University of Defense Technology, China",mil,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 +5,United States,PIPA,pipa,42.3702265,-71.0768929,"Philips Research, Bethesda, MD, USA",company,c76251049b370f8258d6bbb944c696c30b8bbb85,citation,http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w41/Xue_Clothing_Change_Aware_CVPR_2018_paper.pdf,Clothing Change Aware Person Identification,2018 +6,United States,PIPA,pipa,40.47913175,-74.43168868,Rutgers University,edu,c76251049b370f8258d6bbb944c696c30b8bbb85,citation,http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w41/Xue_Clothing_Change_Aware_CVPR_2018_paper.pdf,Clothing Change Aware Person Identification,2018 +7,United States,PIPA,pipa,33.9928298,-81.02685168,University of South Carolina,edu,c76251049b370f8258d6bbb944c696c30b8bbb85,citation,http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w41/Xue_Clothing_Change_Aware_CVPR_2018_paper.pdf,Clothing Change Aware Person Identification,2018 +8,China,PIPA,pipa,22.4162632,114.2109318,Chinese University of Hong Kong,edu,d949fadc9b6c5c8b067fa42265ad30945f9caa99,citation,https://arxiv.org/pdf/1710.00870.pdf,Rethinking Feature Discrimination and Polymerization for Large-scale Recognition,2017 +9,China,PIPA,pipa,22.4162632,114.2109318,Chinese University of Hong Kong,edu,6fed504da4e192fe4c2d452754d23d3db4a4e5e3,citation,https://arxiv.org/pdf/1702.06890.pdf,Learning Deep Features via Congenerous Cosine Loss for Person Recognition,2017 +10,China,PIPA,pipa,23.09461185,113.28788994,Sun Yat-Sen University,edu,30f464c09779c6210397204901d025c0def1fe10,citation,https://arxiv.org/pdf/1807.00504.pdf,Deep Reasoning with Knowledge Graph for Social Relationship Understanding,2018 +11,China,PIPA,pipa,39.993008,116.329882,SenseTime,company,30f464c09779c6210397204901d025c0def1fe10,citation,https://arxiv.org/pdf/1807.00504.pdf,Deep Reasoning with Knowledge Graph for Social Relationship Understanding,2018 +12,United States,PIPA,pipa,40.742252,-74.0270949,Stevens Institute of Technology,edu,1e1d7cbbef67e9e042a3a0a9a1bcefcc4a9adacf,citation,http://openaccess.thecvf.com/content_cvpr_2016/papers/Li_A_Multi-Level_Contextual_CVPR_2016_paper.pdf,A Multi-level Contextual Model for Person Recognition in Photo Albums,2016 +13,Singapore,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 +14,China,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 +15,Germany,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 +16,Germany,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 +17,Germany,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 +18,United States,PIPA,pipa,42.4505507,-76.4783513,Cornell University,edu,6c8dfa770fe4acffaabeae4b6092c2fd5ee2c545,citation,https://arxiv.org/pdf/1805.04049.pdf,Exploiting Unintended Feature Leakage in Collaborative Learning,2018 +19,Germany,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 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Informatics,edu,3e0a1884448bfd7f416c6a45dfcdfc9f2e617268,citation,https://arxiv.org/pdf/1805.05838.pdf,Understanding and Controlling User Linkability in Decentralized Learning,2018 +25,China,PIPA,pipa,22.4162632,114.2109318,Chinese University of Hong Kong,edu,2fe7105ef8e61330a3ddc7f7b35955ca62fc1ab3,citation,https://arxiv.org/pdf/1806.03084.pdf,Unifying Identification and Context Learning for Person Recognition,2018 +26,United States,PIPA,pipa,37.8701543,-122.2712312,University of California at Berkeley,edu,d6a9ea9b40a7377c91c705f4c7f206a669a9eea2,citation,https://pdfs.semanticscholar.org/d6a9/ea9b40a7377c91c705f4c7f206a669a9eea2.pdf,Visual Representations for Fine-grained Categorization,2015 +27,United States,PIPA,pipa,42.44726,-76.480988,Facebook & Cornell University,company,0aaf785d7f21d2b5ad582b456896495d30b0a4e2,citation,,A Face Recognition Application for People with Visual Impairments: Understanding Use Beyond the Lab,2018 +28,United States,PIPA,pipa,42.4505507,-76.4783513,Cornell University,edu,0aaf785d7f21d2b5ad582b456896495d30b0a4e2,citation,,A Face Recognition Application for People with Visual Impairments: Understanding Use Beyond the Lab,2018 +29,United States,PIPA,pipa,37.3936717,-122.0807262,Facebook,company,0aaf785d7f21d2b5ad582b456896495d30b0a4e2,citation,,A Face Recognition Application for People with Visual Impairments: Understanding Use Beyond the Lab,2018 +30,China,PIPA,pipa,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,d94d7ff6f46ad5cab5c20e6ac14c1de333711a0c,citation,http://mirlab.org/conference_papers/International_Conference/ICASSP%202017/pdfs/0003031.pdf,Face Album: Towards automatic photo management based on person identity on mobile phones,2017 +31,United States,PIPA,pipa,42.3702265,-71.0768929,"Philips Research, Bethesda, MD, USA",company,cfd4004054399f3a5f536df71f9b9987f060f434,citation,https://arxiv.org/pdf/1710.03224.pdf,Person Recognition in Social Media Photos,2018 +32,United States,PIPA,pipa,40.47913175,-74.43168868,Rutgers University,edu,cfd4004054399f3a5f536df71f9b9987f060f434,citation,https://arxiv.org/pdf/1710.03224.pdf,Person Recognition in Social Media Photos,2018 +33,United States,PIPA,pipa,33.9928298,-81.02685168,University of South Carolina,edu,cfd4004054399f3a5f536df71f9b9987f060f434,citation,https://arxiv.org/pdf/1710.03224.pdf,Person Recognition in Social Media Photos,2018 +34,Germany,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,2c92839418a64728438c351a42f6dc5ad0c6e686,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Masi_Pose-Aware_Face_Recognition_CVPR_2016_paper.pdf,Pose-Aware Face Recognition in the Wild,2016 +35,Singapore,PIPA,pipa,1.2962018,103.77689944,National University of Singapore,edu,6e50c32f7244e3556eb879f24b7de8410f3177f6,citation,https://arxiv.org/pdf/1812.05917.pdf,Visual Social Relationship Recognition,2018 +36,United States,PIPA,pipa,44.97399,-93.2277285,University of Minnesota-Twin Cities,edu,6e50c32f7244e3556eb879f24b7de8410f3177f6,citation,https://arxiv.org/pdf/1812.05917.pdf,Visual Social Relationship Recognition,2018 +37,United States,PIPA,pipa,40.4441619,-79.94272826,Carnegie Mellon University,edu,95d64ce5b0758bdc213962ce65ac89b31d9fb617,citation,,Learning Pose-Aware Models for Pose-Invariant Face Recognition in the Wild,2018 +38,Israel,PIPA,pipa,32.77824165,34.99565673,Open University of Israel,edu,95d64ce5b0758bdc213962ce65ac89b31d9fb617,citation,,Learning Pose-Aware Models for Pose-Invariant Face Recognition in the Wild,2018 +39,United States,PIPA,pipa,34.0224149,-118.28634407,University of Southern California,edu,95d64ce5b0758bdc213962ce65ac89b31d9fb617,citation,,Learning Pose-Aware Models for Pose-Invariant Face Recognition in the Wild,2018 +40,India,PIPA,pipa,17.4454957,78.34854698,International Institute of Information Technology,edu,01e27c91c7cef926389f913d12410725e7dd35ab,citation,,Semi-supervised annotation of faces in image collection,2018 +41,Switzerland,PIPA,pipa,47.376313,8.5476699,ETH Zurich,edu,503906ca940fa3b01e39d05879c9b6a36524aaf5,citation,,Natural and Effective Obfuscation by Head Inpainting,2018 +42,Germany,PIPA,pipa,49.2578657,7.0457956,Max Planck Institute of Informatics,edu,503906ca940fa3b01e39d05879c9b6a36524aaf5,citation,,Natural and Effective Obfuscation by Head Inpainting,2018 +43,Belgium,PIPA,pipa,50.8784802,4.4348624,"Toyota Motor Europe (TME), Brussels 1140, Belgium",edu,503906ca940fa3b01e39d05879c9b6a36524aaf5,citation,,Natural and Effective Obfuscation by Head Inpainting,2018 +44,Singapore,PIPA,pipa,1.2966426,103.7763939,National University of Singapore & Qihoo 360 AI Institute,edu,af4759f5e636b5d9049010d5f0e2b0df2a69cd72,citation,,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018 +45,Singapore,PIPA,pipa,1.2962018,103.77689944,National