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,China,Market 1501,market_1501,31.83907195,117.26420748,University of Science and Technology of China,edu,5b309f6d98c503efb679eda51bd898543fb746f9,citation,https://arxiv.org/pdf/1809.05864.pdf,In Defense of the Classification Loss for Person Re-Identification,2018
2,United States,Market 1501,market_1501,42.3614256,-71.0812092,Microsoft Research Asia,company,5b309f6d98c503efb679eda51bd898543fb746f9,citation,https://arxiv.org/pdf/1809.05864.pdf,In Defense of the Classification Loss for Person Re-Identification,2018
3,United States,Market 1501,market_1501,39.2899685,-76.62196103,University of Maryland,edu,fe3f8826f615cc5ada33b01777b9f9dc93e0023c,citation,https://arxiv.org/pdf/1901.07702.pdf,Exploring Uncertainty in Conditional Multi-Modal Retrieval Systems,2019
4,China,Market 1501,market_1501,24.4399419,118.09301781,Xiamen University,edu,d95ce873ed42b7c7facaa4c1e9c72b57b4e279f6,citation,https://pdfs.semanticscholar.org/d95c/e873ed42b7c7facaa4c1e9c72b57b4e279f6.pdf,Generalizing a Person Retrieval Model Hetero- and Homogeneously,2018
5,Australia,Market 1501,market_1501,-33.8809651,151.20107299,University of Technology Sydney,edu,d95ce873ed42b7c7facaa4c1e9c72b57b4e279f6,citation,https://pdfs.semanticscholar.org/d95c/e873ed42b7c7facaa4c1e9c72b57b4e279f6.pdf,Generalizing a Person Retrieval Model Hetero- and Homogeneously,2018
6,Australia,Market 1501,market_1501,-35.2776999,149.118527,Australian National University,edu,d95ce873ed42b7c7facaa4c1e9c72b57b4e279f6,citation,https://pdfs.semanticscholar.org/d95c/e873ed42b7c7facaa4c1e9c72b57b4e279f6.pdf,Generalizing a Person Retrieval Model Hetero- and Homogeneously,2018
7,China,Market 1501,market_1501,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,927ec8dde9eb0e3bc5bf0b1a0ae57f9cf745fd9c,citation,https://arxiv.org/pdf/1804.01438.pdf,Learning Discriminative Features with Multiple Granularities for Person Re-Identification,2018
8,China,Market 1501,market_1501,31.83907195,117.26420748,University of Science and Technology of China,edu,04ca65f1454f1014ef5af5bfafb7aee576ee1be6,citation,https://arxiv.org/pdf/1812.08967.pdf,Densely Semantically Aligned Person Re-Identification,2018
9,United States,Market 1501,market_1501,42.3614256,-71.0812092,Microsoft Research Asia,company,04ca65f1454f1014ef5af5bfafb7aee576ee1be6,citation,https://arxiv.org/pdf/1812.08967.pdf,Densely Semantically Aligned Person Re-Identification,2018
10,China,Market 1501,market_1501,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,7daa2c0f76fd3bfc7feadf313d6ac7504d4ecd20,citation,https://arxiv.org/pdf/1803.09937.pdf,Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-identification,2018
11,Singapore,Market 1501,market_1501,1.3484104,103.68297965,Nanyang Technological University,edu,7daa2c0f76fd3bfc7feadf313d6ac7504d4ecd20,citation,https://arxiv.org/pdf/1803.09937.pdf,Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-identification,2018
12,China,Market 1501,market_1501,32.0565957,118.77408833,Nanjing University,edu,08b28a8f2699501d46d87956cbaa37255000daa3,citation,https://arxiv.org/pdf/1804.03864.pdf,MaskReID: A Mask Based Deep Ranking Neural Network for Person Re-identification,2018
13,Australia,Market 1501,market_1501,-34.40505545,150.87834655,University of Wollongong,edu,08b28a8f2699501d46d87956cbaa37255000daa3,citation,https://arxiv.org/pdf/1804.03864.pdf,MaskReID: A Mask Based Deep Ranking Neural Network for Person Re-identification,2018
14,United Kingdom,Market 1501,market_1501,51.5247272,-0.03931035,Queen Mary University of London,edu,baf5ab5e8972e9366951b7e66951e05e2a4b3e36,citation,https://arxiv.org/pdf/1802.08122.pdf,Harmonious Attention Network for Person Re-identification,2018
15,United Kingdom,Market 1501,market_1501,52.3793131,-1.5604252,University of Warwick,edu,124d60fae338b1f87455d1fc4ede5fcfd806da1a,citation,https://arxiv.org/pdf/1807.01440.pdf,Multi-task Mid-level Feature Alignment Network for Unsupervised Cross-Dataset Person Re-Identification,2018
16,Singapore,Market 1501,market_1501,1.3484104,103.68297965,Nanyang Technological University,edu,124d60fae338b1f87455d1fc4ede5fcfd806da1a,citation,https://arxiv.org/pdf/1807.01440.pdf,Multi-task Mid-level Feature Alignment Network for Unsupervised Cross-Dataset Person Re-Identification,2018
17,Australia,Market 1501,market_1501,-35.0636071,147.3552234,Charles Sturt University,edu,124d60fae338b1f87455d1fc4ede5fcfd806da1a,citation,https://arxiv.org/pdf/1807.01440.pdf,Multi-task Mid-level Feature Alignment Network for Unsupervised Cross-Dataset Person Re-Identification,2018
18,United States,Market 1501,market_1501,33.776033,-84.39884086,Georgia Institute of Technology,edu,45a44e61236f7c144d9ec11561e236b2960c7cf6,citation,https://pdfs.semanticscholar.org/4eb8/4fd65703fc92863f9f589e3a07e6c841f7c4.pdf,Multi-object Tracking with Neural Gating Using Bilinear LSTM,2018
19,United States,Market 1501,market_1501,45.5198289,-122.67797964,Oregon State University,edu,45a44e61236f7c144d9ec11561e236b2960c7cf6,citation,https://pdfs.semanticscholar.org/4eb8/4fd65703fc92863f9f589e3a07e6c841f7c4.pdf,Multi-object Tracking with Neural Gating Using Bilinear LSTM,2018
20,China,Market 1501,market_1501,34.1235825,108.83546,Xidian University,edu,55355b0317f6e0c5218887441de71f05da4b42f6,citation,https://arxiv.org/pdf/1811.12150.pdf,Parameter-Free Spatial Attention Network for Person Re-Identification,2018
21,Germany,Market 1501,market_1501,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,55355b0317f6e0c5218887441de71f05da4b42f6,citation,https://arxiv.org/pdf/1811.12150.pdf,Parameter-Free Spatial Attention Network for Person Re-Identification,2018
22,China,Market 1501,market_1501,31.2284923,121.40211389,East China Normal University,edu,e1af55ad7bb26e5e1acde3ec6c5c43cffe884b04,citation,https://pdfs.semanticscholar.org/e1af/55ad7bb26e5e1acde3ec6c5c43cffe884b04.pdf,Person 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