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id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year
0,,Market 1501,market_1501,0.0,0.0,,,,main,,Scalable Person Re-identification: A Benchmark,2015
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
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