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 id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year
 0,,PIPA,pipa,0.0,0.0,,,,main,,Beyond frontal faces: Improving Person Recognition using multiple cues,2015
+1,Australia,PIPA,pipa,-35.2776999,149.118527,Australian National University,edu,9ce12c9f1d1661f56908edc8ef3848e91b24d557,citation,https://arxiv.org/pdf/1810.13103.pdf,Query Adaptive Late Fusion for Image Retrieval,2018
+2,China,PIPA,pipa,40.00229045,116.32098908,Tsinghua University,edu,9ce12c9f1d1661f56908edc8ef3848e91b24d557,citation,https://arxiv.org/pdf/1810.13103.pdf,Query Adaptive Late Fusion for Image Retrieval,2018
+3,Singapore,PIPA,pipa,1.2962018,103.77689944,National University of Singapore,edu,5f771fed91c8e4b666489ba2384d0705bcf75030,citation,https://arxiv.org/pdf/1804.03287.pdf,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018
+4,China,PIPA,pipa,28.2290209,112.99483204,"National University of Defense Technology, China",mil,5f771fed91c8e4b666489ba2384d0705bcf75030,citation,https://arxiv.org/pdf/1804.03287.pdf,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018
+5,United States,PIPA,pipa,42.3702265,-71.0768929,"Philips Research, Bethesda, MD, USA",company,c76251049b370f8258d6bbb944c696c30b8bbb85,citation,http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w41/Xue_Clothing_Change_Aware_CVPR_2018_paper.pdf,Clothing Change Aware Person Identification,2018
+6,United States,PIPA,pipa,40.47913175,-74.43168868,Rutgers University,edu,c76251049b370f8258d6bbb944c696c30b8bbb85,citation,http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w41/Xue_Clothing_Change_Aware_CVPR_2018_paper.pdf,Clothing Change Aware Person Identification,2018
+7,United States,PIPA,pipa,33.9928298,-81.02685168,University of South Carolina,edu,c76251049b370f8258d6bbb944c696c30b8bbb85,citation,http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w41/Xue_Clothing_Change_Aware_CVPR_2018_paper.pdf,Clothing Change Aware Person Identification,2018
+8,China,PIPA,pipa,22.4162632,114.2109318,Chinese University of Hong Kong,edu,d949fadc9b6c5c8b067fa42265ad30945f9caa99,citation,https://arxiv.org/pdf/1710.00870.pdf,Rethinking Feature Discrimination and Polymerization for Large-scale Recognition,2017
+9,China,PIPA,pipa,22.4162632,114.2109318,Chinese University of Hong Kong,edu,6fed504da4e192fe4c2d452754d23d3db4a4e5e3,citation,https://arxiv.org/pdf/1702.06890.pdf,Learning Deep Features via Congenerous Cosine Loss for Person Recognition,2017
+10,China,PIPA,pipa,23.09461185,113.28788994,Sun Yat-Sen University,edu,30f464c09779c6210397204901d025c0def1fe10,citation,https://arxiv.org/pdf/1807.00504.pdf,Deep Reasoning with Knowledge Graph for Social Relationship Understanding,2018
+11,China,PIPA,pipa,39.993008,116.329882,SenseTime,company,30f464c09779c6210397204901d025c0def1fe10,citation,https://arxiv.org/pdf/1807.00504.pdf,Deep Reasoning with Knowledge Graph for Social Relationship Understanding,2018
+12,United States,PIPA,pipa,40.742252,-74.0270949,Stevens Institute of Technology,edu,1e1d7cbbef67e9e042a3a0a9a1bcefcc4a9adacf,citation,http://openaccess.thecvf.com/content_cvpr_2016/papers/Li_A_Multi-Level_Contextual_CVPR_2016_paper.pdf,A Multi-level Contextual Model for Person Recognition in Photo Albums,2016
+13,Singapore,PIPA,pipa,1.2962018,103.77689944,National University of Singapore,edu,b5968e7bb23f5f03213178c22fd2e47af3afa04c,citation,https://arxiv.org/pdf/1705.07206.pdf,Multiple-Human Parsing in the Wild,2017
