id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year
0,,Duke MTMC,duke_mtmc,0.0,0.0,,,,main,,"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking",2016
1,United States,Duke MTMC,duke_mtmc,35.9990522,-78.9290629,Duke University,edu,c9b98c98357a154bceb2287c427c5fa9c17b4a07,citation,https://arxiv.org/pdf/1803.05872.pdf,Virtual CNN Branching: Efficient Feature Ensemble for Person Re-Identification,2018
2,United States,Duke MTMC,duke_mtmc,42.3614256,-71.0812092,Microsoft Research Asia,company,1e2f07f7231eef629c78cba4ada0c9be29d77254,citation,,Group Re-Identification: Leveraging and Integrating Multi-Grain Information,2018
3,China,Duke MTMC,duke_mtmc,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,1e2f07f7231eef629c78cba4ada0c9be29d77254,citation,,Group Re-Identification: Leveraging and Integrating Multi-Grain Information,2018
4,China,Duke MTMC,duke_mtmc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,1e2f07f7231eef629c78cba4ada0c9be29d77254,citation,,Group Re-Identification: Leveraging and Integrating Multi-Grain Information,2018
5,China,Duke MTMC,duke_mtmc,24.4399419,118.09301781,Xiamen University,edu,2788a2461ed0067e2f7aaa63c449a24a237ec341,citation,https://arxiv.org/pdf/1708.04896.pdf,Random Erasing Data Augmentation,2017
6,United States,Duke MTMC,duke_mtmc,32.7768233,-117.0693407,"California State University, San Marcos",edu,9643dabbf1771d2d82ded2fde3baaa15a67f6e56,citation,,Unsupervised Joint Subspace and Dictionary Learning for Enhanced Cross-Domain Person Re-Identification,2018
7,China,Duke MTMC,duke_mtmc,32.0565957,118.77408833,Nanjing University,edu,9643dabbf1771d2d82ded2fde3baaa15a67f6e56,citation,,Unsupervised Joint Subspace and Dictionary Learning for Enhanced Cross-Domain Person Re-Identification,2018
8,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,e323bbaef9ea9a6257b7464e4cc146d690d0d55b,citation,https://arxiv.org/pdf/1811.08400.pdf,Single-Label Multi-Class Image Classification by Deep Logistic Regression,2019
9,China,Duke MTMC,duke_mtmc,28.2290209,112.99483204,"National University of Defense Technology, China",mil,59f357015054bab43fb8cbfd3f3dbf17b1d1f881,citation,https://pdfs.semanticscholar.org/59f3/57015054bab43fb8cbfd3f3dbf17b1d1f881.pdf,Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks,2018
10,United Kingdom,Duke MTMC,duke_mtmc,51.5231607,-0.1282037,University College London,edu,59f357015054bab43fb8cbfd3f3dbf17b1d1f881,citation,https://pdfs.semanticscholar.org/59f3/57015054bab43fb8cbfd3f3dbf17b1d1f881.pdf,Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks,2018
11,China,Duke MTMC,duke_mtmc,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,a0dfc588cd1bc35a06734a31fca81e7adc94b940,citation,https://arxiv.org/pdf/1803.08580.pdf,Weighted Bilinear Coding over Salient Body Parts for Person Re-identification,2018
12,United States,Duke MTMC,duke_mtmc,39.95472495,-75.15346905,Temple University,edu,a0dfc588cd1bc35a06734a31fca81e7adc94b940,citation,https://arxiv.org/pdf/1803.08580.pdf,Weighted Bilinear Coding over Salient Body Parts for Person Re-identification,2018
13,China,Duke MTMC,duke_mtmc,23.0502042,113.39880323,South China University of Technology,edu,a0dfc588cd1bc35a06734a31fca81e7adc94b940,citation,https://arxiv.org/pdf/1803.08580.pdf,Weighted Bilinear Coding over Salient Body Parts for Person Re-identification,2018
14,China,Duke MTMC,duke_mtmc,40.00229045,116.32098908,Tsinghua University,edu,a0dfc588cd1bc35a06734a31fca81e7adc94b940,citation,https://arxiv.org/pdf/1803.08580.pdf,Weighted Bilinear Coding over Salient Body Parts for Person Re-identification,2018
15,China,Duke MTMC,duke_mtmc,30.672721,104.098806,University of Electronic Science and Technology of China,edu,ed2ba6448db8cf945ca24d4df11916c2c5c3edd1,citation,,Rapid Pedestrian Detection Based on Deep Omega-Shape Features with Partial Occlusion Handing,2018
16,China,Duke MTMC,duke_mtmc,30.19331415,120.11930822,Zhejiang University,edu,5b062562a8067baae045df1c7f5a8455d0363b5a,citation,https://arxiv.org/pdf/1810.06996.pdf,SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-Identification,2018
17,China,Duke MTMC,duke_mtmc,40.0044795,116.370238,Chinese Academy of Sciences,edu,5b062562a8067baae045df1c7f5a8455d0363b5a,citation,https://arxiv.org/pdf/1810.06996.pdf,SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-Identification,2018
18,China,Duke MTMC,duke_mtmc,38.88140235,121.52281098,Dalian University of Technology,edu,e8dac6b899e2be56b4d8b4b5bfb422eb1fe2cb68,citation,,A novel two-stream saliency image fusion CNN architecture for person re-identification,2017
19,United States,Duke MTMC,duke_mtmc,29.58333105,-98.61944505,University of Texas at San Antonio,edu,e8dac6b899e2be56b4d8b4b5bfb422eb1fe2cb68,citation,,A novel two-stream saliency image fusion CNN architecture for person re-identification,2017