University of Singapore,edu,af4759f5e636b5d9049010d5f0e2b0df2a69cd72,citation,,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018 diff --git a/site/datasets/verified/prid.csv b/site/datasets/verified/prid.csv index 622bae62..7b6e438f 100644 --- a/site/datasets/verified/prid.csv +++ b/site/datasets/verified/prid.csv @@ -1,2 +1,22 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year 0,,PRID,prid,0.0,0.0,,,,main,,Person Re-identification by Descriptive and Discriminative Classification,2011 +1,China,PRID,prid,22.4162632,114.2109318,Chinese University of Hong Kong,edu,dbb7b563e84903dad4953a8e9f23e3c54c6d7e78,citation,https://arxiv.org/pdf/1710.00983.pdf,Joint Person Re-identification and Camera Network Topology Inference in Multiple Cameras,2017 +2,China,PRID,prid,39.993008,116.329882,SenseTime,company,dbb7b563e84903dad4953a8e9f23e3c54c6d7e78,citation,https://arxiv.org/pdf/1710.00983.pdf,Joint Person Re-identification and Camera Network Topology Inference in Multiple Cameras,2017 +3,China,PRID,prid,23.09461185,113.28788994,Sun Yat-Sen University,edu,dbb7b563e84903dad4953a8e9f23e3c54c6d7e78,citation,https://arxiv.org/pdf/1710.00983.pdf,Joint Person Re-identification and Camera Network Topology Inference in Multiple Cameras,2017 +4,China,PRID,prid,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,147f31b603931c688687c6d64d330c9be2ab2f2f,citation,https://pdfs.semanticscholar.org/147f/31b603931c688687c6d64d330c9be2ab2f2f.pdf,Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-Identification,0 +5,United States,PRID,prid,35.9042272,-78.85565763,"IBM Research, North Carolina",company,147f31b603931c688687c6d64d330c9be2ab2f2f,citation,https://pdfs.semanticscholar.org/147f/31b603931c688687c6d64d330c9be2ab2f2f.pdf,Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-Identification,0 +6,United States,PRID,prid,42.0551164,-87.67581113,Northwestern University,edu,147f31b603931c688687c6d64d330c9be2ab2f2f,citation,https://pdfs.semanticscholar.org/147f/31b603931c688687c6d64d330c9be2ab2f2f.pdf,Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-Identification,0 +7,United States,PRID,prid,41.2097516,-73.8026467,IBM T.J. Watson Research Center,company,147f31b603931c688687c6d64d330c9be2ab2f2f,citation,https://pdfs.semanticscholar.org/147f/31b603931c688687c6d64d330c9be2ab2f2f.pdf,Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-Identification,0 +8,China,PRID,prid,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,5ee96d5c4d467d00909472e3bc0d2c2d82ccb961,citation,https://arxiv.org/pdf/1708.02286.pdf,Jointly Attentive Spatial-Temporal Pooling Networks for Video-Based Person Re-identification,2017 +9,United States,PRID,prid,35.9042272,-78.85565763,"IBM Research, North Carolina",company,5ee96d5c4d467d00909472e3bc0d2c2d82ccb961,citation,https://arxiv.org/pdf/1708.02286.pdf,Jointly Attentive Spatial-Temporal Pooling Networks for Video-Based Person Re-identification,2017 +10,United States,PRID,prid,42.0551164,-87.67581113,Northwestern University,edu,5ee96d5c4d467d00909472e3bc0d2c2d82ccb961,citation,https://arxiv.org/pdf/1708.02286.pdf,Jointly Attentive Spatial-Temporal Pooling Networks for Video-Based Person Re-identification,2017 +11,United States,PRID,prid,41.2097516,-73.8026467,IBM T.J. Watson Research Center,company,5ee96d5c4d467d00909472e3bc0d2c2d82ccb961,citation,https://arxiv.org/pdf/1708.02286.pdf,Jointly Attentive Spatial-Temporal Pooling Networks for Video-Based Person Re-identification,2017 +12,China,PRID,prid,30.60903415,114.3514284,Wuhan University of Technology,edu,76616a2709c03ade176db31fa99c7c61970eba28,citation,https://pdfs.semanticscholar.org/7661/6a2709c03ade176db31fa99c7c61970eba28.pdf,Learning Heterogeneous Dictionary Pair with Feature Projection Matrix for Pedestrian Video Retrieval via Single Query Image,2017 +13,China,PRID,prid,32.105748,118.931701,Nanjing University of Posts and Telecommunications,edu,76616a2709c03ade176db31fa99c7c61970eba28,citation,https://pdfs.semanticscholar.org/7661/6a2709c03ade176db31fa99c7c61970eba28.pdf,Learning Heterogeneous Dictionary Pair with Feature Projection Matrix for Pedestrian Video Retrieval via Single Query Image,2017 +14,China,PRID,prid,39.9808333,116.34101249,Beihang University,edu,76616a2709c03ade176db31fa99c7c61970eba28,citation,https://pdfs.semanticscholar.org/7661/6a2709c03ade176db31fa99c7c61970eba28.pdf,Learning Heterogeneous Dictionary Pair with Feature Projection Matrix for Pedestrian Video Retrieval via Single Query Image,2017 +15,China,PRID,prid,45.7413921,126.62552755,Harbin Institute of Technology,edu,76616a2709c03ade176db31fa99c7c61970eba28,citation,https://pdfs.semanticscholar.org/7661/6a2709c03ade176db31fa99c7c61970eba28.pdf,Learning Heterogeneous Dictionary Pair with Feature Projection Matrix for Pedestrian Video Retrieval via Single Query Image,2017 +16,China,PRID,prid,34.808921,114.369752,Henan University,edu,76616a2709c03ade176db31fa99c7c61970eba28,citation,https://pdfs.semanticscholar.org/7661/6a2709c03ade176db31fa99c7c61970eba28.pdf,Learning Heterogeneous Dictionary Pair with Feature Projection Matrix for Pedestrian Video Retrieval via Single Query Image,2017 +17,China,PRID,prid,23.09461185,113.28788994,Sun Yat-Sen University,edu,76616a2709c03ade176db31fa99c7c61970eba28,citation,https://pdfs.semanticscholar.org/7661/6a2709c03ade176db31fa99c7c61970eba28.pdf,Learning Heterogeneous Dictionary Pair with Feature Projection Matrix for Pedestrian Video Retrieval via Single Query Image,2017 +18,China,PRID,prid,39.993008,116.329882,SenseTime,company,35c51c40338d5d547c34ae7ec2efa7a32479dafa,citation,https://arxiv.org/pdf/1807.05688.pdf,SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification,2018 +19,China,PRID,prid,23.09461185,113.28788994,Sun Yat-Sen University,edu,35c51c40338d5d547c34ae7ec2efa7a32479dafa,citation,https://arxiv.org/pdf/1807.05688.pdf,SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification,2018 +20,China,PRID,prid,22.4162632,114.2109318,Chinese University of Hong Kong,edu,35c51c40338d5d547c34ae7ec2efa7a32479dafa,citation,https://arxiv.org/pdf/1807.05688.pdf,SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification,2018 diff --git a/site/datasets/verified/uccs.csv b/site/datasets/verified/uccs.csv index d7c84820..1cbefd32 100644 --- a/site/datasets/verified/uccs.csv +++ b/site/datasets/verified/uccs.csv @@ -1,9 +1,7 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year 0,,UCCS,uccs,0.0,0.0,,,,main,,Large scale unconstrained open set face database,2013 -1,United States,UCCS,uccs,41.70456775,-86.23822026,University of Notre Dame,edu,841855205818d3a6d6f85ec17a22515f4f062882,citation,https://arxiv.org/pdf/1805.11529.pdf,Low Resolution Face Recognition in the Wild,2018 -2,United States,UCCS,uccs,40.11571585,-88.22750772,Beckman Institute,edu,288d2704205d9ca68660b9f3a8fda17e18329c13,citation,https://arxiv.org/pdf/1601.04153.pdf,Studying Very Low Resolution Recognition Using Deep Networks,2016 -3,United States,UCCS,uccs,38.8920756,-104.79716389,"University of Colorado, Colorado Springs",edu,d4f1eb008eb80595bcfdac368e23ae9754e1e745,citation,,Unconstrained Face Detection and Open-Set Face Recognition Challenge,2017 -4,United Kingdom,UCCS,uccs,51.5247272,-0.03931035,Queen Mary University of London,edu,2306b2a8fba28539306052764a77a0d0f5d1236a,citation,https://arxiv.org/pdf/1804.09691.pdf,Surveillance Face Recognition Challenge,2018 -5,United Kingdom,UCCS,uccs,55.378051,-3.435973,"Vision Semantics Ltd, UK",edu,2306b2a8fba28539306052764a77a0d0f5d1236a,citation,https://arxiv.org/pdf/1804.09691.pdf,Surveillance Face Recognition Challenge,2018 -6,China,UCCS,uccs,39.9808333,116.34101249,Beihang