+14,China,PIPA,pipa,39.94976005,116.33629046,Beijing Jiaotong University,edu,b5968e7bb23f5f03213178c22fd2e47af3afa04c,citation,https://arxiv.org/pdf/1705.07206.pdf,Multiple-Human Parsing in the Wild,2017
+15,Germany,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,23429ef60e7a9c0e2f4d81ed1b4e47cc2616522f,citation,https://arxiv.org/pdf/1704.06456.pdf,A Domain Based Approach to Social Relation Recognition,2017
+16,Germany,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,bfc04ce7752fac884cf5a78b30ededfd5a0ad109,citation,https://arxiv.org/pdf/1804.04779.pdf,A Hybrid Model for Identity Obfuscation by Face Replacement,2018
+17,Germany,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,b68150bfdec373ed8e025f448b7a3485c16e3201,citation,https://arxiv.org/pdf/1703.09471.pdf,Adversarial Image Perturbation for Privacy Protection A Game Theory Perspective,2017
+18,United States,PIPA,pipa,42.4505507,-76.4783513,Cornell University,edu,6c8dfa770fe4acffaabeae4b6092c2fd5ee2c545,citation,https://arxiv.org/pdf/1805.04049.pdf,Exploiting Unintended Feature Leakage in Collaborative Learning,2018
+19,Germany,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,bc27434e376db89fe0e6ef2d2fabc100d2575ec6,citation,https://arxiv.org/pdf/1607.08438.pdf,Faceless Person Recognition; Privacy Implications in Social Media,2016
+20,Switzerland,PIPA,pipa,46.5190557,6.5667576,EPFL,edu,1451e7b11e66c86104f9391b80d9fb422fb11c01,citation,https://pdfs.semanticscholar.org/1451/e7b11e66c86104f9391b80d9fb422fb11c01.pdf,Image privacy protection with secure JPEG transmorphing,2017
+21,United States,PIPA,pipa,42.4505507,-76.4783513,Cornell University,edu,8bdf6f03bde08c424c214188b35be8b2dec7cdea,citation,https://arxiv.org/pdf/1805.04049.pdf,Inference Attacks Against Collaborative Learning,2018
+22,Germany,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,0c59071ddd33849bd431165bc2d21bbe165a81e0,citation,https://arxiv.org/pdf/1509.03502.pdf,Person Recognition in Personal Photo Collections,2015
+23,India,PIPA,pipa,17.4450981,78.3497678,IIIT Hyderabad,edu,d0441970a9f19751e6c047b364f580c30bf9754a,citation,https://arxiv.org/pdf/1705.10120.pdf,Pose-Aware Person Recognition,2017
+24,Germany,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,3e0a1884448bfd7f416c6a45dfcdfc9f2e617268,citation,https://arxiv.org/pdf/1805.05838.pdf,Understanding and Controlling User Linkability in Decentralized Learning,2018
+25,China,PIPA,pipa,22.4162632,114.2109318,Chinese University of Hong Kong,edu,2fe7105ef8e61330a3ddc7f7b35955ca62fc1ab3,citation,https://arxiv.org/pdf/1806.03084.pdf,Unifying Identification and Context Learning for Person Recognition,2018
+26,United States,PIPA,pipa,37.8701543,-122.2712312,University of California at Berkeley,edu,d6a9ea9b40a7377c91c705f4c7f206a669a9eea2,citation,https://pdfs.semanticscholar.org/d6a9/ea9b40a7377c91c705f4c7f206a669a9eea2.pdf,Visual Representations for Fine-grained Categorization,2015
+27,United States,PIPA,pipa,42.44726,-76.480988,Facebook & Cornell University,company,0aaf785d7f21d2b5ad582b456896495d30b0a4e2,citation,,A Face Recognition Application for People with Visual Impairments: Understanding Use Beyond the Lab,2018
+28,United States,PIPA,pipa,42.4505507,-76.4783513,Cornell University,edu,0aaf785d7f21d2b5ad582b456896495d30b0a4e2,citation,,A Face Recognition Application for People with Visual Impairments: Understanding Use Beyond the Lab,2018
+29,United States,PIPA,pipa,37.3936717,-122.0807262,Facebook,company,0aaf785d7f21d2b5ad582b456896495d30b0a4e2,citation,,A Face Recognition Application for People with Visual Impairments: Understanding Use Beyond the Lab,2018
