diff options
| author | jules@lens <julescarbon@gmail.com> | 2019-04-18 16:55:14 +0200 |
|---|---|---|
| committer | jules@lens <julescarbon@gmail.com> | 2019-04-18 16:55:14 +0200 |
| commit | 2e4daed06264f3dc3bbabd8fa4fc0d8ceed4c5af (patch) | |
| tree | 1a17bb4459776ac91f7006a2a407ca12edd3471e /site/datasets | |
| parent | 3d32e5b4ddbfbfe5d4abeda57ff200adf1532f4c (diff) | |
| parent | f8012f88641b0bb378ba79393f277c8918ebe452 (diff) | |
Merge branch 'master' of asdf.us:megapixels_dev
Diffstat (limited to 'site/datasets')
101 files changed, 813 insertions, 0 deletions
diff --git a/site/datasets/verified/50_people_one_question.csv b/site/datasets/verified/50_people_one_question.csv new file mode 100644 index 00000000..ab3b8956 --- /dev/null +++ b/site/datasets/verified/50_people_one_question.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,50 People One Question,50_people_one_question,0.0,0.0,,,,main,,Merging Pose Estimates Across Space and Time,2013 diff --git a/site/datasets/verified/adience.csv b/site/datasets/verified/adience.csv new file mode 100644 index 00000000..deadc399 --- /dev/null +++ b/site/datasets/verified/adience.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,Adience,adience,0.0,0.0,,,,main,,Age and Gender Estimation of Unfiltered Faces,2014 diff --git a/site/datasets/verified/afad.csv b/site/datasets/verified/afad.csv new file mode 100644 index 00000000..b67ff97a --- /dev/null +++ b/site/datasets/verified/afad.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,AFAD,afad,0.0,0.0,,,,main,,Ordinal Regression with Multiple Output CNN for Age Estimation,2016 diff --git a/site/datasets/verified/afw.csv b/site/datasets/verified/afw.csv new file mode 100644 index 00000000..b17652e3 --- /dev/null +++ b/site/datasets/verified/afw.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,AFW,afw,0.0,0.0,,,,main,,"Face detection, pose estimation, and landmark localization in the wild",2012 diff --git a/site/datasets/verified/agedb.csv b/site/datasets/verified/agedb.csv new file mode 100644 index 00000000..ad90a985 --- /dev/null +++ b/site/datasets/verified/agedb.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,AgeDB,agedb,0.0,0.0,,,,main,,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 diff --git a/site/datasets/verified/alert_airport.csv b/site/datasets/verified/alert_airport.csv new file mode 100644 index 00000000..6fa30c1f --- /dev/null +++ b/site/datasets/verified/alert_airport.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,ALERT Airport,alert_airport,0.0,0.0,,,,main,,"A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets",2018 diff --git a/site/datasets/verified/appa_real.csv b/site/datasets/verified/appa_real.csv new file mode 100644 index 00000000..a877dd82 --- /dev/null +++ b/site/datasets/verified/appa_real.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,APPA-REAL,appa_real,0.0,0.0,,,,main,,Apparent and Real Age Estimation in Still Images with Deep Residual Regressors on Appa-Real Database,2017 diff --git a/site/datasets/verified/awe_ears.csv b/site/datasets/verified/awe_ears.csv new file mode 100644 index 00000000..1959b962 --- /dev/null +++ b/site/datasets/verified/awe_ears.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,AWE Ears,awe_ears,0.0,0.0,,,,main,,Ear Recognition: More Than a Survey,2017 diff --git a/site/datasets/verified/bbc_pose.csv b/site/datasets/verified/bbc_pose.csv new file mode 100644 index 00000000..5926c85a --- /dev/null +++ b/site/datasets/verified/bbc_pose.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,BBC Pose,bbc_pose,0.0,0.0,,,,main,,Automatic and Efficient Human Pose Estimation for Sign Language Videos,2013 diff --git a/site/datasets/verified/bpad.csv b/site/datasets/verified/bpad.csv new file mode 100644 index 00000000..bdf32861 --- /dev/null +++ b/site/datasets/verified/bpad.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,BPAD,bpad,0.0,0.0,,,,main,,Describing people: A poselet-based approach to attribute classification,2011 diff --git a/site/datasets/verified/brainwash.csv b/site/datasets/verified/brainwash.csv new file mode 100644 index 00000000..628ca090 --- /dev/null +++ b/site/datasets/verified/brainwash.csv @@ -0,0 +1,5 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,Brainwash,brainwash,0.0,0.0,,,,main,,End-to-End People Detection in Crowded Scenes,2016 +1,China,Brainwash,brainwash,39.9922379,116.30393816,Peking University,edu,7e915bb8e4ada4f8d261bc855a4f587ea97764ca,citation,,People detection in crowded scenes via regional-based convolutional network,2016 +2,China,Brainwash,brainwash,28.2290209,112.99483204,"National University of Defense Technology, China",mil,591a4bfa6380c9fcd5f3ae690e3ac5c09b7bf37b,citation,https://pdfs.semanticscholar.org/591a/4bfa6380c9fcd5f3ae690e3ac5c09b7bf37b.pdf,A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering,2017 +3,China,Brainwash,brainwash,28.2290209,112.99483204,"National University of Defense Technology, China",mil,b02d31c640b0a31fb18c4f170d841d8e21ffb66c,citation,,Localized region context and object feature fusion for people head detection,2016 diff --git a/site/datasets/verified/caltech_10k_web_faces.csv b/site/datasets/verified/caltech_10k_web_faces.csv new file mode 100644 index 00000000..c86cce93 --- /dev/null +++ b/site/datasets/verified/caltech_10k_web_faces.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,Caltech 10K Web Faces,caltech_10k_web_faces,0.0,0.0,,,,main,,Pruning training sets for learning of object categories,2005 diff --git a/site/datasets/verified/caltech_crp.csv b/site/datasets/verified/caltech_crp.csv new file mode 100644 index 00000000..d858c8a7 --- /dev/null +++ b/site/datasets/verified/caltech_crp.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,Caltech CRP,caltech_crp,0.0,0.0,,,,main,,Fine-grained classification of pedestrians in video: Benchmark and state of the art,2015 diff --git a/site/datasets/verified/casia_webface.csv b/site/datasets/verified/casia_webface.csv new file mode 100644 index 00000000..fe39fac8 --- /dev/null +++ b/site/datasets/verified/casia_webface.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,CASIA Webface,casia_webface,0.0,0.0,,,,main,,Learning Face Representation from Scratch,2014 diff --git a/site/datasets/verified/celeba.csv b/site/datasets/verified/celeba.csv new file mode 100644 index 00000000..342dcbbf --- /dev/null +++ b/site/datasets/verified/celeba.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,CelebA,celeba,0.0,0.0,,,,main,,Deep Learning Face Attributes in the Wild,2015 diff --git a/site/datasets/verified/coco.csv b/site/datasets/verified/coco.csv new file mode 100644 index 00000000..0c19b8cf --- /dev/null +++ b/site/datasets/verified/coco.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,COCO,coco,0.0,0.0,,,,main,,Microsoft COCO: Common Objects in Context,2014 diff --git a/site/datasets/verified/cofw.csv b/site/datasets/verified/cofw.csv new file mode 100644 index 00000000..7bd9e598 --- /dev/null +++ b/site/datasets/verified/cofw.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,COFW,cofw,0.0,0.0,,,,main,,Robust Face Landmark Estimation under Occlusion,2013 diff --git a/site/datasets/verified/cuhk_campus_03.csv b/site/datasets/verified/cuhk_campus_03.csv new file mode 100644 index 00000000..cdfd115d --- /dev/null +++ b/site/datasets/verified/cuhk_campus_03.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,CUHK03 Campus,cuhk_campus_03,0.0,0.0,,,,main,,Human Reidentification with Transferred Metric Learning,2012 diff --git a/site/datasets/verified/cuhk_train_station.csv b/site/datasets/verified/cuhk_train_station.csv new file mode 100644 index 00000000..675e473b --- /dev/null +++ b/site/datasets/verified/cuhk_train_station.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,CUHK Train Station Dataset,cuhk_train_station,0.0,0.0,,,,main,,Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents,2012 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 Ltd, UK",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 +93,Canada,Duke MTMC,duke_mtmc,43.4983503,-80.5478382,"Senstar Corporation, Waterloo, Canada",company,8e42568c2b3feaafd1e442e1e861ec50a4ac144f,citation,https://arxiv.org/pdf/1805.06086.pdf,An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-identification,2018 +94,Italy,Duke MTMC,duke_mtmc,45.4377672,12.321807,University Iuav of Venice,edu,eddb1a126eafecad2cead01c6c3bb4b88120d78a,citation,https://arxiv.org/pdf/1802.02181.pdf,Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition,2018 +95,China,Duke MTMC,duke_mtmc,40.00229045,116.32098908,Tsinghua University,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 +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 of Trento,edu,308a13fd1d2847d98930a8e5542f773a9651a0ae,citation,,Group Consistent Similarity Learning via Deep CRF for Person Re-identification,2018 +156,China,Duke MTMC,duke_mtmc,34.250803,108.983693,Xi’an Jiaotong University,edu,308a13fd1d2847d98930a8e5542f773a9651a0ae,citation,,Group Consistent Similarity Learning via Deep CRF for Person Re-identification,2018 +157,Turkey,Duke MTMC,duke_mtmc,41.10427915,29.02231159,Istanbul Technical University,edu,7ba225a614d77efd9bdf66bf74c80dd2da09229a,citation,,Human Semantic Parsing for Person Re-identification,2018 +158,United States,Duke MTMC,duke_mtmc,28.59899755,-81.19712501,University of Central Florida,edu,7ba225a614d77efd9bdf66bf74c80dd2da09229a,citation,,Human Semantic Parsing for Person Re-identification,2018 +159,Australia,Duke MTMC,duke_mtmc,-32.00686365,115.89691775,Curtin University,edu,292286c0024d6625fe606fb5b8a0df54ea3ffe91,citation,,Identity Adaptation for Person Re-Identification,2018 +160,United Kingdom,Duke MTMC,duke_mtmc,54.00975365,-2.78757491,Lancaster University,edu,292286c0024d6625fe606fb5b8a0df54ea3ffe91,citation,,Identity Adaptation for Person Re-Identification,2018 +161,Australia,Duke MTMC,duke_mtmc,-31.95040445,115.79790037,University of Western Australia,edu,292286c0024d6625fe606fb5b8a0df54ea3ffe91,citation,,Identity Adaptation for Person Re-Identification,2018 +162,China,Duke MTMC,duke_mtmc,40.0044795,116.370238,Chinese Academy of Sciences,edu,6cde93a5288e84671a7bee98cf6c94037f42da42,citation,,Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification,2018 +163,Singapore,Duke MTMC,duke_mtmc,1.340216,103.965089,Singapore University of Technology and Design,edu,6cde93a5288e84671a7bee98cf6c94037f42da42,citation,,Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification,2018 +164,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,6cde93a5288e84671a7bee98cf6c94037f42da42,citation,,Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification,2018 +165,China,Duke MTMC,duke_mtmc,39.0607286,117.1256421,Tianjin Normal University,edu,67289bd3b7c9406429c6012eb7292305e50dff0b,citation,,Integration Convolutional Neural Network for Person Re-Identification in Camera Networks,2018 +166,China,Duke MTMC,duke_mtmc,32.05765485,118.7550004,HoHai University,edu,fedb656c45aa332cfc373b413f3000b6228eee08,citation,,Joint Learning of Body and Part Representation for Person Re-Identification,2018 +167,China,Duke MTMC,duke_mtmc,33.5491006,119.035706,"Huaiyin Institute of Technology, Huaian, China",edu,fedb656c45aa332cfc373b413f3000b6228eee08,citation,,Joint Learning of Body and Part Representation for Person Re-Identification,2018 +168,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,fedb656c45aa332cfc373b413f3000b6228eee08,citation,,Joint Learning of Body and Part Representation for Person Re-Identification,2018 +169,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,b37538f9364252eec4182bdbb80ef1e4614c3acd,citation,,Learning a Semantically Discriminative Joint Space for Attribute Based Person Re-identification,2017 +170,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,004acfec16c36649408c561faa102dd9de76f085,citation,,Multi-level Factorisation Net for Person Re-identification,2018 +171,United Kingdom,Duke MTMC,duke_mtmc,55.94951105,-3.19534913,University of Edinburgh,edu,004acfec16c36649408c561faa102dd9de76f085,citation,,Multi-level Factorisation Net for Person Re-identification,2018 +172,China,Duke MTMC,duke_mtmc,39.0607286,117.1256421,Tianjin Normal University,edu,a80d8506fa28334c947989ca153b70aafc63ac7f,citation,,Pedestrian Retrieval via Part-Based Gradation Regularization in Sensor Networks,2018 +173,United States,Duke MTMC,duke_mtmc,35.9990522,-78.9290629,Duke University,edu,96e77135e745385e87fdd0f7ced951bf1fe9a756,citation,,People Tracking and Re-Identification from Multiple Cameras,2018 +174,China,Duke MTMC,duke_mtmc,30.274084,120.15507,Alibaba,company,90c18409b7a3be2cd6da599d02accba4c769e94e,citation,,Person Re-identification with Cascaded Pairwise Convolutions,2018 +175,China,Duke MTMC,duke_mtmc,31.83907195,117.26420748,University of Science and Technology of China,edu,90c18409b7a3be2cd6da599d02accba4c769e94e,citation,,Person Re-identification with Cascaded Pairwise Convolutions,2018 +176,China,Duke MTMC,duke_mtmc,30.5360485,114.3643219,"Wuhan Univeristy, Wuhan, China",edu,90c18409b7a3be2cd6da599d02accba4c769e94e,citation,,Person Re-identification with Cascaded Pairwise Convolutions,2018 +177,China,Duke MTMC,duke_mtmc,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,df4ed9983f7114ca4f0ab71f1476c0bf7521e317,citation,,Pose Transferrable Person Re-identification,2018 +178,United States,Duke MTMC,duke_mtmc,40.4441619,-79.94272826,Carnegie Mellon University,edu,e307c6635472d3d1e512af6e20f2e56c95937bb7,citation,,Semi-Supervised Bayesian Attribute Learning for Person Re-Identification,2018 +179,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,e307c6635472d3d1e512af6e20f2e56c95937bb7,citation,,Semi-Supervised Bayesian Attribute Learning for Person Re-Identification,2018 diff --git a/site/datasets/verified/erce.csv b/site/datasets/verified/erce.csv new file mode 100644 index 00000000..c7594437 --- /dev/null +++ b/site/datasets/verified/erce.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,ERCe,erce,0.0,0.0,,,,main,,Video Synopsis by Heterogeneous Multi-source Correlation,2013 diff --git a/site/datasets/verified/expw.csv b/site/datasets/verified/expw.csv new file mode 100644 index 00000000..bdff0ca8 --- /dev/null +++ b/site/datasets/verified/expw.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,ExpW,expw,0.0,0.0,,,,main,,From Facial Expression Recognition to Interpersonal Relation Prediction,2017 diff --git a/site/datasets/verified/face_scrub.csv b/site/datasets/verified/face_scrub.csv new file mode 100644 index 00000000..9270f6c9 --- /dev/null +++ b/site/datasets/verified/face_scrub.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,FaceScrub,face_scrub,0.0,0.0,,,,main,,A data-driven approach to cleaning large face datasets,2014 diff --git a/site/datasets/verified/face_tracer.csv b/site/datasets/verified/face_tracer.csv new file mode 100644 index 00000000..7d9a786c --- /dev/null +++ b/site/datasets/verified/face_tracer.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,FaceTracer,face_tracer,0.0,0.0,,,,main,,FaceTracer: A Search Engine for Large Collections of Images with Faces,2008 diff --git a/site/datasets/verified/facebook_100.csv b/site/datasets/verified/facebook_100.csv new file mode 100644 index 00000000..7ac8bed2 --- /dev/null +++ b/site/datasets/verified/facebook_100.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,Facebook100,facebook_100,0.0,0.0,,,,main,,Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook,2011 diff --git a/site/datasets/verified/families_in_the_wild.csv b/site/datasets/verified/families_in_the_wild.csv new file mode 100644 index 00000000..f7759a01 --- /dev/null +++ b/site/datasets/verified/families_in_the_wild.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,FIW,families_in_the_wild,0.0,0.0,,,,main,,Visual Kinship Recognition of Families in the Wild,2018 diff --git a/site/datasets/verified/fddb.csv b/site/datasets/verified/fddb.csv new file mode 100644 index 00000000..fad365aa --- /dev/null +++ b/site/datasets/verified/fddb.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,FDDB,fddb,0.0,0.0,,,,main,,FDDB: A benchmark for face detection in unconstrained settings,2010 diff --git a/site/datasets/verified/feret.csv b/site/datasets/verified/feret.csv new file mode 100644 index 00000000..9259d34e --- /dev/null +++ b/site/datasets/verified/feret.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,FERET,feret,0.0,0.0,,,,main,,The FERET Verification Testing Protocol for Face Recognition Algorithms,1998 diff --git a/site/datasets/verified/fiw_300.csv b/site/datasets/verified/fiw_300.csv new file mode 100644 index 00000000..afcd74c1 --- /dev/null +++ b/site/datasets/verified/fiw_300.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,300-W,fiw_300,0.0,0.0,,,,main,,A Semi-automatic Methodology for Facial Landmark Annotation,2013 diff --git a/site/datasets/verified/frgc.csv b/site/datasets/verified/frgc.csv new file mode 100644 index 00000000..1941ce0e --- /dev/null +++ b/site/datasets/verified/frgc.