University,edu,c50e498ede6f5216cffd0645e747ce67fae2096a,citation,https://arxiv.org/pdf/1811.09998.pdf,Low-Resolution Face Recognition in the Wild via Selective Knowledge Distillation,2018 -7,China,UCCS,uccs,39.97426,116.21589,"Institute of Information Engineering, CAS, Beijing, China",edu,c50e498ede6f5216cffd0645e747ce67fae2096a,citation,https://arxiv.org/pdf/1811.09998.pdf,Low-Resolution Face Recognition in the Wild via Selective Knowledge Distillation,2018 +1,United States,UCCS,uccs,40.11571585,-88.22750772,Beckman Institute,edu,288d2704205d9ca68660b9f3a8fda17e18329c13,citation,https://arxiv.org/pdf/1601.04153.pdf,Studying Very Low Resolution Recognition Using Deep Networks,2016 +2,United States,UCCS,uccs,38.8920756,-104.79716389,"University of Colorado, Colorado Springs",edu,d4f1eb008eb80595bcfdac368e23ae9754e1e745,citation,,Unconstrained Face Detection and Open-Set Face Recognition Challenge,2017 +3,United States,UCCS,uccs,41.70456775,-86.23822026,University of Notre Dame,edu,841855205818d3a6d6f85ec17a22515f4f062882,citation,https://arxiv.org/pdf/1805.11529.pdf,Low Resolution Face Recognition in the Wild,2018 +4,China,UCCS,uccs,39.9808333,116.34101249,Beihang University,edu,c50e498ede6f5216cffd0645e747ce67fae2096a,citation,https://arxiv.org/pdf/1811.09998.pdf,Low-Resolution Face Recognition in the Wild via Selective Knowledge Distillation,2018 +5,China,UCCS,uccs,39.97426,116.21589,"Institute of Information Engineering, CAS, Beijing, China",edu,c50e498ede6f5216cffd0645e747ce67fae2096a,citation,https://arxiv.org/pdf/1811.09998.pdf,Low-Resolution Face Recognition in the Wild via Selective Knowledge Distillation,2018 diff --git a/site/datasets/verified/used.csv b/site/datasets/verified/used.csv index 52c7be2f..c63ece10 100644 --- a/site/datasets/verified/used.csv +++ b/site/datasets/verified/used.csv @@ -1,2 +1,5 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year 0,,USED Social Event Dataset,used,0.0,0.0,,,,main,,USED: a large-scale social event detection dataset,2016 +1,Japan,USED Social Event Dataset,used,32.8164178,130.72703969,Kumamoto University,edu,d1bca67dd26d719b3e7a51acecd7c54c7b78b34a,citation,https://arxiv.org/pdf/1612.04062.pdf,Spatial Pyramid Convolutional Neural Network for Social Event Detection in Static Image,2016 +2,Italy,USED Social Event Dataset,used,46.0658836,11.1159894,University of Trento,edu,27f8b01e628f20ebfcb58d14ea40573d351bbaad,citation,https://pdfs.semanticscholar.org/27f8/b01e628f20ebfcb58d14ea40573d351bbaad.pdf,Events based Multimedia Indexing and Retrieval,2017 +3,Italy,USED Social Event Dataset,used,46.0658836,11.1159894,University of Trento,edu,4bf85ef995c684b841d0a5a002d175fadd922ff0,citation,,Ensemble of Deep Models for Event Recognition,2018 diff --git a/site/datasets/verified/voc.csv b/site/datasets/verified/voc.csv index 89a14200..75397740 100644 --- a/site/datasets/verified/voc.csv +++ b/site/datasets/verified/voc.csv @@ -1,2 +1,145 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year 0,,VOC,voc,0.0,0.0,,,,main,,The Pascal Visual Object Classes (VOC) Challenge,2009 +1,China,VOC,voc,28.2290209,112.99483204,"National University of Defense Technology, China",mil,ca4e0a2cd761f52e6c0bc06ef8ac79e3c7649083,citation,https://arxiv.org/pdf/1804.04606.pdf,Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors,2018 +2,United States,VOC,voc,39.0298587,-76.9638027,"U.S. Army Research Laboratory, Adelphi, MD, USA",mil,e7895feb2de9007ea1e47b0ea5952afd5af08b3d,citation,https://arxiv.org/pdf/1704.01069.pdf,ME R-CNN: Multi-Expert R-CNN for Object Detection,2017 +3,United States,VOC,voc,37.8718992,-122.2585399,"University of Califonia, Berkeley",edu,0547c44cb896e1cc38130ae8cc6b04dc21179045,citation,http://courses.cs.washington.edu/courses/cse590v/13au/FastMatch_cvpr_2013.pdf,Fast-Match: Fast Affine Template Matching,2013 +4,Israel,VOC,voc,32.1119889,34.80459702,Tel Aviv University,edu,0547c44cb896e1cc38130ae8cc6b04dc21179045,citation,http://courses.cs.washington.edu/courses/cse590v/13au/FastMatch_cvpr_2013.pdf,Fast-Match: Fast Affine Template Matching,2013 +5,Israel,VOC,voc,31.904187,34.807378,"Weizmann Institute, Rehovot, Israel",edu,0547c44cb896e1cc38130ae8cc6b04dc21179045,citation,http://courses.cs.washington.edu/courses/cse590v/13au/FastMatch_cvpr_2013.pdf,Fast-Match: Fast Affine Template Matching,2013 +6,Israel,VOC,voc,32.7940463,34.989571,"Yahoo Research Labs, Haifa, Israel",company,0547c44cb896e1cc38130ae8cc6b04dc21179045,citation,http://courses.cs.washington.edu/courses/cse590v/13au/FastMatch_cvpr_2013.pdf,Fast-Match: Fast Affine Template Matching,2013 +7,Netherlands,VOC,voc,52.3553655,4.9501644,University of Amsterdam,edu,19a3e5495b420c1f5da283bf39708a6e833a6cc5,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1A_020.pdf,Attributes and categories for generic instance search from one example,2015 +8,United States,VOC,voc,40.8419836,-73.94368971,Columbia University,edu,19a3e5495b420c1f5da283bf39708a6e833a6cc5,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1A_020.pdf,Attributes and categories for generic instance search from one example,2015 +9,China,VOC,voc,39.103355,117.164927,NanKai University,edu,55968c9906e13eff2a7fb03d7c416a6d0f9f53e0,citation,http://cg.cs.tsinghua.edu.cn/papers/ECCV-2016-Hfs.pdf,HFS: Hierarchical Feature Selection for Efficient Image Segmentation,2016 +10,United Kingdom,VOC,voc,51.7520849,-1.2516646,Oxford University,edu,55968c9906e13eff2a7fb03d7c416a6d0f9f53e0,citation,http://cg.cs.tsinghua.edu.cn/papers/ECCV-2016-Hfs.pdf,HFS: Hierarchical Feature Selection for Efficient Image Segmentation,2016 +11,China,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,55968c9906e13eff2a7fb03d7c416a6d0f9f53e0,citation,http://cg.cs.tsinghua.edu.cn/papers/ECCV-2016-Hfs.pdf,HFS: Hierarchical Feature Selection for Efficient Image Segmentation,2016 +12,United States,VOC,voc,32.87935255,-117.23110049,"University of California, San Diego",edu,55968c9906e13eff2a7fb03d7c416a6d0f9f53e0,citation,http://cg.cs.tsinghua.edu.cn/papers/ECCV-2016-Hfs.pdf,HFS: Hierarchical Feature Selection for Efficient Image Segmentation,2016 +13,United States,VOC,voc,40.4441619,-79.94272826,Carnegie Mellon University,edu,46c82cfadd9f885f5480b2d7155f0985daf949fc,citation,http://openaccess.thecvf.com/content_cvpr_2016/papers/Fouhey_3D_Shape_Attributes_CVPR_2016_paper.pdf,3D Shape Attributes,2016 +14,United Kingdom,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,46c82cfadd9f885f5480b2d7155f0985daf949fc,citation,http://openaccess.thecvf.com/content_cvpr_2016/papers/Fouhey_3D_Shape_Attributes_CVPR_2016_paper.pdf,3D Shape Attributes,2016 +15,United States,VOC,voc,47.6423318,-122.1369302,Microsoft,company,57642aa16d29bbd9f89f95e3f3dcb8291552db60,citation,http://www.cs.toronto.edu/~pekhimenko/Papers/iiswc18-tbd.pdf,Benchmarking and Analyzing Deep Neural Network Training,2018 +16,Canada,VOC,voc,49.25839375,-123.24658161,University of British Columbia,edu,57642aa16d29bbd9f89f95e3f3dcb8291552db60,citation,http://www.cs.toronto.edu/~pekhimenko/Papers/iiswc18-tbd.pdf,Benchmarking and Analyzing Deep Neural Network Training,2018 +17,Canada,VOC,voc,43.66333345,-79.39769975,University of Toronto,edu,57642aa16d29bbd9f89f95e3f3dcb8291552db60,citation,http://www.cs.toronto.edu/~pekhimenko/Papers/iiswc18-tbd.pdf,Benchmarking and Analyzing Deep Neural Network Training,2018 +18,China,VOC,voc,39.9808333,116.34101249,Beihang University,edu,df0e280cae018cebd5b16ad701ad101265c369fa,citation,https://arxiv.org/pdf/1509.02470.pdf,Deep Attributes from Context-Aware Regional Neural Codes,2015 +19,China,VOC,voc,39.966244,116.3270039,Intel Labs China,company,df0e280cae018cebd5b16ad701ad101265c369fa,citation,https://arxiv.org/pdf/1509.02470.pdf,Deep