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diff --git a/site/datasets/verified/duke_mtmc.csv b/site/datasets/verified/duke_mtmc.csv new file mode 100644 index 00000000..929b84c1 --- /dev/null +++ b/site/datasets/verified/duke_mtmc.csv @@ -0,0 +1,181 @@ +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,United States,Duke MTMC,duke_mtmc,37.8718992,-122.2585399,University of California,edu,fefa8f07d998f8f4a6c85a7da781b19bf6b78d7d,citation,https://arxiv.org/pdf/1902.00749.pdf,Online Multi-Object Tracking with Dual Matching Attention Networks,2018 +47,China,Duke MTMC,duke_mtmc,39.9808333,116.34101249,Beihang University,edu,7bfc5bbad852f9e6bea3b86c25179d81e2e7fff6,citation,,Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology,2018 +48,China,Duke MTMC,duke_mtmc,40.00229045,116.32098908,Tsinghua University,edu,be79ad118d0524d9b493f4a14a662c8184e6405a,citation,,Attend and Align: Improving Deep Representations with Feature Alignment Layer for Person Retrieval,2018 +49,China,Duke MTMC,duke_mtmc,40.00229045,116.32098908,Tsinghua University,edu,13ea9a2ed134a9e238d33024fba34d3dd6a010e0,citation,https://arxiv.org/pdf/1703.05693.pdf,SVDNet for Pedestrian Retrieval,2017 +50,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,13ea9a2ed134a9e238d33024fba34d3dd6a010e0,citation,https://arxiv.org/pdf/1703.05693.pdf,SVDNet for Pedestrian Retrieval,2017 +51,China,Duke MTMC,duke_mtmc,30.19331415,120.11930822,Zhejiang University,edu,608dede56161fd5f76bcf9228b4dd8c639d65b02,citation,https://arxiv.org/pdf/1807.00537.pdf,SphereReID: Deep Hypersphere Manifold Embedding for Person Re-Identification,2018 +52,United States,Duke MTMC,duke_mtmc,42.7298459,-73.67950216,Rensselaer Polytechnic Institute,edu,24d6d3adf2176516ef0de2e943ce2084e27c4f94,citation,https://arxiv.org/pdf/1811.07487.pdf,Re-Identification with Consistent Attentive Siamese Networks,2018 +53,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,15e1af79939dbf90790b03d8aa02477783fb1d0f,citation,https://arxiv.org/pdf/1701.07717.pdf,Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro,2017 +54,China,Duke MTMC,duke_mtmc,30.778621,103.961236,XiHua University,edu,ec9c20ed6cce15e9b63ac96bb5a6d55e69661e0b,citation,https://pdfs.semanticscholar.org/ec9c/20ed6cce15e9b63ac96bb5a6d55e69661e0b.pdf,Robust Pedestrian Detection for Semi-automatic Construction of a Crowded Person Re-Identification Dataset,2018 +55,United Kingdom,Duke MTMC,duke_mtmc,51.24303255,-0.59001382,University of Surrey,edu,ec9c20ed6cce15e9b63ac96bb5a6d55e69661e0b,citation,https://pdfs.semanticscholar.org/ec9c/20ed6cce15e9b63ac96bb5a6d55e69661e0b.pdf,Robust Pedestrian Detection for Semi-automatic Construction of a Crowded Person Re-Identification Dataset,2018 +56,China,Duke MTMC,duke_mtmc,31.4854255,120.2739581,Jiangnan University,edu,ec9c20ed6cce15e9b63ac96bb5a6d55e69661e0b,citation,https://pdfs.semanticscholar.org/ec9c/20ed6cce15e9b63ac96bb5a6d55e69661e0b.pdf,Robust Pedestrian Detection for Semi-automatic Construction of a Crowded Person Re-Identification Dataset,2018 +57,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,fa3fb32fe0cd392960549b0adb7a535eb3656abd,citation,https://arxiv.org/pdf/1711.08106.pdf,The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching,2017 +58,United Kingdom,Duke MTMC,duke_mtmc,55.94951105,-3.19534913,University of Edinburgh,edu,fa3fb32fe0cd392960549b0adb7a535eb3656abd,citation,https://arxiv.org/pdf/1711.08106.pdf,The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching,2017 +59,United States,Duke MTMC,duke_mtmc,40.1019523,-88.2271615,UIUC,edu,54c28bf64debbdb21c246795182f97d4f7917b74,citation,https://arxiv.org/pdf/1811.04129.pdf,STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification,2018 +60,United States,Duke MTMC,duke_mtmc,34.0803829,-118.3909947,Tencent,company,3b311a1ce30f9c0f3dc1d9c0cf25f13127a5e48c,citation,https://arxiv.org/pdf/1810.12193.pdf,A Coarse-to-fine Pyramidal Model for Person Re-identification via Multi-Loss Dynamic Training,2018 +61,United States,Duke MTMC,duke_mtmc,37.3860784,-121.9877807,Google and Hewlett-Packard Labs,edu,4d799f6e09f442bde583a50a0a9f81131ef707bb,citation,,TAR: Enabling Fine-Grained Targeted Advertising in Retail Stores,2018 +62,United States,Duke MTMC,duke_mtmc,37.3860784,-121.9877807,Hewlett-Packard Labs,edu,4d799f6e09f442bde583a50a0a9f81131ef707bb,citation,,TAR: Enabling Fine-Grained Targeted Advertising in Retail Stores,2018 +63,United States,Duke MTMC,duke_mtmc,39.6321923,-76.3038146,LinkedIn and Hewlett-Packard Labs,edu,4d799f6e09f442bde583a50a0a9f81131ef707bb,citation,,TAR: Enabling Fine-Grained Targeted Advertising in Retail Stores,2018 +64,United States,Duke MTMC,duke_mtmc,34.0224149,-118.28634407,University of Southern California,edu,4d799f6e09f442bde583a50a0a9f81131ef707bb,citation,,TAR: Enabling Fine-Grained Targeted Advertising in Retail Stores,2018 +65,Canada,Duke MTMC,duke_mtmc,49.2767454,-122.91777375,Simon Fraser University,edu,5137ca9f0a7cf4c61f2254d4a252a0c56e5dcfcc,citation,https://arxiv.org/pdf/1811.07130.pdf,Batch Feature Erasing for Person Re-identification and Beyond,2018 +66,China,Duke MTMC,duke_mtmc,32.0565957,118.77408833,Nanjing University,edu,c37c3853ab428725f13906bb0ff4936ffe15d6af,citation,https://arxiv.org/pdf/1809.02874.pdf,Unsupervised Person Re-identification by Deep Learning Tracklet Association,2018 +67,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,c37c3853ab428725f13906bb0ff4936ffe15d6af,citation,https://arxiv.org/pdf/1809.02874.pdf,Unsupervised Person Re-identification by Deep Learning Tracklet Association,2018 +68,United States,Duke MTMC,duke_mtmc,37.8687126,-122.25586815,"University of California, Berkeley",edu,a8d665fa7357f696dcfd188b91fda88da47b964e,citation,https://arxiv.org/pdf/1809.02318.pdf,Scaling Video Analytics Systems to Large Camera Deployments,2018 +69,United States,Duke MTMC,duke_mtmc,47.6423318,-122.1369302,Microsoft,company,a8d665fa7357f696dcfd188b91fda88da47b964e,citation,https://arxiv.org/pdf/1809.02318.pdf,Scaling Video Analytics Systems to Large Camera Deployments,2018 +70,United States,Duke MTMC,duke_mtmc,41.78468745,-87.60074933,University of Chicago,edu,a8d665fa7357f696dcfd188b91fda88da47b964e,citation,https://arxiv.org/pdf/1809.02318.pdf,Scaling Video Analytics Systems to Large Camera Deployments,2018 +71,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,dda0b381c162695f21b8d1149aab22188b3c2bc0,citation,https://arxiv.org/pdf/1804.02792.pdf,Occluded Person Re-Identification,2018 +72,China,Duke MTMC,duke_mtmc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,33f358f1d2b54042c524d69b20e80d98dde3dacd,citation,https://arxiv.org/pdf/1811.11405.pdf,Spectral Feature Transformation for Person Re-identification,2018 +73,United States,Duke MTMC,duke_mtmc,32.8734455,-117.2065636,TuSimple,edu,33f358f1d2b54042c524d69b20e80d98dde3dacd,citation,https://arxiv.org/pdf/1811.11405.pdf,Spectral Feature Transformation for Person Re-identification,2018 +74,China,Duke MTMC,duke_mtmc,30.672721,104.098806,University of Electronic Science and Technology of China,edu,8ffc49aead99fdacb0b180468a36984759f2fc1e,citation,https://arxiv.org/pdf/1809.04976.pdf,Sparse Label Smoothing for Semi-supervised Person Re-Identification,2018 +75,Germany,Duke MTMC,duke_mtmc,50.7791703,6.06728733,RWTH Aachen University,edu,10b36c003542545f1e2d73e8897e022c0c260c32,citation,https://arxiv.org/pdf/1705.04608.pdf,Towards a Principled Integration of