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,FRGC,frgc,0.0,0.0,,,,main,,Overview of the face recognition grand challenge,2005 diff --git a/site/datasets/verified/gallagher.csv b/site/datasets/verified/gallagher.csv new file mode 100644 index 00000000..60f38cab --- /dev/null +++ b/site/datasets/verified/gallagher.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,Gallagher,gallagher,0.0,0.0,,,,main,,Clothing cosegmentation for recognizing people,2008 diff --git a/site/datasets/verified/geofaces.csv b/site/datasets/verified/geofaces.csv new file mode 100644 index 00000000..9331c186 --- /dev/null +++ b/site/datasets/verified/geofaces.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,GeoFaces,geofaces,0.0,0.0,,,,main,,Exploring the geo-dependence of human face appearance,2014 diff --git a/site/datasets/verified/gfw.csv b/site/datasets/verified/gfw.csv new file mode 100644 index 00000000..5acd8bf1 --- /dev/null +++ b/site/datasets/verified/gfw.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,Grouping Face in the Wild,gfw,0.0,0.0,,,,main,,Merge or Not? Learning to Group Faces via Imitation Learning,2018 diff --git a/site/datasets/verified/helen.csv b/site/datasets/verified/helen.csv new file mode 100644 index 00000000..a9f9a846 --- /dev/null +++ b/site/datasets/verified/helen.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,Helen,helen,0.0,0.0,,,,main,,Interactive Facial Feature Localization,2012 diff --git a/site/datasets/verified/hipsterwars.csv b/site/datasets/verified/hipsterwars.csv new file mode 100644 index 00000000..7d6bd213 --- /dev/null +++ b/site/datasets/verified/hipsterwars.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,Hipsterwars,hipsterwars,0.0,0.0,,,,main,,Hipster Wars: Discovering Elements of Fashion Styles,2014 diff --git a/site/datasets/verified/hrt_transgender.csv b/site/datasets/verified/hrt_transgender.csv new file mode 100644 index 00000000..76cb4c41 --- /dev/null +++ b/site/datasets/verified/hrt_transgender.csv @@ -0,0 +1,6 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,HRT Transgender,hrt_transgender,0.0,0.0,,,,main,,Is the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset,2013 +1,United States,HRT Transgender,hrt_transgender,34.2249827,-77.86907744,University of North Carolina at Wilmington,edu,2f43b614607163abf41dfe5d17ef6749a1b61304,citation,,Investigating the Periocular-Based Face Recognition Across Gender Transformation,2014 +2,India,HRT Transgender,hrt_transgender,17.4454957,78.34854698,International Institute of Information Technology,edu,e6d46d923f201da644ae8d8bd04721dd9ac0e73d,citation,,Robust transgender face recognition: Approach based on appearance and therapy factors,2016 +3,Norway,HRT Transgender,hrt_transgender,60.7897318,10.6821927,"Norwegian Biometrics Laboratory, NTNU, Norway",edu,e6d46d923f201da644ae8d8bd04721dd9ac0e73d,citation,,Robust transgender face recognition: Approach based on appearance and therapy factors,2016 +4,Sweden,HRT Transgender,hrt_transgender,56.66340325,12.87929727,Halmstad University,edu,555f75077a02f33a05841f9b63a1388ec5fbcba5,citation,https://arxiv.org/pdf/1810.03360.pdf,A Survey on Periocular Biometrics Research,2016 diff --git a/site/datasets/verified/ibm_dif.csv b/site/datasets/verified/ibm_dif.csv new file mode 100644 index 00000000..4a78dc92 --- /dev/null +++ b/site/datasets/verified/ibm_dif.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,IBM Diversity in Faces,ibm_dif,0.0,0.0,,,,main,,Facial Coding Scheme Reference 1 Craniofacial Distances,2019 diff --git a/site/datasets/verified/ifad.csv b/site/datasets/verified/ifad.csv new file mode 100644 index 00000000..ca30c779 --- /dev/null +++ b/site/datasets/verified/ifad.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,IFAD,ifad,0.0,0.0,,,,main,,Indian Face Age Database: A Database for Face Recognition with Age Variation,2015 diff --git a/site/datasets/verified/ifdb.csv b/site/datasets/verified/ifdb.csv new file mode 100644 index 00000000..5d7eb156 --- /dev/null +++ b/site/datasets/verified/ifdb.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,IFDB,ifdb,0.0,0.0,,,,main,,Iranian Face Database and Evaluation with a New Detection Algorithm,2007 diff --git a/site/datasets/verified/ijb_a.csv b/site/datasets/verified/ijb_a.csv new file mode 100644 index 00000000..f3abe597 --- /dev/null +++ b/site/datasets/verified/ijb_a.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,IJB-A,ijb_a,0.0,0.0,,,,main,,Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A,2015 diff --git a/site/datasets/verified/ijb_b.csv b/site/datasets/verified/ijb_b.csv new file mode 100644 index 00000000..6a78ed81 --- /dev/null +++ b/site/datasets/verified/ijb_b.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,IJB-B,ijb_b,0.0,0.0,,,,main,,IARPA Janus Benchmark-B Face Dataset,2017 diff --git a/site/datasets/verified/ijb_c.csv b/site/datasets/verified/ijb_c.csv new file mode 100644 index 00000000..4b8c251d --- /dev/null +++ b/site/datasets/verified/ijb_c.csv @@ -0,0 +1,6 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,IJB-C,ijb_c,0.0,0.0,,,,main,,IARPA Janus Benchmark - C: Face Dataset and Protocol,2018 +1,United Kingdom,IJB-C,ijb_c,51.7520849,-1.2516646,Oxford University,edu,9286eab328444401a848cd2e13186840be8f0409,citation,https://arxiv.org/pdf/1807.09192.pdf,Multicolumn Networks for Face Recognition,2018 +2,United Kingdom,IJB-C,ijb_c,51.7520849,-1.2516646,Oxford University,edu,ac5ab8f71edde6d1a2129da12d051ed03a8446a1,citation,https://arxiv.org/pdf/1807.11440.pdf,Comparator Networks,2018 +3,United States,IJB-C,ijb_c,29.7207902,-95.34406271,University of Houston,edu,3b3941524d97e7f778367a1250ba1efb9205d5fc,citation,https://arxiv.org/pdf/1901.09447.pdf,Open Source Face Recognition Performance Evaluation Package,2019 +4,United States,IJB-C,ijb_c,42.718568,-84.47791571,Michigan State University,edu,fa03cac5aa5192822a85273852090ca20a6c47aa,citation,https://arxiv.org/pdf/1805.00611.pdf,Towards Interpretable Face Recognition,2018 diff --git a/site/datasets/verified/ilids_mcts_vid.csv b/site/datasets/verified/ilids_mcts_vid.csv new file mode 100644 index 00000000..a8c49b3e --- /dev/null +++ b/site/datasets/verified/ilids_mcts_vid.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,iLIDS-VID,ilids_mcts_vid,0.0,0.0,,,,main,,Person Re-identification by Video Ranking,2014 diff --git a/site/datasets/verified/images_of_groups.csv b/site/datasets/verified/images_of_groups.csv new file mode 100644 index 00000000..cb1ca5b7 --- /dev/null +++ b/site/datasets/verified/images_of_groups.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,Images of Groups,images_of_groups,0.0,0.0,,,,main,,Understanding images of groups of people,2009 diff --git a/site/datasets/verified/imdb_face.csv b/site/datasets/verified/imdb_face.csv new file mode 100644 index 00000000..57609d4b --- /dev/null +++ b/site/datasets/verified/imdb_face.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,IMDb Face,imdb_face,0.0,0.0,,,,main,,The Devil of Face Recognition is in the Noise,2018 diff --git a/site/datasets/verified/imdb_wiki.csv b/site/datasets/verified/imdb_wiki.csv new file mode 100644 index 00000000..913f9f8d --- /dev/null +++ b/site/datasets/verified/imdb_wiki.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,IMDB,imdb_wiki,0.0,0.0,,,,main,,Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks,2016 diff --git a/site/datasets/verified/imfdb.csv b/site/datasets/verified/imfdb.csv new file mode 100644 index 00000000..d82b1665 --- /dev/null +++ b/site/datasets/verified/imfdb.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,IMFDB,imfdb,0.0,0.0,,,,main,,Indian Movie Face Database: A benchmark for face recognition under wide variations,2013 diff --git a/site/datasets/verified/kin_face.csv b/site/datasets/verified/kin_face.csv new file mode 100644 index 00000000..5f1a02c6 --- /dev/null +++ b/site/datasets/verified/kin_face.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,UB KinFace,kin_face,0.0,0.0,,,,main,,Understanding Kin Relationships in a Photo,2012 diff --git a/site/datasets/verified/lag.csv b/site/datasets/verified/lag.csv new file mode 100644 index 00000000..7021aad2 --- /dev/null +++ b/site/datasets/verified/lag.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,LAG,lag,0.0,0.0,,,,main,,Large age-gap face verification by feature injection in deep networks,2017 diff --git a/site/datasets/verified/laofiw.csv b/site/datasets/verified/laofiw.csv new file mode 100644 index 00000000..9bcabfed --- /dev/null +++ b/site/datasets/verified/laofiw.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,LAOFIW,laofiw,0.0,0.0,,,,main,,Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings,2018 diff --git a/site/datasets/verified/lfpw.csv b/site/datasets/verified/lfpw.csv new file mode 100644 index 00000000..a2b6a265 --- /dev/null +++ b/site/datasets/verified/lfpw.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,LFWP,lfpw,0.0,0.0,,,,main,,Localizing Parts of Faces Using a Consensus of Exemplars,2011 diff --git a/site/datasets/verified/lfw.csv b/site/datasets/verified/lfw.csv new file mode 100644 index 00000000..e22ec3c1 --- /dev/null +++ b/site/datasets/verified/lfw.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,LFW,lfw,0.0,0.0,,,,main,,Labeled Faces in the Wild : Updates and New Reporting Procedures,2014 diff --git a/site/datasets/verified/market_1501.csv b/site/datasets/verified/market_1501.csv new file mode 100644 index 00000000..8561b33f --- /dev/null +++ b/site/datasets/verified/market_1501.csv @@ -0,0 +1,177 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,Market 1501,market_1501,0.0,0.0,,,,main,,Scalable Person Re-identification: A Benchmark,2015 +1,China,Market 1501,market_1501,31.83907195,117.26420748,University of Science and Technology of China,edu,5b309f6d98c503efb679eda51bd898543fb746f9,citation,https://arxiv.org/pdf/1809.05864.pdf,In Defense of the Classification Loss for Person Re-Identification,2018 +2,United States,Market 1501,market_1501,42.3614256,-71.0812092,Microsoft Research Asia,company,5b309f6d98c503efb679eda51bd898543fb746f9,citation,https://arxiv.org/pdf/1809.05864.pdf,In Defense of the Classification Loss for Person Re-Identification,2018 +3,United States,Market 1501,market_1501,39.2899685,-76.62196103,University of Maryland,edu,fe3f8826f615cc5ada33b01777b9f9dc93e0023c,citation,https://arxiv.org/pdf/1901.07702.pdf,Exploring Uncertainty in Conditional Multi-Modal Retrieval Systems,2019 +4,China,Market 1501,market_1501,24.4399419,118.09301781,Xiamen University,edu,d95ce873ed42b7c7facaa4c1e9c72b57b4e279f6,citation,https://pdfs.semanticscholar.org/d95c/e873ed42b7c7facaa4c1e9c72b57b4e279f6.pdf,Generalizing a Person Retrieval Model Hetero- and Homogeneously,2018 +5,Australia,Market 1501,market_1501,-33.8809651,151.20107299,University of Technology Sydney,edu,d95ce873ed42b7c7facaa4c1e9c72b57b4e279f6,citation,https://pdfs.semanticscholar.org/d95c/e873ed42b7c7facaa4c1e9c72b57b4e279f6.pdf,Generalizing a Person Retrieval Model Hetero- and Homogeneously,2018 +6,Australia,Market 1501,market_1501,-35.2776999,149.118527,Australian National University,edu,d95ce873ed42b7c7facaa4c1e9c72b57b4e279f6,citation,https://pdfs.semanticscholar.org/d95c/e873ed42b7c7facaa4c1e9c72b57b4e279f6.pdf,Generalizing a Person Retrieval Model Hetero- and Homogeneously,2018 +7,China,Market 1501,market_1501,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,927ec8dde9eb0e3bc5bf0b1a0ae57f9cf745fd9c,citation,https://arxiv.org/pdf/1804.01438.pdf,Learning Discriminative Features with Multiple Granularities for Person Re-Identification,2018 +8,China,Market 1501,market_1501,31.83907195,117.26420748,University of Science and Technology of China,edu,04ca65f1454f1014ef5af5bfafb7aee576ee1be6,citation,https://arxiv.org/pdf/1812.08967.pdf,Densely Semantically Aligned Person Re-Identification,2018 +9,United States,Market 1501,market_1501,42.3614256,-71.0812092,Microsoft Research Asia,company,04ca65f1454f1014ef5af5bfafb7aee576ee1be6,citation,https://arxiv.org/pdf/1812.08967.pdf,Densely Semantically Aligned Person Re-Identification,2018 +10,China,Market 1501,market_1501,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,7daa2c0f76fd3bfc7feadf313d6ac7504d4ecd20,citation,https://arxiv.org/pdf/1803.09937.pdf,Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-identification,2018 +11,Singapore,Market 1501,market_1501,1.3484104,103.68297965,Nanyang Technological University,edu,7daa2c0f76fd3bfc7feadf313d6ac7504d4ecd20,citation,https://arxiv.org/pdf/1803.09937.pdf,Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-identification,2018 +12,China,Market 1501,market_1501,32.0565957,118.77408833,Nanjing University,edu,08b28a8f2699501d46d87956cbaa37255000daa3,citation,https://arxiv.org/pdf/1804.03864.pdf,MaskReID: A Mask Based Deep Ranking Neural Network for Person Re-identification,2018 +13,Australia,Market 1501,market_1501,-34.40505545,150.87834655,University of Wollongong,edu,08b28a8f2699501d46d87956cbaa37255000daa3,citation,https://arxiv.org/pdf/1804.03864.pdf,MaskReID: A Mask Based Deep Ranking Neural Network for Person Re-identification,2018 +14,United Kingdom,Market 1501,market_1501,51.5247272,-0.03931035,Queen Mary University of London,edu,baf5ab5e8972e9366951b7e66951e05e2a4b3e36,citation,https://arxiv.org/pdf/1802.08122.pdf,Harmonious Attention Network for Person Re-identification,2018 +15,United Kingdom,Market 1501,market_1501,52.3793131,-1.5604252,University of Warwick,edu,124d60fae338b1f87455d1fc4ede5fcfd806da1a,citation,https://arxiv.org/pdf/1807.01440.pdf,Multi-task Mid-level Feature Alignment Network for Unsupervised Cross-Dataset Person Re-Identification,2018 +16,Singapore,Market 1501,market_1501,1.3484104,103.68297965,Nanyang Technological University,edu,124d60fae338b1f87455d1fc4ede5fcfd806da1a,citation,https://arxiv.org/pdf/1807.01440.pdf,Multi-task Mid-level Feature Alignment Network for Unsupervised Cross-Dataset Person Re-Identification,2018 +17,Australia,Market 1501,market_1501,-35.0636071,147.3552234,Charles Sturt University,edu,124d60fae338b1f87455d1fc4ede5fcfd806da1a,citation,https://arxiv.org/pdf/1807.01440.pdf,Multi-task Mid-level Feature Alignment Network for Unsupervised Cross-Dataset Person Re-Identification,2018 +18,United States,Market 1501,market_1501,33.776033,-84.39884086,Georgia Institute of Technology,edu,45a44e61236f7c144d9ec11561e236b2960c7cf6,citation,https://pdfs.semanticscholar.org/4eb8/4fd65703fc92863f9f589e3a07e6c841f7c4.pdf,Multi-object Tracking with Neural Gating Using Bilinear LSTM,2018 +19,United States,Market 1501,market_1501,45.5198289,-122.67797964,Oregon State University,edu,45a44e61236f7c144d9ec11561e236b2960c7cf6,citation,https://pdfs.semanticscholar.org/4eb8/4fd65703fc92863f9f589e3a07e6c841f7c4.pdf,Multi-object Tracking with Neural Gating Using Bilinear LSTM,2018 +20,China,Market 1501,market_1501,34.1235825,108.83546,Xidian University,edu,55355b0317f6e0c5218887441de71f05da4b42f6,citation,https://arxiv.org/pdf/1811.12150.pdf,Parameter-Free Spatial Attention Network for Person Re-Identification,2018 +21,Germany,Market 1501,market_1501,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,55355b0317f6e0c5218887441de71f05da4b42f6,citation,https://arxiv.org/pdf/1811.12150.pdf,Parameter-Free Spatial Attention Network for Person Re-Identification,2018 +22,China,Market 1501,market_1501,31.2284923,121.40211389,East China Normal University,edu,e1af55ad7bb26e5e1acde3ec6c5c43cffe884b04,citation,https://pdfs.semanticscholar.org/e1af/55ad7bb26e5e1acde3ec6c5c43cffe884b04.pdf,Person Re-identification by Mid-level Attribute and Part-based Identity Learning,2018 +23,Australia,Market 1501,market_1501,-35.2776999,149.118527,Australian National University,edu,c66350cbdee8c6873cc99807d342e932594aa0b9,citation,https://arxiv.org/pdf/1812.02162.pdf,Dissecting Person Re-identification from the Viewpoint of Viewpoint,2018 +24,Brazil,Market 1501,market_1501,-27.5953995,-48.6154218,University of Campinas,edu,b986a535e45751cef684a30631a74476e911a749,citation,https://arxiv.org/pdf/1807.05618.pdf,Improved Person Re-Identification Based on Saliency and Semantic Parsing with Deep Neural Network Models,2018 +25,South Korea,Market 1501,market_1501,37.26728,126.9841151,Seoul National University,edu,315df9b7dd354ae78ddf1049fb428b086eee632c,citation,https://arxiv.org/pdf/1804.07094.pdf,Part-Aligned Bilinear Representations for Person Re-identification,2018 +26,Germany,Market 1501,market_1501,48.7468939,9.0805141,Max Planck Institute for Intelligent Systems,edu,315df9b7dd354ae78ddf1049fb428b086eee632c,citation,https://arxiv.org/pdf/1804.07094.pdf,Part-Aligned Bilinear Representations for Person Re-identification,2018 +27,United States,Market 