id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year
0,,VOC,voc,0.0,0.0,,,,main,,The Pascal Visual Object Classes (VOC) Challenge,2009
1,China,VOC,voc,28.2290209,112.99483204,"National University of Defense Technology, China",mil,ca4e0a2cd761f52e6c0bc06ef8ac79e3c7649083,citation,https://arxiv.org/pdf/1804.04606.pdf,Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors,2018
2,United States,VOC,voc,39.0298587,-76.9638027,"U.S. Army Research Laboratory, Adelphi, MD, USA",mil,e7895feb2de9007ea1e47b0ea5952afd5af08b3d,citation,https://arxiv.org/pdf/1704.01069.pdf,ME R-CNN: Multi-Expert R-CNN for Object Detection,2017
3,United States,VOC,voc,37.8718992,-122.2585399,"University of Califonia, Berkeley",edu,0547c44cb896e1cc38130ae8cc6b04dc21179045,citation,http://courses.cs.washington.edu/courses/cse590v/13au/FastMatch_cvpr_2013.pdf,Fast-Match: Fast Affine Template Matching,2013
4,Israel,VOC,voc,32.1119889,34.80459702,Tel Aviv University,edu,0547c44cb896e1cc38130ae8cc6b04dc21179045,citation,http://courses.cs.washington.edu/courses/cse590v/13au/FastMatch_cvpr_2013.pdf,Fast-Match: Fast Affine Template Matching,2013
5,Israel,VOC,voc,31.904187,34.807378,"Weizmann Institute, Rehovot, Israel",edu,0547c44cb896e1cc38130ae8cc6b04dc21179045,citation,http://courses.cs.washington.edu/courses/cse590v/13au/FastMatch_cvpr_2013.pdf,Fast-Match: Fast Affine Template Matching,2013
6,Israel,VOC,voc,32.7940463,34.989571,"Yahoo Research Labs, Haifa, Israel",company,0547c44cb896e1cc38130ae8cc6b04dc21179045,citation,http://courses.cs.washington.edu/courses/cse590v/13au/FastMatch_cvpr_2013.pdf,Fast-Match: Fast Affine Template Matching,2013
7,Netherlands,VOC,voc,52.3553655,4.9501644,University of Amsterdam,edu,19a3e5495b420c1f5da283bf39708a6e833a6cc5,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1A_020.pdf,Attributes and categories for generic instance search from one example,2015
8,United States,VOC,voc,40.8419836,-73.94368971,Columbia University,edu,19a3e5495b420c1f5da283bf39708a6e833a6cc5,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1A_020.pdf,Attributes and categories for generic instance search from one example,2015
9,China,VOC,voc,39.103355,117.164927,NanKai University,edu,55968c9906e13eff2a7fb03d7c416a6d0f9f53e0,citation,http://cg.cs.tsinghua.edu.cn/papers/ECCV-2016-Hfs.pdf,HFS: Hierarchical Feature Selection for Efficient Image Segmentation,2016
10,United Kingdom,VOC,voc,51.7520849,-1.2516646,Oxford University,edu,55968c9906e13eff2a7fb03d7c416a6d0f9f53e0,citation,http://cg.cs.tsinghua.edu.cn/papers/ECCV-2016-Hfs.pdf,HFS: Hierarchical Feature Selection for Efficient Image Segmentation,2016
11,China,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,55968c9906e13eff2a7fb03d7c416a6d0f9f53e0,citation,http://cg.cs.tsinghua.edu.cn/papers/ECCV-2016-Hfs.pdf,HFS: Hierarchical Feature Selection for Efficient Image Segmentation,2016
12,United States,VOC,voc,32.87935255,-117.23110049,"University of California, San Diego",edu,55968c9906e13eff2a7fb03d7c416a6d0f9f53e0,citation,http://cg.cs.tsinghua.edu.cn/papers/ECCV-2016-Hfs.pdf,HFS: Hierarchical Feature Selection for Efficient Image Segmentation,2016
13,United States,VOC,voc,40.4441619,-79.94272826,Carnegie Mellon University,edu,46c82cfadd9f885f5480b2d7155f0985daf949fc,citation,http://openaccess.thecvf.com/content_cvpr_2016/papers/Fouhey_3D_Shape_Attributes_CVPR_2016_paper.pdf,3D Shape Attributes,2016
14,United Kingdom,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,46c82cfadd9f885f5480b2d7155f0985daf949fc,citation,http://openaccess.thecvf.com/content_cvpr_2016/papers/Fouhey_3D_Shape_Attributes_CVPR_2016_paper.pdf,3D Shape Attributes,2016
15,United States,VOC,voc,47.6423318,-122.1369302,Microsoft,company,57642aa16d29bbd9f89f95e3f3dcb8291552db60,citation,http://www.cs.toronto.edu/~pekhimenko/Papers/iiswc18-tbd.pdf,Benchmarking and Analyzing Deep Neural Network Training,2018
16,Canada,VOC,voc,49.25839375,-123.24658161,University of British Columbia,edu,57642aa16d29bbd9f89f95e3f3dcb8291552db60,citation,http://www.cs.toronto.edu/~pekhimenko/Papers/iiswc18-tbd.pdf,Benchmarking and Analyzing Deep Neural Network Training,2018
17,Canada,VOC,voc,43.66333345,-79.39769975,University of Toronto,edu,57642aa16d29bbd9f89f95e3f3dcb8291552db60,citation,http://www.cs.toronto.edu/~pekhimenko/Papers/iiswc18-tbd.pdf,Benchmarking and Analyzing Deep Neural Network Training,2018
18,China,VOC,voc,39.9808333,116.34101249,Beihang University,edu,df0e280cae018cebd5b16ad701ad101265c369fa,citation,https://arxiv.org/pdf/1509.02470.pdf,Deep Attributes from Context-Aware Regional Neural Codes,2015