20,China,Duke MTMC,duke_mtmc,31.83907195,117.26420748,University of Science and Technology of China,edu,d4a5c9b2197b6bc476aa296b8d59515c9684e97d,citation,,CA3Net: Contextual-Attentional Attribute-Appearance Network for Person Re-Identification,2018
21,United States,Duke MTMC,duke_mtmc,40.1019523,-88.2271615,UIUC,edu,c2a5f27d97744bc1f96d7e1074395749e3c59bc8,citation,https://arxiv.org/pdf/1804.05275.pdf,Horizontal Pyramid Matching for Person Re-identification,2019
22,United States,Duke MTMC,duke_mtmc,37.8718992,-122.2585399,UC Berkeley,edu,8ba606d7667c50054d74083867230abbed755574,citation,https://arxiv.org/pdf/1811.01268.pdf,"ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Enterprise Scale",2018
23,United States,Duke MTMC,duke_mtmc,41.78468745,-87.60074933,University of Chicago,edu,8ba606d7667c50054d74083867230abbed755574,citation,https://arxiv.org/pdf/1811.01268.pdf,"ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Enterprise Scale",2018
24,United States,Duke MTMC,duke_mtmc,47.6423318,-122.1369302,Microsoft,company,8ba606d7667c50054d74083867230abbed755574,citation,https://arxiv.org/pdf/1811.01268.pdf,"ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Enterprise Scale",2018
25,China,Duke MTMC,duke_mtmc,30.491766,114.396237,South-Central University for Nationalities,edu,cbf5b3469c7216c37733efca6c2cdb94357b14a7,citation,,Person Re-identification Based on Feature Fusion and Triplet Loss Function,2018
26,China,Duke MTMC,duke_mtmc,30.60903415,114.3514284,Wuhan University of Technology,edu,cbf5b3469c7216c37733efca6c2cdb94357b14a7,citation,,Person Re-identification Based on Feature Fusion and Triplet Loss Function,2018
27,China,Duke MTMC,duke_mtmc,32.0565957,118.77408833,Nanjing University,edu,3b24dcb3a1ff4811386b3467943c0ccad266bc99,citation,https://arxiv.org/pdf/1811.08561.pdf,Adaptive Re-ranking of Deep Feature for Person Re-identification,2018
28,Australia,Duke MTMC,duke_mtmc,-37.8087465,144.9638875,RMIT University,edu,3b24dcb3a1ff4811386b3467943c0ccad266bc99,citation,https://arxiv.org/pdf/1811.08561.pdf,Adaptive Re-ranking of Deep Feature for Person Re-identification,2018
29,China,Duke MTMC,duke_mtmc,22.3874201,114.2082222,Hong Kong Baptist University,edu,3cbf60c4a73fadd05b59c3abd19df032303e8577,citation,,Incremental Deep Hidden Attribute Learning,2018
30,China,Duke MTMC,duke_mtmc,30.508964,114.410577,Huazhong University of Science of Technology,edu,3cbf60c4a73fadd05b59c3abd19df032303e8577,citation,,Incremental Deep Hidden Attribute Learning,2018
31,Japan,Duke MTMC,duke_mtmc,35.6924853,139.7582533,"National Institute of Informatics, Japan",edu,3cbf60c4a73fadd05b59c3abd19df032303e8577,citation,,Incremental Deep Hidden Attribute Learning,2018
32,Japan,Duke MTMC,duke_mtmc,35.6924853,139.7582533,"National Institute of Informatics, Japan, Tokyo, Japan",edu,3cbf60c4a73fadd05b59c3abd19df032303e8577,citation,,Incremental Deep Hidden Attribute Learning,2018
33,South Korea,Duke MTMC,duke_mtmc,35.2265288,126.839987,Gwangju Institute of Science and Technology,edu,5317bd54ad696f40594d78c3464d86d8e39bd75b,citation,https://arxiv.org/pdf/1901.08787.pdf,Multiple Hypothesis Tracking Algorithm for Multi-Target Multi-Camera Tracking with Disjoint Views,2018
34,China,Duke MTMC,duke_mtmc,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,0db41739f514c4c911c54a4c90ab5f07db3862dc,citation,https://pdfs.semanticscholar.org/0db4/1739f514c4c911c54a4c90ab5f07db3862dc.pdf,NCA-Net for Tracking Multiple Objects across Multiple Cameras,2018
35,United Kingdom,Duke MTMC,duke_mtmc,51.4584837,-2.6097752,University of Bristol,edu,92939c68b2075d0446fee540bd174b6da26fea05,citation,https://arxiv.org/pdf/1806.04074.pdf,Semantically Selective Augmentation for Deep Compact Person Re-Identification,2018
36,China,Duke MTMC,duke_mtmc,40.00229045,116.32098908,Tsinghua University,edu,69a7c8bca699ee4100fbe6a83b72459c132a6f10,citation,https://pdfs.semanticscholar.org/69a7/c8bca699ee4100fbe6a83b72459c132a6f10.pdf,Aware Person Re-identification across Multiple Resolutions,2018
37,Thailand,Duke MTMC,duke_mtmc,13.74311795,100.53287901,Chulalongkorn University,edu,fcec633bbdeaab2d61fcc6d86f74383ccc3621f9,citation,,Robust video editing detection using Scalable Color and Color Layout Descriptors,2017
38,China,Duke MTMC,duke_mtmc,30.672721,104.098806,University of Electronic Science and Technology of China,edu,a20f132a30e99541aa7ba6dddac86e6a393778e8,citation,https://arxiv.org/pdf/1809.08556.pdf,Self Attention Grid for Person Re-Identification,2018