Multi-camera Re-identification and Tracking Through Optimal Bayes Filters,2017 +76,United Kingdom,Duke MTMC,duke_mtmc,51.7534538,-1.25400997,University of Oxford,edu,94ed6dc44842368b457851b43023c23fd78d5390,citation,https://arxiv.org/pdf/1806.01794.pdf,"Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects",2018 +77,China,Duke MTMC,duke_mtmc,39.9041999,116.4073963,"Beijing, China",edu,280976bbb41d2948a5c0208f86605977397181cd,citation,https://arxiv.org/pdf/1811.08073.pdf,Factorized Distillation: Training Holistic Person Re-identification Model by Distilling an Ensemble of Partial ReID Models,2018 +78,China,Duke MTMC,duke_mtmc,40.00229045,116.32098908,Tsinghua University,edu,280976bbb41d2948a5c0208f86605977397181cd,citation,https://arxiv.org/pdf/1811.08073.pdf,Factorized Distillation: Training Holistic Person Re-identification Model by Distilling an Ensemble of Partial ReID Models,2018 +79,China,Duke MTMC,duke_mtmc,39.9922379,116.30393816,Peking University,edu,014e249422b6bd6ff32b3f7d385b5a0e8c4c9fcf,citation,https://arxiv.org/pdf/1810.05866.pdf,Attention driven person re-identification,2019 +80,Singapore,Duke MTMC,duke_mtmc,1.3484104,103.68297965,Nanyang Technological University,edu,014e249422b6bd6ff32b3f7d385b5a0e8c4c9fcf,citation,https://arxiv.org/pdf/1810.05866.pdf,Attention driven person re-identification,2019 +81,China,Duke MTMC,duke_mtmc,39.9808333,116.34101249,Beihang University,edu,e9d549989926f36abfa5dc7348ae3d79a567bf30,citation,,Orientation-Guided Similarity Learning for Person Re-identification,2018 +82,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,95bdd45fed0392418e0e5d3e51d34714917e3c87,citation,https://arxiv.org/pdf/1812.03282.pdf,Spatial-Temporal Person Re-identification,2019 +83,China,Duke MTMC,duke_mtmc,31.30104395,121.50045497,Fudan University,edu,00e3957212517a252258baef833833921dd308d4,citation,,Adaptively Weighted Multi-task Deep Network for Person Attribute Classification,2017 +84,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,705073015bb8ae97212532a30488c05d50894bec,citation,https://arxiv.org/pdf/1803.09786.pdf,Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-identification,2018 +85,United States,Duke MTMC,duke_mtmc,35.9990522,-78.9290629,Duke University,edu,9e644b1e33dd9367be167eb9d832174004840400,citation,https://users.cs.duke.edu/~tomasi/papers/ristani/ristaniTCAS16.pdf,Tracking Social Groups Within and Across Cameras,2017 +86,Italy,Duke MTMC,duke_mtmc,44.6451046,10.9279268,University of Modena,edu,9e644b1e33dd9367be167eb9d832174004840400,citation,https://users.cs.duke.edu/~tomasi/papers/ristani/ristaniTCAS16.pdf,Tracking Social Groups Within and Across Cameras,2017 +87,United States,Duke MTMC,duke_mtmc,35.9990522,-78.9290629,Duke University,edu,27a2fad58dd8727e280f97036e0d2bc55ef5424c,citation,https://arxiv.org/pdf/1609.01775.pdf,"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking",2016 +88,Switzerland,Duke MTMC,duke_mtmc,46.5190557,6.5667576,EPFL,edu,4e4e3ddb55607e127a4abdef45d92adf1ff78de2,citation,http://openaccess.thecvf.com/content_ICCV_2017/papers/Maksai_Non-Markovian_Globally_Consistent_ICCV_2017_paper.pdf,Non-Markovian Globally Consistent Multi-object Tracking,2017 +89,Switzerland,Duke MTMC,duke_mtmc,46.109237,7.08453549,IDIAP Research Institute,edu,4e4e3ddb55607e127a4abdef45d92adf1ff78de2,citation,http://openaccess.thecvf.com/content_ICCV_2017/papers/Maksai_Non-Markovian_Globally_Consistent_ICCV_2017_paper.pdf,Non-Markovian Globally Consistent Multi-object Tracking,2017 +90,United States,Duke MTMC,duke_mtmc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,4e4e3ddb55607e127a4abdef45d92adf1ff78de2,citation,http://openaccess.thecvf.com/content_ICCV_2017/papers/Maksai_Non-Markovian_Globally_Consistent_ICCV_2017_paper.pdf,Non-Markovian