19,China,VOC,voc,39.966244,116.3270039,Intel Labs China,company,df0e280cae018cebd5b16ad701ad101265c369fa,citation,https://arxiv.org/pdf/1509.02470.pdf,Deep Attributes from Context-Aware Regional Neural Codes,2015
20,United States,VOC,voc,40.8419836,-73.94368971,Columbia University,edu,df0e280cae018cebd5b16ad701ad101265c369fa,citation,https://arxiv.org/pdf/1509.02470.pdf,Deep Attributes from Context-Aware Regional Neural Codes,2015
21,United Kingdom,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,a63104ad235f98bc5ee0b44fefbcdb49e32c205a,citation,http://groups.inf.ed.ac.uk/calvin/Publications/Jammalamadaka12eccv.pdf,Has my algorithm succeeded? an evaluator for human pose estimators,2012
22,Switzerland,VOC,voc,47.376313,8.5476699,ETH Zurich,edu,a63104ad235f98bc5ee0b44fefbcdb49e32c205a,citation,http://groups.inf.ed.ac.uk/calvin/Publications/Jammalamadaka12eccv.pdf,Has my algorithm succeeded? an evaluator for human pose estimators,2012
23,United Kingdom,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,a63104ad235f98bc5ee0b44fefbcdb49e32c205a,citation,http://groups.inf.ed.ac.uk/calvin/Publications/Jammalamadaka12eccv.pdf,Has my algorithm succeeded? an evaluator for human pose estimators,2012
24,China,VOC,voc,36.3693473,120.673818,Shandong University,edu,ddde8f2c0209f11c2579dfaa13ac4053dedbf2fe,citation,https://arxiv.org/pdf/1811.02804.pdf,Image smoothing via unsupervised learning,2018
25,United States,VOC,voc,42.3614256,-71.0812092,Microsoft Research Asia,company,ddde8f2c0209f11c2579dfaa13ac4053dedbf2fe,citation,https://arxiv.org/pdf/1811.02804.pdf,Image smoothing via unsupervised learning,2018
26,China,VOC,voc,39.9922379,116.30393816,Peking University,edu,ddde8f2c0209f11c2579dfaa13ac4053dedbf2fe,citation,https://arxiv.org/pdf/1811.02804.pdf,Image smoothing via unsupervised learning,2018
27,United States,VOC,voc,32.87935255,-117.23110049,"University of California, San Diego",edu,16161051ee13dd3d836a39a280df822bf6442c84,citation,https://pdfs.semanticscholar.org/4bd3/f187f3e09483b1f0f92150a4a77409691b0f.pdf,Learning Efficient Object Detection Models with Knowledge Distillation,2017
28,United States,VOC,voc,38.926761,-92.29193783,University of Missouri,edu,16161051ee13dd3d836a39a280df822bf6442c84,citation,https://pdfs.semanticscholar.org/4bd3/f187f3e09483b1f0f92150a4a77409691b0f.pdf,Learning Efficient Object Detection Models with Knowledge Distillation,2017
29,United States,VOC,voc,37.3239177,-122.0129693,"NEC Labs, Cupertino, CA",company,16161051ee13dd3d836a39a280df822bf6442c84,citation,https://pdfs.semanticscholar.org/4bd3/f187f3e09483b1f0f92150a4a77409691b0f.pdf,Learning Efficient Object Detection Models with Knowledge Distillation,2017
30,China,VOC,voc,39.966244,116.3270039,Intel Labs China,company,19d4855f064f0d53cb851e9342025bd8503922e2,citation,http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/4989d468.pdf,Learning SURF Cascade for Fast and Accurate Object Detection,2013
31,China,VOC,voc,23.09461185,113.28788994,Sun Yat-Sen University,edu,ee098ed493af3abe873ce89354599e1f6bdf65be,citation,https://arxiv.org/pdf/1702.05839.pdf,Progressively Diffused Networks for Semantic Image Segmentation,2017
32,China,VOC,voc,22.4162632,114.2109318,Chinese University of Hong Kong,edu,ee098ed493af3abe873ce89354599e1f6bdf65be,citation,https://arxiv.org/pdf/1702.05839.pdf,Progressively Diffused Networks for Semantic Image Segmentation,2017
33,China,VOC,voc,39.993008,116.329882,SenseTime,company,ee098ed493af3abe873ce89354599e1f6bdf65be,citation,https://arxiv.org/pdf/1702.05839.pdf,Progressively Diffused Networks for Semantic Image Segmentation,2017
34,United States,VOC,voc,37.4092265,-122.0236615,Baidu,company,99f95595c45bd7a4fe2cffff07850754955e5e2a,citation,https://nicsefc.ee.tsinghua.edu.cn/media/publications/2015/IEEE%20TCAD_170.pdf,RRAM-Based Analog Approximate Computing,2015
35,United States,VOC,voc,40.44415295,-79.96243993,University of Pittsburgh,edu,99f95595c45bd7a4fe2cffff07850754955e5e2a,citation,https://nicsefc.ee.tsinghua.edu.cn/media/publications/2015/IEEE%20TCAD_170.pdf,RRAM-Based Analog Approximate Computing,2015
36,China,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,99f95595c45bd7a4fe2cffff07850754955e5e2a,citation,https://nicsefc.ee.tsinghua.edu.cn/media/publications/2015/IEEE%20TCAD_170.pdf,RRAM-Based Analog Approximate Computing,2015
37,United States,VOC,voc,33.7756178,-84.396285,Georgia Tech,edu,5a0209515ab62e008efeca31f80fa0a97031cd9d,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/3B_046.pdf,Dataset fingerprints: Exploring image collections through data mining,2015