39,China,Duke MTMC,duke_mtmc,39.98177,116.330086,Chinese Academy of Sciences & University of Chinese Academy of Sciences,edu,56423685e039d82d3cc88f797fc2b73f2d93e200,citation,,A Unified Generative Adversarial Framework for Image Generation and Person Re-identification,2018
40,China,Duke MTMC,duke_mtmc,39.9922379,116.30393816,Peking University,edu,56423685e039d82d3cc88f797fc2b73f2d93e200,citation,,A Unified Generative Adversarial Framework for Image Generation and Person Re-identification,2018
41,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,f8f92624c8794d54e08b3a8f94910952ae03cade,citation,,CamStyle: A Novel Data Augmentation Method for Person Re-Identification,2019
42,China,Duke MTMC,duke_mtmc,24.4399419,118.09301781,Xiamen University,edu,f8f92624c8794d54e08b3a8f94910952ae03cade,citation,,CamStyle: A Novel Data Augmentation Method for Person Re-Identification,2019
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,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
47,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
48,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
49,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
50,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
51,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
52,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
53,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
54,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
55,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
56,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
57,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
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
133,Germany,Duke MTMC,duke_mtmc,49.01546,8.4257999,Fraunhofer,company,978716708762dab46e91059e170d43551be74732,citation,,A Pose-Sensitive Embedding for Person Re-identification with Expanded Cross Neighborhood Re-ranking,2018
134,Germany,Duke MTMC,duke_mtmc,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,978716708762dab46e91059e170d43551be74732,citation,,A Pose-Sensitive Embedding for Person Re-identification with Expanded Cross Neighborhood Re-ranking,2018
135,Taiwan,Duke MTMC,duke_mtmc,25.01682835,121.53846924,National Taiwan University,edu,d9216cc2a3c03659cb2392b7cc8509feb7829579,citation,,Adaptation and Re-identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-identification,2018
136,China,Duke MTMC,duke_mtmc,39.979203,116.33287,"CRIPAC & NLPR, CASIA",edu,1bfe59be5b42d6b7257da4b35a408239c01ab79d,citation,,Adversarially Occluded Samples for Person Re-identification,2018
137,China,Duke MTMC,duke_mtmc,40.0044795,116.370238,Chinese Academy 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 MTMC,duke_mtmc,24.4399419,118.09301781,Xiamen University,edu,1822ca8db58b0382b0c64f310840f0f875ea02c0,citation,,Camera Style Adaptation for Person Re-identification,2018
143,China,Duke MTMC,duke_mtmc,36.16161795,120.49355276,Ocean University of China,edu,38259235a1c7b2c68ca09f3bc0930987ae99cf00,citation,,Deep Feature Ranking for Person Re-Identification,2019
144,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
145,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
146,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
147,China,Duke MTMC,duke_mtmc,23.0502042,113.39880323,South China University of Technology,edu,cb68c60ac046a0ec1c7f67487f14b999037313e1,citation,,Exploit the Unknown Gradually: One-Shot Video-Based Person Re-identification by Stepwise Learning,2018
148,Australia,Duke MTMC,duke_mtmc,-33.88890695,151.18943366,University of Sydney,edu,cb68c60ac046a0ec1c7f67487f14b999037313e1,citation,,Exploit the Unknown Gradually: One-Shot Video-Based Person Re-identification by Stepwise Learning,2018
149,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,cb68c60ac046a0ec1c7f67487f14b999037313e1,citation,,Exploit the Unknown Gradually: One-Shot Video-Based Person Re-identification by Stepwise Learning,2018
150,United States,Duke MTMC,duke_mtmc,35.9990522,-78.9290629,Duke University,edu,c0f01b8174a632448c20eb5472cd9d5b2c595e39,citation,,Features for Multi-target Multi-camera Tracking and Re-identification,2018
151,China,Duke 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
152,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
153,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
154,Turkey,Duke MTMC,duke_mtmc,41.10427915,29.02231159,Istanbul Technical University,edu,7ba225a614d77efd9bdf66bf74c80dd2da09229a,citation,,Human Semantic Parsing for Person Re-identification,2018
155,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
156,Australia,Duke MTMC,duke_mtmc,-32.00686365,115.89691775,Curtin University,edu,292286c0024d6625fe606fb5b8a0df54ea3ffe91,citation,,Identity Adaptation for Person Re-Identification,2018