Globally Consistent Multi-object Tracking,2017 +91,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,fc26fc2340a863d6da0b427cd924fb4cb101051b,citation,http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w37/Chen_Person_Re-Identification_by_ICCV_2017_paper.pdf,Person Re-identification by Deep Learning Multi-scale Representations,2017 +92,United Kingdom,Duke MTMC,duke_mtmc,55.378051,-3.435973,"Vision Semantics 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Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline),2018 +96,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,fc068f7f8a3b2921ec4f3246e9b6c6015165df9a,citation,https://arxiv.org/pdf/1711.09349.pdf,Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline),2018 +97,United States,Duke MTMC,duke_mtmc,29.58333105,-98.61944505,University of Texas at San Antonio,edu,fc068f7f8a3b2921ec4f3246e9b6c6015165df9a,citation,https://arxiv.org/pdf/1711.09349.pdf,Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline),2018 +98,United States,Duke MTMC,duke_mtmc,43.0008093,-78.7889697,University at Buffalo,edu,fdd1bde7066c7e9c7515f330546e0b3a8de8a4a6,citation,https://arxiv.org/pdf/1811.06582.pdf,CAN: Composite Appearance Network and a Novel Evaluation Metric for Person Tracking,2018 +99,United States,Duke MTMC,duke_mtmc,43.0008093,-78.7889697,University at Buffalo,edu,3144c9b3bedb6e3895dcd36998bcb0903271841d,citation,https://arxiv.org/pdf/1811.06582.pdf,CAN: Composite Appearance Network and a Novel Evaluation Metric for Person Tracking,2018 +100,China,Duke MTMC,duke_mtmc,29.1416432,119.7889248,"Alibaba Group, Zhejiang, People’s Republic of China",edu,f4e65ab81a0f4ffa50d0c9bc308d7365e012cc75,citation,https://arxiv.org/pdf/1812.05785.pdf,Deep Active Learning for Video-based Person Re-identification,2018 +101,China,Duke MTMC,duke_mtmc,30.19331415,120.11930822,Zhejiang University,edu,f4e65ab81a0f4ffa50d0c9bc308d7365e012cc75,citation,https://arxiv.org/pdf/1812.05785.pdf,Deep Active Learning for Video-based Person Re-identification,2018 +102,China,Duke MTMC,duke_mtmc,38.88140235,121.52281098,Dalian University of Technology,edu,5be74c6fa7f890ea530e427685dadf0d0a371fc1,citation,https://arxiv.org/pdf/1804.11027.pdf,Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification,2018 +103,Australia,Duke MTMC,duke_mtmc,-27.49741805,153.01316956,University of Queensland,edu,5be74c6fa7f890ea530e427685dadf0d0a371fc1,citation,https://arxiv.org/pdf/1804.11027.pdf,Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification,2018 +104,Australia,Duke MTMC,duke_mtmc,-33.88890695,151.18943366,University of Sydney,edu,5be74c6fa7f890ea530e427685dadf0d0a371fc1,citation,https://arxiv.org/pdf/1804.11027.pdf,Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification,2018 +105,Switzerland,Duke MTMC,duke_mtmc,46.5184121,6.5684654,École Polytechnique Fédérale de Lausanne,edu,0f3eb3719b6f6f544b766e0bfeb8f962c9bd59f4,citation,https://arxiv.org/pdf/1811.10984.pdf,Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking,2018 +106,Italy,Duke MTMC,duke_mtmc,45.434532,12.326197,"DAIS, Università Ca’ Foscari, Venice, Italy",edu,6dce5866ebc46355a35b8667c1e04a4790c2289b,citation,https://pdfs.semanticscholar.org/6dce/5866ebc46355a35b8667c1e04a4790c2289b.pdf,Extensions of dominant sets and their applications in computer vision,2018 +107,United States,Duke MTMC,duke_mtmc,42.3383668,-71.08793524,Northeastern University,edu,8abe89ab85250fd7a8117da32bc339a71c67dc21,citation,https://arxiv.org/pdf/1709.07065.pdf,Multi-camera Multi-Object Tracking,2017 +108,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,b856c0eb039effce7da9ff45c3f5987f18928bef,citation,https://arxiv.org/pdf/1707.00408.pdf,Pedestrian