38,United States,VOC,voc,40.4441619,-79.94272826,Carnegie Mellon University,edu,2c953b06c1c312e36f1fdb9919567b42c9322384,citation,http://people.csail.mit.edu/tomasz/papers/malisiewicz_iccv11.pdf,Ensemble of exemplar-SVMs for object detection and beyond,2011
39,China,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,5907ca4b91c8e8d846871e045bce9a4ca851053a,citation,http://eiger.ddns.comp.nus.edu.sg/pubs/fusionofmultichannelstructures-tip2014.pdf,Fusion of Multichannel Local and Global Structural Cues for Photo Aesthetics Evaluation,2014
40,United States,VOC,voc,29.58333105,-98.61944505,University of Texas at San Antonio,edu,5907ca4b91c8e8d846871e045bce9a4ca851053a,citation,http://eiger.ddns.comp.nus.edu.sg/pubs/fusionofmultichannelstructures-tip2014.pdf,Fusion of Multichannel Local and Global Structural Cues for Photo Aesthetics Evaluation,2014
41,Singapore,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,5907ca4b91c8e8d846871e045bce9a4ca851053a,citation,http://eiger.ddns.comp.nus.edu.sg/pubs/fusionofmultichannelstructures-tip2014.pdf,Fusion of Multichannel Local and Global Structural Cues for Photo Aesthetics Evaluation,2014
42,China,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,5907ca4b91c8e8d846871e045bce9a4ca851053a,citation,http://eiger.ddns.comp.nus.edu.sg/pubs/fusionofmultichannelstructures-tip2014.pdf,Fusion of Multichannel Local and Global Structural Cues for Photo Aesthetics Evaluation,2014
43,China,VOC,voc,22.4162632,114.2109318,Chinese University of Hong Kong,edu,931282732f0be57f7fb895238e94bdda00a52cad,citation,https://pdfs.semanticscholar.org/9312/82732f0be57f7fb895238e94bdda00a52cad.pdf,Gated Bi-directional CNN for Object Detection,2016
44,China,VOC,voc,39.993008,116.329882,SenseTime,company,931282732f0be57f7fb895238e94bdda00a52cad,citation,https://pdfs.semanticscholar.org/9312/82732f0be57f7fb895238e94bdda00a52cad.pdf,Gated Bi-directional CNN for Object Detection,2016
45,Germany,VOC,voc,48.7468939,9.0805141,Max Planck Institute for Intelligent Systems,edu,cfa48bc1015b88809e362b4da19fe4459acb1d89,citation,https://pdfs.semanticscholar.org/cfa4/8bc1015b88809e362b4da19fe4459acb1d89.pdf,Learning to Filter Object Detections,2017
46,United States,VOC,voc,47.6423318,-122.1369302,Microsoft,company,cfa48bc1015b88809e362b4da19fe4459acb1d89,citation,https://pdfs.semanticscholar.org/cfa4/8bc1015b88809e362b4da19fe4459acb1d89.pdf,Learning to Filter Object Detections,2017
47,United States,VOC,voc,40.34829285,-74.66308325,Princeton University,edu,420c46d7cafcb841309f02ad04cf51cb1f190a48,citation,https://arxiv.org/pdf/1511.07122.pdf,Multi-Scale Context Aggregation by Dilated Convolutions,2015
48,United States,VOC,voc,40.4439789,-79.9464634,Intel Labs,company,420c46d7cafcb841309f02ad04cf51cb1f190a48,citation,https://arxiv.org/pdf/1511.07122.pdf,Multi-Scale Context Aggregation by Dilated Convolutions,2015
49,France,VOC,voc,48.708759,2.164006,"Center for Visual Computing, École Centrale Paris, France",edu,2603a85b305d041bf749934fe538315ecbc300c2,citation,http://www.ee.oulu.fi/~jkannala/publications/scia2013a.pdf,Non Maximal Suppression in Cascaded Ranking Models,2013
50,France,VOC,voc,48.840579,2.586968,"LIGM (UMR CNRS), École des Ponts ParisTech, Université Paris-Est, France",edu,2603a85b305d041bf749934fe538315ecbc300c2,citation,http://www.ee.oulu.fi/~jkannala/publications/scia2013a.pdf,Non Maximal Suppression in Cascaded Ranking Models,2013
51,Finland,VOC,voc,65.0592157,25.46632601,University of Oulu,edu,2603a85b305d041bf749934fe538315ecbc300c2,citation,http://www.ee.oulu.fi/~jkannala/publications/scia2013a.pdf,Non Maximal Suppression in Cascaded Ranking Models,2013
52,France,VOC,voc,48.7146403,2.2056539,"Équipe Galen, INRIA Saclay, Île-de-France, France",edu,2603a85b305d041bf749934fe538315ecbc300c2,citation,http://www.ee.oulu.fi/~jkannala/publications/scia2013a.pdf,Non Maximal Suppression in Cascaded Ranking Models,2013
53,United States,VOC,voc,42.3354481,-71.16813864,Boston College,edu,18ccd8bd64b50c1b6a83a71792fd808da7076bc9,citation,http://ttic.uchicago.edu/~mmaire/papers/pdf/seg_obj_iccv2011.pdf,Object detection and segmentation from joint embedding of parts and pixels,2011
54,United States,VOC,voc,34.13710185,-118.12527487,California Institute of Technology,edu,18ccd8bd64b50c1b6a83a71792fd808da7076bc9,citation,http://ttic.uchicago.edu/~mmaire/papers/pdf/seg_obj_iccv2011.pdf,Object detection and segmentation from joint embedding of parts and pixels,2011