157,United Kingdom,Duke MTMC,duke_mtmc,54.00975365,-2.78757491,Lancaster University,edu,292286c0024d6625fe606fb5b8a0df54ea3ffe91,citation,,Identity Adaptation for Person Re-Identification,2018
158,Australia,Duke MTMC,duke_mtmc,-31.95040445,115.79790037,University of Western Australia,edu,292286c0024d6625fe606fb5b8a0df54ea3ffe91,citation,,Identity Adaptation for Person Re-Identification,2018
159,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
160,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
161,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
162,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
163,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
164,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
165,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
166,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
167,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
168,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
169,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
170,United States,Duke MTMC,duke_mtmc,35.9990522,-78.9290629,Duke University,edu,96e77135e745385e87fdd0f7ced951bf1fe9a756,citation,,People Tracking and Re-Identification from Multiple Cameras,2018
171,China,Duke MTMC,duke_mtmc,30.274084,120.15507,Alibaba,company,90c18409b7a3be2cd6da599d02accba4c769e94e,citation,,Person Re-identification with Cascaded Pairwise Convolutions,2018
172,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
173,China,Duke MTMC,duke_mtmc,30.5360485,114.3643219,"Wuhan Univeristy, Wuhan, China",edu,90c18409b7a3be2cd6da599d02accba4c769e94e,citation,,Person Re-identification with Cascaded Pairwise Convolutions,2018
174,China,Duke MTMC,duke_mtmc,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,df4ed9983f7114ca4f0ab71f1476c0bf7521e317,citation,,Pose Transferrable Person Re-identification,2018
175,United States,Duke MTMC,duke_mtmc,40.4441619,-79.94272826,Carnegie Mellon University,edu,e307c6635472d3d1e512af6e20f2e56c95937bb7,citation,,Semi-Supervised Bayesian Attribute Learning for Person Re-Identification,2018
176,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,e307c6635472d3d1e512af6e20f2e56c95937bb7,citation,,Semi-Supervised Bayesian Attribute Learning for Person Re-Identification,2018
177,China,Duke MTMC,duke_mtmc,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
178,United States,Duke MTMC,duke_mtmc,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
179,United States,Duke MTMC,duke_mtmc,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
180,China,Duke MTMC,duke_mtmc,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
181,Australia,Duke MTMC,duke_mtmc,-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
182,Australia,Duke MTMC,duke_mtmc,-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
183,China,Duke MTMC,duke_mtmc,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
184,China,Duke MTMC,duke_mtmc,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
185,United States,Duke MTMC,duke_mtmc,42.3614256,-71.0812092,Microsoft Research Asia,company,04ca65f1454f1014ef5af5bfafb7aee576ee1be6,citation,https://arxiv.org/pdf/1812.08967.pdf,Densely Semantically Aligned Person Re-Identification,2018
186,China,Duke MTMC,duke_mtmc,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
187,Singapore,Duke MTMC,duke_mtmc,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
188,China,Duke MTMC,duke_mtmc,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
189,Australia,Duke MTMC,duke_mtmc,-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
190,United Kingdom,Duke MTMC,duke_mtmc,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
191,United Kingdom,Duke MTMC,duke_mtmc,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
192,Singapore,Duke MTMC,duke_mtmc,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
193,Australia,Duke MTMC,duke_mtmc,-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
194,China,Duke MTMC,duke_mtmc,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
195,Germany,Duke MTMC,duke_mtmc,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
196,China,Duke MTMC,duke_mtmc,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
197,Brazil,Duke MTMC,duke_mtmc,-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
198,South Korea,Duke MTMC,duke_mtmc,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
199,Germany,Duke MTMC,duke_mtmc,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
200,United States,Duke MTMC,duke_mtmc,47.6423318,-122.1369302,Microsoft,company,315df9b7dd354ae78ddf1049fb428b086eee632c,citation,https://arxiv.org/pdf/1804.07094.pdf,Part-Aligned Bilinear Representations for Person Re-identification,2018
