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index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year
0,YouTube Pose,youtube_poses,0.0,0.0,,,1c2802c2199b6d15ecefe7ba0c39bfe44363de38,main,http://arxiv.org/pdf/1511.06676v1.pdf,Personalizing Human Video Pose Estimation,2016
1,YouTube Pose,youtube_poses,50.7338124,7.1022465,University of Bonn,edu,267bd60e442d87c44eaae3290610138e63d663ab,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Iqbal_PoseTrack_Joint_Multi-Person_CVPR_2017_paper.pdf,PoseTrack: Joint Multi-person Pose Estimation and Tracking,2017
2,YouTube Pose,youtube_poses,-34.9189226,138.60423668,University of Adelaide,edu,267bd60e442d87c44eaae3290610138e63d663ab,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Iqbal_PoseTrack_Joint_Multi-Person_CVPR_2017_paper.pdf,PoseTrack: Joint Multi-person Pose Estimation and Tracking,2017
3,YouTube Pose,youtube_poses,17.4454957,78.34854698,International Institute of Information Technology,edu,185263189a30986e31566394680d6d16b0089772,citation,https://pdfs.semanticscholar.org/1852/63189a30986e31566394680d6d16b0089772.pdf,Efficient Annotation of Objects for Video Analysis,2018
4,YouTube Pose,youtube_poses,52.17638955,0.14308882,University of Cambridge,edu,cd87fea30b68ad1c9ebcb71a224c53cde3516adb,citation,https://pdfs.semanticscholar.org/cd87/fea30b68ad1c9ebcb71a224c53cde3516adb.pdf,EXTRACTING THE X FACTOR IN HUMAN PARSING 3 Factored module Factored task Aggregation module Input Main task Shared features Silhouette Body parts The X Factor bottleneck layers bottleneck layers bottleneck layers Initial module bottleneck layers initial block,2018
5,YouTube Pose,youtube_poses,51.49887085,-0.17560797,Imperial College London,edu,37aa876f5202d1db6919f0a0dd5a0f76508c02fb,citation,https://arxiv.org/pdf/1711.10872.pdf,Occlusion-Aware Hand Pose Estimation Using Hierarchical Mixture Density Network,2018
6,YouTube Pose,youtube_poses,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,0ca2f48fad7f69fb415ecbb99945250cbf8f011c,citation,https://pdfs.semanticscholar.org/0ca2/f48fad7f69fb415ecbb99945250cbf8f011c.pdf,Outliers Cleaning in Dynamic Systems,2017
7,YouTube Pose,youtube_poses,42.3383668,-71.08793524,Northeastern University,edu,0ca2f48fad7f69fb415ecbb99945250cbf8f011c,citation,https://pdfs.semanticscholar.org/0ca2/f48fad7f69fb415ecbb99945250cbf8f011c.pdf,Outliers Cleaning in Dynamic Systems,2017
8,YouTube Pose,youtube_poses,37.43131385,-122.16936535,Stanford University,edu,815e77b8f2e8f17205e46162b3addd02b2ea8ff0,citation,http://pdfs.semanticscholar.org/815e/77b8f2e8f17205e46162b3addd02b2ea8ff0.pdf,Marker-less Pose Estimation,2017
9,YouTube Pose,youtube_poses,39.9492344,-75.19198985,University of Pennsylvania,edu,bbd9b5e4d4761d923d21a060513e826bf5bfc620,citation,https://arxiv.org/pdf/1704.04793.pdf,Harvesting Multiple Views for Marker-Less 3D Human Pose Annotations,2017
10,YouTube Pose,youtube_poses,43.65815275,-79.3790801,Ryerson University,edu,bbd9b5e4d4761d923d21a060513e826bf5bfc620,citation,https://arxiv.org/pdf/1704.04793.pdf,Harvesting Multiple Views for Marker-Less 3D Human Pose Annotations,2017
11,YouTube Pose,youtube_poses,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,10d255fb0bb651b6e9cc69855a970c44f121f2c9,citation,https://arxiv.org/pdf/1710.06513.pdf,Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation,2018
12,YouTube Pose,youtube_poses,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,3e682d368422ff31632760611039372a07eeabc6,citation,https://pdfs.semanticscholar.org/a254/e59f6fd1f8c51e3a5398c01cc1b45aebc66e.pdf,Articulated Multi-person Tracking in the Wild,2016
13,YouTube Pose,youtube_poses,-35.2776999,149.118527,Australian National University,edu,ce2fd44a8c43642b76f219fe32291c1b2644cb73,citation,https://arxiv.org/pdf/1707.09240.pdf,Human Pose Forecasting via Deep Markov Models,2017
14,YouTube Pose,youtube_poses,52.17638955,0.14308882,University of Cambridge,edu,4065d038ecbda579a0791aaf46fc62bbcba5b1f3,citation,http://pdfs.semanticscholar.org/4065/d038ecbda579a0791aaf46fc62bbcba5b1f3.pdf,Real-time Factored ConvNets: Extracting the X Factor in Human Parsing,2017
15,YouTube Pose,youtube_poses,50.7338124,7.1022465,University of Bonn,edu,7a0cd36d02ad962f628d9d504d02a850e27d5bfb,citation,https://arxiv.org/pdf/1710.10000.pdf,PoseTrack: A Benchmark for Human Pose Estimation and Tracking,2017
16,YouTube Pose,youtube_poses,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,8e74244e220a1c9e89417caa1ad22f649884d311,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.142,ArtTrack: Articulated Multi-Person Tracking in the Wild,2017
17,YouTube Pose,youtube_poses,65.0592157,25.46632601,University of Oulu,edu,a287643d3eddca3dcc09b3532f2b070a28d4a022,citation,http://pdfs.semanticscholar.org/a287/643d3eddca3dcc09b3532f2b070a28d4a022.pdf,Real-time Human Pose Estimation from Video with Convolutional Neural Networks,2016
18,YouTube Pose,youtube_poses,60.18558755,24.8242733,Aalto University,edu,a287643d3eddca3dcc09b3532f2b070a28d4a022,citation,http://pdfs.semanticscholar.org/a287/643d3eddca3dcc09b3532f2b070a28d4a022.pdf,Real-time Human Pose Estimation from Video with Convolutional Neural Networks,2016
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