55,Japan,VOC,voc,34.7275714,135.2371,Kobe University,edu,75d0a8e80a75312571951144aaa2d5dd5ae30e43,citation,http://eprints.whiterose.ac.uk/132227/1/TMM_camera_ready.pdf,Polar Transformation on Image Features for Orientation-Invariant Representations,2019
56,China,VOC,voc,26.0252776,119.2117845,Fujian Normal University,edu,75d0a8e80a75312571951144aaa2d5dd5ae30e43,citation,http://eprints.whiterose.ac.uk/132227/1/TMM_camera_ready.pdf,Polar Transformation on Image Features for Orientation-Invariant Representations,2019
57,United Kingdom,VOC,voc,53.94540365,-1.03138878,University of York,edu,75d0a8e80a75312571951144aaa2d5dd5ae30e43,citation,http://eprints.whiterose.ac.uk/132227/1/TMM_camera_ready.pdf,Polar Transformation on Image Features for Orientation-Invariant Representations,2019
58,China,VOC,voc,24.4399419,118.09301781,Xiamen University,edu,75d0a8e80a75312571951144aaa2d5dd5ae30e43,citation,http://eprints.whiterose.ac.uk/132227/1/TMM_camera_ready.pdf,Polar Transformation on Image Features for Orientation-Invariant Representations,2019
59,United Kingdom,VOC,voc,51.5247272,-0.03931035,Queen Mary University of London,edu,b1045a2de35d0adf784353f90972118bc1162f8d,citation,http://eecs.qmul.ac.uk/~jason/Research/PreprintVersion/Quantifying%20and%20Transferring%20Contextual%20Information%20in%20Object%20Detection.pdf,Quantifying and Transferring Contextual Information in Object Detection,2012
60,China,VOC,voc,23.09461185,113.28788994,Sun Yat-Sen University,edu,b1045a2de35d0adf784353f90972118bc1162f8d,citation,http://eecs.qmul.ac.uk/~jason/Research/PreprintVersion/Quantifying%20and%20Transferring%20Contextual%20Information%20in%20Object%20Detection.pdf,Quantifying and Transferring Contextual Information in Object Detection,2012
61,China,VOC,voc,23.09461185,113.28788994,Sun Yat-Sen University,edu,ab781f035720d991e244adb35f1d04e671af1999,citation,https://arxiv.org/pdf/1712.07465.pdf,Recurrent Attentional Reinforcement Learning for Multi-Label Image Recognition,2018
62,China,VOC,voc,39.993008,116.329882,SenseTime,company,ab781f035720d991e244adb35f1d04e671af1999,citation,https://arxiv.org/pdf/1712.07465.pdf,Recurrent Attentional Reinforcement Learning for Multi-Label Image Recognition,2018
63,Canada,VOC,voc,43.66333345,-79.39769975,University of Toronto,edu,1bb0dd8d349cdb1bbc065f1f0e111a8334072257,citation,http://jmlr.csail.mit.edu/proceedings/papers/v22/tarlow12a/tarlow12a.pdf,Structured Output Learning with High Order Loss Functions,2012
64,United States,VOC,voc,41.7846982,-87.5925848,Toyota Technological Institute at Chicago,company,3a4c70ca0bbd461fe2e4de3448a01f06c0217459,citation,https://arxiv.org/pdf/1510.09171.pdf,Accurate Vision-based Vehicle Localization using Satellite Imagery,2015
65,Netherlands,VOC,voc,52.3553655,4.9501644,University of Amsterdam,edu,26c58e24687ccbe9737e41837aab74e4a499d259,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Li_Codemaps_-_Segment_2013_ICCV_paper.pdf,"Codemaps - Segment, Classify and Search Objects Locally",2013
66,Netherlands,VOC,voc,52.356678,4.95187,"Centrum Wiskunde & Informatica, Amsterdam, The Netherlands",edu,26c58e24687ccbe9737e41837aab74e4a499d259,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Li_Codemaps_-_Segment_2013_ICCV_paper.pdf,"Codemaps - Segment, Classify and Search Objects Locally",2013
67,United States,VOC,voc,47.6423318,-122.1369302,Microsoft,company,c9abf6cb2d916262425033db12cf0181d40be7cb,citation,https://pdfs.semanticscholar.org/c9ab/f6cb2d916262425033db12cf0181d40be7cb.pdf,Entropy-based Latent Structured Output Prediction-Supplementary materials,2015
68,China,VOC,voc,31.83907195,117.26420748,University of Science and Technology of China,edu,ce43209fc68e51ef05fa06cc0fe6210cbd021e3f,citation,http://min.sjtu.edu.cn/files%5Cpapers%5C2016%5CJournal%5C2016-TIP-CV-ZHANGXIAOPENG%5C2016-TIP-CV-02.pdf,Fused One-vs-All Features With Semantic Alignments for Fine-Grained Visual Categorization,2016
69,United States,VOC,voc,29.58333105,-98.61944505,University of Texas at San Antonio,edu,ce43209fc68e51ef05fa06cc0fe6210cbd021e3f,citation,http://min.sjtu.edu.cn/files%5Cpapers%5C2016%5CJournal%5C2016-TIP-CV-ZHANGXIAOPENG%5C2016-TIP-CV-02.pdf,Fused One-vs-All Features With Semantic Alignments for Fine-Grained Visual Categorization,2016
70,China,VOC,voc,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,ce43209fc68e51ef05fa06cc0fe6210cbd021e3f,citation,http://min.sjtu.edu.cn/files%5Cpapers%5C2016%5CJournal%5C2016-TIP-CV-ZHANGXIAOPENG%5C2016-TIP-CV-02.pdf,Fused One-vs-All Features With Semantic Alignments for Fine-Grained Visual Categorization,2016