201,United States,Duke MTMC,duke_mtmc,40.1019523,-88.2271615,UIUC,edu,cc78e3f1e531342f639e4a1fc8107a7a778ae1cf,citation,https://arxiv.org/pdf/1811.10144.pdf,One Shot Domain Adaptation for Person Re-Identification,2018
202,China,Duke MTMC,duke_mtmc,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
203,China,Duke MTMC,duke_mtmc,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
204,China,Duke MTMC,duke_mtmc,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
205,China,Duke MTMC,duke_mtmc,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
206,United States,Duke MTMC,duke_mtmc,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
207,China,Duke MTMC,duke_mtmc,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
208,China,Duke MTMC,duke_mtmc,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
209,Canada,Duke MTMC,duke_mtmc,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
210,Australia,Duke MTMC,duke_mtmc,-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
211,China,Duke MTMC,duke_mtmc,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
212,Australia,Duke MTMC,duke_mtmc,-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
213,China,Duke MTMC,duke_mtmc,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
214,China,Duke MTMC,duke_mtmc,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
215,China,Duke MTMC,duke_mtmc,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
216,China,Duke MTMC,duke_mtmc,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
217,China,Duke MTMC,duke_mtmc,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
218,Italy,Duke MTMC,duke_mtmc,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
219,Italy,Duke MTMC,duke_mtmc,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
220,India,Duke MTMC,duke_mtmc,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
221,China,Duke MTMC,duke_mtmc,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
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
224,China,Duke MTMC,duke_mtmc,30.19331415,120.11930822,Zhejiang University,edu,b350b567b13ab2b7ba94159767a41917fc38a2cb,citation,https://arxiv.org/pdf/1903.07071.pdf,Bag of Tricks and A Strong Baseline for Deep Person Re-identification,2019
225,China,Duke MTMC,duke_mtmc,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
226,China,Duke MTMC,duke_mtmc,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
227,China,Duke MTMC,duke_mtmc,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
228,China,Duke MTMC,duke_mtmc,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
229,United States,Duke MTMC,duke_mtmc,22.5447154,113.9357164,Tencent,company,57c144f668d11ef7e2c89fdfcf67341a4733dd64,citation,https://pdfs.semanticscholar.org/57c1/44f668d11ef7e2c89fdfcf67341a4733dd64.pdf,Unlabeled images Auxiliary reference person images Backbone ResNet ‐ 50 Reference learning,2019
230,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,57c144f668d11ef7e2c89fdfcf67341a4733dd64,citation,https://pdfs.semanticscholar.org/57c1/44f668d11ef7e2c89fdfcf67341a4733dd64.pdf,Unlabeled images Auxiliary reference person images Backbone ResNet ‐ 50 Reference learning,2019
231,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,57c144f668d11ef7e2c89fdfcf67341a4733dd64,citation,https://pdfs.semanticscholar.org/57c1/44f668d11ef7e2c89fdfcf67341a4733dd64.pdf,Unlabeled images Auxiliary reference person images Backbone ResNet ‐ 50 Reference learning,2019
232,China,Duke MTMC,duke_mtmc,31.83907195,117.26420748,University of Science and Technology of China,edu,59a4cec1afb2804eeff1774c4eb315701443af76,citation,https://arxiv.org/pdf/1904.02998.pdf,Relation-Aware Global Attention,2019
233,United States,Duke MTMC,duke_mtmc,42.3614256,-71.0812092,Microsoft Research Asia,company,59a4cec1afb2804eeff1774c4eb315701443af76,citation,https://arxiv.org/pdf/1904.02998.pdf,Relation-Aware Global Attention,2019
234,China,Duke MTMC,duke_mtmc,32.0565957,118.77408833,Nanjing University,edu,9a433055551c1f5c670f2a69a57f6aad3a5810d9,citation,https://arxiv.org/pdf/1904.03425.pdf,A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification,2019
235,Australia,Duke MTMC,duke_mtmc,-34.40505545,150.87834655,University of Wollongong,edu,9a433055551c1f5c670f2a69a57f6aad3a5810d9,citation,https://arxiv.org/pdf/1904.03425.pdf,A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification,2019
236,Australia,Duke MTMC,duke_mtmc,-33.88890695,151.18943366,University of Sydney,edu,9a433055551c1f5c670f2a69a57f6aad3a5810d9,citation,https://arxiv.org/pdf/1904.03425.pdf,A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification,2019
237,China,Duke MTMC,duke_mtmc,24.4399419,118.09301781,Xiamen University,edu,b9cc54c5f94371cfc8e79179c20ec559a1a43cbb,citation,https://arxiv.org/pdf/1904.01990.pdf,Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification,2019
