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diff --git a/site/datasets/verified/duke_mtmc.csv b/site/datasets/verified/duke_mtmc.csv index b85d9458..5ede8ed5 100644 --- a/site/datasets/verified/duke_mtmc.csv +++ b/site/datasets/verified/duke_mtmc.csv @@ -223,3 +223,79 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,t 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 |