71,United Kingdom,VOC,voc,51.7555205,-1.2261597,Oxford Brookes University,edu,70d71c2f8c865438c0158bed9f7d64e57e245535,citation,http://cms.brookes.ac.uk/research/visiongroup/publications/2013/intr_obj_vrt_nips13.pdf,"Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation",2013
72,United Kingdom,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,70d71c2f8c865438c0158bed9f7d64e57e245535,citation,http://cms.brookes.ac.uk/research/visiongroup/publications/2013/intr_obj_vrt_nips13.pdf,"Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation",2013
73,China,VOC,voc,34.2469152,108.91061982,Northwestern Polytechnical University,edu,50953b9a15aca6ef3351e613e7215abdcae1435e,citation,http://sunw.csail.mit.edu/papers/63_Cheng_SUNw.pdf,Learning coarse-to-fine sparselets for efficient object detection and scene classification,2015
74,Thailand,VOC,voc,13.65450525,100.49423171,Robotics Institute,edu,d6d7dcdcf66fe83e49d175cd9d8ac0b507d0e9d8,citation,http://dhoiem.cs.illinois.edu/publications/ijcv2010_occlusion.pdf,Recovering Occlusion Boundaries from an Image,2010
75,United States,VOC,voc,40.4441619,-79.94272826,Carnegie Mellon University,edu,d6d7dcdcf66fe83e49d175cd9d8ac0b507d0e9d8,citation,http://dhoiem.cs.illinois.edu/publications/ijcv2010_occlusion.pdf,Recovering Occlusion Boundaries from an Image,2010
76,United States,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,d6d7dcdcf66fe83e49d175cd9d8ac0b507d0e9d8,citation,http://dhoiem.cs.illinois.edu/publications/ijcv2010_occlusion.pdf,Recovering Occlusion Boundaries from an Image,2010
77,China,VOC,voc,28.727339,115.816633,Jiangxi University of Finance and Economics,edu,1642358cd9410abe9ee512d34ba68296b308770e,citation,https://arxiv.org/pdf/1807.04562.pdf,Robustness Analysis of Pedestrian Detectors for Surveillance,2018
78,Singapore,VOC,voc,1.3484104,103.68297965,Nanyang Technological University,edu,1642358cd9410abe9ee512d34ba68296b308770e,citation,https://arxiv.org/pdf/1807.04562.pdf,Robustness Analysis of Pedestrian Detectors for Surveillance,2018
79,China,VOC,voc,34.250803,108.983693,Xi’an Jiaotong University,edu,1642358cd9410abe9ee512d34ba68296b308770e,citation,https://arxiv.org/pdf/1807.04562.pdf,Robustness Analysis of Pedestrian Detectors for Surveillance,2018
80,Netherlands,VOC,voc,52.3553655,4.9501644,University of Amsterdam,edu,25d7da85858a4d89b7de84fd94f0c0a51a9fc67a,citation,http://graphics.cs.cmu.edu/courses/16-824/2016_spring/slides/seg_3.pdf,Selective Search for Object Recognition,2013
81,Italy,VOC,voc,46.0658836,11.1159894,University of Trento,edu,25d7da85858a4d89b7de84fd94f0c0a51a9fc67a,citation,http://graphics.cs.cmu.edu/courses/16-824/2016_spring/slides/seg_3.pdf,Selective Search for Object Recognition,2013
82,United States,VOC,voc,37.4219999,-122.0840575,Google,company,0690ba31424310a90028533218d0afd25a829c8d,citation,https://arxiv.org/pdf/1412.7062.pdf,Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs,2015
83,Germany,VOC,voc,53.8338371,10.7035939,Institute of Systems and Robotics,edu,7fb8d9c36c23f274f2dd84945dd32ec2cc143de1,citation,http://home.isr.uc.pt/~joaoluis/papers/eccv2012.pdf,Semantic segmentation with second-order pooling,2012
84,Germany,VOC,voc,50.7338124,7.1022465,University of Bonn,edu,7fb8d9c36c23f274f2dd84945dd32ec2cc143de1,citation,http://home.isr.uc.pt/~joaoluis/papers/eccv2012.pdf,Semantic segmentation with second-order pooling,2012
85,United Kingdom,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,4682fee7dc045aea7177d7f3bfe344aabf153bd5,citation,http://cs.brown.edu/~ls/teaching_CMU_16-824/slides_tz-1.pdf,Tabula rasa: Model transfer for object category detection,2011
86,United States,VOC,voc,42.3614256,-71.0812092,Microsoft Research Asia,company,35f345ebe3831e4741dcdc1931da59043acf4b83,citation,https://pdfs.semanticscholar.org/35f3/45ebe3831e4741dcdc1931da59043acf4b83.pdf,Towards High Performance Video Object Detection for Mobiles 3 2 Revisiting Video Object Detection Baseline,2018
87,Canada,VOC,voc,49.8091536,-97.13304179,University of Manitoba,edu,488fff23542ff397cdb1ced64db2c96320afc560,citation,http://www.cs.umanitoba.ca/~ywang/papers/cvpr15.pdf,Weakly supervised localization of novel objects using appearance transfer,2015
88,United States,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,032bde9da87439c781a6c81ba7933985ed95d88e,citation,https://arxiv.org/pdf/1506.02106.pdf,What's the point: Semantic segmentation with point supervision,2016