238,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,b9cc54c5f94371cfc8e79179c20ec559a1a43cbb,citation,https://arxiv.org/pdf/1904.01990.pdf,Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification,2019
239,Australia,Duke MTMC,duke_mtmc,-35.2776999,149.118527,Australian National University,edu,b9cc54c5f94371cfc8e79179c20ec559a1a43cbb,citation,https://arxiv.org/pdf/1904.01990.pdf,Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification,2019
240,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,123478b496a3fa39a9043ccaa660e81c473a14e9,citation,https://pdfs.semanticscholar.org/1234/78b496a3fa39a9043ccaa660e81c473a14e9.pdf,A Bottom-Up Clustering Approach to Unsupervised Person Re-identification,2019
241,United States,Duke MTMC,duke_mtmc,29.888411,-97.938351,Texas State University,edu,123478b496a3fa39a9043ccaa660e81c473a14e9,citation,https://pdfs.semanticscholar.org/1234/78b496a3fa39a9043ccaa660e81c473a14e9.pdf,A Bottom-Up Clustering Approach to Unsupervised Person Re-identification,2019
242,United States,Duke MTMC,duke_mtmc,42.3383668,-71.08793524,Northeastern University,edu,78fde57462fb68530a49f913c89343da5727580d,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w17/papers/Gou_DukeMTMC4ReID_A_Large-Scale_CVPR_2017_paper.pdf,DukeMTMC4ReID: A Large-Scale Multi-camera Person Re-identification Dataset,2017
243,United States,Duke MTMC,duke_mtmc,42.7298459,-73.67950216,Rensselaer Polytechnic Institute,edu,78fde57462fb68530a49f913c89343da5727580d,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w17/papers/Gou_DukeMTMC4ReID_A_Large-Scale_CVPR_2017_paper.pdf,DukeMTMC4ReID: A Large-Scale Multi-camera Person Re-identification Dataset,2017
244,United States,Duke MTMC,duke_mtmc,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
245,China,Duke MTMC,duke_mtmc,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
246,China,Duke MTMC,duke_mtmc,30.2931534,120.1620458,Zhejiang University of Technology,edu,8fbb73bc6fb74e119b5fdf02482fa90afb7e443e,citation,https://pdfs.semanticscholar.org/8fbb/73bc6fb74e119b5fdf02482fa90afb7e443e.pdf,Parts Semantic Segmentation Aware Representation Learning for Person Re-Identification,2019
247,China,Duke MTMC,duke_mtmc,39.061004,117.142023,Tianjin University of Technology,edu,8fbb73bc6fb74e119b5fdf02482fa90afb7e443e,citation,https://pdfs.semanticscholar.org/8fbb/73bc6fb74e119b5fdf02482fa90afb7e443e.pdf,Parts Semantic Segmentation Aware Representation Learning for Person Re-Identification,2019
248,China,Duke MTMC,duke_mtmc,27.712328,112.006373,Hunan University of Humanities,edu,2ff0f94f1a05fb4e6cb906f8b5aa59d50c9754be,citation,https://arxiv.org/pdf/1807.11042.pdf,Towards Good Practices on Building Effective CNN Baseline Model for Person Re-identification,2018
249,Singapore,Duke MTMC,duke_mtmc,1.2988926,103.7873107,"A*STAR, Singapore",edu,2ff0f94f1a05fb4e6cb906f8b5aa59d50c9754be,citation,https://arxiv.org/pdf/1807.11042.pdf,Towards Good Practices on Building Effective CNN Baseline Model for Person Re-identification,2018
250,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,5f12ca6b863b5bc28f58443ba2b70a102af965bd,citation,https://arxiv.org/pdf/1903.09776.pdf,Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification,2019
251,Italy,Duke MTMC,duke_mtmc,46.0658836,11.1159894,University of Trento,edu,4c903009e7b963f1cd4f02482ea4b242d71e8058,citation,https://arxiv.org/pdf/1904.01308.pdf,Camera Adversarial Transfer for Unsupervised Person Re-Identification,2019
252,United States,Duke MTMC,duke_mtmc,47.6543238,-122.30800894,University of Washington,edu,17829aec0f06dc8f45f417e667e3d92eeba923dc,citation,https://arxiv.org/pdf/1903.09254.pdf,CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification,2019
253,China,Duke MTMC,duke_mtmc,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
254,United States,Duke MTMC,duke_mtmc,28.59899755,-81.19712501,University of Central Florida,edu,427aee2aaf7d2d67738b046aea2782f9b8892c68,citation,https://arxiv.org/pdf/1904.11397.pdf,Deep Constrained Dominant Sets for Person Re-identification,2019
255,China,Duke MTMC,duke_mtmc,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
256,China,Duke MTMC,duke_mtmc,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
257,China,Duke MTMC,duke_mtmc,22.053565,113.39913285,Jilin University,edu,05f9d47bcc438ffcd4efcc5d77792a7b1984342a,citation,https://arxiv.org/pdf/1811.11510.pdf,Identity Preserving Generative Adversarial Network for Cross-Domain Person Re-identification,2018