Attributes from Context-Aware Regional Neural Codes,2015 +20,United States,VOC,voc,40.8419836,-73.94368971,Columbia University,edu,df0e280cae018cebd5b16ad701ad101265c369fa,citation,https://arxiv.org/pdf/1509.02470.pdf,Deep Attributes from Context-Aware Regional Neural Codes,2015 +21,United Kingdom,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,a63104ad235f98bc5ee0b44fefbcdb49e32c205a,citation,http://groups.inf.ed.ac.uk/calvin/Publications/Jammalamadaka12eccv.pdf,Has my algorithm succeeded? an evaluator for human pose estimators,2012 +22,Switzerland,VOC,voc,47.376313,8.5476699,ETH Zurich,edu,a63104ad235f98bc5ee0b44fefbcdb49e32c205a,citation,http://groups.inf.ed.ac.uk/calvin/Publications/Jammalamadaka12eccv.pdf,Has my algorithm succeeded? an evaluator for human pose estimators,2012 +23,United Kingdom,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,a63104ad235f98bc5ee0b44fefbcdb49e32c205a,citation,http://groups.inf.ed.ac.uk/calvin/Publications/Jammalamadaka12eccv.pdf,Has my algorithm succeeded? an evaluator for human pose estimators,2012 +24,China,VOC,voc,36.3693473,120.673818,Shandong University,edu,ddde8f2c0209f11c2579dfaa13ac4053dedbf2fe,citation,https://arxiv.org/pdf/1811.02804.pdf,Image smoothing via unsupervised learning,2018 +25,United States,VOC,voc,42.3614256,-71.0812092,Microsoft Research Asia,company,ddde8f2c0209f11c2579dfaa13ac4053dedbf2fe,citation,https://arxiv.org/pdf/1811.02804.pdf,Image smoothing via unsupervised learning,2018 +26,China,VOC,voc,39.9922379,116.30393816,Peking University,edu,ddde8f2c0209f11c2579dfaa13ac4053dedbf2fe,citation,https://arxiv.org/pdf/1811.02804.pdf,Image smoothing via unsupervised learning,2018 +27,United States,VOC,voc,32.87935255,-117.23110049,"University of California, San Diego",edu,16161051ee13dd3d836a39a280df822bf6442c84,citation,https://pdfs.semanticscholar.org/4bd3/f187f3e09483b1f0f92150a4a77409691b0f.pdf,Learning Efficient Object Detection Models with Knowledge Distillation,2017 +28,United States,VOC,voc,38.926761,-92.29193783,University of Missouri,edu,16161051ee13dd3d836a39a280df822bf6442c84,citation,https://pdfs.semanticscholar.org/4bd3/f187f3e09483b1f0f92150a4a77409691b0f.pdf,Learning Efficient Object Detection Models with Knowledge Distillation,2017 +29,United States,VOC,voc,37.3239177,-122.0129693,"NEC Labs, Cupertino, CA",company,16161051ee13dd3d836a39a280df822bf6442c84,citation,https://pdfs.semanticscholar.org/4bd3/f187f3e09483b1f0f92150a4a77409691b0f.pdf,Learning Efficient Object Detection Models with Knowledge Distillation,2017 +30,China,VOC,voc,39.966244,116.3270039,Intel Labs China,company,19d4855f064f0d53cb851e9342025bd8503922e2,citation,http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/4989d468.pdf,Learning SURF Cascade for Fast and Accurate Object Detection,2013 +31,China,VOC,voc,23.09461185,113.28788994,Sun Yat-Sen University,edu,ee098ed493af3abe873ce89354599e1f6bdf65be,citation,https://arxiv.org/pdf/1702.05839.pdf,Progressively Diffused Networks for Semantic Image Segmentation,2017 +32,China,VOC,voc,22.4162632,114.2109318,Chinese University of Hong Kong,edu,ee098ed493af3abe873ce89354599e1f6bdf65be,citation,https://arxiv.org/pdf/1702.05839.pdf,Progressively Diffused Networks for Semantic Image Segmentation,2017 +33,China,VOC,voc,39.993008,116.329882,SenseTime,company,ee098ed493af3abe873ce89354599e1f6bdf65be,citation,https://arxiv.org/pdf/1702.05839.pdf,Progressively Diffused Networks for Semantic Image Segmentation,2017 +34,United States,VOC,voc,37.4092265,-122.0236615,Baidu,company,99f95595c45bd7a4fe2cffff07850754955e5e2a,citation,https://nicsefc.ee.tsinghua.edu.cn/media/publications/2015/IEEE%20TCAD_170.pdf,RRAM-Based Analog Approximate Computing,2015 +35,United States,VOC,voc,40.44415295,-79.96243993,University of Pittsburgh,edu,99f95595c45bd7a4fe2cffff07850754955e5e2a,citation,https://nicsefc.ee.tsinghua.edu.cn/media/publications/2015/IEEE%20TCAD_170.pdf,RRAM-Based Analog Approximate Computing,2015 +36,China,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,99f95595c45bd7a4fe2cffff07850754955e5e2a,citation,https://nicsefc.ee.tsinghua.edu.cn/media/publications/2015/IEEE%20TCAD_170.pdf,RRAM-Based Analog Approximate Computing,2015 +37,United States,VOC,voc,33.7756178,-84.396285,Georgia Tech,edu,5a0209515ab62e008efeca31f80fa0a97031cd9d,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/3B_046.pdf,Dataset fingerprints: Exploring image collections through data mining,2015 +38,United States,VOC,voc,40.4441619,-79.94272826,Carnegie Mellon University,edu,2c953b06c1c312e36f1fdb9919567b42c9322384,citation,http://people.csail.mit.edu/tomasz/papers/malisiewicz_iccv11.pdf,Ensemble of exemplar-SVMs for object detection and beyond,2011 +39,China,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,5907ca4b91c8e8d846871e045bce9a4ca851053a,citation,http://eiger.ddns.comp.nus.edu.sg/pubs/fusionofmultichannelstructures-tip2014.pdf,Fusion of Multichannel Local and Global Structural Cues for Photo Aesthetics Evaluation,2014 +40,United States,VOC,voc,29.58333105,-98.61944505,University of Texas at San Antonio,edu,5907ca4b91c8e8d846871e045bce9a4ca851053a,citation,http://eiger.ddns.comp.nus.edu.sg/pubs/fusionofmultichannelstructures-tip2014.pdf,Fusion of Multichannel Local and Global Structural Cues for Photo Aesthetics Evaluation,2014 +41,Singapore,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,5907ca4b91c8e8d846871e045bce9a4ca851053a,citation,http://eiger.ddns.comp.nus.edu.sg/pubs/fusionofmultichannelstructures-tip2014.pdf,Fusion of Multichannel Local and Global Structural Cues for Photo Aesthetics Evaluation,2014 +42,China,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,5907ca4b91c8e8d846871e045bce9a4ca851053a,citation,http://eiger.ddns.comp.nus.edu.sg/pubs/fusionofmultichannelstructures-tip2014.pdf,Fusion of Multichannel Local and Global Structural Cues for Photo Aesthetics Evaluation,2014 +43,China,VOC,voc,22.4162632,114.2109318,Chinese University of Hong Kong,edu,931282732f0be57f7fb895238e94bdda00a52cad,citation,https://pdfs.semanticscholar.org/9312/82732f0be57f7fb895238e94bdda00a52cad.pdf,Gated Bi-directional CNN for Object Detection,2016 +44,China,VOC,voc,39.993008,116.329882,SenseTime,company,931282732f0be57f7fb895238e94bdda00a52cad,citation,https://pdfs.semanticscholar.org/9312/82732f0be57f7fb895238e94bdda00a52cad.pdf,Gated Bi-directional CNN for Object Detection,2016 +45,Germany,VOC,voc,48.7468939,9.0805141,Max Planck Institute for Intelligent Systems,edu,cfa48bc1015b88809e362b4da19fe4459acb1d89,citation,https://pdfs.semanticscholar.org/cfa4/8bc1015b88809e362b4da19fe4459acb1d89.pdf,Learning to Filter Object Detections,2017 +46,United States,VOC,voc,47.6423318,-122.1369302,Microsoft,company,cfa48bc1015b88809e362b4da19fe4459acb1d89,citation,https://pdfs.semanticscholar.org/cfa4/8bc1015b88809e362b4da19fe4459acb1d89.pdf,Learning to Filter Object Detections,2017 +47,United States,VOC,voc,40.34829285,-74.66308325,Princeton University,edu,420c46d7cafcb841309f02ad04cf51cb1f190a48,citation,https://arxiv.org/pdf/1511.07122.pdf,Multi-Scale Context Aggregation by Dilated Convolutions,2015 +48,United States,VOC,voc,40.4439789,-79.9464634,Intel Labs,company,420c46d7cafcb841309f02ad04cf51cb1f190a48,citation,https://arxiv.org/pdf/1511.07122.pdf,Multi-Scale Context Aggregation by Dilated Convolutions,2015 +49,France,VOC,voc,48.708759,2.164006,"Center for Visual Computing, École Centrale Paris, France",edu,2603a85b305d041bf749934fe538315ecbc300c2,citation,http://www.ee.oulu.fi/~jkannala/publications/scia2013a.pdf,Non Maximal Suppression in Cascaded Ranking Models,2013 +50,France,VOC,voc,48.840579,2.586968,"LIGM (UMR