89,United States,VOC,voc,40.4441619,-79.94272826,Carnegie Mellon University,edu,032bde9da87439c781a6c81ba7933985ed95d88e,citation,https://arxiv.org/pdf/1506.02106.pdf,What's the point: Semantic segmentation with point supervision,2016
90,United Kingdom,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,032bde9da87439c781a6c81ba7933985ed95d88e,citation,https://arxiv.org/pdf/1506.02106.pdf,What's the point: Semantic segmentation with point supervision,2016
91,Australia,VOC,voc,-42.902631,147.3273381,University of Tasmania,edu,c2a2093b4163616b83398e503ae9ed948f4f6a2b,citation,http://mima.sdu.edu.cn/(X(1)S(ar3myg55nqom1l55ttix5kjj))/Images/publication/Dual-CNN-ML.pdf,A Dual-CNN Model for Multi-label Classification by Leveraging Co-occurrence Dependencies Between Labels,2017
92,China,VOC,voc,36.3693473,120.673818,Shandong University,edu,c2a2093b4163616b83398e503ae9ed948f4f6a2b,citation,http://mima.sdu.edu.cn/(X(1)S(ar3myg55nqom1l55ttix5kjj))/Images/publication/Dual-CNN-ML.pdf,A Dual-CNN Model for Multi-label Classification by Leveraging Co-occurrence Dependencies Between Labels,2017
93,United States,VOC,voc,34.068921,-118.4451811,UCLA,edu,c4fc07072d7ebfbca471d2394b20199d8107e517,citation,https://pdfs.semanticscholar.org/c4fc/07072d7ebfbca471d2394b20199d8107e517.pdf,Active Mask Hierarchies for Object Detection,2010
94,United States,VOC,voc,42.3583961,-71.09567788,MIT,edu,c4fc07072d7ebfbca471d2394b20199d8107e517,citation,https://pdfs.semanticscholar.org/c4fc/07072d7ebfbca471d2394b20199d8107e517.pdf,Active Mask Hierarchies for Object Detection,2010
95,China,VOC,voc,38.88140235,121.52281098,Dalian University of Technology,edu,39afeceb57a7fde266ddd842aa23d2eea7ad5665,citation,https://arxiv.org/pdf/1802.06960.pdf,Agile Amulet: Real-Time Salient Object Detection with Contextual Attention,2018
96,Australia,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,39afeceb57a7fde266ddd842aa23d2eea7ad5665,citation,https://arxiv.org/pdf/1802.06960.pdf,Agile Amulet: Real-Time Salient Object Detection with Contextual Attention,2018
97,United States,VOC,voc,42.3583961,-71.09567788,MIT,edu,732e4016225280b485c557a119ec50cffb8fee98,citation,https://arxiv.org/pdf/1311.6510.pdf,Are all training examples equally valuable?,2013
98,Spain,VOC,voc,41.40657415,2.1945341,Universitat Oberta de Catalunya,edu,732e4016225280b485c557a119ec50cffb8fee98,citation,https://arxiv.org/pdf/1311.6510.pdf,Are all training examples equally valuable?,2013
99,United States,VOC,voc,39.2899685,-76.62196103,University of Maryland,edu,38b4ac4a0802fdb63dea6769dd1aee075cc3f87d,citation,https://arxiv.org/pdf/1712.08675.pdf,Boundary-sensitive Network for Portrait Segmentation,2017
100,United States,VOC,voc,37.4019735,-122.0477876,Samsung Research America,edu,38b4ac4a0802fdb63dea6769dd1aee075cc3f87d,citation,https://arxiv.org/pdf/1712.08675.pdf,Boundary-sensitive Network for Portrait Segmentation,2017
101,Switzerland,VOC,voc,47.3764534,8.54770931,ETH Zürich,edu,10f13579084670291019c6e8ef55f5cd35c926b6,citation,https://pdfs.semanticscholar.org/7088/0e0ba2478c7250918ee9b7accc6993d13ba4.pdf,Closed-Form Approximate CRF Training for Scalable Image Segmentation,2014
102,United Kingdom,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,10f13579084670291019c6e8ef55f5cd35c926b6,citation,https://pdfs.semanticscholar.org/7088/0e0ba2478c7250918ee9b7accc6993d13ba4.pdf,Closed-Form Approximate CRF Training for Scalable Image Segmentation,2014
103,Singapore,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,5250f319cae32437489bb97b2ed9a1dc962d4d39,citation,https://arxiv.org/pdf/1411.2861.pdf,Computational Baby Learning.,2014
104,China,VOC,voc,39.94976005,116.33629046,Beijing Jiaotong University,edu,5250f319cae32437489bb97b2ed9a1dc962d4d39,citation,https://arxiv.org/pdf/1411.2861.pdf,Computational Baby Learning.,2014
105,Switzerland,VOC,voc,46.5190557,6.5667576,"EPFL, Lausanne (Switzerland)",edu,7b8ace072475a9a42d6ceb293c8b4a8c9b573284,citation,http://www.vision.ee.ethz.ch/en/publications/papers/proceedings/eth_biwi_00855.pdf,Conditional Random Fields for multi-camera object detection,2011
106,Switzerland,VOC,voc,47.376313,8.5476699,"ETHZ, Zurich (Switzerland)",edu,7b8ace072475a9a42d6ceb293c8b4a8c9b573284,citation,http://www.vision.ee.ethz.ch/en/publications/papers/proceedings/eth_biwi_00855.pdf,Conditional Random Fields for multi-camera object detection,2011
107,United States,VOC,voc,37.2283843,-80.4234167,Virginia Tech,edu,3d0660e18c17db305b9764bb86b21a429241309e,citation,https://arxiv.org/pdf/1604.03505.pdf,Counting Everyday Objects in Everyday Scenes,2017