1501,market_1501,47.6423318,-122.1369302,Microsoft,company,315df9b7dd354ae78ddf1049fb428b086eee632c,citation,https://arxiv.org/pdf/1804.07094.pdf,Part-Aligned Bilinear Representations for Person Re-identification,2018 +28,Australia,Market 1501,market_1501,-33.8809651,151.20107299,University of Technology Sydney,edu,7f23a4bb0c777dd72cca7665a5f370ac7980217e,citation,https://arxiv.org/pdf/1703.07220.pdf,Improving Person Re-identification by Attribute and Identity Learning,2017 +29,United States,Market 1501,market_1501,40.1019523,-88.2271615,UIUC,edu,cc78e3f1e531342f639e4a1fc8107a7a778ae1cf,citation,https://arxiv.org/pdf/1811.10144.pdf,One Shot Domain Adaptation for Person Re-Identification,2018 +30,China,Market 1501,market_1501,22.053565,113.39913285,Jilin University,edu,4abf902cefca527f707e4f76dd4e14fcd5d47361,citation,https://arxiv.org/pdf/1811.11510.pdf,Identity Preserving Generative Adversarial Network for Cross-Domain Person Re-identification,2018 +31,China,Market 1501,market_1501,32.0565957,118.77408833,Nanjing University,edu,088e7b24bd1cf6e5922ae6c80d37439e05fadce9,citation,https://arxiv.org/pdf/1711.07155.pdf,Let Features Decide for Themselves: Feature Mask Network for Person Re-identification,2017 +32,China,Market 1501,market_1501,22.4162632,114.2109318,Chinese University of Hong Kong,edu,4f8e06ac894e9cc1eb1617a293e43448930c7d4f,citation,https://arxiv.org/pdf/1810.02936.pdf,FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification,2018 +33,China,Market 1501,market_1501,39.993008,116.329882,SenseTime,company,4f8e06ac894e9cc1eb1617a293e43448930c7d4f,citation,https://arxiv.org/pdf/1810.02936.pdf,FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification,2018 +34,United States,Market 1501,market_1501,39.3299013,-76.6205177,Johns Hopkins University,edu,4f8e06ac894e9cc1eb1617a293e43448930c7d4f,citation,https://arxiv.org/pdf/1810.02936.pdf,FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification,2018 +35,China,Market 1501,market_1501,31.83907195,117.26420748,University of Science and Technology of China,edu,4f8e06ac894e9cc1eb1617a293e43448930c7d4f,citation,https://arxiv.org/pdf/1810.02936.pdf,FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification,2018 +36,China,Market 1501,market_1501,30.19331415,120.11930822,Zhejiang University,edu,84984c7201a7e5bc8ef4c01f0a7cfbe08c2c523b,citation,https://arxiv.org/pdf/1804.06964.pdf,GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning,2018 +37,China,Market 1501,market_1501,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,c753521ba6fb06c12369d6fff814bb704c682ef5,citation,https://pdfs.semanticscholar.org/c753/521ba6fb06c12369d6fff814bb704c682ef5.pdf,Mancs: A Multi-task Attentional Network with Curriculum Sampling for Person Re-Identification,2018 +38,China,Market 1501,market_1501,22.4162632,114.2109318,Chinese University of Hong Kong,edu,0a808a17f5c86413bd552a324ee6ba180a12f46d,citation,https://arxiv.org/pdf/1808.01571.pdf,Improving Deep Visual Representation for Person Re-identification by Global and Local Image-language Association,2018 +39,China,Market 1501,market_1501,39.993008,116.329882,SenseTime,company,0a808a17f5c86413bd552a324ee6ba180a12f46d,citation,https://arxiv.org/pdf/1808.01571.pdf,Improving Deep Visual Representation for Person Re-identification by Global and Local Image-language Association,2018 +40,China,Market 1501,market_1501,34.250803,108.983693,Xi’an Jiaotong University,edu,0a808a17f5c86413bd552a324ee6ba180a12f46d,citation,https://arxiv.org/pdf/1808.01571.pdf,Improving Deep Visual Representation for Person Re-identification by Global and Local Image-language Association,2018 +41,Germany,Market 1501,market_1501,48.7468939,9.0805141,"Max Planck Instutite for Intelligent Systems, Tüebingen",edu,9db841848aa96f60e765299de4cce7abe5ccb47d,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Tang_Multiple_People_Tracking_CVPR_2017_paper.pdf,Multiple People Tracking by Lifted Multicut and Person Re-identification,2017 +42,Germany,Market 1501,market_1501,49.2578657,7.0457956,"Max-Planck-Institut für Informatik, Saarbrücken, Germany",edu,9db841848aa96f60e765299de4cce7abe5ccb47d,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Tang_Multiple_People_Tracking_CVPR_2017_paper.pdf,Multiple People Tracking by Lifted Multicut and Person Re-identification,2017 +43,France,Market 1501,market_1501,48.8457981,2.3567236,Pierre and Marie Curie University,edu,231a12de5dedddf1184ae9caafbc4a954ce584c3,citation,https://pdfs.semanticscholar.org/231a/12de5dedddf1184ae9caafbc4a954ce584c3.pdf,Closed and Open World Multi-shot Person Re-identification. (Ré-identification de personnes à partir de multiples images dans le cadre de bases d'identités fermées et ouvertes),2017 +44,China,Market 1501,market_1501,30.5097537,114.4062881,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 +45,Canada,Market 1501,market_1501,46.7817463,-71.2747424,Université Laval,edu,a743127b44397b7a017a65a7ad52d0d7ccb4db93,citation,https://arxiv.org/pdf/1804.10094.pdf,Domain Adaptation Through Synthesis for Unsupervised Person Re-identification,2018 +46,Australia,Market 1501,market_1501,-35.2776999,149.118527,Australian National University,edu,12d62f1360587fdecee728e6c509acc378f38dc9,citation,https://arxiv.org/pdf/1805.06118.pdf,Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification,2018 +47,China,Market 1501,market_1501,32.20541,118.726956,Nanjing University of Information Science & Technology,edu,12d62f1360587fdecee728e6c509acc378f38dc9,citation,https://arxiv.org/pdf/1805.06118.pdf,Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification,2018 +48,Australia,Market 1501,market_1501,-33.8809651,151.20107299,University of Technology Sydney,edu,12d62f1360587fdecee728e6c509acc378f38dc9,citation,https://arxiv.org/pdf/1805.06118.pdf,Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification,2018 +49,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,14b3a7aa61c15fd9cab0a4d8bc2a205a89fb572e,citation,https://arxiv.org/pdf/1807.11206.pdf,Hard-Aware Point-to-Set Deep Metric for Person Re-identification,2018 +50,China,Market 1501,market_1501,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,14b3a7aa61c15fd9cab0a4d8bc2a205a89fb572e,citation,https://arxiv.org/pdf/1807.11206.pdf,Hard-Aware Point-to-Set Deep Metric for Person Re-identification,2018 +51,China,Market 1501,market_1501,22.304572,114.17976285,Hong Kong Polytechnic University,edu,fea0895326b663bf72be89151a751362db8ae881,citation,https://arxiv.org/pdf/1804.08866.pdf,Homocentric Hypersphere Feature Embedding for Person Re-identification,2018 +52,China,Market 1501,market_1501,22.4162632,114.2109318,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 +53,China,Market 1501,market_1501,39.993008,116.329882,SenseTime,company,0c769c19d894e0dbd6eb314781dc1db3c626df57,citation,https://arxiv.org/pdf/1604.01850.pdf,Joint Detection and Identification Feature Learning for Person Search,2017 +54,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,0c769c19d894e0dbd6eb314781dc1db3c626df57,citation,https://arxiv.org/pdf/1604.01850.pdf,Joint Detection and Identification Feature Learning for Person Search,2017 +55,China,Market 1501,market_1501,30.209484,120.220912,"Hikvision Digital Technology Co., Ltd.",company,ed3991046e6dfba0c5cebdbbe914cc3aa06d0235,citation,https://arxiv.org/pdf/1812.06576.pdf,Learning Incremental Triplet Margin for Person Re-identification,2019 +56,China,Market 1501,market_1501,24.4399419,118.09301781,Xiamen University,edu,e746447afc4898713a0bcf2bb560286eb4d20019,citation,https://arxiv.org/pdf/1811.02074.pdf,Leveraging Virtual and Real Person for Unsupervised Person Re-identification,2018 +57,United States,Market 1501,market_1501,40.4441619,-79.94272826,Carnegie Mellon University,edu,76fb9e2963928bf8e940944d45c13d52db947702,citation,https://arxiv.org/pdf/1710.00478.pdf,Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification,2017 +58,China,Market 1501,market_1501,30.19331415,120.11930822,Zhejiang University,edu,76fb9e2963928bf8e940944d45c13d52db947702,citation,https://arxiv.org/pdf/1710.00478.pdf,Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification,2017 +59,Italy,Market 1501,market_1501,45.434532,12.326197,"DAIS, Università Ca’ Foscari, Venice, Italy",edu,bee609ea6e71aba9b449731242efdb136d556222,citation,https://arxiv.org/pdf/1706.06196.pdf,Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets,2017 +60,Italy,Market 1501,market_1501,45.4377672,12.321807,University Iuav of Venice,edu,bee609ea6e71aba9b449731242efdb136d556222,citation,https://arxiv.org/pdf/1706.06196.pdf,Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets,2017 +61,India,Market 1501,market_1501,13.0222347,77.56718325,Indian Institute of Science Bangalore,edu,317f5a56519df95884cce81cfba180ee3adaf5a5,citation,https://arxiv.org/pdf/1807.07295.pdf,Operator-In-The-Loop Deep Sequential Multi-camera Feature Fusion for Person Re-identification,2018 +62,Spain,Market 1501,market_1501,41.5007811,2.11143663,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 +63,China,Market 1501,market_1501,39.10041,121.821932,Dalian University,edu,ae5983048e59a339c77fee89e9279a4a787ba985,citation,https://arxiv.org/pdf/1705.02145.pdf,Part-Based Deep Hashing for Large-Scale Person Re-Identification,2017 +64,Australia,Market 1501,market_1501,-33.8809651,151.20107299,University of Technology Sydney,edu,ae5983048e59a339c77fee89e9279a4a787ba985,citation,https://arxiv.org/pdf/1705.02145.pdf,Part-Based Deep Hashing for Large-Scale Person Re-Identification,2017 +65,United States,Market 1501,market_1501,29.58333105,-98.61944505,University of Texas at San Antonio,edu,ae5983048e59a339c77fee89e9279a4a787ba985,citation,https://arxiv.org/pdf/1705.02145.pdf,Part-Based Deep Hashing for Large-Scale Person Re-Identification,2017 +66,Germany,Market 1501,market_1501,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,9812542cae5a470ea601e7c3a871331694105093,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w17/papers/Schumann_Person_Re-Identification_by_CVPR_2017_paper.pdf,Person Re-identification by Deep Learning Attribute-Complementary Information,2017 +67,China,Market 1501,market_1501,34.250803,108.983693,Xi’an Jiaotong University,edu,e1dcc3946fa750da4bc05b1154b6321db163ad62,citation,http://gr.xjtu.edu.cn/c/document_library/get_file?folderId=1540809&name=DLFE-80365.pdf,Similarity Learning with Spatial Constraints for Person Re-identification,2016 +68,United States,Market 1501,market_1501,42.366183,-71.092455,Mitsubishi Electric Research Laboratories,company,bb4f83458976755e9310b241a689c8d21b481238,citation,http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w23/Jones_Improving_Face_Verification_ICCV_2017_paper.pdf,Improving Face Verification and Person Re-Identification Accuracy Using Hyperplane Similarity,2017 +69,United States,Market 1501,market_1501,42.3383668,-71.08793524,Northeastern University,edu,32dc3e04dea2306ec34ca3f39db27a2b0a49e0a1,citation,http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w21/Gou_moM_Mean_of_ICCV_2017_paper.pdf,moM: Mean of Moments Feature for Person Re-identification,2017 +70,United States,Market 1501,market_1501,42.3383668,-71.08793524,Northeastern University,edu,0deca8c53adcc13d8da72050d9a4b638da52264b,citation,https://pdfs.semanticscholar.org/0dec/a8c53adcc13d8da72050d9a4b638da52264b.pdf,"A Comprehensive Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets",2016 +71,Australia,Market 1501,market_1501,-33.8809651,151.20107299,University of Technology Sydney,edu,193089d56758ab88391d846edd08d359b1f9a863,citation,https://arxiv.org/pdf/1611.05666.pdf,A Discriminatively Learned CNN Embedding for Person Reidentification,2017 +72,China,Market 1501,market_1501,31.821994,117.28059,"USTC, Hefei, China",edu,83c19722450e8f7dcb89dabb38265f19efafba27,citation,https://arxiv.org/pdf/1803.02983.pdf,A framework with updateable joint images re-ranking for Person Re-identification.,2018 +73,Singapore,Market 1501,market_1501,1.3484104,103.68297965,Nanyang Technological University,edu,6bb8a5f9e2ddf1bdcd42aa7212eb0499992c1e9e,citation,https://arxiv.org/pdf/1607.08381.pdf,A Siamese Long Short-Term Memory Architecture for Human Re-Identification,2016 +74,China,Market 1501,market_1501,40.00229045,116.32098908,Tsinghua University,edu,6bb8a5f9e2ddf1bdcd42aa7212eb0499992c1e9e,citation,https://arxiv.org/pdf/1607.08381.pdf,A Siamese Long Short-Term Memory Architecture for Human Re-Identification,2016 +75,Australia,Market 1501,market_1501,-33.88890695,151.18943366,University of Sydney,edu,6bb8a5f9e2ddf1bdcd42aa7212eb0499992c1e9e,citation,https://arxiv.org/pdf/1607.08381.pdf,A Siamese Long Short-Term Memory Architecture for Human Re-Identification,2016 +76,Germany,Market 1501,market_1501,49.4109266,8.6979529,Heidelberg University,edu,5fdb3533152f9862e3e4c2282cd5f1400af18956,citation,https://arxiv.org/pdf/1804.04694.pdf,A Variational U-Net for Conditional Appearance and Shape Generation,2018 +77,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,635efc8bddec1cf94b1ee4951e4d216331758422,citation,https://arxiv.org/pdf/1812.00914.pdf,Accelerating Large Scale Knowledge Distillation via Dynamic Importance Sampling,2018 +78,Canada,Market 1501,market_1501,53.5238572,-113.52282665,University of Alberta,edu,635efc8bddec1cf94b1ee4951e4d216331758422,citation,https://arxiv.org/pdf/1812.00914.pdf,Accelerating Large Scale Knowledge Distillation via Dynamic Importance Sampling,2018 +79,China,Market 1501,market_1501,39.9808333,116.34101249,Beihang University,edu,19be4580df2e76b70a39af6e749bf189e1ca3975,citation,https://arxiv.org/pdf/1803.10914.pdf,Adversarial Binary Coding for Efficient Person Re-identification,2018 +80,United Kingdom,Market 1501,market_1501,51.7534538,-1.25400997,University of Oxford,edu,47f4dec5f733e933c8b9a8fdcda9419741f2bf62,citation,https://arxiv.org/pdf/1901.10650.pdf,Adversarial Metric Attack for Person Re-identification,2019 +81,United States,Market 1501,market_1501,39.3299013,-76.6205177,Johns Hopkins University,edu,47f4dec5f733e933c8b9a8fdcda9419741f2bf62,citation,https://arxiv.org/pdf/1901.10650.pdf,Adversarial Metric Attack for Person Re-identification,2019 +82,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,eee4cc389ca85d23700cba9627fa11e5ee65d740,citation,https://arxiv.org/pdf/1807.10482.pdf,Adversarial Open-World Person Re-Identification,2018 +83,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,7969cc315bbafcd38a637eb8cd5d45ba897be319,citation,https://arxiv.org/pdf/1604.07807.pdf,An enhanced deep feature representation for person re-identification,2016 +84,China,Market 1501,market_1501,22.3874201,114.2082222,Hong Kong Baptist University,edu,c0e9d06383442d89426808d723ca04586db91747,citation,https://pdfs.semanticscholar.org/c0e9/d06383442d89426808d723ca04586db91747.pdf,Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification,2018 +85,China,Market 1501,market_1501,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,c0e9d06383442d89426808d723ca04586db91747,citation,https://pdfs.semanticscholar.org/c0e9/d06383442d89426808d723ca04586db91747.pdf,Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification,2018 +86,Japan,Market 1501,market_1501,35.6924853,139.7582533,"National Institute of Informatics, Japan",edu,c0e9d06383442d89426808d723ca04586db91747,citation,https://pdfs.semanticscholar.org/c0e9/d06383442d89426808d723ca04586db91747.pdf,Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification,2018 +87,China,Market 1501,market_1501,40.00229045,116.32098908,Tsinghua University,edu,5e1514de6d20d3b1d148d6925edc89a6c891ce47,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Lin_Consistent-Aware_Deep_Learning_CVPR_2017_paper.pdf,Consistent-Aware Deep Learning for Person Re-identification in a Camera Network,2017 +88,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,bff1e1ecf00c37ec91edc7c5c85c1390726c3687,citation,https://arxiv.org/pdf/1511.07545.pdf,Constrained Deep Metric Learning for Person Re-identification,2015 +89,China,Market 1501,market_1501,40.00229045,116.32098908,Tsinghua University,edu,6ce6da7a6b2d55fac604d986595ba6979580393b,citation,https://arxiv.org/pdf/1611.06026.pdf,Cross Domain Knowledge Transfer for Person Re-identification,2016 +90,China,Market 1501,market_1501,23.0502042,113.39880323,South China University of Technology,edu,c249f0aa1416c51bf82be5bb47cbeb8aac6dee35,citation,https://arxiv.org/pdf/1806.04533.pdf,Cross-Dataset Person Re-identification Using Similarity Preserved Generative Adversarial Networks,2018 +91,China,Market 1501,market_1501,40.00229045,116.32098908,Tsinghua University,edu,4f83ef534c164bd7fbd1e71fe6a3d09a30326b26,citation,https://arxiv.org/pdf/1810.10221.pdf,Cross-Resolution Person Re-identification with Deep Antithetical Learning,2018 +92,China,Market 1501,market_1501,28.16437,112.93251,Central South