CNRS), École des Ponts ParisTech, Université Paris-Est, France",edu,2603a85b305d041bf749934fe538315ecbc300c2,citation,http://www.ee.oulu.fi/~jkannala/publications/scia2013a.pdf,Non Maximal Suppression in Cascaded Ranking Models,2013 +51,Finland,VOC,voc,65.0592157,25.46632601,University of Oulu,edu,2603a85b305d041bf749934fe538315ecbc300c2,citation,http://www.ee.oulu.fi/~jkannala/publications/scia2013a.pdf,Non Maximal Suppression in Cascaded Ranking Models,2013 +52,France,VOC,voc,48.7146403,2.2056539,"Équipe Galen, INRIA Saclay, Île-de-France, France",edu,2603a85b305d041bf749934fe538315ecbc300c2,citation,http://www.ee.oulu.fi/~jkannala/publications/scia2013a.pdf,Non Maximal Suppression in Cascaded Ranking Models,2013 +53,United States,VOC,voc,42.3354481,-71.16813864,Boston College,edu,18ccd8bd64b50c1b6a83a71792fd808da7076bc9,citation,http://ttic.uchicago.edu/~mmaire/papers/pdf/seg_obj_iccv2011.pdf,Object detection and segmentation from joint embedding of parts and pixels,2011 +54,United States,VOC,voc,34.13710185,-118.12527487,California Institute of Technology,edu,18ccd8bd64b50c1b6a83a71792fd808da7076bc9,citation,http://ttic.uchicago.edu/~mmaire/papers/pdf/seg_obj_iccv2011.pdf,Object detection and segmentation from joint embedding of parts and pixels,2011 +55,Japan,VOC,voc,34.7275714,135.2371,Kobe University,edu,75d0a8e80a75312571951144aaa2d5dd5ae30e43,citation,http://eprints.whiterose.ac.uk/132227/1/TMM_camera_ready.pdf,Polar Transformation on Image Features for Orientation-Invariant Representations,2019 +56,China,VOC,voc,26.0252776,119.2117845,Fujian Normal University,edu,75d0a8e80a75312571951144aaa2d5dd5ae30e43,citation,http://eprints.whiterose.ac.uk/132227/1/TMM_camera_ready.pdf,Polar Transformation on Image Features for Orientation-Invariant Representations,2019 +57,United Kingdom,VOC,voc,53.94540365,-1.03138878,University of York,edu,75d0a8e80a75312571951144aaa2d5dd5ae30e43,citation,http://eprints.whiterose.ac.uk/132227/1/TMM_camera_ready.pdf,Polar Transformation on Image Features for Orientation-Invariant Representations,2019 +58,China,VOC,voc,24.4399419,118.09301781,Xiamen University,edu,75d0a8e80a75312571951144aaa2d5dd5ae30e43,citation,http://eprints.whiterose.ac.uk/132227/1/TMM_camera_ready.pdf,Polar Transformation on Image Features for Orientation-Invariant Representations,2019 +59,United Kingdom,VOC,voc,51.5247272,-0.03931035,Queen Mary University of London,edu,b1045a2de35d0adf784353f90972118bc1162f8d,citation,http://eecs.qmul.ac.uk/~jason/Research/PreprintVersion/Quantifying%20and%20Transferring%20Contextual%20Information%20in%20Object%20Detection.pdf,Quantifying and Transferring Contextual Information in Object Detection,2012 +60,China,VOC,voc,23.09461185,113.28788994,Sun Yat-Sen University,edu,b1045a2de35d0adf784353f90972118bc1162f8d,citation,http://eecs.qmul.ac.uk/~jason/Research/PreprintVersion/Quantifying%20and%20Transferring%20Contextual%20Information%20in%20Object%20Detection.pdf,Quantifying and Transferring Contextual Information in Object Detection,2012 +61,China,VOC,voc,23.09461185,113.28788994,Sun Yat-Sen University,edu,ab781f035720d991e244adb35f1d04e671af1999,citation,https://arxiv.org/pdf/1712.07465.pdf,Recurrent Attentional Reinforcement Learning for Multi-Label Image Recognition,2018 +62,China,VOC,voc,39.993008,116.329882,SenseTime,company,ab781f035720d991e244adb35f1d04e671af1999,citation,https://arxiv.org/pdf/1712.07465.pdf,Recurrent Attentional Reinforcement Learning for Multi-Label Image Recognition,2018 +63,Canada,VOC,voc,43.66333345,-79.39769975,University of Toronto,edu,1bb0dd8d349cdb1bbc065f1f0e111a8334072257,citation,http://jmlr.csail.mit.edu/proceedings/papers/v22/tarlow12a/tarlow12a.pdf,Structured Output Learning with High Order Loss Functions,2012 +64,United States,VOC,voc,41.7846982,-87.5925848,Toyota Technological Institute at Chicago,company,3a4c70ca0bbd461fe2e4de3448a01f06c0217459,citation,https://arxiv.org/pdf/1510.09171.pdf,Accurate Vision-based Vehicle Localization using Satellite Imagery,2015 +65,Netherlands,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 +66,Netherlands,VOC,voc,52.356678,4.95187,"Centrum Wiskunde & Informatica, Amsterdam, The Netherlands",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 +67,United States,VOC,voc,47.6423318,-122.1369302,Microsoft,company,c9abf6cb2d916262425033db12cf0181d40be7cb,citation,https://pdfs.semanticscholar.org/c9ab/f6cb2d916262425033db12cf0181d40be7cb.pdf,Entropy-based Latent Structured Output Prediction-Supplementary materials,2015 +68,China,VOC,voc,31.83907195,117.26420748,University of Science and Technology of China,edu,ce43209fc68e51ef05fa06cc0fe6210cbd021e3f,citation,http://min.sjtu.edu.cn/files%5Cpapers%5C2016%5CJournal%5C2016-TIP-CV-ZHANGXIAOPENG%5C2016-TIP-CV-02.pdf,Fused One-vs-All Features With Semantic Alignments for Fine-Grained Visual Categorization,2016 +69,United States,VOC,voc,29.58333105,-98.61944505,University of Texas at San Antonio,edu,ce43209fc68e51ef05fa06cc0fe6210cbd021e3f,citation,http://min.sjtu.edu.cn/files%5Cpapers%5C2016%5CJournal%5C2016-TIP-CV-ZHANGXIAOPENG%5C2016-TIP-CV-02.pdf,Fused One-vs-All Features With Semantic Alignments for Fine-Grained Visual Categorization,2016 +70,China,VOC,voc,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,ce43209fc68e51ef05fa06cc0fe6210cbd021e3f,citation,http://min.sjtu.edu.cn/files%5Cpapers%5C2016%5CJournal%5C2016-TIP-CV-ZHANGXIAOPENG%5C2016-TIP-CV-02.pdf,Fused One-vs-All Features With Semantic Alignments for Fine-Grained Visual Categorization,2016 +71,United Kingdom,VOC,voc,51.7555205,-1.2261597,Oxford Brookes University,edu,70d71c2f8c865438c0158bed9f7d64e57e245535,citation,http://cms.brookes.ac.uk/research/visiongroup/publications/2013/intr_obj_vrt_nips13.pdf,"Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation",2013 +72,United Kingdom,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,70d71c2f8c865438c0158bed9f7d64e57e245535,citation,http://cms.brookes.ac.uk/research/visiongroup/publications/2013/intr_obj_vrt_nips13.pdf,"Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation",2013 +73,China,VOC,voc,34.2469152,108.91061982,Northwestern Polytechnical University,edu,50953b9a15aca6ef3351e613e7215abdcae1435e,citation,http://sunw.csail.mit.edu/papers/63_Cheng_SUNw.pdf,Learning coarse-to-fine sparselets for efficient object detection and scene classification,2015 +74,Thailand,VOC,voc,13.65450525,100.49423171,Robotics Institute,edu,d6d7dcdcf66fe83e49d175cd9d8ac0b507d0e9d8,citation,http://dhoiem.cs.illinois.edu/publications/ijcv2010_occlusion.pdf,Recovering Occlusion Boundaries from an Image,2010 +75,United States,VOC,voc,40.4441619,-79.94272826,Carnegie Mellon University,edu,d6d7dcdcf66fe83e49d175cd9d8ac0b507d0e9d8,citation,http://dhoiem.cs.illinois.edu/publications/ijcv2010_occlusion.pdf,Recovering Occlusion Boundaries from an Image,2010 +76,United States,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,d6d7dcdcf66fe83e49d175cd9d8ac0b507d0e9d8,citation,http://dhoiem.cs.illinois.edu/publications/ijcv2010_occlusion.pdf,Recovering Occlusion Boundaries from an Image,2010 +77,China,VOC,voc,28.727339,115.816633,Jiangxi University of Finance and Economics,edu,1642358cd9410abe9ee512d34ba68296b308770e,citation,https://arxiv.org/pdf/1807.04562.pdf,Robustness Analysis of Pedestrian Detectors for Surveillance,2018 +78,Singapore,VOC,voc,1.3484104,103.68297965,Nanyang Technological University,edu,1642358cd9410abe9ee512d34ba68296b308770e,citation,https://arxiv.org/pdf/1807.04562.pdf,Robustness Analysis of Pedestrian Detectors for Surveillance,2018 +79,China,VOC,voc,34.250803,108.983693,Xi’an Jiaotong