108,United States,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,3d0660e18c17db305b9764bb86b21a429241309e,citation,https://arxiv.org/pdf/1604.03505.pdf,Counting Everyday Objects in Everyday Scenes,2017
109,United States,VOC,voc,37.3239177,-122.0129693,"NEC Labs, Cupertino, CA",company,8f76401847d3e3f0331bab24b17f76953be66220,citation,http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2010_1077.pdf,Deep Coding Network,2010
110,United States,VOC,voc,40.47913175,-74.43168868,Rutgers University,edu,8f76401847d3e3f0331bab24b17f76953be66220,citation,http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2010_1077.pdf,Deep Coding Network,2010
111,China,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,fe7ae13bf5fc80cf0837bacbe44905bd8749f03f,citation,http://ivg.au.tsinghua.edu.cn/paper/2017_Deep%20coupled%20metric%20learning%20for%20cross-modal%20matching.pdf,Deep Coupled Metric Learning for Cross-Modal Matching,2017
112,Singapore,VOC,voc,1.3484104,103.68297965,Nanyang Technological University,edu,fe7ae13bf5fc80cf0837bacbe44905bd8749f03f,citation,http://ivg.au.tsinghua.edu.cn/paper/2017_Deep%20coupled%20metric%20learning%20for%20cross-modal%20matching.pdf,Deep Coupled Metric Learning for Cross-Modal Matching,2017
113,Canada,VOC,voc,43.7743911,-79.50481085,York University,edu,cdeee5eed68e7c8eb06185f7fcb1a072af784886,citation,https://arxiv.org/pdf/1505.01173.pdf,Deep Learning for Object Saliency Detection and Image Segmentation,2015
114,United States,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,cdeee5eed68e7c8eb06185f7fcb1a072af784886,citation,https://arxiv.org/pdf/1505.01173.pdf,Deep Learning for Object Saliency Detection and Image Segmentation,2015
115,Canada,VOC,voc,49.8091536,-97.13304179,University of Manitoba,edu,64b9675e924974fdec78a7272b27c7e7ec63a608,citation,http://www.cs.umanitoba.ca/~ywang/papers/icip17.pdf,Depth-aware object instance segmentation,2017
116,China,VOC,voc,31.32235655,121.38400941,Shanghai University,edu,64b9675e924974fdec78a7272b27c7e7ec63a608,citation,http://www.cs.umanitoba.ca/~ywang/papers/icip17.pdf,Depth-aware object instance segmentation,2017
117,Thailand,VOC,voc,13.65450525,100.49423171,Robotics Institute,edu,7d520f474f2fc59422d910b980f8485716ce0a3e,citation,https://pdfs.semanticscholar.org/2128/4a9310a4b4c836b8dfb6af39c682b7348128.pdf,Designing Convolutional Neural Networks for Urban Scene Understanding,2017
118,United States,VOC,voc,40.4441619,-79.94272826,Carnegie Mellon University,edu,7d520f474f2fc59422d910b980f8485716ce0a3e,citation,https://pdfs.semanticscholar.org/2128/4a9310a4b4c836b8dfb6af39c682b7348128.pdf,Designing Convolutional Neural Networks for Urban Scene Understanding,2017
119,India,VOC,voc,17.4450981,78.3497678,IIIT Hyderabad,edu,f23114073e0e513b1c1c55e8777bda503721718c,citation,https://arxiv.org/pdf/1811.10016.pdf,Dissimilarity Coefficient based Weakly Supervised Object Detection,2018
120,United Kingdom,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,f23114073e0e513b1c1c55e8777bda503721718c,citation,https://arxiv.org/pdf/1811.10016.pdf,Dissimilarity Coefficient based Weakly Supervised Object Detection,2018
121,United States,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,280d632ef3234c5ab06018c6eaccead75bc173b3,citation,http://ai.stanford.edu/~ajoulin/article/eccv14-vidcoloc.pdf,Efficient Image and Video Co-localization with Frank-Wolfe Algorithm,2014
122,United States,VOC,voc,37.3239177,-122.0129693,NEC,company,44a3ee0429a6d1b79d431b4d396962175c28ace6,citation,http://openaccess.thecvf.com/content_cvpr_2016/papers/Yang_Exploit_All_the_CVPR_2016_paper.pdf,Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers,2016
123,United States,VOC,voc,38.99203005,-76.9461029,University of Maryland College Park,edu,44a3ee0429a6d1b79d431b4d396962175c28ace6,citation,http://openaccess.thecvf.com/content_cvpr_2016/papers/Yang_Exploit_All_the_CVPR_2016_paper.pdf,Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers,2016
124,United States,VOC,voc,34.13710185,-118.12527487,California Institute of Technology,edu,1a54a8b0c7b3fc5a21c6d33656690585c46ca08b,citation,http://authors.library.caltech.edu/49239/7/DollarPAMI14pyramids_0.pdf,Fast Feature Pyramids for Object Detection,2014
125,United States,VOC,voc,42.4505507,-76.4783513,Cornell University,edu,1a54a8b0c7b3fc5a21c6d33656690585c46ca08b,citation,http://authors.library.caltech.edu/49239/7/DollarPAMI14pyramids_0.pdf,Fast Feature Pyramids for Object Detection,2014