University,edu,a6bc69831dea3efc5804b8ab65cf5a06688ddae0,citation,https://arxiv.org/pdf/1801.01760.pdf,Crossing Generative Adversarial Networks for Cross-View Person Re-identification,2018 +93,Australia,Market 1501,market_1501,-27.49741805,153.01316956,University of Queensland,edu,a6bc69831dea3efc5804b8ab65cf5a06688ddae0,citation,https://arxiv.org/pdf/1801.01760.pdf,Crossing Generative Adversarial Networks for Cross-View Person Re-identification,2018 +94,Australia,Market 1501,market_1501,-33.91758275,151.23124025,University of New South Wales,edu,a6bc69831dea3efc5804b8ab65cf5a06688ddae0,citation,https://arxiv.org/pdf/1801.01760.pdf,Crossing Generative Adversarial Networks for Cross-View Person Re-identification,2018 +95,China,Market 1501,market_1501,39.98177,116.330086,National Laboratory of Pattern Recognition,edu,34b8e675d4651db45e484da34f3c415c60ef3ea2,citation,https://arxiv.org/pdf/1707.01220.pdf,DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer,2018 +96,China,Market 1501,market_1501,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,34b8e675d4651db45e484da34f3c415c60ef3ea2,citation,https://arxiv.org/pdf/1707.01220.pdf,DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer,2018 +97,Australia,Market 1501,market_1501,-27.49741805,153.01316956,University of Queensland,edu,d1ba33106567c880bf99daba2bd31fe88df4ecba,citation,https://arxiv.org/pdf/1706.03160.pdf,Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification,2018 +98,Australia,Market 1501,market_1501,-33.91758275,151.23124025,University of New South Wales,edu,d1ba33106567c880bf99daba2bd31fe88df4ecba,citation,https://arxiv.org/pdf/1706.03160.pdf,Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification,2018 +99,Australia,Market 1501,market_1501,-33.88890695,151.18943366,University of Sydney,edu,d1ba33106567c880bf99daba2bd31fe88df4ecba,citation,https://arxiv.org/pdf/1706.03160.pdf,Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification,2018 +100,China,Market 1501,market_1501,39.9922379,116.30393816,Peking University,edu,2788f382e4396290acfc8b21df45cc811586e66e,citation,https://arxiv.org/pdf/1605.03259.pdf,Deep Attributes Driven Multi-Camera Person Re-identification,2016 +101,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,2788f382e4396290acfc8b21df45cc811586e66e,citation,https://arxiv.org/pdf/1605.03259.pdf,Deep Attributes Driven Multi-Camera Person Re-identification,2016 +102,United States,Market 1501,market_1501,29.58333105,-98.61944505,University of Texas at San Antonio,edu,2788f382e4396290acfc8b21df45cc811586e66e,citation,https://arxiv.org/pdf/1605.03259.pdf,Deep Attributes Driven Multi-Camera Person Re-identification,2016 +103,United States,Market 1501,market_1501,40.4441619,-79.94272826,Carnegie Mellon University,edu,63e1ce7de0fdbce6e03d25b5001c670c30139aa8,citation,https://arxiv.org/pdf/1707.07791.pdf,Deep Feature Learning via Structured Graph Laplacian Embedding for Person Re-Identification,2018 +104,China,Market 1501,market_1501,34.250803,108.983693,Xi’an Jiaotong University,edu,63e1ce7de0fdbce6e03d25b5001c670c30139aa8,citation,https://arxiv.org/pdf/1707.07791.pdf,Deep Feature Learning via Structured Graph Laplacian Embedding for Person Re-Identification,2018 +105,United Kingdom,Market 1501,market_1501,51.5247272,-0.03931035,Queen Mary University of London,edu,e3e36ccd836458d51676789fb133b092d42dac16,citation,https://arxiv.org/pdf/1610.05047.pdf,Deep learning prototype domains for person re-identification,2017 +106,Australia,Market 1501,market_1501,-34.9189226,138.60423668,University of Adelaide,edu,63ac85ec1bff6009bb36f0b24ef189438836bc91,citation,https://arxiv.org/pdf/1606.01595.pdf,Deep linear discriminant analysis on fisher networks: A hybrid architecture for person re-identification,2017 +107,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,9a81f46fcf8c6c0efbe34649552b5056ce419a3d,citation,https://arxiv.org/pdf/1705.03332.pdf,Deep person re-identification with improved embedding and efficient training,2017 +108,China,Market 1501,market_1501,34.250803,108.983693,Xi’an Jiaotong University,edu,6562c40932ea734f46e5068555fbf3a185a345de,citation,https://arxiv.org/pdf/1707.00409.pdf,Deep Ranking Model by Large Adaptive Margin Learning for Person Re-identification,2018 +109,United Kingdom,Market 1501,market_1501,51.5247272,-0.03931035,Queen Mary University of London,edu,35b9af6057801fb2f28881840c8427c9cf648757,citation,https://arxiv.org/pdf/1707.02785.pdf,Deep Reinforcement Learning Attention Selection For Person Re-Identification,2017 +110,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,8961677300a9ee30ca51e1a3cf9815b4a162265b,citation,https://arxiv.org/pdf/1707.00798.pdf,Deep Representation Learning with Part Loss for Person Re-Identification,2017 +111,China,Market 1501,market_1501,39.9922379,116.30393816,Peking University,edu,8961677300a9ee30ca51e1a3cf9815b4a162265b,citation,https://arxiv.org/pdf/1707.00798.pdf,Deep Representation Learning with Part Loss for Person Re-Identification,2017 +112,United States,Market 1501,market_1501,29.58333105,-98.61944505,University of Texas at San Antonio,edu,8961677300a9ee30ca51e1a3cf9815b4a162265b,citation,https://arxiv.org/pdf/1707.00798.pdf,Deep Representation Learning with Part Loss for Person Re-Identification,2017 +113,China,Market 1501,market_1501,34.250803,108.983693,Xi’an Jiaotong University,edu,123286df95d93600f4281c60a60c69121c6440c7,citation,https://arxiv.org/pdf/1710.05711.pdf,Deep Self-Paced Learning for Person Re-Identification,2018 +114,China,Market 1501,market_1501,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,d8949f4f4085b15978e20ed7c5c34a080dd637f2,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w17/papers/Chen_Deep_Spatial-Temporal_Fusion_CVPR_2017_paper.pdf,Deep Spatial-Temporal Fusion Network for Video-Based Person Re-identification,2017 +115,China,Market 1501,market_1501,39.9922379,116.30393816,Peking University,edu,31c0968fb5f587918f1c49bf7fa51453b3e89cf7,citation,https://arxiv.org/pdf/1611.05244.pdf,Deep Transfer Learning for Person Re-Identification,2018 +116,China,Market 1501,market_1501,30.19331415,120.11930822,Zhejiang University,edu,50bf4f77d8b66ec838ad59a869630eace7e0e4a7,citation,https://arxiv.org/pdf/1707.07256.pdf,Deeply-Learned Part-Aligned Representations for Person Re-identification,2017 +117,United States,Market 1501,market_1501,47.6423318,-122.1369302,Microsoft,company,50bf4f77d8b66ec838ad59a869630eace7e0e4a7,citation,https://arxiv.org/pdf/1707.07256.pdf,Deeply-Learned Part-Aligned Representations for Person Re-identification,2017 +118,China,Market 1501,market_1501,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,d497543834f23f72f4092252b613bf3adaefc606,citation,https://arxiv.org/pdf/1805.07698.pdf,Density-Adaptive Kernel based Re-Ranking for Person Re-Identification,2018 +119,China,Market 1501,market_1501,23.09461185,113.28788994,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 +120,China,Market 1501,market_1501,34.250803,108.983693,Xi’an Jiaotong University,edu,7b2e0c87aece7ff1404ef2034d4c5674770301b2,citation,https://arxiv.org/pdf/1807.01455.pdf,Discriminative Feature Learning with Foreground Attention for Person Re-Identification,2018 +121,China,Market 1501,market_1501,31.2284923,121.40211389,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 +122,United Kingdom,Market 1501,market_1501,55.94951105,-3.19534913,University of Edinburgh,edu,68621721705e3115355268450b4b447362e455c6,citation,https://arxiv.org/pdf/1812.02605.pdf,Disjoint Label Space Transfer Learning with Common Factorised Space,2019 +123,China,Market 1501,market_1501,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,d950af49c44bc5d9f4a5cc1634e606004790b1e5,citation,https://arxiv.org/pdf/1708.04169.pdf,Divide and Fuse: A Re-ranking Approach for Person Re-identification,2017 +124,United Arab Emirates,Market 1501,market_1501,24.453884,54.3773438,New York University Abu Dhabi,edu,a94b832facb57ea37b18927b13d2dd4c5fa3a9ea,citation,https://arxiv.org/pdf/1803.09733.pdf,Domain transfer convolutional attribute embedding,2018 +125,China,Market 1501,market_1501,39.9106327,116.3356321,Chinese Academy of Science,edu,7f8d4494aba2a2b11a88bf7de4b8879b047dd69b,citation,http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_Easy_Identification_From_CVPR_2018_paper.pdf,Easy Identification from Better Constraints: Multi-shot Person Re-identification from Reference Constraints,2018 +126,United States,Market 1501,market_1501,42.0551164,-87.67581113,Northwestern University,edu,7f8d4494aba2a2b11a88bf7de4b8879b047dd69b,citation,http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_Easy_Identification_From_CVPR_2018_paper.pdf,Easy Identification from Better Constraints: Multi-shot Person Re-identification from Reference Constraints,2018 +127,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,ca1db9dc493a045e3fadf8d8209eaa4311bbdc70,citation,https://arxiv.org/pdf/1709.09304.pdf,Effective Image Retrieval via Multilinear Multi-index Fusion,2017 +128,United States,Market 1501,market_1501,29.58333105,-98.61944505,University of Texas at San Antonio,edu,ca1db9dc493a045e3fadf8d8209eaa4311bbdc70,citation,https://arxiv.org/pdf/1709.09304.pdf,Effective Image Retrieval via Multilinear Multi-index Fusion,2017 +129,United States,Market 1501,market_1501,42.0551164,-87.67581113,Northwestern University,edu,00bf7bcf31ee71f5f325ca5307883157ba3d580f,citation,http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhou_Efficient_Online_Local_ICCV_2017_paper.pdf,Efficient Online Local Metric Adaptation via Negative Samples for Person Re-identification,2017 +130,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,febff0f6faa8dde77848845e4b3e6f6c91180d33,citation,https://arxiv.org/pdf/1611.00137.pdf,Embedding Deep Metric for Person Re-identication A Study Against Large Variations,2016 +131,China,Market 1501,market_1501,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,febff0f6faa8dde77848845e4b3e6f6c91180d33,citation,https://arxiv.org/pdf/1611.00137.pdf,Embedding Deep Metric for Person Re-identication A Study Against Large Variations,2016 +132,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,febff0f6faa8dde77848845e4b3e6f6c91180d33,citation,https://arxiv.org/pdf/1611.00137.pdf,Embedding Deep Metric for Person Re-identication A Study Against Large Variations,2016 +133,China,Market 1501,market_1501,31.846918,117.29053367,Hefei University of Technology,edu,fd0e1fecf7e72318a4c53463fd5650720df40281,citation,https://arxiv.org/pdf/1606.04404.pdf,End-to-End Comparative Attention Networks for Person Re-Identification,2017 +134,China,Market 1501,market_1501,39.9041999,116.4073963,"Qihoo 360 AI Institute, Beijing, China",edu,fd0e1fecf7e72318a4c53463fd5650720df40281,citation,https://arxiv.org/pdf/1606.04404.pdf,End-to-End Comparative Attention Networks for Person Re-Identification,2017 +135,Singapore,Market 1501,market_1501,1.2966426,103.7763939,Singapore / National University of Singapore,edu,fd0e1fecf7e72318a4c53463fd5650720df40281,citation,https://arxiv.org/pdf/1606.04404.pdf,End-to-End Comparative Attention Networks for Person Re-Identification,2017 +136,China,Market 1501,market_1501,31.970907,118.8128989,PLA Army Engineering University,edu,c8ac121e9c4eb9964be9c5713f22a95c1c3b57e9,citation,https://arxiv.org/pdf/1901.05798.pdf,Ensemble Feature for Person Re-Identification,2019 +137,Spain,Market 1501,market_1501,41.5008957,2.111553,Autonomous University of Barcelona,edu,fe54a5a10288648f3bd0a71b053cdb896716b552,citation,https://arxiv.org/pdf/1804.04419.pdf,"Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification",2018 +138,Spain,Market 1501,market_1501,41.40657415,2.1945341,Universitat Oberta de Catalunya,edu,fe54a5a10288648f3bd0a71b053cdb896716b552,citation,https://arxiv.org/pdf/1804.04419.pdf,"Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification",2018 +139,Spain,Market 1501,market_1501,41.3868913,2.16352385,University of Barcelona,edu,fe54a5a10288648f3bd0a71b053cdb896716b552,citation,https://arxiv.org/pdf/1804.04419.pdf,"Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification",2018 +140,United States,Market 1501,market_1501,33.2416008,-111.8839083,Intel,company,6a9c3011b5092daa1d0cacda23f20ca4ae74b902,citation,https://arxiv.org/pdf/1812.02465.pdf,Fast and Accurate Person Re-Identification with RMNet.,2018 +141,China,Market 1501,market_1501,39.9808333,116.34101249,Beihang University,edu,91cc3981c304227e13ae151a43fbb124419bc0ce,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Chen_Fast_Person_Re-Identification_CVPR_2017_paper.pdf,Fast Person Re-identification via Cross-Camera Semantic Binary Transformation,2017 +142,United Kingdom,Market 1501,market_1501,52.6221571,1.2409136,University of East Anglia,edu,91cc3981c304227e13ae151a43fbb124419bc0ce,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Chen_Fast_Person_Re-Identification_CVPR_2017_paper.pdf,Fast Person Re-identification via Cross-Camera Semantic Binary Transformation,2017 +143,Singapore,Market 1501,market_1501,1.3484104,103.68297965,Nanyang Technological University,edu,6123e52c1a560c88817d8720e05fbff8565271fb,citation,https://arxiv.org/pdf/1607.08378.pdf,Gated Siamese Convolutional Neural Network Architecture for Human Re-Identification,2016 +144,United States,Market 1501,market_1501,38.5336349,-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 +145,China,Market 1501,market_1501,30.19331415,120.11930822,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 +146,Taiwan,Market 1501,market_1501,25.0410728,121.6147562,Institute of Information Science,edu,3cbb4cf942ee95d14505c0f83a48ba224abdd00b,citation,https://arxiv.org/pdf/1712.06820.pdf,Hierarchical Cross Network for Person Re-identification,2017 +147,Japan,Market 1501,market_1501,33.8941968,130.8394083,Kyushu Institute of Technology,edu,7da961cb039b1a01cad9b78d93bdfe2a69ed3ccf,citation,https://arxiv.org/pdf/1706.04318.pdf,Hierarchical Gaussian Descriptors with Application to Person Re-Identification,2017 +148,Japan,Market 1501,market_1501,33.59914655,130.22359848,Kyushu University,edu,7da961cb039b1a01cad9b78d93bdfe2a69ed3ccf,citation,https://arxiv.org/pdf/1706.04318.pdf,Hierarchical Gaussian Descriptors with Application to Person Re-Identification,2017 +149,Japan,Market 1501,market_1501,35.9020448,139.93622009,University of Tokyo,edu,7da961cb039b1a01cad9b78d93bdfe2a69ed3ccf,citation,https://arxiv.org/pdf/1706.04318.pdf,Hierarchical Gaussian Descriptors with Application to Person Re-Identification,2017 +150,United States,Market 1501,market_1501,42.3504253,-71.10056114,Boston University,edu,7c25ed788da1f5f61d8d1da23dd319dfb4e5ac2d,citation,https://arxiv.org/pdf/1612.01345.pdf,Human-In-The-Loop Person Re-Identification,2016 +151,United Kingdom,Market 1501,market_1501,51.5247272,-0.03931035,Queen Mary University of London,edu,7c25ed788da1f5f61d8d1da23dd319dfb4e5ac2d,citation,https://arxiv.org/pdf/1612.01345.pdf,Human-In-The-Loop Person Re-Identification,2016 +152,United Kingdom,Market 1501,market_1501,55.378051,-3.435973,"Vision Semantics Ltd, UK",edu,7c25ed788da1f5f61d8d1da23dd319dfb4e5ac2d,citation,https://arxiv.org/pdf/1612.01345.pdf,Human-In-The-Loop Person Re-Identification,2016 +153,Australia,Market 1501,market_1501,-37.9062737,145.1319449,"CSIRO, Australia",edu,53492cb14b33a26b10c91102daa2d5a2a3ed069d,citation,https://arxiv.org/pdf/1806.07592.pdf,Improving Online Multiple Object tracking with Deep Metric Learning,2018 +154,Germany,Market 1501,market_1501,50.7791703,6.06728733,RWTH Aachen University,edu,a3d11e98794896849ab2304a42bf83e2979e5fb5,citation,https://arxiv.org/pdf/1703.07737.pdf,In Defense of the Triplet Loss for Person Re-Identification,2017 +155,China,Market 1501,market_1501,34.250803,108.983693,Xi’an Jiaotong University,edu,cb8567f074573a0d66d50e75b5a91df283ccd503,citation,https://arxiv.org/pdf/1708.05512.pdf,Large Margin Learning in Set-to-Set Similarity Comparison for Person Reidentification,2018 +156,United Kingdom,Market 1501,market_1501,51.5247272,-0.03931035,Queen Mary University of London,edu,207e0ac5301a3c79af862951b70632ed650f74f7,citation,https://arxiv.org/pdf/1603.02139.pdf,Learning a Discriminative Null Space for Person Re-identification,2016 +157,China,Market 1501,market_1501,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,34cf90fcbf83025666c5c86ec30ac58b632b27b0,citation,https://arxiv.org/pdf/1710.06555.pdf,Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification,2017 +158,United States,Market 1501,market_1501,40.007581,-105.2659417,University of Colorado,edu,ad3be20fe0106d80c567def71fef01146564df4b,citation,https://arxiv.org/pdf/1802.05312.pdf,Learning Deep Disentangled Embeddings With the F-Statistic Loss,2018 +159,Russia,Market 1501,market_1501,55.6846566,37.3407539,"Skolkovo Institute of Science and Technology, Skolkovo, Moscow",edu,218603147709344d4ff66625d83603deee2854bf,citation,https://arxiv.org/pdf/1611.00822.pdf,Learning