Alignment Network for Large-scale Person Re-identification,2017 +109,Germany,Duke MTMC,duke_mtmc,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,bab66082d01b393e6b9e841e5e06782a6c61ec88,citation,https://arxiv.org/pdf/1803.08709.pdf,Pose-Driven Deep Models for Person Re-Identification,2018 +110,China,Duke MTMC,duke_mtmc,31.30104395,121.50045497,Fudan University,edu,e6d8f332ae26e9983d5b42af4466ff95b55f2341,citation,https://arxiv.org/pdf/1712.02225.pdf,Pose-Normalized Image Generation for Person Re-identification,2018 +111,Japan,Duke MTMC,duke_mtmc,34.7321121,135.7328585,Nara Institute of Science and Technology,edu,e6d8f332ae26e9983d5b42af4466ff95b55f2341,citation,https://arxiv.org/pdf/1712.02225.pdf,Pose-Normalized Image Generation for Person Re-identification,2018 +112,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,e6d8f332ae26e9983d5b42af4466ff95b55f2341,citation,https://arxiv.org/pdf/1712.02225.pdf,Pose-Normalized Image Generation for Person Re-identification,2018 +113,China,Duke MTMC,duke_mtmc,22.8376,108.289839,Guangxi University,edu,4a91be40e6b382c3ddf3385ac44062b2399336a8,citation,https://arxiv.org/pdf/1809.09970.pdf,Random Occlusion-recovery for Person Re-identification,2018 +114,China,Duke MTMC,duke_mtmc,31.28473925,121.49694909,Tongji University,edu,4a91be40e6b382c3ddf3385ac44062b2399336a8,citation,https://arxiv.org/pdf/1809.09970.pdf,Random Occlusion-recovery for Person Re-identification,2018 +115,France,Duke MTMC,duke_mtmc,45.2173989,5.7921349,"Naver Labs Europe, Meylan, France",edu,4d8347a69e77cc02c1e1aba3a8b6646eac1a0b3d,citation,https://arxiv.org/pdf/1801.05339.pdf,Re-ID done right: towards good practices for person re-identification.,2018 +116,United States,Duke MTMC,duke_mtmc,28.59899755,-81.19712501,University of Central Florida,edu,a1e97c4043d5cc9896dc60ae7ca135782d89e5fc,citation,https://arxiv.org/pdf/1612.02155.pdf,"Re-identification of Humans in Crowds using Personal, Social and Environmental Constraints",2016 +117,China,Duke MTMC,duke_mtmc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,0e36bf238d2db6c970ade0b5f68811ed6debc4e8,citation,https://arxiv.org/pdf/1810.07399.pdf,Recognizing Partial Biometric Patterns,2018 +118,United States,Duke MTMC,duke_mtmc,42.4505507,-76.4783513,Cornell University,edu,6d76eefecdcaa130a000d1d6c93cf57166ebd18e,citation,https://arxiv.org/pdf/1805.08805.pdf,Resource Aware Person Re-identification Across Multiple Resolutions,2018 +119,China,Duke MTMC,duke_mtmc,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,6d76eefecdcaa130a000d1d6c93cf57166ebd18e,citation,https://arxiv.org/pdf/1805.08805.pdf,Resource Aware Person Re-identification Across Multiple Resolutions,2018 +120,China,Duke MTMC,duke_mtmc,40.00229045,116.32098908,Tsinghua University,edu,6d76eefecdcaa130a000d1d6c93cf57166ebd18e,citation,https://arxiv.org/pdf/1805.08805.pdf,Resource Aware Person Re-identification Across Multiple Resolutions,2018 +121,China,Duke MTMC,duke_mtmc,31.846918,117.29053367,Hefei University of Technology,edu,42dc432f58adfaa7bf6af07e5faf9e75fea29122,citation,https://arxiv.org/pdf/1811.08115.pdf,Sequence-based Person Attribute Recognition with Joint CTC-Attention Model,2018 +122,China,Duke MTMC,duke_mtmc,31.1675446,121.3974873,"Tencent, Shanghai, China",company,42dc432f58adfaa7bf6af07e5faf9e75fea29122,citation,https://arxiv.org/pdf/1811.08115.pdf,Sequence-based Person Attribute Recognition with Joint CTC-Attention Model,2018 +123,United States,Duke MTMC,duke_mtmc,47.6423318,-122.1369302,Microsoft,company,8a77025bde5479a1366bb93c6f2366b5a6293720,citation,https://arxiv.org/pdf/1805.02336.pdf,Sharp Attention Network via Adaptive Sampling for Person Re-identification,2018 +124,United States,Duke MTMC,duke_mtmc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,8a77025bde5479a1366bb93c6f2366b5a6293720,citation,https://arxiv.org/pdf/1805.02336.pdf,Sharp Attention Network via Adaptive Sampling for Person Re-identification,2018 +125,China,Duke 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 +126,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 +127,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 +128,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 +129,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 +130,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 +131,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 +132,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 +133,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 +134,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 +135,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 +136,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 +137,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 +138,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 +139,China,Duke MTMC,duke_mtmc,39.979203,116.33287,"CRIPAC & NLPR, CASIA",edu,1bfe59be5b42d6b7257da4b35a408239c01ab79d,citation,,Adversarially Occluded Samples for Person Re-identification,2018 +140,China,Duke MTMC,duke_mtmc,40.0044795,116.370238,Chinese Academy of Sciences,edu,1bfe59be5b42d6b7257da4b35a408239c01ab79d,citation,,Adversarially Occluded Samples for Person Re-identification,2018 +141,China,Duke MTMC,duke_mtmc,22.543096,114.057865,"SenseNets Corporation, Shenzhen, China",company,14ce502bc19b225466126b256511f9c05cadcb6e,citation,,Attention-Aware Compositional Network for Person Re-identification,2018 +142,China,Duke MTMC,duke_mtmc,39.993008,116.329882,SenseTime,company,14ce502bc19b225466126b256511f9c05cadcb6e,citation,,Attention-Aware Compositional Network for Person Re-identification,2018 +143,Australia,Duke MTMC,duke_mtmc,-33.88890695,151.18943366,University of Sydney,edu,14ce502bc19b225466126b256511f9c05cadcb6e,citation,,Attention-Aware Compositional Network for Person Re-identification,2018 +144,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,1822ca8db58b0382b0c64f310840f0f875ea02c0,citation,,Camera Style Adaptation for Person Re-identification,2018 +145,China,Duke MTMC,duke_mtmc,24.4399419,118.09301781,Xiamen University,edu,1822ca8db58b0382b0c64f310840f0f875ea02c0,citation,,Camera Style Adaptation for Person Re-identification,2018 +146,China,Duke MTMC,duke_mtmc,36.16161795,120.49355276,Ocean University of China,edu,38259235a1c7b2c68ca09f3bc0930987ae99cf00,citation,,Deep Feature Ranking for Person Re-Identification,2019 +147,South Korea,Duke MTMC,duke_mtmc,35.84658875,127.1350133,Chonbuk National University,edu,c635564fe2f7d91b578bd6959904982aaa61234d,citation,,Deep Multi-Task Network for Learning Person Identity and Attributes,2018 +148,China,Duke MTMC,duke_mtmc,22.4162632,114.2109318,Chinese University of Hong Kong,edu,947954cafdefd471b75da8c3bb4c21b9e6d57838,citation,,End-to-End Deep Kronecker-Product Matching for Person Re-identification,2018 +149,China,Duke MTMC,duke_mtmc,39.993008,116.329882,SenseTime,company,947954cafdefd471b75da8c3bb4c21b9e6d57838,citation,,End-to-End Deep Kronecker-Product Matching for Person Re-identification,2018 +150,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 +151,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 +152,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 +153,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 +154,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 +155,Italy,Duke MTMC,duke_mtmc,46.0658836,11.1159894,University 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