126,United States,VOC,voc,47.6418392,-122.1407465,"Microsoft Research Redmond, Redmond, USA",company,1a54a8b0c7b3fc5a21c6d33656690585c46ca08b,citation,http://authors.library.caltech.edu/49239/7/DollarPAMI14pyramids_0.pdf,Fast Feature Pyramids for Object Detection,2014
127,Singapore,VOC,voc,1.29500195,103.84909214,Singapore Management University,edu,742d5b4590284b632ca043a16507fb5a459dceb2,citation,https://arxiv.org/pdf/1712.00721.pdf,Feature Agglomeration Networks for Single Stage Face Detection,2017
128,China,VOC,voc,30.19331415,120.11930822,Zhejiang University,edu,742d5b4590284b632ca043a16507fb5a459dceb2,citation,https://arxiv.org/pdf/1712.00721.pdf,Feature Agglomeration Networks for Single Stage Face Detection,2017
129,United States,VOC,voc,42.2745754,-71.8062724,Worcester Polytechnic Institute,edu,bd433d471af50b571d7284afb5ee435654ace99f,citation,https://pdfs.semanticscholar.org/bd43/3d471af50b571d7284afb5ee435654ace99f.pdf,Going Deeper with Convolutional Neural Network for Intelligent Transportation,2016
130,United States,VOC,voc,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,bd433d471af50b571d7284afb5ee435654ace99f,citation,https://pdfs.semanticscholar.org/bd43/3d471af50b571d7284afb5ee435654ace99f.pdf,Going Deeper with Convolutional Neural Network for Intelligent Transportation,2016
131,Israel,VOC,voc,32.76162915,35.01986304,University of Haifa,edu,fe683e48f373fa14c07851966474d15588b8c28b,citation,https://pdfs.semanticscholar.org/fe68/3e48f373fa14c07851966474d15588b8c28b.pdf,Hinge-Minimax Learner for the Ensemble of Hyperplanes,2018
132,Israel,VOC,voc,32.7767783,35.0231271,Technion - Israel Institute of Technology,edu,fe683e48f373fa14c07851966474d15588b8c28b,citation,https://pdfs.semanticscholar.org/fe68/3e48f373fa14c07851966474d15588b8c28b.pdf,Hinge-Minimax Learner for the Ensemble of Hyperplanes,2018
133,United States,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,4e65c9f0a64b6a4333b12e2adc3861ad75aca83b,citation,https://pdfs.semanticscholar.org/4e65/c9f0a64b6a4333b12e2adc3861ad75aca83b.pdf,Image Classification Using Super-Vector Coding of Local Image Descriptors,2010
134,United States,VOC,voc,40.47913175,-74.43168868,Rutgers University,edu,4e65c9f0a64b6a4333b12e2adc3861ad75aca83b,citation,https://pdfs.semanticscholar.org/4e65/c9f0a64b6a4333b12e2adc3861ad75aca83b.pdf,Image Classification Using Super-Vector Coding of Local Image Descriptors,2010
135,United States,VOC,voc,41.7847112,-87.59260567,"Toyota Technological Institute, Chicago",edu,a1f33473ea3b8e98fee37e32ecbecabc379e07a0,citation,http://cs.brown.edu/people/ren/publications/cvpr2013/cascade_final.pdf,Image Segmentation by Cascaded Region Agglomeration,2013
136,China,VOC,voc,30.19331415,120.11930822,Zhejiang University,edu,a1f33473ea3b8e98fee37e32ecbecabc379e07a0,citation,http://cs.brown.edu/people/ren/publications/cvpr2013/cascade_final.pdf,Image Segmentation by Cascaded Region Agglomeration,2013
137,Canada,VOC,voc,49.8091536,-97.13304179,University of Manitoba,edu,3b60af814574ebe389856e9f7008bb83b0539abc,citation,https://arxiv.org/pdf/1703.00551.pdf,Label Refinement Network for Coarse-to-Fine Semantic Segmentation.,2017
138,United States,VOC,voc,39.86948105,-84.87956905,Indiana University,edu,3b60af814574ebe389856e9f7008bb83b0539abc,citation,https://arxiv.org/pdf/1703.00551.pdf,Label Refinement Network for Coarse-to-Fine Semantic Segmentation.,2017
139,United States,VOC,voc,47.6543238,-122.30800894,University of Washington,edu,214f552070a7eb5ef5efe0d6ffeaaa594a3c3535,citation,http://allenai.org/content/publications/objectNgrams_cvpr14.pdf,Learning Everything about Anything: Webly-Supervised Visual Concept Learning,2014
140,Germany,VOC,voc,48.14955455,11.56775314,Technical University Munich,edu,472541ccd941b9b4c52e1f088cc1152de9b3430f,citation,https://arxiv.org/pdf/1612.00197.pdf,Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses,2017
141,United States,VOC,voc,39.3299013,-76.6205177,Johns Hopkins University,edu,472541ccd941b9b4c52e1f088cc1152de9b3430f,citation,https://arxiv.org/pdf/1612.00197.pdf,Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses,2017
142,United States,VOC,voc,40.11571585,-88.22750772,Beckman Institute,edu,0bbb40e5b9e546a3f4e7340b2980059065c99203,citation,https://arxiv.org/pdf/1712.00886.pdf,Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids,2017
143,China,VOC,voc,31.30104395,121.50045497,Fudan University,edu,0bbb40e5b9e546a3f4e7340b2980059065c99203,citation,https://arxiv.org/pdf/1712.00886.pdf,Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids,2017