University,edu,1642358cd9410abe9ee512d34ba68296b308770e,citation,https://arxiv.org/pdf/1807.04562.pdf,Robustness Analysis of Pedestrian Detectors for Surveillance,2018 +80,Netherlands,VOC,voc,52.3553655,4.9501644,University of Amsterdam,edu,25d7da85858a4d89b7de84fd94f0c0a51a9fc67a,citation,http://graphics.cs.cmu.edu/courses/16-824/2016_spring/slides/seg_3.pdf,Selective Search for Object Recognition,2013 +81,Italy,VOC,voc,46.0658836,11.1159894,University of Trento,edu,25d7da85858a4d89b7de84fd94f0c0a51a9fc67a,citation,http://graphics.cs.cmu.edu/courses/16-824/2016_spring/slides/seg_3.pdf,Selective Search for Object Recognition,2013 +82,United States,VOC,voc,37.4219999,-122.0840575,Google,company,0690ba31424310a90028533218d0afd25a829c8d,citation,https://arxiv.org/pdf/1412.7062.pdf,Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs,2015 +83,Germany,VOC,voc,53.8338371,10.7035939,Institute of Systems and Robotics,edu,7fb8d9c36c23f274f2dd84945dd32ec2cc143de1,citation,http://home.isr.uc.pt/~joaoluis/papers/eccv2012.pdf,Semantic segmentation with second-order pooling,2012 +84,Germany,VOC,voc,50.7338124,7.1022465,University of Bonn,edu,7fb8d9c36c23f274f2dd84945dd32ec2cc143de1,citation,http://home.isr.uc.pt/~joaoluis/papers/eccv2012.pdf,Semantic segmentation with second-order pooling,2012 +85,United Kingdom,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,4682fee7dc045aea7177d7f3bfe344aabf153bd5,citation,http://cs.brown.edu/~ls/teaching_CMU_16-824/slides_tz-1.pdf,Tabula rasa: Model transfer for object category detection,2011 +86,United States,VOC,voc,42.3614256,-71.0812092,Microsoft Research Asia,company,35f345ebe3831e4741dcdc1931da59043acf4b83,citation,https://pdfs.semanticscholar.org/35f3/45ebe3831e4741dcdc1931da59043acf4b83.pdf,Towards High Performance Video Object Detection for Mobiles 3 2 Revisiting Video Object Detection Baseline,2018 +87,Canada,VOC,voc,49.8091536,-97.13304179,University of Manitoba,edu,488fff23542ff397cdb1ced64db2c96320afc560,citation,http://www.cs.umanitoba.ca/~ywang/papers/cvpr15.pdf,Weakly supervised localization of novel objects using appearance transfer,2015 +88,United States,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,032bde9da87439c781a6c81ba7933985ed95d88e,citation,https://arxiv.org/pdf/1506.02106.pdf,What's the point: Semantic segmentation with point supervision,2016 +89,United States,VOC,voc,40.4441619,-79.94272826,Carnegie Mellon University,edu,032bde9da87439c781a6c81ba7933985ed95d88e,citation,https://arxiv.org/pdf/1506.02106.pdf,What's the point: Semantic segmentation with point supervision,2016 +90,United Kingdom,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,032bde9da87439c781a6c81ba7933985ed95d88e,citation,https://arxiv.org/pdf/1506.02106.pdf,What's the point: Semantic segmentation with point supervision,2016 +91,Australia,VOC,voc,-42.902631,147.3273381,University of Tasmania,edu,c2a2093b4163616b83398e503ae9ed948f4f6a2b,citation,http://mima.sdu.edu.cn/(X(1)S(ar3myg55nqom1l55ttix5kjj))/Images/publication/Dual-CNN-ML.pdf,A Dual-CNN Model for Multi-label Classification by Leveraging Co-occurrence Dependencies Between Labels,2017 +92,China,VOC,voc,36.3693473,120.673818,Shandong University,edu,c2a2093b4163616b83398e503ae9ed948f4f6a2b,citation,http://mima.sdu.edu.cn/(X(1)S(ar3myg55nqom1l55ttix5kjj))/Images/publication/Dual-CNN-ML.pdf,A Dual-CNN Model for Multi-label Classification by Leveraging Co-occurrence Dependencies Between Labels,2017 +93,United States,VOC,voc,34.068921,-118.4451811,UCLA,edu,c4fc07072d7ebfbca471d2394b20199d8107e517,citation,https://pdfs.semanticscholar.org/c4fc/07072d7ebfbca471d2394b20199d8107e517.pdf,Active Mask Hierarchies for Object Detection,2010 +94,United States,VOC,voc,42.3583961,-71.09567788,MIT,edu,c4fc07072d7ebfbca471d2394b20199d8107e517,citation,https://pdfs.semanticscholar.org/c4fc/07072d7ebfbca471d2394b20199d8107e517.pdf,Active Mask Hierarchies for Object Detection,2010 +95,China,VOC,voc,38.88140235,121.52281098,Dalian University of Technology,edu,39afeceb57a7fde266ddd842aa23d2eea7ad5665,citation,https://arxiv.org/pdf/1802.06960.pdf,Agile Amulet: Real-Time Salient Object Detection with Contextual Attention,2018 +96,Australia,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,39afeceb57a7fde266ddd842aa23d2eea7ad5665,citation,https://arxiv.org/pdf/1802.06960.pdf,Agile Amulet: Real-Time Salient Object Detection with Contextual Attention,2018 +97,United States,VOC,voc,42.3583961,-71.09567788,MIT,edu,732e4016225280b485c557a119ec50cffb8fee98,citation,https://arxiv.org/pdf/1311.6510.pdf,Are all training examples equally valuable?,2013 +98,Spain,VOC,voc,41.40657415,2.1945341,Universitat Oberta de Catalunya,edu,732e4016225280b485c557a119ec50cffb8fee98,citation,https://arxiv.org/pdf/1311.6510.pdf,Are all training examples equally valuable?,2013 +99,United States,VOC,voc,39.2899685,-76.62196103,University of Maryland,edu,38b4ac4a0802fdb63dea6769dd1aee075cc3f87d,citation,https://arxiv.org/pdf/1712.08675.pdf,Boundary-sensitive Network for Portrait Segmentation,2017 +100,United States,VOC,voc,37.4019735,-122.0477876,Samsung Research America,edu,38b4ac4a0802fdb63dea6769dd1aee075cc3f87d,citation,https://arxiv.org/pdf/1712.08675.pdf,Boundary-sensitive Network for Portrait Segmentation,2017 +101,Switzerland,VOC,voc,47.3764534,8.54770931,ETH Zürich,edu,10f13579084670291019c6e8ef55f5cd35c926b6,citation,https://pdfs.semanticscholar.org/7088/0e0ba2478c7250918ee9b7accc6993d13ba4.pdf,Closed-Form Approximate CRF Training for Scalable Image Segmentation,2014 +102,United Kingdom,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,10f13579084670291019c6e8ef55f5cd35c926b6,citation,https://pdfs.semanticscholar.org/7088/0e0ba2478c7250918ee9b7accc6993d13ba4.pdf,Closed-Form Approximate CRF Training for Scalable Image Segmentation,2014 +103,Singapore,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,5250f319cae32437489bb97b2ed9a1dc962d4d39,citation,https://arxiv.org/pdf/1411.2861.pdf,Computational Baby Learning.,2014 +104,China,VOC,voc,39.94976005,116.33629046,Beijing Jiaotong University,edu,5250f319cae32437489bb97b2ed9a1dc962d4d39,citation,https://arxiv.org/pdf/1411.2861.pdf,Computational Baby Learning.,2014 +105,Switzerland,VOC,voc,46.5190557,6.5667576,"EPFL, Lausanne (Switzerland)",edu,7b8ace072475a9a42d6ceb293c8b4a8c9b573284,citation,http://www.vision.ee.ethz.ch/en/publications/papers/proceedings/eth_biwi_00855.pdf,Conditional Random Fields for multi-camera object detection,2011 +106,Switzerland,VOC,voc,47.376313,8.5476699,"ETHZ, Zurich (Switzerland)",edu,7b8ace072475a9a42d6ceb293c8b4a8c9b573284,citation,http://www.vision.ee.ethz.ch/en/publications/papers/proceedings/eth_biwi_00855.pdf,Conditional Random Fields for multi-camera object detection,2011 +107,United States,VOC,voc,37.2283843,-80.4234167,Virginia Tech,edu,3d0660e18c17db305b9764bb86b21a429241309e,citation,https://arxiv.org/pdf/1604.03505.pdf,Counting Everyday Objects in Everyday Scenes,2017 +108,United States,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 +109,United States,VOC,voc,37.3239177,-122.0129693,"NEC Labs, Cupertino, CA",company,8f76401847d3e3f0331bab24b17f76953be66220,citation,http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2010_1077.pdf,Deep Coding Network,2010 +110,United States,VOC,voc,40.47913175,-74.43168868,Rutgers University,edu,8f76401847d3e3f0331bab24b17f76953be66220,citation,http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2010_1077.pdf,Deep Coding Network,2010 +111,China,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,fe7ae13bf5fc80cf0837bacbe44905bd8749f03f,citation,http://ivg.au.tsinghua.edu.cn/paper/2017_Deep%20coupled%20metric%20learning%20for%20cross-modal%20matching.pdf,Deep Coupled Metric