258,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,424cce55355f2fa4b3c020d56967e1f7b82b1de9,citation,https://pdfs.semanticscholar.org/424c/ce55355f2fa4b3c020d56967e1f7b82b1de9.pdf,M 2 M-GAN : Many-to-Many Generative Adversarial Transfer Learning for Person Re-Identification,2018
259,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,8824638e8077f62283d292804006ce94c92764bf,citation,https://arxiv.org/pdf/1811.03768.pdf,M2M-GAN: Many-to-Many Generative Adversarial Transfer Learning for Person Re-Identification,2018
260,China,Duke MTMC,duke_mtmc,31.28473925,121.49694909,Tongji University,edu,74e38dfeb5abc7ddf077abc01de90f4d0a49c142,citation,https://arxiv.org/pdf/1812.05319.pdf,Omni-directional Feature Learning for Person Re-identification,2018
261,United States,Duke MTMC,duke_mtmc,40.1019523,-88.2271615,UIUC,edu,040c0612e0f006fa93f140ccb97b9738efcf74a5,citation,https://arxiv.org/pdf/1811.10144.pdf,One Shot Domain Adaptation for Person Re-Identification,2018
262,Spain,Duke MTMC,duke_mtmc,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
263,China,Duke MTMC,duke_mtmc,22.53521465,113.9315911,Shenzhen University,edu,1e3cb57830fde3bb588acbe2784b01e922f031b0,citation,https://arxiv.org/pdf/1904.00355.pdf,Pedestrian re-identification based on Tree branch network with local and global learning,2019
264,United States,Duke MTMC,duke_mtmc,43.0008093,-78.7889697,University at Buffalo,edu,1ba61a4fedc217f7bd052d1b2904567c9985dc44,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w6/papers/Narayan_Person_Re-Identification_for_CVPR_2017_paper.pdf,Person Re-identification for Improved Multi-person Multi-camera Tracking by Continuous Entity Association,2017
265,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
266,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
267,United States,Duke MTMC,duke_mtmc,42.7298459,-73.67950216,Rensselaer Polytechnic Institute,edu,afc01c33b7dd9de9e5c84c063aaccc4e0c839e74,citation,https://arxiv.org/pdf/1811.07487.pdf,Re-Identification with Consistent Attentive Siamese Networks,2018
268,China,Duke MTMC,duke_mtmc,30.19331415,120.11930822,Zhejiang University,edu,74bfaacd4e86a1304d2b5e7340591cffb38d84dd,citation,https://arxiv.org/pdf/1807.00537.pdf,SphereReID: Deep Hypersphere Manifold Embedding for Person Re-Identification,2019
269,United States,Duke MTMC,duke_mtmc,35.9990522,-78.9290629,Duke University,edu,0c0e26737fbc27d2dc7aab58783b155b009a06cf,citation,https://arxiv.org/pdf/1803.05872.pdf,Virtual CNN Branching: Efficient Feature Ensemble for Person Re-Identification,2018
270,China,Duke MTMC,duke_mtmc,40.00229045,116.32098908,Tsinghua University,edu,753d2a35c9edf5dfcac4ef3a6adc993b657b01f0,citation,https://arxiv.org/pdf/1711.09349.pdf,Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline),2017
271,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,753d2a35c9edf5dfcac4ef3a6adc993b657b01f0,citation,https://arxiv.org/pdf/1711.09349.pdf,Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline),2017
272,United States,Duke MTMC,duke_mtmc,29.58333105,-98.61944505,University of Texas at San Antonio,edu,753d2a35c9edf5dfcac4ef3a6adc993b657b01f0,citation,https://arxiv.org/pdf/1711.09349.pdf,Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline),2017
273,China,Duke MTMC,duke_mtmc,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,26ac3ee756d4a24ec31de918f54098012e17fd25,citation,https://arxiv.org/pdf/1711.10658.pdf,Deep-Person: Learning Discriminative Deep Features for Person Re-Identification,2017
274,China,Duke MTMC,duke_mtmc,40.0044795,116.370238,Chinese Academy of Sciences,edu,3c89455d9a91560eb08e59237dbc4f9fac16ff09,citation,https://arxiv.org/pdf/1904.04975.pdf,Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification,2019
275,Australia,Duke MTMC,duke_mtmc,-35.2776999,149.118527,Australian National University,edu,48b4b0bbbfee08604b68bb0246b295e357444ed1,citation,https://arxiv.org/pdf/1904.07223.pdf,Joint Discriminative and Generative Learning for Person Re-identification,2019
276,United States,Duke MTMC,duke_mtmc,37.3706254,-121.9671894,NVIDIA,company,48b4b0bbbfee08604b68bb0246b295e357444ed1,citation,https://arxiv.org/pdf/1904.07223.pdf,Joint Discriminative and Generative Learning for Person Re-identification,2019
277,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,48b4b0bbbfee08604b68bb0246b295e357444ed1,citation,https://arxiv.org/pdf/1904.07223.pdf,Joint Discriminative and Generative Learning for Person Re-identification,2019
278,China,Duke MTMC,duke_mtmc,35.86166,104.195397,"Megvii Inc. (Face++), China",company,10c20cf47d61063032dce4af73a4b8e350bf1128,citation,https://arxiv.org/pdf/1712.09531.pdf,"Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project",2017