+30,China,PIPA,pipa,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,d94d7ff6f46ad5cab5c20e6ac14c1de333711a0c,citation,http://mirlab.org/conference_papers/International_Conference/ICASSP%202017/pdfs/0003031.pdf,Face Album: Towards automatic photo management based on person identity on mobile phones,2017
+31,United States,PIPA,pipa,42.3702265,-71.0768929,"Philips Research, Bethesda, MD, USA",company,cfd4004054399f3a5f536df71f9b9987f060f434,citation,https://arxiv.org/pdf/1710.03224.pdf,Person Recognition in Social Media Photos,2018
+32,United States,PIPA,pipa,40.47913175,-74.43168868,Rutgers University,edu,cfd4004054399f3a5f536df71f9b9987f060f434,citation,https://arxiv.org/pdf/1710.03224.pdf,Person Recognition in Social Media Photos,2018
+33,United States,PIPA,pipa,33.9928298,-81.02685168,University of South Carolina,edu,cfd4004054399f3a5f536df71f9b9987f060f434,citation,https://arxiv.org/pdf/1710.03224.pdf,Person Recognition in Social Media Photos,2018
+34,Germany,PIPA,pipa,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,2c92839418a64728438c351a42f6dc5ad0c6e686,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Masi_Pose-Aware_Face_Recognition_CVPR_2016_paper.pdf,Pose-Aware Face Recognition in the Wild,2016
+35,Singapore,PIPA,pipa,1.2962018,103.77689944,National University of Singapore,edu,6e50c32f7244e3556eb879f24b7de8410f3177f6,citation,https://arxiv.org/pdf/1812.05917.pdf,Visual Social Relationship Recognition,2018
+36,United States,PIPA,pipa,44.97399,-93.2277285,University of Minnesota-Twin Cities,edu,6e50c32f7244e3556eb879f24b7de8410f3177f6,citation,https://arxiv.org/pdf/1812.05917.pdf,Visual Social Relationship Recognition,2018
+37,United States,PIPA,pipa,40.4441619,-79.94272826,Carnegie Mellon University,edu,95d64ce5b0758bdc213962ce65ac89b31d9fb617,citation,,Learning Pose-Aware Models for Pose-Invariant Face Recognition in the Wild,2018
+38,Israel,PIPA,pipa,32.77824165,34.99565673,Open University of Israel,edu,95d64ce5b0758bdc213962ce65ac89b31d9fb617,citation,,Learning Pose-Aware Models for Pose-Invariant Face Recognition in the Wild,2018
+39,United States,PIPA,pipa,34.0224149,-118.28634407,University of Southern California,edu,95d64ce5b0758bdc213962ce65ac89b31d9fb617,citation,,Learning Pose-Aware Models for Pose-Invariant Face Recognition in the Wild,2018
+40,India,PIPA,pipa,17.4454957,78.34854698,International Institute of Information Technology,edu,01e27c91c7cef926389f913d12410725e7dd35ab,citation,,Semi-supervised annotation of faces in image collection,2018
+41,Switzerland,PIPA,pipa,47.376313,8.5476699,ETH Zurich,edu,503906ca940fa3b01e39d05879c9b6a36524aaf5,citation,,Natural and Effective Obfuscation by Head Inpainting,2018
+42,Germany,PIPA,pipa,49.2578657,7.0457956,Max Planck Institute of Informatics,edu,503906ca940fa3b01e39d05879c9b6a36524aaf5,citation,,Natural and Effective Obfuscation by Head Inpainting,2018
+43,Belgium,PIPA,pipa,50.8784802,4.4348624,"Toyota Motor Europe (TME), Brussels 1140, Belgium",edu,503906ca940fa3b01e39d05879c9b6a36524aaf5,citation,,Natural and Effective Obfuscation by Head Inpainting,2018
+44,Singapore,PIPA,pipa,1.2966426,103.7763939,National University of Singapore & Qihoo 360 AI Institute,edu,af4759f5e636b5d9049010d5f0e2b0df2a69cd72,citation,,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018
+45,Singapore,PIPA,pipa,1.2962018,103.77689944,National University of Singapore,edu,af4759f5e636b5d9049010d5f0e2b0df2a69cd72,citation,,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018