Learning for Cross-Modal Matching,2017 +112,Singapore,VOC,voc,1.3484104,103.68297965,Nanyang Technological University,edu,fe7ae13bf5fc80cf0837bacbe44905bd8749f03f,citation,http://ivg.au.tsinghua.edu.cn/paper/2017_Deep%20coupled%20metric%20learning%20for%20cross-modal%20matching.pdf,Deep Coupled Metric Learning for Cross-Modal Matching,2017 +113,Canada,VOC,voc,43.7743911,-79.50481085,York University,edu,cdeee5eed68e7c8eb06185f7fcb1a072af784886,citation,https://arxiv.org/pdf/1505.01173.pdf,Deep Learning for Object Saliency Detection and Image Segmentation,2015 +114,United States,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,cdeee5eed68e7c8eb06185f7fcb1a072af784886,citation,https://arxiv.org/pdf/1505.01173.pdf,Deep Learning for Object Saliency Detection and Image Segmentation,2015 +115,Canada,VOC,voc,49.8091536,-97.13304179,University of Manitoba,edu,64b9675e924974fdec78a7272b27c7e7ec63a608,citation,http://www.cs.umanitoba.ca/~ywang/papers/icip17.pdf,Depth-aware object instance segmentation,2017 +116,China,VOC,voc,31.32235655,121.38400941,Shanghai University,edu,64b9675e924974fdec78a7272b27c7e7ec63a608,citation,http://www.cs.umanitoba.ca/~ywang/papers/icip17.pdf,Depth-aware object instance segmentation,2017 +117,Thailand,VOC,voc,13.65450525,100.49423171,Robotics Institute,edu,7d520f474f2fc59422d910b980f8485716ce0a3e,citation,https://pdfs.semanticscholar.org/2128/4a9310a4b4c836b8dfb6af39c682b7348128.pdf,Designing Convolutional Neural Networks for Urban Scene Understanding,2017 +118,United States,VOC,voc,40.4441619,-79.94272826,Carnegie Mellon University,edu,7d520f474f2fc59422d910b980f8485716ce0a3e,citation,https://pdfs.semanticscholar.org/2128/4a9310a4b4c836b8dfb6af39c682b7348128.pdf,Designing Convolutional Neural Networks for Urban Scene Understanding,2017 +119,India,VOC,voc,17.4450981,78.3497678,IIIT Hyderabad,edu,f23114073e0e513b1c1c55e8777bda503721718c,citation,https://arxiv.org/pdf/1811.10016.pdf,Dissimilarity Coefficient based Weakly Supervised Object Detection,2018 +120,United Kingdom,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,f23114073e0e513b1c1c55e8777bda503721718c,citation,https://arxiv.org/pdf/1811.10016.pdf,Dissimilarity Coefficient based Weakly Supervised Object Detection,2018 +121,United States,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,280d632ef3234c5ab06018c6eaccead75bc173b3,citation,http://ai.stanford.edu/~ajoulin/article/eccv14-vidcoloc.pdf,Efficient Image and Video Co-localization with Frank-Wolfe Algorithm,2014 +122,United States,VOC,voc,37.3239177,-122.0129693,NEC,company,44a3ee0429a6d1b79d431b4d396962175c28ace6,citation,http://openaccess.thecvf.com/content_cvpr_2016/papers/Yang_Exploit_All_the_CVPR_2016_paper.pdf,Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers,2016 +123,United States,VOC,voc,38.99203005,-76.9461029,University of Maryland College Park,edu,44a3ee0429a6d1b79d431b4d396962175c28ace6,citation,http://openaccess.thecvf.com/content_cvpr_2016/papers/Yang_Exploit_All_the_CVPR_2016_paper.pdf,Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers,2016 +124,United States,VOC,voc,34.13710185,-118.12527487,California Institute of Technology,edu,1a54a8b0c7b3fc5a21c6d33656690585c46ca08b,citation,http://authors.library.caltech.edu/49239/7/DollarPAMI14pyramids_0.pdf,Fast Feature Pyramids for Object Detection,2014 +125,United States,VOC,voc,42.4505507,-76.4783513,Cornell University,edu,1a54a8b0c7b3fc5a21c6d33656690585c46ca08b,citation,http://authors.library.caltech.edu/49239/7/DollarPAMI14pyramids_0.pdf,Fast Feature Pyramids for Object Detection,2014 +126,United States,VOC,voc,47.6418392,-122.1407465,"Microsoft Research Redmond, Redmond, USA",company,1a54a8b0c7b3fc5a21c6d33656690585c46ca08b,citation,http://authors.library.caltech.edu/49239/7/DollarPAMI14pyramids_0.pdf,Fast Feature Pyramids for Object Detection,2014 +127,Singapore,VOC,voc,1.29500195,103.84909214,Singapore Management University,edu,742d5b4590284b632ca043a16507fb5a459dceb2,citation,https://arxiv.org/pdf/1712.00721.pdf,Feature Agglomeration Networks for Single Stage Face Detection,2017 +128,China,VOC,voc,30.19331415,120.11930822,Zhejiang University,edu,742d5b4590284b632ca043a16507fb5a459dceb2,citation,https://arxiv.org/pdf/1712.00721.pdf,Feature Agglomeration Networks for Single Stage Face Detection,2017 +129,United States,VOC,voc,42.2745754,-71.8062724,Worcester Polytechnic Institute,edu,bd433d471af50b571d7284afb5ee435654ace99f,citation,https://pdfs.semanticscholar.org/bd43/3d471af50b571d7284afb5ee435654ace99f.pdf,Going Deeper with Convolutional Neural Network for Intelligent Transportation,2016 +130,United States,VOC,voc,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,bd433d471af50b571d7284afb5ee435654ace99f,citation,https://pdfs.semanticscholar.org/bd43/3d471af50b571d7284afb5ee435654ace99f.pdf,Going Deeper with Convolutional Neural Network for Intelligent Transportation,2016 +131,Israel,VOC,voc,32.76162915,35.01986304,University of Haifa,edu,fe683e48f373fa14c07851966474d15588b8c28b,citation,https://pdfs.semanticscholar.org/fe68/3e48f373fa14c07851966474d15588b8c28b.pdf,Hinge-Minimax Learner for the Ensemble of Hyperplanes,2018 +132,Israel,VOC,voc,32.7767783,35.0231271,Technion - Israel Institute of Technology,edu,fe683e48f373fa14c07851966474d15588b8c28b,citation,https://pdfs.semanticscholar.org/fe68/3e48f373fa14c07851966474d15588b8c28b.pdf,Hinge-Minimax Learner for the Ensemble of Hyperplanes,2018 +133,United States,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,4e65c9f0a64b6a4333b12e2adc3861ad75aca83b,citation,https://pdfs.semanticscholar.org/4e65/c9f0a64b6a4333b12e2adc3861ad75aca83b.pdf,Image Classification Using Super-Vector Coding of Local Image Descriptors,2010 +134,United States,VOC,voc,40.47913175,-74.43168868,Rutgers University,edu,4e65c9f0a64b6a4333b12e2adc3861ad75aca83b,citation,https://pdfs.semanticscholar.org/4e65/c9f0a64b6a4333b12e2adc3861ad75aca83b.pdf,Image Classification Using Super-Vector Coding of Local Image Descriptors,2010 +135,United States,VOC,voc,41.7847112,-87.59260567,"Toyota Technological Institute, Chicago",edu,a1f33473ea3b8e98fee37e32ecbecabc379e07a0,citation,http://cs.brown.edu/people/ren/publications/cvpr2013/cascade_final.pdf,Image Segmentation by Cascaded Region Agglomeration,2013 +136,China,VOC,voc,30.19331415,120.11930822,Zhejiang University,edu,a1f33473ea3b8e98fee37e32ecbecabc379e07a0,citation,http://cs.brown.edu/people/ren/publications/cvpr2013/cascade_final.pdf,Image Segmentation by Cascaded Region Agglomeration,2013 +137,Canada,VOC,voc,49.8091536,-97.13304179,University of Manitoba,edu,3b60af814574ebe389856e9f7008bb83b0539abc,citation,https://arxiv.org/pdf/1703.00551.pdf,Label Refinement Network for Coarse-to-Fine Semantic Segmentation.,2017 +138,United States,VOC,voc,39.86948105,-84.87956905,Indiana University,edu,3b60af814574ebe389856e9f7008bb83b0539abc,citation,https://arxiv.org/pdf/1703.00551.pdf,Label Refinement Network for Coarse-to-Fine Semantic Segmentation.,2017 +139,United States,VOC,voc,47.6543238,-122.30800894,University of Washington,edu,214f552070a7eb5ef5efe0d6ffeaaa594a3c3535,citation,http://allenai.org/content/publications/objectNgrams_cvpr14.pdf,Learning Everything about Anything: Webly-Supervised Visual Concept Learning,2014 +140,Germany,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 +141,United States,VOC,voc,39.3299013,-76.6205177,Johns Hopkins University,edu,472541ccd941b9b4c52e1f088cc1152de9b3430f,citation,https://arxiv.org/pdf/1612.00197.pdf,Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses,2017 +142,United States,VOC,voc,40.11571585,-88.22750772,Beckman