Deep Embeddings with Histogram Loss,2016 +160,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,489decd84645b77d31001d17a66abb92bb96c731,citation,https://arxiv.org/pdf/1803.11333.pdf,Learning View-Specific Deep Networks for Person Re-Identification,2018 +161,Norway,Market 1501,market_1501,63.419499,10.4020771,Norwegian University of Science and Technology,edu,2102915d0c51cfda4d85133bd593ecb9508fa4bb,citation,https://arxiv.org/pdf/1701.03153.pdf,Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification,2018 +162,Italy,Market 1501,market_1501,41.9037626,12.5144384,Sapienza University of Rome,edu,2102915d0c51cfda4d85133bd593ecb9508fa4bb,citation,https://arxiv.org/pdf/1701.03153.pdf,Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification,2018 +163,Italy,Market 1501,market_1501,45.437398,11.003376,University of Verona,edu,2102915d0c51cfda4d85133bd593ecb9508fa4bb,citation,https://arxiv.org/pdf/1701.03153.pdf,Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification,2018 +164,China,Market 1501,market_1501,40.00229045,116.32098908,Tsinghua University,edu,c0387e788a52f10bf35d4d50659cfa515d89fbec,citation,https://pdfs.semanticscholar.org/c038/7e788a52f10bf35d4d50659cfa515d89fbec.pdf,MARS: A Video Benchmark for Large-Scale Person Re-Identification,2016 +165,China,Market 1501,market_1501,40.00229045,116.32098908,Tsinghua University,edu,1e83e2abcb258cd62b160e3f31a490a6bc042e83,citation,https://arxiv.org/pdf/1704.02492.pdf,Metric Learning in Codebook Generation of Bag-of-Words for Person Re-identification,2017 +166,China,Market 1501,market_1501,31.8405068,117.2638057,Hefei University,edu,7c9d8593cdf2f8ba9f27906b2b5827b145631a0b,citation,https://arxiv.org/pdf/1810.08534.pdf,MsCGAN: Multi-scale Conditional Generative Adversarial Networks for Person Image Generation,2018 +167,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,1565bf91f8fdfe5f5168a5050b1418debc662151,citation,https://arxiv.org/pdf/1711.03368.pdf,One-pass Person Re-identification by Sketch Online Discriminant Analysis,2017 +168,Australia,Market 1501,market_1501,-33.8809651,151.20107299,University of Technology Sydney,edu,592e555ebe4bd2d821230e7074d7e9626af716b0,citation,https://arxiv.org/pdf/1809.02681.pdf,Open Set Adversarial Examples,2018 +169,China,Market 1501,market_1501,40.0044795,116.370238,Chinese Academy of Sciences,edu,fcaa88dcb1a440ef09c4e5d724ed209bfc5d3367,citation,https://arxiv.org/pdf/1811.09928.pdf,PCGAN: Partition-Controlled Human Image Generation,2019 +170,China,Market 1501,market_1501,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,fcaa88dcb1a440ef09c4e5d724ed209bfc5d3367,citation,https://arxiv.org/pdf/1811.09928.pdf,PCGAN: Partition-Controlled Human Image Generation,2019 +171,China,Market 1501,market_1501,22.4162632,114.2109318,Chinese University of Hong Kong,edu,2fad06ed34169a5b1f736112364c58140577a6b4,citation,https://pdfs.semanticscholar.org/2fad/06ed34169a5b1f736112364c58140577a6b4.pdf,Pedestrian Color Naming via Convolutional Neural Network,2016 +172,China,Market 1501,market_1501,22.4162632,114.2109318,Chinese University of Hong Kong,edu,25bb4212af72d64ec20cac533f58f7af1472e057,citation,https://arxiv.org/pdf/1703.08837.pdf,Person Re-Identification by Camera Correlation Aware Feature Augmentation,2018 +173,China,Market 1501,market_1501,28.2290209,112.99483204,"National University of Defense Technology, China",mil,25bb4212af72d64ec20cac533f58f7af1472e057,citation,https://arxiv.org/pdf/1703.08837.pdf,Person Re-Identification by Camera Correlation Aware Feature Augmentation,2018 +174,China,Market 1501,market_1501,23.09461185,113.28788994,Sun Yat-Sen University,edu,25bb4212af72d64ec20cac533f58f7af1472e057,citation,https://arxiv.org/pdf/1703.08837.pdf,Person Re-Identification by Camera Correlation Aware Feature Augmentation,2018 +175,United Kingdom,Market 1501,market_1501,51.5247272,-0.03931035,Queen Mary University of London,edu,744cc8c69255cbe9d992315e456b9efb06f42e20,citation,https://arxiv.org/pdf/1705.04724.pdf,Person Re-Identification by Deep Joint Learning of Multi-Loss Classification,2017 diff --git a/site/datasets/verified/mars.csv b/site/datasets/verified/mars.csv new file mode 100644 index 00000000..cb6901a5 --- /dev/null +++ b/site/datasets/verified/mars.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,MARS,mars,0.0,0.0,,,,main,,MARS: A Video Benchmark for Large-Scale Person Re-Identification,2016 diff --git a/site/datasets/verified/megaage.csv b/site/datasets/verified/megaage.csv new file mode 100644 index 00000000..04702674 --- /dev/null +++ b/site/datasets/verified/megaage.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,MegaAge,megaage,0.0,0.0,,,,main,,Quantifying Facial Age by Posterior of Age Comparisons,2017 diff --git a/site/datasets/verified/megaface.csv b/site/datasets/verified/megaface.csv new file mode 100644 index 00000000..d9f78ec3 --- /dev/null +++ b/site/datasets/verified/megaface.csv @@ -0,0 +1,4 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,MegaFace,megaface,0.0,0.0,,,,main,,Level Playing Field for Million Scale Face Recognition,2017 +1,Netherlands,MegaFace,megaface,53.21967825,6.56251482,University of Groningen,edu,8efda5708bbcf658d4f567e3866e3549fe045bbb,citation,https://pdfs.semanticscholar.org/8efd/a5708bbcf658d4f567e3866e3549fe045bbb.pdf,Pre-trained Deep Convolutional Neural Networks for Face Recognition,2018 +2,United States,MegaFace,megaface,41.70456775,-86.23822026,University of Notre Dame,edu,e64c166dc5bb33bc61462a8b5ac92edb24d905a1,citation,https://arxiv.org/pdf/1811.01474.pdf,Fast Face Image Synthesis with Minimal Training.,2018 diff --git a/site/datasets/verified/mifs.csv b/site/datasets/verified/mifs.csv new file mode 100644 index 00000000..4e127e79 --- /dev/null +++ b/site/datasets/verified/mifs.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,MIFS,mifs,0.0,0.0,,,,main,,Spoofing faces using makeup: An investigative study,2017 diff --git a/site/datasets/verified/miw.csv b/site/datasets/verified/miw.csv new file mode 100644 index 00000000..11bc2e33 --- /dev/null +++ b/site/datasets/verified/miw.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,MIW,miw,0.0,0.0,,,,main,,Automatic facial makeup detection with application in face recognition,2013 diff --git a/site/datasets/verified/morph.csv b/site/datasets/verified/morph.csv new file mode 100644 index 00000000..b0a66a5f --- /dev/null +++ b/site/datasets/verified/morph.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,MORPH Commercial,morph,0.0,0.0,,,,main,,MORPH: a longitudinal image database of normal adult age-progression,2006 diff --git a/site/datasets/verified/morph_nc.csv b/site/datasets/verified/morph_nc.csv new file mode 100644 index 00000000..a14720dd --- /dev/null +++ b/site/datasets/verified/morph_nc.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,MORPH Non-Commercial,morph_nc,0.0,0.0,,,,main,,MORPH: a longitudinal image database of normal adult age-progression,2006 diff --git a/site/datasets/verified/mot.csv b/site/datasets/verified/mot.csv new file mode 100644 index 00000000..ae532522 --- /dev/null +++ b/site/datasets/verified/mot.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,MOT,mot,0.0,0.0,,,,main,,Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics,2008 diff --git a/site/datasets/verified/msceleb.csv b/site/datasets/verified/msceleb.csv new file mode 100644 index 00000000..d1a7ec8c --- /dev/null +++ b/site/datasets/verified/msceleb.csv @@ -0,0 +1,127 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,MsCeleb,msceleb,0.0,0.0,,,,main,,MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition,2016 +1,China,MsCeleb,msceleb,22.4162632,114.2109318,Chinese University of Hong Kong,edu,2011d4da646f794456bebb617d1500ddf71989ed,citation,https://pdfs.semanticscholar.org/2011/d4da646f794456bebb617d1500ddf71989ed.pdf,Transductive Centroid Projection for Semi-supervised Large-Scale Recognition,2018 +2,China,MsCeleb,msceleb,39.993008,116.329882,SenseTime,company,2011d4da646f794456bebb617d1500ddf71989ed,citation,https://pdfs.semanticscholar.org/2011/d4da646f794456bebb617d1500ddf71989ed.pdf,Transductive Centroid Projection for Semi-supervised Large-Scale Recognition,2018 +3,United States,MsCeleb,msceleb,39.2899685,-76.62196103,University of Maryland,edu,23dd8d17ce09c22d367e4d62c1ccf507bcbc64da,citation,https://pdfs.semanticscholar.org/23dd/8d17ce09c22d367e4d62c1ccf507bcbc64da.pdf,Deep Density Clustering of Unconstrained Faces ( Supplementary Material ),2018 +4,United States,MsCeleb,msceleb,37.3936717,-122.0807262,Facebook,company,628a3f027b7646f398c68a680add48c7969ab1d9,citation,https://pdfs.semanticscholar.org/628a/3f027b7646f398c68a680add48c7969ab1d9.pdf,Plan for Final Year Project : HKU-Face : A Large Scale Dataset for Deep Face Recognition,2017 +5,United States,MsCeleb,msceleb,37.4219999,-122.0840575,Google,company,628a3f027b7646f398c68a680add48c7969ab1d9,citation,https://pdfs.semanticscholar.org/628a/3f027b7646f398c68a680add48c7969ab1d9.pdf,Plan for Final Year Project : HKU-Face : A Large Scale Dataset for Deep Face Recognition,2017 +6,France,MsCeleb,msceleb,46.1476461,-1.1549415,University of La Rochelle,edu,153fbae25efd061f9046970071d0cfe739a35a0e,citation,,FaceLiveNet: End-to-End Networks Combining Face Verification with Interactive Facial Expression-Based Liveness Detection,2018 +7,China,MsCeleb,msceleb,26.89887,112.590435,University of South China,edu,98518fc368d7e1478cef40f5f8fd4468763645ad,citation,http://downloads.hindawi.com/journals/cin/2018/4512473.pdf,A Community Detection Approach to Cleaning Extremely Large Face Database,2018 +8,China,MsCeleb,msceleb,28.2290209,112.99483204,"National University of Defense Technology, China",mil,98518fc368d7e1478cef40f5f8fd4468763645ad,citation,http://downloads.hindawi.com/journals/cin/2018/4512473.pdf,A Community Detection Approach to Cleaning Extremely Large Face Database,2018 +9,China,MsCeleb,msceleb,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,6cdbbced12bff53bcbdde3cdb6d20b4bd02a9d6c,citation,https://arxiv.org/pdf/1811.12026.pdf,Attacks on State-of-the-Art Face Recognition using Attentional Adversarial Attack Generative Network,2018 +10,China,MsCeleb,msceleb,39.98177,116.330086,National Laboratory of Pattern Recognition,edu,e47f4a127f41c055fb7893ddc295932ead783c63,citation,https://arxiv.org/pdf/1709.03675.pdf,Adversarial Discriminative Heterogeneous Face Recognition,2018 +11,China,MsCeleb,msceleb,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,e47f4a127f41c055fb7893ddc295932ead783c63,citation,https://arxiv.org/pdf/1709.03675.pdf,Adversarial Discriminative Heterogeneous Face Recognition,2018 +12,China,MsCeleb,msceleb,22.4162632,114.2109318,Chinese University of Hong Kong,edu,2401cd5606c6bc5390acc352d00c1685f0c8af60,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018 +13,China,MsCeleb,msceleb,39.993008,116.329882,SenseTime,company,2401cd5606c6bc5390acc352d00c1685f0c8af60,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018 +14,Singapore,MsCeleb,msceleb,1.3484104,103.68297965,Nanyang Technological University,edu,2401cd5606c6bc5390acc352d00c1685f0c8af60,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018 +15,United States,MsCeleb,msceleb,42.718568,-84.47791571,Michigan State University,edu,b446bcd7fb78adfe346cf7a01a38e4f43760f363,citation,https://pdfs.semanticscholar.org/b446/bcd7fb78adfe346cf7a01a38e4f43760f363.pdf,To appear in ICB 2018 Longitudinal Study of Child Face Recognition,2017 +16,United Kingdom,MsCeleb,msceleb,51.3791442,-2.3252332,University of Bath,edu,26567da544239cc6628c5696b0b10539144cbd57,citation,https://arxiv.org/pdf/1811.12784.pdf,The GAN that Warped: Semantic Attribute Editing with Unpaired Data,2018 +17,United Kingdom,MsCeleb,msceleb,51.49887085,-0.17560797,Imperial College London,edu,40bb090a4e303f11168dce33ed992f51afe02ff7,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Deng_Marginal_Loss_for_CVPR_2017_paper.pdf,Marginal Loss for Deep Face Recognition,2017 +18,China,MsCeleb,msceleb,40.0044795,116.370238,Chinese Academy of Sciences,edu,4cdb6144d56098b819076a8572a664a2c2d27f72,citation,https://arxiv.org/pdf/1806.01196.pdf,Face Synthesis for Eyeglass-Robust Face Recognition,2018 +19,China,MsCeleb,msceleb,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,4cdb6144d56098b819076a8572a664a2c2d27f72,citation,https://arxiv.org/pdf/1806.01196.pdf,Face Synthesis for Eyeglass-Robust Face Recognition,2018 +20,United States,MsCeleb,msceleb,39.2899685,-76.62196103,University of Maryland,edu,872dfdeccf99bbbed7c8f1ea08afb2d713ebe085,citation,https://arxiv.org/pdf/1703.09507.pdf,L2-constrained Softmax Loss for Discriminative Face Verification,2017 +21,United States,MsCeleb,msceleb,42.718568,-84.47791571,Michigan State University,edu,3011b5fce49112228711a9e5f92d6f191687c1ea,citation,https://arxiv.org/pdf/1803.09014.pdf,Feature Transfer Learning for Deep Face Recognition with Long-Tail Data,2018 +22,United Kingdom,MsCeleb,msceleb,51.49887085,-0.17560797,Imperial College London,edu,1929863fff917ee7f6dc428fc1ce732777668eca,citation,https://arxiv.org/pdf/1712.04695.pdf,UV-GAN: Adversarial Facial UV Map Completion for Pose-Invariant Face Recognition,2018 +23,China,MsCeleb,msceleb,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 +24,China,MsCeleb,msceleb,31.30104395,121.50045497,Fudan University,edu,5a259f2f5337435f841d39dada832ab24e7b3325,citation,,Face Recognition via Active Annotation and Learning,2016 +25,China,MsCeleb,msceleb,40.0044795,116.370238,Chinese Academy of Sciences,edu,5a259f2f5337435f841d39dada832ab24e7b3325,citation,,Face Recognition via Active Annotation and Learning,2016 +26,China,MsCeleb,msceleb,39.993008,116.329882,SenseTime,company,c72a2ea819df9b0e8cd267eebcc6528b8741e03d,citation,https://arxiv.org/pdf/1708.09687.pdf,Quantifying Facial Age by Posterior of Age Comparisons,2017 +27,China,MsCeleb,msceleb,22.4162632,114.2109318,Chinese University of Hong Kong,edu,c72a2ea819df9b0e8cd267eebcc6528b8741e03d,citation,https://arxiv.org/pdf/1708.09687.pdf,Quantifying Facial Age by Posterior of Age Comparisons,2017 +28,United States,MsCeleb,msceleb,39.2899685,-76.62196103,University of Maryland,edu,b6f758be954d34817d4ebaa22b30c63a4b8ddb35,citation,https://arxiv.org/pdf/1703.04835.pdf,A Proximity-Aware Hierarchical Clustering of Faces,2017 +29,United States,MsCeleb,msceleb,42.718568,-84.47791571,Michigan State University,edu,19fa871626df604639550c6445d2f76cd369dd13,citation,https://arxiv.org/pdf/1805.02283.pdf,DocFace: Matching ID Document Photos to Selfies,2018 +30,United States,MsCeleb,msceleb,32.87935255,-117.23110049,"University of California, San Diego",edu,d35534f3f59631951011539da2fe83f2844ca245,citation,https://arxiv.org/pdf/1705.07904.pdf,Semantically Decomposing the Latent Spaces of Generative Adversarial Networks,2017 +31,United States,MsCeleb,msceleb,37.43131385,-122.16936535,Stanford University,edu,d35534f3f59631951011539da2fe83f2844ca245,citation,https://arxiv.org/pdf/1705.07904.pdf,Semantically Decomposing the Latent Spaces of Generative Adversarial Networks,2017 +32,United States,MsCeleb,msceleb,40.4441619,-79.94272826,Carnegie Mellon University,edu,d35534f3f59631951011539da2fe83f2844ca245,citation,https://arxiv.org/pdf/1705.07904.pdf,Semantically Decomposing the Latent Spaces of Generative Adversarial Networks,2017 +33,Canada,MsCeleb,msceleb,49.2767454,-122.91777375,Simon Fraser University,edu,b301fd2fc33f24d6f75224e7c0991f4f04b64a65,citation,https://arxiv.org/pdf/1803.06340.pdf,Faces as Lighting Probes via Unsupervised Deep Highlight Extraction,2018 +34,China,MsCeleb,msceleb,28.2290209,112.99483204,"National University of Defense Technology, China",mil,b301fd2fc33f24d6f75224e7c0991f4f04b64a65,citation,https://arxiv.org/pdf/1803.06340.pdf,Faces as Lighting Probes via Unsupervised Deep Highlight Extraction,2018 +35,United States,MsCeleb,msceleb,42.3614256,-71.0812092,Microsoft Research Asia,company,b301fd2fc33f24d6f75224e7c0991f4f04b64a65,citation,https://arxiv.org/pdf/1803.06340.pdf,Faces as Lighting Probes via Unsupervised Deep Highlight Extraction,2018 +36,United Kingdom,MsCeleb,msceleb,51.7534538,-1.25400997,University of Oxford,edu,70c59dc3470ae867016f6ab0e008ac8ba03774a1,citation,https://arxiv.org/pdf/1710.08092.pdf,VGGFace2: A Dataset for Recognising Faces across Pose and Age,2018 +37,China,MsCeleb,msceleb,39.9041999,116.4073963,"Beijing, China",edu,7fa4e972da46735971aad52413d17c4014c49e6e,citation,https://arxiv.org/pdf/1709.02940.pdf,How to Train Triplet Networks with 100K Identities?,2017 +38,China,MsCeleb,msceleb,39.94976005,116.33629046,Beijing Jiaotong University,edu,d7cbedbee06293e78661335c7dd9059c70143a28,citation,https://arxiv.org/pdf/1804.07573.pdf,MobileFaceNets: Efficient CNNs for Accurate Real-time Face Verification on Mobile Devices,2018 +39,Singapore,MsCeleb,msceleb,1.2962018,103.77689944,National University of