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