Institute,edu,0bbb40e5b9e546a3f4e7340b2980059065c99203,citation,https://arxiv.org/pdf/1712.00886.pdf,Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids,2017 +143,China,VOC,voc,31.30104395,121.50045497,Fudan University,edu,0bbb40e5b9e546a3f4e7340b2980059065c99203,citation,https://arxiv.org/pdf/1712.00886.pdf,Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids,2017 diff --git a/site/datasets/verified/yfcc_100m.csv b/site/datasets/verified/yfcc_100m.csv index c7b3cd1f..a7625e9d 100644 --- a/site/datasets/verified/yfcc_100m.csv +++ b/site/datasets/verified/yfcc_100m.csv @@ -1,2 +1,104 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year 0,,YFCC100M,yfcc_100m,0.0,0.0,,,,main,,YFCC100M: the new data in multimedia research,2016 +1,United States,YFCC100M,yfcc_100m,38.7768106,-94.9442982,Amazon,company,d2067c7d31bebf89249966c3d8ee9395dd8531b8,citation,http://skamalas.com/docs/ICPR_2016.pdf,Visual congruent ads for image search,2016 +2,Netherlands,YFCC100M,yfcc_100m,52.356678,4.95187,"Centrum Wiskunde & Informatica (CWI), The Netherlands",edu,d2067c7d31bebf89249966c3d8ee9395dd8531b8,citation,http://skamalas.com/docs/ICPR_2016.pdf,Visual congruent ads for image search,2016 +3,Spain,YFCC100M,yfcc_100m,41.3789689,2.1797941,"DTIC, Universitat Pompeu Fabra & DCC, Universidad de Chile, Chile",edu,d2067c7d31bebf89249966c3d8ee9395dd8531b8,citation,http://skamalas.com/docs/ICPR_2016.pdf,Visual congruent ads for image search,2016 +4,United States,YFCC100M,yfcc_100m,33.0723372,-96.810299,"Futurewei Technologies Inc., USA",company,d2067c7d31bebf89249966c3d8ee9395dd8531b8,citation,http://skamalas.com/docs/ICPR_2016.pdf,Visual congruent ads for image search,2016 +5,United States,YFCC100M,yfcc_100m,40.7574714,-73.9877318,Yahoo,company,d2067c7d31bebf89249966c3d8ee9395dd8531b8,citation,http://skamalas.com/docs/ICPR_2016.pdf,Visual congruent ads for image search,2016 +6,United States,YFCC100M,yfcc_100m,40.4441619,-79.94272826,Carnegie Mellon University,edu,010f0f4929e6a6644fb01f0e43820f91d0fad292,citation,,YFCC100M: the new data in multimedia research,2016 +7,United States,YFCC100M,yfcc_100m,37.4523809,-122.1797586,In-Q-Tel,mil,010f0f4929e6a6644fb01f0e43820f91d0fad292,citation,,YFCC100M: the new data in multimedia research,2016 +8,United States,YFCC100M,yfcc_100m,40.7574714,-73.9877318,Yahoo,company,010f0f4929e6a6644fb01f0e43820f91d0fad292,citation,,YFCC100M: the new data in multimedia research,2016 +9,United States,YFCC100M,yfcc_100m,39.1254938,-77.22293475,National Institute of Standards and Technology,edu,36631dcbb9452ea3d35b19b2de6ef709022531a6,citation,https://pdfs.semanticscholar.org/0109/93ae9742f7f4c40763a25ded237723de60b5.pdf,"TRECVID 2016 : Evaluating Video Search , Video Event Detection , Localization , and Hyperlinking",2016 +10,Ireland,YFCC100M,yfcc_100m,53.38522185,-6.25740874,Dublin City University,edu,36631dcbb9452ea3d35b19b2de6ef709022531a6,citation,https://pdfs.semanticscholar.org/0109/93ae9742f7f4c40763a25ded237723de60b5.pdf,"TRECVID 2016 : Evaluating Video Search , Video Event Detection , Localization , and Hyperlinking",2016 +11,Netherlands,YFCC100M,yfcc_100m,51.816701,5.865272,Radboud University,edu,36631dcbb9452ea3d35b19b2de6ef709022531a6,citation,https://pdfs.semanticscholar.org/0109/93ae9742f7f4c40763a25ded237723de60b5.pdf,"TRECVID 2016 : Evaluating Video Search , Video Event Detection , Localization , and Hyperlinking",2016 +12,Netherlands,YFCC100M,yfcc_100m,52.2380139,6.8566761,University of Twente,edu,36631dcbb9452ea3d35b19b2de6ef709022531a6,citation,https://pdfs.semanticscholar.org/0109/93ae9742f7f4c40763a25ded237723de60b5.pdf,"TRECVID 2016 : Evaluating Video Search , Video Event Detection , Localization , and Hyperlinking",2016 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States,YFCC100M,yfcc_100m,40.7574714,-73.9877318,Yahoo,company,61b17f719bab899dd50bcc3be9d55673255fe102,citation,https://arxiv.org/pdf/1608.02289.pdf,Detecting Sarcasm in Multimodal Social Platforms,2016 +18,United States,YFCC100M,yfcc_100m,40.4441619,-79.94272826,Carnegie Mellon University,edu,2577211aeaaa1f2245ddc379564813bee3d46c06,citation,https://arxiv.org/pdf/1512.06974.pdf,Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels,2016 +19,United States,YFCC100M,yfcc_100m,47.6423318,-122.1369302,Microsoft,company,2577211aeaaa1f2245ddc379564813bee3d46c06,citation,https://arxiv.org/pdf/1512.06974.pdf,Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels,2016 +20,United States,YFCC100M,yfcc_100m,37.3936717,-122.0807262,Facebook,company,b6397f818f67faad6a36de8480212f6e7e82e71c,citation,,Tag Prediction at Flickr: A View from the Darkroom,2017 +21,United States,YFCC100M,yfcc_100m,47.6543238,-122.30800894,University of Washington,edu,b6397f818f67faad6a36de8480212f6e7e82e71c,citation,,Tag Prediction at Flickr: A View from the Darkroom,2017 +22,United States,YFCC100M,yfcc_100m,37.7749295,-122.4194155,"Yahoo Research, San Francisco, CA",company,b6397f818f67faad6a36de8480212f6e7e82e71c,citation,,Tag Prediction at Flickr: A View from the Darkroom,2017 +23,United States,YFCC100M,yfcc_100m,37.36883,-122.0363496,"Yahoo Research, Sunnyvale, CA, USA",edu,b6397f818f67faad6a36de8480212f6e7e82e71c,citation,,Tag Prediction at Flickr: A View from the Darkroom,2017 +24,Germany,YFCC100M,yfcc_100m,53.1474921,8.1817645,University of Oldenburg,edu,d3dae5c4f47a0457ebe2297d7e70432521c82cc6,citation,https://pdfs.semanticscholar.org/d3da/e5c4f47a0457ebe2297d7e70432521c82cc6.pdf,The Benchmarking Initiative for Multimedia Evaluation: MediaEval 2016,2017 +25,Netherlands,YFCC100M,yfcc_100m,51.816701,5.865272,Radboud 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Melbourne,edu,3ad6bd5c34b0866019b54f5976d644326069cb3d,citation,http://people.eng.unimelb.edu.au/limk2/2016-ICAPS-groupTourRec.pdf,Towards next generation touring: personalized group tours,2016 +49,Australia,YFCC100M,yfcc_100m,-33.917347,151.2312675,National ICT Australia,edu,3ad6bd5c34b0866019b54f5976d644326069cb3d,citation,http://people.eng.unimelb.edu.au/limk2/2016-ICAPS-groupTourRec.pdf,Towards next generation touring: personalized group tours,2016 +50,Australia,YFCC100M,yfcc_100m,-37.8087465,144.9638875,RMIT University,edu,3ad6bd5c34b0866019b54f5976d644326069cb3d,citation,http://people.eng.unimelb.edu.au/limk2/2016-ICAPS-groupTourRec.pdf,Towards next generation touring: personalized group tours,2016 +51,Denmark,YFCC100M,yfcc_100m,55.659635,12.590958,IT University of Copenhagen,edu,92fb2cb7f9a54360ea4442f902472aded5e88c74,citation,https://pure.itu.dk/portal/files/82406569/tmm_2017_blackthorn.pdf,Blackthorn: Large-Scale Interactive Multimodal Learning,2018 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University,edu,5ed63317cdef429f77499d9de0e58402ed1f687e,citation,https://arxiv.org/pdf/1702.05878.pdf,From Photo Streams to Evolving Situations,2017 +60,Thailand,YFCC100M,yfcc_100m,13.7972777,100.3263216,Mahidol University,edu,5ed63317cdef429f77499d9de0e58402ed1f687e,citation,https://arxiv.org/pdf/1702.05878.pdf,From Photo Streams to Evolving Situations,2017 +61,United States,YFCC100M,yfcc_100m,38.0333742,-84.5017758,University of Kentucky,edu,a851f32d4a4bffd6f95ac67c2ef1b25b8c4e5480,citation,http://bmvc2018.org/contents/papers/0586.pdf,Learning Geo-Temporal Image Features.,2018 +62,United States,YFCC100M,yfcc_100m,38.6480445,-90.3099667,Washington University,edu,a851f32d4a4bffd6f95ac67c2ef1b25b8c4e5480,citation,http://bmvc2018.org/contents/papers/0586.pdf,Learning Geo-Temporal Image Features.,2018 +63,Canada,YFCC100M,yfcc_100m,48.4634067,-123.3116935,University of Victoria,edu,8a2e3453d5f88ce6ce73cc7731800cd512f95e64,citation,https://arxiv.org/pdf/1711.05971.pdf,Learning to Find Good 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