Singapore,edu,fca9ebaa30d69ccec8bb577c31693c936c869e72,citation,https://arxiv.org/pdf/1809.00338.pdf,Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition,2018 +40,China,MsCeleb,msceleb,40.0044795,116.370238,Chinese Academy of Sciences,edu,fca9ebaa30d69ccec8bb577c31693c936c869e72,citation,https://arxiv.org/pdf/1809.00338.pdf,Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition,2018 +41,Japan,MsCeleb,msceleb,35.6992503,139.7721568,"Hitachi, Ltd., Tokyo, Japan",company,3b4da93fbdf7ae520fa00d39ffa694e850b85162,citation,,Face-Voice Matching using Cross-modal Embeddings,2018 +42,China,MsCeleb,msceleb,30.19331415,120.11930822,Zhejiang University,edu,85860d38c66a5cf2e6ffd6475a3a2ba096ea2920,citation,,Celeb-500K: A Large Training Dataset for Face Recognition,2018 +43,China,MsCeleb,msceleb,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 +44,China,MsCeleb,msceleb,40.0044795,116.370238,Chinese Academy of Sciences,edu,6f5309d8cc76d3d300b72745887addd2a2480ba8,citation,,KinNet: Fine-to-Coarse Deep Metric Learning for Kinship Verification,2017 +45,China,MsCeleb,msceleb,40.00229045,116.32098908,Tsinghua University,edu,09ad80c4e80e1e02afb8fa4cb6dab260fb66df53,citation,,Feature Learning for One-Shot Face Recognition,2018 +46,United States,MsCeleb,msceleb,40.4441619,-79.94272826,Carnegie Mellon University,edu,c71217b2b111a51a31cf1107c71d250348d1ff68,citation,https://arxiv.org/pdf/1703.09912.pdf,One Network to Solve Them All — Solving Linear Inverse Problems Using Deep Projection Models,2017 +47,United Kingdom,MsCeleb,msceleb,51.7534538,-1.25400997,University of Oxford,edu,05ee231749c9ce97f036c71c1d2d599d660a8c81,citation,https://arxiv.org/pdf/1810.09951.pdf,GhostVLAD for set-based face recognition,2018 +48,United States,MsCeleb,msceleb,45.57022705,-122.63709346,Concordia University,edu,db374308655256da1479c272582d7c7139c97173,citation,https://arxiv.org/pdf/1811.11080.pdf,MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices,2018 +49,United States,MsCeleb,msceleb,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,db374308655256da1479c272582d7c7139c97173,citation,https://arxiv.org/pdf/1811.11080.pdf,MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices,2018 +50,United States,MsCeleb,msceleb,36.0678324,-94.1736551,University of Arkansas,edu,db374308655256da1479c272582d7c7139c97173,citation,https://arxiv.org/pdf/1811.11080.pdf,MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices,2018 +51,China,MsCeleb,msceleb,22.4162632,114.2109318,Chinese University of Hong Kong,edu,de7d36173f9ca0e89e7a1991d541aed7c65127ea,citation,https://arxiv.org/pdf/1812.01288.pdf,FaceFeat-GAN: a Two-Stage Approach for Identity-Preserving Face Synthesis,2018 +52,China,MsCeleb,msceleb,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,de7d36173f9ca0e89e7a1991d541aed7c65127ea,citation,https://arxiv.org/pdf/1812.01288.pdf,FaceFeat-GAN: a Two-Stage Approach for Identity-Preserving Face Synthesis,2018 +53,China,MsCeleb,msceleb,40.0044795,116.370238,Chinese Academy of Sciences,edu,212608e00fc1e8912ff845ee7a4a67f88ba938fc,citation,https://arxiv.org/pdf/1704.02450.pdf,Coupled Deep Learning for Heterogeneous Face Recognition,2018 +54,China,MsCeleb,msceleb,22.4162632,114.2109318,Chinese University of Hong Kong,edu,1fd5d08394a3278ef0a89639e9bfec7cb482e0bf,citation,https://arxiv.org/pdf/1804.03487.pdf,Exploring Disentangled Feature Representation Beyond Face Identification,2018 +55,China,MsCeleb,msceleb,39.993008,116.329882,SenseTime,company,1fd5d08394a3278ef0a89639e9bfec7cb482e0bf,citation,https://arxiv.org/pdf/1804.03487.pdf,Exploring Disentangled Feature Representation Beyond Face Identification,2018 +56,United States,MsCeleb,msceleb,40.8722825,-73.89489171,City University of New York,edu,f74917fc0e55f4f5682909dcf6929abd19d33e2e,citation,https://pdfs.semanticscholar.org/f749/17fc0e55f4f5682909dcf6929abd19d33e2e.pdf,GAN Q UALITY I NDEX ( GQI ) BY GAN-INDUCED C LASSIFIER,2018 +57,United States,MsCeleb,msceleb,42.3383668,-71.08793524,Northeastern University,edu,f74917fc0e55f4f5682909dcf6929abd19d33e2e,citation,https://pdfs.semanticscholar.org/f749/17fc0e55f4f5682909dcf6929abd19d33e2e.pdf,GAN Q UALITY I NDEX ( GQI ) BY GAN-INDUCED C LASSIFIER,2018 +58,United States,MsCeleb,msceleb,47.6423318,-122.1369302,Microsoft,company,f74917fc0e55f4f5682909dcf6929abd19d33e2e,citation,https://pdfs.semanticscholar.org/f749/17fc0e55f4f5682909dcf6929abd19d33e2e.pdf,GAN Q UALITY I NDEX ( GQI ) BY GAN-INDUCED C LASSIFIER,2018 +59,China,MsCeleb,msceleb,32.0565957,118.77408833,Nanjing University,edu,8ff8c64288a2f7e4e8bf8fda865820b04ab3dbe8,citation,https://pdfs.semanticscholar.org/0056/92b9fa6728df3a7f14578c43410867bba425.pdf,Age Estimation Using Expectation of Label Distribution Learning,2018 +60,China,MsCeleb,msceleb,32.0575279,118.78682252,Southeast University,edu,8ff8c64288a2f7e4e8bf8fda865820b04ab3dbe8,citation,https://pdfs.semanticscholar.org/0056/92b9fa6728df3a7f14578c43410867bba425.pdf,Age Estimation Using Expectation of Label Distribution Learning,2018 +61,United States,MsCeleb,msceleb,42.4505507,-76.4783513,Cornell University,edu,dec0c26855da90876c405e9fd42830c3051c2f5f,citation,https://pdfs.semanticscholar.org/dec0/c26855da90876c405e9fd42830c3051c2f5f.pdf,Supplementary Material : Learning Compositional Visual Concepts with Mutual Consistency,2018 +62,France,MsCeleb,msceleb,48.8476037,2.2639934,"Université Paris-Saclay, France",edu,96e318f8ff91ba0b10348d4de4cb7c2142eb8ba9,citation,,State-of-the-art face recognition performance using publicly available software and datasets,2018 +63,United States,MsCeleb,msceleb,29.7207902,-95.34406271,University of Houston,edu,38d8ff137ff753f04689e6b76119a44588e143f3,citation,https://arxiv.org/pdf/1709.06532.pdf,When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition,2017 +64,United States,MsCeleb,msceleb,38.0333742,-84.5017758,University of Kentucky,edu,455a7e03a0c5ab618d0e86a06c9910ac179f0479,citation,https://arxiv.org/pdf/1807.08772.pdf,Identity Preserving Face Completion for Large Ocular Region Occlusion,2018 +65,United States,MsCeleb,msceleb,34.0224149,-118.28634407,University of Southern California,edu,455a7e03a0c5ab618d0e86a06c9910ac179f0479,citation,https://arxiv.org/pdf/1807.08772.pdf,Identity Preserving Face Completion for Large Ocular Region Occlusion,2018 +66,China,MsCeleb,msceleb,45.7413921,126.62552755,Harbin Institute of Technology,edu,455a7e03a0c5ab618d0e86a06c9910ac179f0479,citation,https://arxiv.org/pdf/1807.08772.pdf,Identity 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Recognition",edu,3ac09c2589178dac0b6a2ea2edf04b7629672d81,citation,https://arxiv.org/pdf/1708.02412.pdf,Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition,2018 +117,China,MsCeleb,msceleb,40.0044795,116.370238,Chinese Academy of Sciences,edu,3ac09c2589178dac0b6a2ea2edf04b7629672d81,citation,https://arxiv.org/pdf/1708.02412.pdf,Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition,2018 +118,United States,MsCeleb,msceleb,38.99203005,-76.9461029,University of Maryland College Park,edu,b35ff9985aaee9371588330bcef0dfc88d1401d7,citation,,Deep Density Clustering of Unconstrained Faces,2018 +119,United States,MsCeleb,msceleb,30.6108365,-96.352128,Texas A&M University,edu,e36fdb50844132fc7925550398e68e7ae95467de,citation,,Face Verification with Disguise Variations via Deep Disguise Recognizer,2018 +120,United States,MsCeleb,msceleb,39.65404635,-79.96475355,West Virginia University,edu,e36fdb50844132fc7925550398e68e7ae95467de,citation,,Face Verification with Disguise Variations via Deep Disguise Recognizer,2018 +121,United States,MsCeleb,msceleb,42.4505507,-76.4783513,Cornell University,edu,9ccf528ef8df99372ce6286ffbb0bf6f9a505cca,citation,,Learning Compositional Visual Concepts with Mutual Consistency,2018 +122,United States,MsCeleb,msceleb,40.3442079,-74.5924599,"Siemens Corporate Research, Princeton, NJ",edu,9ccf528ef8df99372ce6286ffbb0bf6f9a505cca,citation,,Learning Compositional Visual Concepts with Mutual Consistency,2018 +123,United States,MsCeleb,msceleb,42.3383668,-71.08793524,Northeastern University,edu,3827f1cab643a57e3cd22fbffbf19dd5e8a298a8,citation,,One-Shot Face Recognition via Generative Learning,2018 +124,China,MsCeleb,msceleb,39.9106327,116.3356321,Chinese Academy of Science,edu,20f87ed94a423b5d8599d85d1f2f80bab8902107,citation,,Pose-Guided Photorealistic Face Rotation,2018 +125,United States,MsCeleb,msceleb,40.4441619,-79.94272826,Carnegie Mellon University,edu,67a9659de0bf671fafccd7f39b7587f85fb6dfbd,citation,,Ring Loss: Convex Feature Normalization for Face Recognition,2018 diff --git a/site/datasets/verified/mug_faces.csv b/site/datasets/verified/mug_faces.csv new file mode 100644 index 00000000..0ad9226e --- /dev/null +++ b/site/datasets/verified/mug_faces.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,MUG Faces,mug_faces,0.0,0.0,,,,main,,The MUG facial expression database,2010 diff --git a/site/datasets/verified/names_and_faces.csv b/site/datasets/verified/names_and_faces.csv new file mode 100644 index 00000000..56f2a57a --- /dev/null +++ b/site/datasets/verified/names_and_faces.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,News Dataset,names_and_faces,0.0,0.0,,,,main,,Names and faces in the news,2004 diff --git a/site/datasets/verified/orl.csv b/site/datasets/verified/orl.csv new file mode 100644 index 00000000..b9d29530 --- /dev/null +++ b/site/datasets/verified/orl.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,ORL,orl,0.0,0.0,,,,main,,Parameterisation of a stochastic model for human face identification,1994 diff --git a/site/datasets/verified/oxford_town_centre.csv b/site/datasets/verified/oxford_town_centre.csv new file mode 100644 index 00000000..8fb0f336 --- /dev/null +++ b/site/datasets/verified/oxford_town_centre.csv @@ -0,0 +1,114 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,TownCentre,oxford_town_centre,0.0,0.0,,,,main,,Stable multi-target tracking in real-time surveillance video,2011 +1,United States,TownCentre,oxford_town_centre,40.4441619,-79.94272826,Carnegie Mellon University,edu,03ae36b2ed0215b15c5bc7d42fbe20b1491e551a,citation,http://vishnu.boddeti.net/papers/cvpr-2015-abstract.pdf,Learning scene-specific pedestrian detectors without real data,2015 +2,United States,TownCentre,oxford_town_centre,37.3675905,-121.9133491,Sony,company,03ae36b2ed0215b15c5bc7d42fbe20b1491e551a,citation,http://vishnu.boddeti.net/papers/cvpr-2015-abstract.pdf,Learning scene-specific pedestrian detectors without real data,2015 +3,United States,TownCentre,oxford_town_centre,42.3504253,-71.10056114,Boston University,edu,9363bf52a5bb2ac94bf247ca56e7cf55fb29ee4e,citation,http://cs-www.bu.edu/groups/ivc/software/TrackerHierarchy/AVSS2012_TrackerHierarchy.pdf,Online Multi-person Tracking by Tracker Hierarchy,2012 +4,United States,TownCentre,oxford_town_centre,28.59899755,-81.19712501,University of Central Florida,edu,80b41fb824f3751b03017bf7ec8c5f71b7e214b2,citation,http://crcv-web.eecs.ucf.edu/papers/cvpr2013/CVPR2013_Yang_FinalVersion_HumanDetection.pdf,Semi-supervised Learning of Feature Hierarchies for Object Detection in a Video,2013 +5,United States,TownCentre,oxford_town_centre,39.2899685,-76.62196103,University of Maryland,edu,2b9410889dc6870cc6e0476dbc681049b28ccacb,citation,http://drum.lib.umd.edu/bitstream/1903/13339/1/CS-TR-5018.pdf,Learning to Detect Carried Objects with Minimal Supervision,2013 +6,United States,TownCentre,oxford_town_centre,28.59899755,-81.19712501,University of Central Florida,edu,5369b021f2abf5daa77fa5602569bb3b8bb18546,citation,http://crcv-web.eecs.ucf.edu/papers/cvpr2015/AfshinDehghan_GMMCP_CVPR15.pdf,GMMCP tracker: Globally optimal Generalized Maximum Multi Clique problem for multiple object tracking,2015 +7,United States,TownCentre,oxford_town_centre,28.59899755,-81.19712501,University of Central Florida,edu,076fd6fd85b93858155a1c775f1897f83d52b4c2,citation,http://crcv-web.eecs.ucf.edu/papers/cvpr2013/CVPR13_final_guang.pdf,Improving an Object Detector and Extracting Regions Using Superpixels,2013 +8,United Kingdom,TownCentre,oxford_town_centre,55.91029135,-3.32345777,Heriot-Watt University,edu,b02581323ad03125e9b18d74ba0c1909d6485dda,citation,https://pure.qub.ac.uk/portal/files/57462725/Anomaly1_s2.0_S0167865513004625_main.pdf,Contextual anomaly detection in crowded surveillance scenes,2014 +9,United Kingdom,TownCentre,oxford_town_centre,51.7534538,-1.25400997,University of Oxford,edu,184c3e66a746376716d5e816d95e1a7cb8e04390,citation,http://ben.benfold.com/docs/benfold_reid_iccv2011-poster.pdf,Unsupervised learning of a scene-specific coarse gaze estimator,2011 +10,United Kingdom,TownCentre,oxford_town_centre,51.7520209,-1.2577263,"Oxford, UK",edu,184c3e66a746376716d5e816d95e1a7cb8e04390,citation,http://ben.benfold.com/docs/benfold_reid_iccv2011-poster.pdf,Unsupervised learning of a scene-specific coarse gaze estimator,2011 +11,Israel,TownCentre,oxford_town_centre,31.262218,34.801461,Ben-Gurion University,edu,880e232f260b0f9d649a4e6408b1cf82f270bd6d,citation,http://www.cs.bgu.ac.il/~ben-shahar/Publications/2013-Ben_Ari_and_Ben_Shahar-A_Computationally_Efficient_Tracker_with_Direct_Appearance-Kinematic_Measure_and_Adaptive%20Kalman_Filter.pdf,A computationally efficient tracker with direct appearance-kinematic measure and adaptive Kalman filter,2013 +12,Israel,TownCentre,oxford_town_centre,31.8878767,34.7359885,"Orbotech Ltd., Yavne, Israel",company,880e232f260b0f9d649a4e6408b1cf82f270bd6d,citation,http://www.cs.bgu.ac.il/~ben-shahar/Publications/2013-Ben_Ari_and_Ben_Shahar-A_Computationally_Efficient_Tracker_with_Direct_Appearance-Kinematic_Measure_and_Adaptive%20Kalman_Filter.pdf,A computationally efficient tracker with direct appearance-kinematic measure and adaptive Kalman filter,2013 +13,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,3e0db33884ca8c756b26dc0df85c498c18d5f2ec,citation,http://is.tuebingen.mpg.de/uploads_file/attachment/attachment/137/LeaPonRos11SocialLP.pdf,Exploiting pedestrian interaction via global optimization and social behaviors,2011 +14,United States,TownCentre,oxford_town_centre,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,b28eb219db9370cf20063288225cc2f3e6e5f984,citation,http://faculty.ucmerced.edu/mhyang/papers/iccv15_pose.pdf,Fast and Accurate Head Pose Estimation via Random Projection Forests,2015 +15,United States,TownCentre,oxford_town_centre,37.3641651,-120.4254615,University of California at Merced,edu,b28eb219db9370cf20063288225cc2f3e6e5f984,citation,http://faculty.ucmerced.edu/mhyang/papers/iccv15_pose.pdf,Fast and Accurate Head Pose Estimation via Random Projection Forests,2015 +16,Austria,TownCentre,oxford_town_centre,47.05821,15.46019568,Graz University of Technology,edu,356ec17af375b63a015d590562381a62f352f7d5,citation,http://lrs.icg.tugraz.at/pubs/possegger_cvpr14.pdf,Occlusion Geodesics for Online Multi-object Tracking,2014 +17,United States,TownCentre,oxford_town_centre,45.57022705,-122.63709346,Concordia University,edu,b53289f3f3b17dad91fa4fd25d09fdbc14f8c8cc,citation,http://faculty.ucmerced.edu/mhyang/papers/cviu16_MOT.pdf,Online multi-object tracking via robust collaborative model and sample selection,2017 +18,United States,TownCentre,oxford_town_centre,37.8718992,-122.2585399,University of California,edu,b53289f3f3b17dad91fa4fd25d09fdbc14f8c8cc,citation,http://faculty.ucmerced.edu/mhyang/papers/cviu16_MOT.pdf,Online multi-object tracking via robust collaborative model and sample selection,2017 +19,United States,TownCentre,oxford_town_centre,28.59899755,-81.19712501,University of Central Florida,edu,920246280e7e70900762ddfa7c41a79ec4517350,citation,http://crcv-web.eecs.ucf.edu/papers/eccv2012/MPMPT-ECCV12.pdf,(MP) 2 T: multiple people multiple parts tracker,2012 +20,United States,TownCentre,oxford_town_centre,37.8718992,-122.2585399,University of California,edu,14d5bd23667db4413a7f362565be21d462d3fc93,citation,http://alumni.cs.ucr.edu/~zqin001/cvpr2014.pdf,An Online Learned Elementary Grouping Model for Multi-target Tracking,2014 +21,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,9070045c1a9564a5f25b42f3facc7edf4c302483,citation,http://virtualhumans.mpi-inf.mpg.de/papers/lealPonsmollICCVW2011/lealPonsmollICCVW2011.pdf,Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker,2011 +22,Singapore,TownCentre,oxford_town_centre,1.3484104,103.68297965,Nanyang Technological University,edu,2323cb559c9e18673db836ffc283c27e4a002ed9,citation,http://arxiv.org/pdf/1605.04502v1.pdf,Joint Learning of Convolutional Neural Networks and Temporally Constrained Metrics for Tracklet Association,2016 +23,China,TownCentre,oxford_town_centre,39.905838,116.375516,"Huawei Technologies, Beijing, China",company,434627a03d4433b0df03058724524c3ac1c07478,citation,http://jianghz.com/pubs/mtt_tip_final.pdf,Online Multi-Target Tracking With Unified Handling of Complex Scenarios,2015 +24,China,TownCentre,oxford_town_centre,34.250803,108.983693,Xi’an