279,France,Duke MTMC,duke_mtmc,45.7833244,4.8781984,University of Lyon,edu,19650d66be1bf350fe784467da3ff7074c94c940,citation,https://pdfs.semanticscholar.org/1965/0d66be1bf350fe784467da3ff7074c94c940.pdf,Person re-identification in images with deep learning,2018
280,Singapore,Duke MTMC,duke_mtmc,1.3392609,103.8916077,Panasonic Singapore,company,70ce1a17f257320fc718d61964b21e7aeabd8cd5,citation,https://arxiv.org/pdf/1803.10630.pdf,Person re-identification with fusion of hand-crafted and deep pose-based body region features,2018
281,China,Duke MTMC,duke_mtmc,31.30104395,121.50045497,Fudan University,edu,66e4f5e354240a022789353798ce92e4ab68e109,citation,https://arxiv.org/pdf/1712.02225.pdf,Pose-Normalized Image Generation for Person Re-identification,2018
282,Japan,Duke MTMC,duke_mtmc,34.7321121,135.7328585,"Nara Institute of Science and Technology, Japan",edu,66e4f5e354240a022789353798ce92e4ab68e109,citation,https://arxiv.org/pdf/1712.02225.pdf,Pose-Normalized Image Generation for Person Re-identification,2018
283,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,66e4f5e354240a022789353798ce92e4ab68e109,citation,https://arxiv.org/pdf/1712.02225.pdf,Pose-Normalized Image Generation for Person Re-identification,2018
284,China,Duke MTMC,duke_mtmc,28.2290209,112.99483204,"National University of Defense Technology, China",mil,e799c5c7e169f471950eb76dbb329c2d031347ae,citation,https://arxiv.org/pdf/1809.03137.pdf,Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers,2018
285,United Kingdom,Duke MTMC,duke_mtmc,54.6141723,-5.9002151,Queen's University Belfast,edu,05c4eace439fcc011aaa70c8c00c7386a0cf479e,citation,https://pdfs.semanticscholar.org/05c4/eace439fcc011aaa70c8c00c7386a0cf479e.pdf,Video Person Re-Identification for Wide Area Tracking based on Recurrent Neural Networks,2017
286,China,Duke MTMC,duke_mtmc,39.979203,116.33287,"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China",edu,f12e2888e6db23433166db72ff77c448cb6845e8,citation,,GLAD: Global–Local-Alignment Descriptor for Scalable Person Re-Identification,2018
287,China,Duke MTMC,duke_mtmc,39.9922379,116.30393816,Peking University,edu,f12e2888e6db23433166db72ff77c448cb6845e8,citation,,GLAD: Global–Local-Alignment Descriptor for Scalable Person Re-Identification,2018
288,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,a34f8768b10d928aa4f4105afb971819c26a2219,citation,,Multi-Pseudo Regularized Label for Generated Data in Person Re-Identification,2018
289,China,Duke MTMC,duke_mtmc,40.0044795,116.370238,Chinese Academy of Sciences,edu,a34f8768b10d928aa4f4105afb971819c26a2219,citation,,Multi-Pseudo Regularized Label for Generated Data in Person Re-Identification,2018
290,China,Duke MTMC,duke_mtmc,31.0252201,121.4337784,Shanghai Jiaotong University,edu,f8c4959ca67846d0c08f371ee884bb8a0845af1e,citation,,Enhancing Model Performance of Person Re-Indentification on Unknown Target Domain,2018
291,China,Duke MTMC,duke_mtmc,31.83907195,117.26420748,University of Science and Technology of China,edu,f81f69570113e5171203ac121d1ec1d8b91df4a4,citation,,Local Convolutional Neural Networks for Person Re-Identification,2018
292,China,Duke MTMC,duke_mtmc,34.1235825,108.83546,Xidian University,edu,03df42c643872aa664a7d6a8f5dbb12cbc3d09f3,citation,,An End-to-End Noise-Weakened Person Re-Identification and Tracking With Adaptive Partial Information,2019
293,China,Duke MTMC,duke_mtmc,39.0607286,117.1256421,Tianjin Normal University,edu,59161bd01e739ad69a93f88303fa2b6e21f6ea98,citation,,Discrimination-Aware Integration for Person Re-Identification in Camera Networks,2019
294,China,Duke MTMC,duke_mtmc,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,960cdda2dcd299ecdf64e867a7538e24ee4e2a99,citation,,Learning deep embedding with mini-cluster loss for person re-identification,2019
295,China,Duke MTMC,duke_mtmc,22.8376,108.289839,Guangxi University,edu,aaca2ebcd26ed668788f364dd7af8b4615492b66,citation,,Omnidirectional Feature Learning for Person Re-Identification,2019
296,China,Duke MTMC,duke_mtmc,31.28473925,121.49694909,Tongji University,edu,aaca2ebcd26ed668788f364dd7af8b4615492b66,citation,,Omnidirectional Feature Learning for Person Re-Identification,2019
297,China,Duke MTMC,duke_mtmc,34.2469152,108.91061982,Northwestern Polytechnical University,edu,11cb49d8f19f0491e1930d9471988a3c07b70bb4,citation,,Person Re-Identification With Triplet Focal Loss,2018
298,China,Duke MTMC,duke_mtmc,34.250803,108.983693,Xi’an Jiaotong University,edu,11cb49d8f19f0491e1930d9471988a3c07b70bb4,citation,,Person Re-Identification With Triplet Focal Loss,2018
299,United States,Duke MTMC,duke_mtmc,42.0551164,-87.67581113,Northwestern University,edu,665b263ce030bcb3356fcd6e45b219c9184d09e1,citation,,Random linear interpolation data augmentation for person re-identification,2018