Jiaotong University,edu,434627a03d4433b0df03058724524c3ac1c07478,citation,http://jianghz.com/pubs/mtt_tip_final.pdf,Online Multi-Target Tracking With Unified Handling of Complex Scenarios,2015 +25,United States,TownCentre,oxford_town_centre,28.59899755,-81.19712501,University of Central Florida,edu,084352b63e98d3b3310521fb3bda8cb4a77a0254,citation,http://crcv.ucf.edu/papers/1439.pdf,Part-based multiple-person tracking with partial occlusion handling,2012 +26,United States,TownCentre,oxford_town_centre,39.5469449,-119.81346566,University of Nevada,edu,084352b63e98d3b3310521fb3bda8cb4a77a0254,citation,http://crcv.ucf.edu/papers/1439.pdf,Part-based multiple-person tracking with partial occlusion handling,2012 +27,United Kingdom,TownCentre,oxford_town_centre,55.7782474,-4.1040988,University of the West of Scotland,edu,32b9be86de4f82c5a43da2a1a0a892515da8910d,citation,http://users.informatik.haw-hamburg.de/~ubicomp/arbeiten/papers/ICISP2014.pdf,Robust False Positive Detection for Real-Time Multi-target Tracking,2014 +28,Italy,TownCentre,oxford_town_centre,43.7776426,11.259765,"Università degli Studi di Firenze, Firenze",edu,2914a20df10f3bb55c5d4764ece85101c1a3e5a8,citation,http://www.micc.unifi.it/seidenari/wp-content/papercite-data/pdf/icpr_16.pdf,User interest profiling using tracking-free coarse gaze estimation,2016 +29,United States,TownCentre,oxford_town_centre,40.4441619,-79.94272826,Carnegie Mellon University,edu,1f4fed0183048d9014e22a72fd50e1e5fbe0777c,citation,https://pdfs.semanticscholar.org/6b7b/1760ed23ef15ec210b2d6795fdf9ad36d0e2.pdf,A Game-Theoretic Approach to Multi-Pedestrian Activity Forecasting,2016 +30,United States,TownCentre,oxford_town_centre,37.43131385,-122.16936535,Stanford University,edu,1f4fed0183048d9014e22a72fd50e1e5fbe0777c,citation,https://pdfs.semanticscholar.org/6b7b/1760ed23ef15ec210b2d6795fdf9ad36d0e2.pdf,A Game-Theoretic Approach to Multi-Pedestrian Activity Forecasting,2016 +31,United States,TownCentre,oxford_town_centre,42.3354481,-71.16813864,Boston College,edu,869df5e8221129850e81e77d4dc36e6c0f854fe6,citation,https://arxiv.org/pdf/1601.03094.pdf,A metric for sets of trajectories that is practical and mathematically consistent,2016 +32,United States,TownCentre,oxford_town_centre,34.1579742,-118.2894729,Disney Research,company,d8bc2e2537cecbe6e751d4791837251a249cd06d,citation,http://www.cse.psu.edu/~rtc12/Papers/wacv2016CarrCollins.pdf,Assessing tracking performance in complex scenarios using mean time between failures,2016 +33,United States,TownCentre,oxford_town_centre,40.7982133,-77.8599084,The Pennsylvania State University,edu,d8bc2e2537cecbe6e751d4791837251a249cd06d,citation,http://www.cse.psu.edu/~rtc12/Papers/wacv2016CarrCollins.pdf,Assessing tracking performance in complex scenarios using mean time between failures,2016 +34,United States,TownCentre,oxford_town_centre,28.59899755,-81.19712501,University of Central Florida,edu,2dfba157e0b5db5becb99b3c412ac729cf3bb32d,citation,https://pdfs.semanticscholar.org/7fb2/f6ce372db950f26f9395721651d6c6aa7b76.pdf,Automatic Detection and Tracking of Pedestrians in Videos with Various Crowd Densities,2012 +35,India,TownCentre,oxford_town_centre,12.9914929,80.2336907,"IIT Madras, India",edu,37f2e03c7cbec9ffc35eac51578e7e8fdfee3d4e,citation,http://www.cse.iitm.ac.in/~amittal/wacv2015_review.pdf,Co-operative Pedestrians Group Tracking in Crowded Scenes Using an MST Approach,2015 +36,United Kingdom,TownCentre,oxford_town_centre,55.91029135,-3.32345777,Heriot-Watt University,edu,b8af24279c58a718091817236f878c805a7843e1,citation,https://pdfs.semanticscholar.org/b8af/24279c58a718091817236f878c805a7843e1.pdf,Context Aware Anomalous Behaviour Detection in Crowded Surveillance,2013 +37,Russia,TownCentre,oxford_town_centre,55.8067104,37.5416381,"Faculty of Computer Science, Moscow, Russia",edu,224547337e1ace6411a69c2e06ce538bc67923f7,citation,https://pdfs.semanticscholar.org/2245/47337e1ace6411a69c2e06ce538bc67923f7.pdf,Convolutional Neural Network for Camera Pose Estimation from Object Detections,2017 +38,Germany,TownCentre,oxford_town_centre,48.7468939,9.0805141,Max Planck Institute for Intelligent Systems,edu,b6d0e461535116a675a0354e7da65b2c1d2958d4,citation,https://arxiv.org/pdf/1805.03430.pdf,Deep Directional Statistics: Pose Estimation with Uncertainty Quantification,2018 +39,United States,TownCentre,oxford_town_centre,38.7768106,-94.9442982,Amazon,company,b6d0e461535116a675a0354e7da65b2c1d2958d4,citation,https://arxiv.org/pdf/1805.03430.pdf,Deep Directional Statistics: Pose Estimation with Uncertainty Quantification,2018 +40,United States,TownCentre,oxford_town_centre,47.6423318,-122.1369302,Microsoft,company,b6d0e461535116a675a0354e7da65b2c1d2958d4,citation,https://arxiv.org/pdf/1805.03430.pdf,Deep Directional Statistics: Pose Estimation with Uncertainty Quantification,2018 +41,United Kingdom,TownCentre,oxford_town_centre,55.91029135,-3.32345777,Heriot-Watt University,edu,70be5432677c0fbe000ac0c28dda351a950e0536,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2014/W14/papers/Leach_Detecting_Social_Groups_2014_CVPR_paper.pdf,Detecting Social Groups in Crowded Surveillance Videos Using Visual Attention,2014 +42,Switzerland,TownCentre,oxford_town_centre,47.376313,8.5476699,ETH Zurich,edu,9458642e7645bfd865911140ee8413e2f5f9fcd6,citation,https://pdfs.semanticscholar.org/9458/642e7645bfd865911140ee8413e2f5f9fcd6.pdf,Efficient Multiple People Tracking Using Minimum Cost Arborescences,2014 +43,United Kingdom,TownCentre,oxford_town_centre,54.6141723,-5.9002151,Queen's University Belfast,edu,2a7935706d43c01789d43a81a1d391418f220a0a,citation,https://pure.qub.ac.uk/portal/files/31960902/285.pdf,Enhancing Linear Programming with Motion Modeling for Multi-target Tracking,2015 +44,Sri Lanka,TownCentre,oxford_town_centre,6.7970862,79.9019094,University of Moratuwa,edu,b183914d0b16647a41f0bfd4af64bf94a83a2b14,citation,http://iwinlab.eng.usf.edu/papers/Extensible%20video%20surveillance%20software%20with%20simultaneous%20event%20detection%20for%20low%20and%20high%20density%20crowd%20analysis.pdf,Extensible video surveillance software with simultaneous event detection for low and high density crowd analysis,2014 +45,United States,TownCentre,oxford_town_centre,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,fa5aca45965e312362d2d75a69312a0678fdf5d7,citation,https://pdfs.semanticscholar.org/fa5a/ca45965e312362d2d75a69312a0678fdf5d7.pdf,Fast and Accurate Head Pose Estimation via Random Projection Forests : Supplementary Material,2015 +46,United States,TownCentre,oxford_town_centre,37.3641651,-120.4254615,University of California at Merced,edu,fa5aca45965e312362d2d75a69312a0678fdf5d7,citation,https://pdfs.semanticscholar.org/fa5a/ca45965e312362d2d75a69312a0678fdf5d7.pdf,Fast and Accurate Head Pose Estimation via Random Projection Forests : Supplementary Material,2015 +47,Australia,TownCentre,oxford_town_centre,-32.8892352,151.6998983,"University of Newcastle, Australia",edu,2feb7c57d51df998aafa6f3017662263a91625b4,citation,https://pdfs.semanticscholar.org/d344/9eaaf392fd07b676e744410049f4095b4b5c.pdf,Feature Selection for Intelligent Transportation Systems,2014 +48,Germany,TownCentre,oxford_town_centre,49.01546,8.4257999,Fraunhofer,company,1f82eebadc3ffa41820ad1a0f53770247fc96dcd,citation,https://pdfs.semanticscholar.org/c5ac/81b17b8fcc028f375fbbd090b558ba9a437a.pdf,Using Trajectories derived by Dense Optical Flows as a Spatial Component in Background Subtraction,2016 +49,United States,TownCentre,oxford_town_centre,42.3583961,-71.09567788,MIT,edu,b18f94c5296a9cebe9e779d50d193fd180f78ed9,citation,https://arxiv.org/pdf/1604.01431.pdf,Forecasting Interactive Dynamics of Pedestrians with Fictitious Play,2017 +50,United Kingdom,TownCentre,oxford_town_centre,51.7520849,-1.2516646,Oxford University,edu,b18f94c5296a9cebe9e779d50d193fd180f78ed9,citation,https://arxiv.org/pdf/1604.01431.pdf,Forecasting Interactive Dynamics of Pedestrians with Fictitious Play,2017 +51,United States,TownCentre,oxford_town_centre,37.43131385,-122.16936535,Stanford University,edu,b18f94c5296a9cebe9e779d50d193fd180f78ed9,citation,https://arxiv.org/pdf/1604.01431.pdf,Forecasting Interactive Dynamics of Pedestrians with Fictitious Play,2017 +52,Netherlands,TownCentre,oxford_town_centre,52.3553655,4.9501644,University of Amsterdam,edu,687ec23addf5a1279e49cc46b78e3245af94ac7b,citation,https://pdfs.semanticscholar.org/687e/c23addf5a1279e49cc46b78e3245af94ac7b.pdf,UvA-DARE ( Digital Academic Repository ) Visual Tracking : An Experimental Survey Smeulders,2013 +53,Italy,TownCentre,oxford_town_centre,45.1847248,9.1582069,"Italian Institute of Technology, Genova, Italy",edu,5ab9f00a707a55f4955b378981ad425aa1cb8ea3,citation,https://arxiv.org/pdf/1901.02000.pdf,Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets,2019 +54,Germany,TownCentre,oxford_town_centre,48.1820038,11.5978282,"OSRAM GmbH, Germany",company,5ab9f00a707a55f4955b378981ad425aa1cb8ea3,citation,https://arxiv.org/pdf/1901.02000.pdf,Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets,2019 +55,Italy,TownCentre,oxford_town_centre,45.437398,11.003376,University of Verona,edu,5ab9f00a707a55f4955b378981ad425aa1cb8ea3,citation,https://arxiv.org/pdf/1901.02000.pdf,Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets,2019 +56,United Kingdom,TownCentre,oxford_town_centre,51.7534538,-1.25400997,University of Oxford,edu,3ed9730e5ec8716e8cdf55f207ef973a9c854574,citation,https://arxiv.org/pdf/1612.05234.pdf,Visual Compiler: Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator,2016 +57,United States,TownCentre,oxford_town_centre,29.7207902,-95.34406271,University of Houston,edu,58eba9930b63cc14715368acf40017293b8dc94f,citation,https://pdfs.semanticscholar.org/7508/ac08dd7b9694bcfe71a617df7fcf3df80952.pdf,What Do I See? Modeling Human Visual Perception for Multi-person Tracking,2014 +58,United States,TownCentre,oxford_town_centre,29.7207902,-95.34406271,University of Houston,edu,a0b489eeb4f7fd2249da756d829e179a6718d9d1,citation,,"""Seeing is Believing"": Pedestrian Trajectory Forecasting Using Visual Frustum of Attention",2018 +59,Belgium,TownCentre,oxford_town_centre,50.8779545,4.7002953,"KULeuven, EAVISE",edu,4ec4392246a7760d189cd6ea48a81664cd2fe4bf,citation,https://pdfs.semanticscholar.org/4ec4/392246a7760d189cd6ea48a81664cd2fe4bf.pdf,GPU Accelerated ACF Detector,2018 +60,United States,TownCentre,oxford_town_centre,40.7982133,-77.8599084,The Pennsylvania State University,edu,6e32c368a6157fb911c9363dc3e967a7fb2ad9f7,citation,https://pdfs.semanticscholar.org/8268/d68f6aa510a765466b2c7f2ba2ea34a48c51.pdf,Hybrid Stochastic / Deterministic Optimization for Tracking Sports Players and Pedestrians,2014 +61,United States,TownCentre,oxford_town_centre,40.4439789,-79.9464634,Disney Research Pittsburgh,edu,6e32c368a6157fb911c9363dc3e967a7fb2ad9f7,citation,https://pdfs.semanticscholar.org/8268/d68f6aa510a765466b2c7f2ba2ea34a48c51.pdf,Hybrid Stochastic / Deterministic Optimization for Tracking Sports Players and Pedestrians,2014 +62,India,TownCentre,oxford_town_centre,13.0304619,77.5646862,"M.S. Ramaiah Institute of Technology, Bangalore, India",edu,6f089f9959cc711e16f1ebe0c6251aaf8a65959a,citation,https://pdfs.semanticscholar.org/6f08/9f9959cc711e16f1ebe0c6251aaf8a65959a.pdf,Improvement in object detection using Super Pixels,2016 +63,United States,TownCentre,oxford_town_centre,38.99203005,-76.9461029,University of Maryland College Park,edu,4e82908e6482d973c280deb79c254631a60f1631,citation,https://pdfs.semanticscholar.org/4e82/908e6482d973c280deb79c254631a60f1631.pdf,Improving Efficiency and Scalability in Visual Surveillance Applications,2013 +64,United States,TownCentre,oxford_town_centre,37.8718992,-122.2585399,University of California,edu,38b5a83f7941fea5fd82466f8ce1ce4ed7749f59,citation,http://rlair.cs.ucr.edu/papers/docs/grouptracking.pdf,Improving multi-target tracking via social grouping,2012 +65,Singapore,TownCentre,oxford_town_centre,1.3484104,103.68297965,Nanyang Technological University,edu,13caf4d2e0a4b6fcfcd4b9e8e2341b8ebd38258d,citation,https://arxiv.org/pdf/1605.04502.pdf,Joint Learning of Siamese CNNs and Temporally Constrained Metrics for Tracklet Association,2016 +66,United States,TownCentre,oxford_town_centre,35.9049122,-79.0469134,The University of North Carolina at Chapel Hill,edu,45e459462a80af03e1bb51a178648c10c4250925,citation,https://arxiv.org/pdf/1606.08998.pdf,LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning,2016 +67,China,TownCentre,oxford_town_centre,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,c0262e24324a6a4e6af5bd99fc79e2eb802519b3,citation,https://arxiv.org/pdf/1611.03968.pdf,Learning Scene-specific Object Detectors Based on a Generative-Discriminative Model with Minimal Supervision,2016 +68,China,TownCentre,oxford_town_centre,30.527151,114.400762,China University of Geosciences,edu,c0262e24324a6a4e6af5bd99fc79e2eb802519b3,citation,https://arxiv.org/pdf/1611.03968.pdf,Learning Scene-specific Object Detectors Based on a Generative-Discriminative Model with Minimal Supervision,2016 +69,China,TownCentre,oxford_town_centre,32.0565957,118.77408833,Nanjing University,edu,c0262e24324a6a4e6af5bd99fc79e2eb802519b3,citation,https://arxiv.org/pdf/1611.03968.pdf,Learning Scene-specific Object Detectors Based on a Generative-Discriminative Model with Minimal Supervision,2016 +70,United Kingdom,TownCentre,oxford_town_centre,51.5247272,-0.03931035,Queen Mary University of London,edu,1883387726897d94b663cc4de4df88e5c31df285,citation,http://www.eecs.qmul.ac.uk/~andrea/papers/2014_TIP_MultiTargetTrackingEvaluation_Tahir_Poiesi_Cavallaro.pdf,Measures of Effective Video Tracking,2014 +71,United States,TownCentre,oxford_town_centre,35.9113971,-79.0504529,University of North Carolina at Chapel Hill,edu,8d2bf6ecbfda94f57000b84509bf77f4c47c1c66,citation,https://arxiv.org/pdf/1707.09100.pdf,MixedPeds: Pedestrian Detection in Unannotated Videos Using Synthetically Generated Human-Agents for Training,2018 +72,United States,TownCentre,oxford_town_centre,37.8718992,-122.2585399,University of California,edu,b506aa23949b6d1f0c868ad03aaaeb5e5f7f6b57,citation,http://rlair.cs.ucr.edu/papers/docs/zqin-phd.pdf,Modeling Social and Temporal Context for Video Analysis,2015 +73,Australia,TownCentre,oxford_town_centre,-34.920603,138.6062277,Adelaide University,edu,5bae9822d703c585a61575dced83fa2f4dea1c6d,citation,https://arxiv.org/pdf/1504.01942.pdf,MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking,2015 +74,Switzerland,TownCentre,oxford_town_centre,47.376313,8.5476699,ETH Zurich,edu,5bae9822d703c585a61575dced83fa2f4dea1c6d,citation,https://arxiv.org/pdf/1504.01942.pdf,MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking,2015 +75,Germany,TownCentre,oxford_town_centre,49.8748277,8.6563281,TU Darmstadt,edu,5bae9822d703c585a61575dced83fa2f4dea1c6d,citation,https://arxiv.org/pdf/1504.01942.pdf,MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking,2015 +76,United States,TownCentre,oxford_town_centre,37.8718992,-122.2585399,University of California,edu,e6d48d23308a9e0a215f7b5ba6ae30ee5d2f0ef5,citation,https://pdfs.semanticscholar.org/e6d4/8d23308a9e0a215f7b5ba6ae30ee5d2f0ef5.pdf,Multi-person Tracking by Online Learned Grouping Model with Non-linear Motion Context,2015 +77,France,TownCentre,oxford_town_centre,45.217886,5.807369,INRIA,edu,fc30d7dbf4c3cdd377d8cd4e7eeabd5d73814b8f,citation,https://pdfs.semanticscholar.org/fc30/d7dbf4c3cdd377d8cd4e7eeabd5d73814b8f.pdf,Multiple Object Tracking by Efficient Graph Partitioning,2014 +78,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,290eda31bc13cbd5933acec8b6a25b3e3761c788,citation,https://arxiv.org/pdf/1411.7935.pdf,Multiple object tracking with context awareness,2014 +79,Czech Republic,TownCentre,oxford_town_centre,49.20172,16.6033168,Brno University of Technology,edu,dc53c4bb04e787a0d45dd761ba2101cc51c17b82,citation,https://pdfs.semanticscholar.org/dc53/c4bb04e787a0d45dd761ba2101cc51c17b82.pdf,Multiple-Person Tracking by Detection,2016 +80,Germany,TownCentre,oxford_town_centre,48.1820038,11.5978282,"OSRAM GmbH, Germany",company,943b1b92b5bdee0b5770418c645a4a17bded1ccf,citation,https://arxiv.org/pdf/1805.00652.pdf,MX-LSTM: Mixing Tracklets and Vislets to Jointly Forecast Trajectories and Head Poses,2018 +81,Italy,TownCentre,oxford_town_centre,45.437398,11.003376,University of Verona,edu,943b1b92b5bdee0b5770418c645a4a17bded1ccf,citation,https://arxiv.org/pdf/1805.00652.pdf,MX-LSTM: Mixing Tracklets and Vislets to Jointly Forecast Trajectories and Head Poses,2018 +82,France,TownCentre,oxford_town_centre,48.8422058,2.3451689,"INRIA / Ecole Normale Supérieure, France",edu,47119c99f5aa1e47bbeb86de0f955e7c500e6a93,citation,https://arxiv.org/pdf/1408.3304.pdf,On pairwise costs for network flow multi-object tracking,2015 +83,United States,TownCentre,oxford_town_centre,42.3504253,-71.10056114,Boston University,edu,1ae3dd081b93c46cda4d72100d8b1d59eb585157,citation,https://pdfs.semanticscholar.org/fea1/0f39b0a77035fb549fc580fd951384b79f9b.pdf,Online Motion Agreement Tracking,2013 +84,Malaysia,TownCentre,oxford_town_centre,4.3400673,101.1429799,Universiti Tunku Abdul Rahman,edu,e1f815c50a6c0c6d790c60a1348393264f829e60,citation,https://pdfs.semanticscholar.org/e1f8/15c50a6c0c6d790c60a1348393264f829e60.pdf,PEDESTRIAN DETECTION AND TRACKING IN SURVEILLANCE VIDEO By PENNY CHONG,2016 +85,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,422d352a7d26fef692a3cd24466bfb5b4526efea,citation,https://pdfs.semanticscholar.org/422d/352a7d26fef692a3cd24466bfb5b4526efea.pdf,Pedestrian interaction in tracking : the social force model and global optimization methods,2012 +86,Sweden,TownCentre,oxford_town_centre,57.6897063,11.9741654,Chalmers University of Technology,edu,367b5b814aa991329c2ae7f8793909ad8c0a56f1,citation,https://arxiv.org/pdf/1211.0191.pdf,Performance evaluation of random set based pedestrian tracking algorithms,2013 +87,Japan,TownCentre,oxford_town_centre,35.5152072,134.1733553,Tottori University,edu,9d89f1bc88fd65e90b31a2129719384796bed17a,citation,http://vision.unipv.it/CV/materiale2016-17/2nd%20Choice/0225.pdf,Person re-identification using co-occurrence attributes of physical and adhered human characteristics,2016 +88,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,48705017d91a157949cfaaeb19b826014899a36b,citation,https://pdfs.semanticscholar.org/4870/5017d91a157949cfaaeb19b826014899a36b.pdf,PROBABILISTIC MULTI-PERSON TRACKING USING DYNAMIC BAYES NETWORKS,2015 +89,Italy,TownCentre,oxford_town_centre,39.2173657,9.1149218,"Università degli Studi di Cagliari, Italy",edu,7c1f47ca50a8a55f93bf69791d9df2f994019758,citation,http://veprints.unica.it/1295/1/PhD_ThesisPalaF.pdf,Re-identification and semantic retrieval of pedestrians in video surveillance scenarios,2016 +90,United Kingdom,TownCentre,oxford_town_centre,51.5247272,-0.03931035,Queen Mary University of London,edu,3a28059df29b74775f77fd20a15dc6b5fe857556,citation,https://pdfs.semanticscholar.org/3a28/059df29b74775f77fd20a15dc6b5fe857556.pdf,Riccardo Mazzon PhD Thesis 2013,2013 +91,Brazil,TownCentre,oxford_town_centre,-30.0338248,-51.218828,Federal University of Rio Grande do Sul,edu,057517452369751bd63d83902ea91558d58161da,citation,http://inf.ufrgs.br/~gfuhr/papers/102095_3.pdf,Robust Patch-Based Pedestrian Tracking Using Monocular Calibrated Cameras,2012 +92,China,TownCentre,oxford_town_centre,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 +93,Singapore,TownCentre,oxford_town_centre,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 +94,China,TownCentre,oxford_town_centre,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 +95,Singapore,TownCentre,oxford_town_centre,1.3484104,103.68297965,Nanyang Technological University,edu,7c132e0a2b7e13c78784287af38ad74378da31e5,citation,https://pdfs.semanticscholar.org/7c13/2e0a2b7e13c78784287af38ad74378da31e5.pdf,Salient Parts based Multi-people Tracking,2015 +96,China,TownCentre,oxford_town_centre,40.0044795,116.370238,Chinese Academy of Sciences,edu,679136c2844eeddca34e98e483aca1ff6ef5e902,citation,https://arxiv.org/pdf/1712.08745.pdf,Scene-Specific Pedestrian Detection Based on Parallel Vision,2017 +97,China,TownCentre,oxford_town_centre,34.250803,108.983693,Xi’an Jiaotong University,edu,679136c2844eeddca34e98e483aca1ff6ef5e902,citation,https://arxiv.org/pdf/1712.08745.pdf,Scene-Specific Pedestrian Detection Based on Parallel Vision,2017 +98,China,TownCentre,oxford_town_centre,40.0044795,116.370238,Chinese Academy of Sciences,edu,57e9b0d3ab6295e914d5a30cfaa3b2c81189abc1,citation,https://arxiv.org/pdf/1611.07544.pdf,Self-Learning Scene-Specific Pedestrian Detectors Using a Progressive Latent Model,2017 +99,United States,TownCentre,oxford_town_centre,35.9990522,-78.9290629,Duke University,edu,57e9b0d3ab6295e914d5a30cfaa3b2c81189abc1,citation,https://arxiv.org/pdf/1611.07544.pdf,Self-Learning Scene-Specific Pedestrian Detectors Using a Progressive Latent Model,2017 +100,Switzerland,TownCentre,oxford_town_centre,47.3764534,8.54770931,ETH Zürich,edu,70b42bbd76e6312d39ea06b8a0c24beb4a93e022,citation,http://www.tnt.uni-hannover.de/papers/data/1075/WACV2015_Abstract.pdf,Solving Multiple People Tracking in a Minimum Cost Arborescence,2015 +101,United States,TownCentre,oxford_town_centre,42.718568,-84.47791571,Michigan State University,edu,acf0db156406ddad1ace2ff2696cb60d0a04cf7c,citation,http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf,Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,2018 +102,United Kingdom,TownCentre,oxford_town_centre,51.7534538,-1.25400997,University of Oxford,edu,acf0db156406ddad1ace2ff2696cb60d0a04cf7c,citation,http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf,Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,2018 +103,Japan,TownCentre,oxford_town_centre,36.05238585,140.11852361,Institute of Industrial Science,edu,acf0db156406ddad1ace2ff2696cb60d0a04cf7c,citation,http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf,Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,2018 +104,United States,TownCentre,oxford_town_centre,40.4441619,-79.94272826,Carnegie Mellon University,edu,acf0db156406ddad1ace2ff2696cb60d0a04cf7c,citation,http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf,Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,2018 +105,Sweden,TownCentre,oxford_town_centre,57.7172004,11.9218558,"Volvo Construction Equipment, Göthenburg, Sweden",company,acf0db156406ddad1ace2ff2696cb60d0a04cf7c,citation,http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf,Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,2018 +106,United States,TownCentre,oxford_town_centre,35.9990522,-78.9290629,Duke University,edu,64e0690dd176a93de9d4328f6e31fc4afe1e7536,citation,https://pdfs.semanticscholar.org/64e0/690dd176a93de9d4328f6e31fc4afe1e7536.pdf,Tracking Multiple People Online and in Real Time,2014 +107,Switzerland,TownCentre,oxford_town_centre,47.3764534,8.54770931,ETH Zürich,edu,64c78c8bf779a27e819fd9d5dba91247ab5a902b,citation,https://arxiv.org/pdf/1607.07304.pdf,Tracking with multi-level features.,2016 +108,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,64c78c8bf779a27e819fd9d5dba91247ab5a902b,citation,https://arxiv.org/pdf/1607.07304.pdf,Tracking with multi-level features.,2016 +109,Germany,TownCentre,oxford_town_centre,48.14955455,11.56775314,Technical University Munich,edu,64c78c8bf779a27e819fd9d5dba91247ab5a902b,citation,https://arxiv.org/pdf/1607.07304.pdf,Tracking with multi-level features.,2016 +110,Singapore,TownCentre,oxford_town_centre,1.3484104,103.68297965,Nanyang Technological University,edu,7d3698c0e828d05f147682b0f5bfcd3b681ff205,citation,https://arxiv.org/pdf/1511.06654.pdf,Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation,2017 +111,Australia,TownCentre,oxford_town_centre,-35.2809368,149.1300092,"NICTA, Canberra",edu,f0cc615b14c97482faa9c47eb855303c71ff03a7,citation,https://pdfs.semanticscholar.org/f0cc/615b14c97482faa9c47eb855303c71ff03a7.pdf,Tracklet clustering for robust multiple object tracking using distance dependent Chinese restaurant processes,2016 +112,Germany,TownCentre,oxford_town_centre,52.5180641,13.3250425,TU Berlin,edu,c4cd19cf41a2f5cd543d81b94afe6cc42785920a,citation,http://elvera.nue.tu-berlin.de/files/1491Bochinski2016.pdf,Training a convolutional neural network for multi-class object detection using solely virtual world data,2016 diff --git a/site/datasets/verified/pa_100k.csv b/site/datasets/verified/pa_100k.csv new file mode 100644 index 00000000..b79ce7f3 --- /dev/null +++ b/site/datasets/verified/pa_100k.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,PA-100K,pa_100k,0.0,0.0,,,,main,,HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis,2017 diff --git a/site/datasets/verified/penn_fudan.csv b/site/datasets/verified/penn_fudan.csv new file mode 100644 index 00000000..10427ed0 --- /dev/null +++ b/site/datasets/verified/penn_fudan.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,Penn Fudan,penn_fudan,0.0,0.0,,,,main,,Object Detection Combining Recognition and Segmentation,2007 diff --git a/site/datasets/verified/peta.csv b/site/datasets/verified/peta.csv new file mode 100644 index 00000000..e999095c --- /dev/null +++ b/site/datasets/verified/peta.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,PETA,peta,0.0,0.0,,,,main,,Pedestrian Attribute Recognition At Far Distance,2014 diff --git a/site/datasets/verified/pilot_parliament.csv b/site/datasets/verified/pilot_parliament.csv new file mode 100644 index 00000000..45279348 --- /dev/null +++ b/site/datasets/verified/pilot_parliament.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,PPB,pilot_parliament,0.0,0.0,,,,main,,Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification,2018 diff --git a/site/datasets/verified/pipa.csv b/site/datasets/verified/pipa.csv new file mode 100644 index 00000000..3acdccff --- /dev/null +++ b/site/datasets/verified/pipa.csv @@ -0,0 +1,2 @@ +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 diff --git a/site/datasets/verified/pku_reid.csv b/site/datasets/verified/pku_reid.csv new file mode 100644 index 00000000..46dea72b --- /dev/null +++ b/site/datasets/verified/pku_reid.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,PKU-Reid,pku_reid,0.0,0.0,,,,main,,Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification,2015 diff --git a/site/datasets/verified/prid.csv b/site/datasets/verified/prid.csv new file mode 100644 index 00000000..622bae62 --- /dev/null +++ b/site/datasets/verified/prid.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,PRID,prid,0.0,0.0,,,,main,,Person Re-identification by Descriptive and Discriminative Classification,2011 diff --git a/site/datasets/verified/pubfig.csv b/site/datasets/verified/pubfig.csv new file mode 100644 index 00000000..5152566a --- /dev/null +++ b/site/datasets/verified/pubfig.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,PubFig,pubfig,0.0,0.0,,,,main,,Attribute and simile classifiers for face verification,2009 diff --git a/site/datasets/verified/pubfig_83.csv b/site/datasets/verified/pubfig_83.csv new file mode 100644 index 00000000..9385e8cd --- /dev/null +++ b/site/datasets/verified/pubfig_83.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,pubfig83,pubfig_83,0.0,0.0,,,,main,,Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook,2011 diff --git a/site/datasets/verified/social_relation.csv b/site/datasets/verified/social_relation.csv new file mode 100644 index 00000000..eb7f473e --- /dev/null +++ b/site/datasets/verified/social_relation.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,Social Relation,social_relation,0.0,0.0,,,,main,,Learning Social Relation Traits from Face Images,2015 diff --git a/site/datasets/verified/tisi.csv b/site/datasets/verified/tisi.csv new file mode 100644 index 00000000..80f164c4 --- /dev/null +++ b/site/datasets/verified/tisi.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,Times Square Intersection,tisi,0.0,0.0,,,,main,,Video Synopsis by Heterogeneous Multi-source Correlation,2013 diff --git a/site/datasets/verified/uccs.csv b/site/datasets/verified/uccs.csv new file mode 100644 index 00000000..d7c84820 --- /dev/null +++ b/site/datasets/verified/uccs.csv @@ -0,0 +1,9 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,UCCS,uccs,0.0,0.0,,,,main,,Large scale unconstrained open set face database,2013 +1,United States,UCCS,uccs,41.70456775,-86.23822026,University of Notre Dame,edu,841855205818d3a6d6f85ec17a22515f4f062882,citation,https://arxiv.org/pdf/1805.11529.pdf,Low Resolution Face Recognition in the Wild,2018 +2,United States,UCCS,uccs,40.11571585,-88.22750772,Beckman Institute,edu,288d2704205d9ca68660b9f3a8fda17e18329c13,citation,https://arxiv.org/pdf/1601.04153.pdf,Studying Very Low Resolution Recognition Using Deep Networks,2016 +3,United States,UCCS,uccs,38.8920756,-104.79716389,"University of Colorado, Colorado Springs",edu,d4f1eb008eb80595bcfdac368e23ae9754e1e745,citation,,Unconstrained Face Detection and Open-Set Face Recognition Challenge,2017 +4,United Kingdom,UCCS,uccs,51.5247272,-0.03931035,Queen Mary University of London,edu,2306b2a8fba28539306052764a77a0d0f5d1236a,citation,https://arxiv.org/pdf/1804.09691.pdf,Surveillance Face Recognition Challenge,2018 +5,United Kingdom,UCCS,uccs,55.378051,-3.435973,"Vision Semantics Ltd, UK",edu,2306b2a8fba28539306052764a77a0d0f5d1236a,citation,https://arxiv.org/pdf/1804.09691.pdf,Surveillance Face Recognition Challenge,2018 +6,China,UCCS,uccs,39.9808333,116.34101249,Beihang University,edu,c50e498ede6f5216cffd0645e747ce67fae2096a,citation,https://arxiv.org/pdf/1811.09998.pdf,Low-Resolution Face Recognition in the Wild via Selective Knowledge Distillation,2018 +7,China,UCCS,uccs,39.97426,116.21589,"Institute of Information Engineering, CAS, Beijing, China",edu,c50e498ede6f5216cffd0645e747ce67fae2096a,citation,https://arxiv.org/pdf/1811.09998.pdf,Low-Resolution Face Recognition in the Wild via Selective Knowledge Distillation,2018 diff --git a/site/datasets/verified/ucf_selfie.csv b/site/datasets/verified/ucf_selfie.csv new file mode 100644 index 00000000..c32488ba --- /dev/null +++ b/site/datasets/verified/ucf_selfie.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,UCF Selfie,ucf_selfie,0.0,0.0,,,,main,,How to Take a Good Selfie?,2015 diff --git a/site/datasets/verified/ufdd.csv b/site/datasets/verified/ufdd.csv new file mode 100644 index 00000000..cec3e352 --- /dev/null +++ b/site/datasets/verified/ufdd.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,UFDD,ufdd,0.0,0.0,,,,main,,Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results,2018 diff --git a/site/datasets/verified/umd_faces.csv b/site/datasets/verified/umd_faces.csv new file mode 100644 index 00000000..03a3ed68 --- /dev/null +++ b/site/datasets/verified/umd_faces.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,UMD,umd_faces,0.0,0.0,,,,main,,UMDFaces: An annotated face dataset for training deep networks,2017 diff --git a/site/datasets/verified/urban_tribes.csv b/site/datasets/verified/urban_tribes.csv new file mode 100644 index 00000000..be8799f6 --- /dev/null +++ b/site/datasets/verified/urban_tribes.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,Urban Tribes,urban_tribes,0.0,0.0,,,,main,,From Bikers to Surfers: Visual Recognition of Urban Tribes,2013 diff --git a/site/datasets/verified/used.csv b/site/datasets/verified/used.csv new file mode 100644 index 00000000..52c7be2f --- /dev/null +++ b/site/datasets/verified/used.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,USED Social Event Dataset,used,0.0,0.0,,,,main,,USED: a large-scale social event detection dataset,2016 diff --git a/site/datasets/verified/vadana.csv b/site/datasets/verified/vadana.csv new file mode 100644 index 00000000..43b21fa4 --- /dev/null +++ b/site/datasets/verified/vadana.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,VADANA,vadana,0.0,0.0,,,,main,,VADANA: A dense dataset for facial image analysis,2011 diff --git a/site/datasets/verified/vgg_celebs_in_places.csv b/site/datasets/verified/vgg_celebs_in_places.csv new file mode 100644 index 00000000..41086905 --- /dev/null +++ b/site/datasets/verified/vgg_celebs_in_places.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,CIP,vgg_celebs_in_places,0.0,0.0,,,,main,,Faces in Places: compound query retrieval,2016 diff --git a/site/datasets/verified/vgg_faces.csv b/site/datasets/verified/vgg_faces.csv new file mode 100644 index 00000000..9d95ac17 --- /dev/null +++ b/site/datasets/verified/vgg_faces.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,VGG Face,vgg_faces,0.0,0.0,,,,main,,Deep Face Recognition,2015 diff --git a/site/datasets/verified/vgg_faces2.csv b/site/datasets/verified/vgg_faces2.csv new file mode 100644 index 00000000..689b801e --- /dev/null +++ b/site/datasets/verified/vgg_faces2.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,VGG Face2,vgg_faces2,0.0,0.0,,,,main,,VGGFace2: A Dataset for Recognising Faces across Pose and Age,2018 diff --git a/site/datasets/verified/viper.csv b/site/datasets/verified/viper.csv new file mode 100644 index 00000000..9885dfe5 --- /dev/null +++ b/site/datasets/verified/viper.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,VIPeR,viper,0.0,0.0,,,,main,,"Evaluating Appearance Models for Recognition, Reacquisition, and Tracking",2007 diff --git a/site/datasets/verified/vmu.csv b/site/datasets/verified/vmu.csv new file mode 100644 index 00000000..dd40d38b --- /dev/null +++ b/site/datasets/verified/vmu.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,VMU,vmu,0.0,0.0,,,,main,,Can facial cosmetics affect the matching accuracy of face recognition systems?,2012 diff --git a/site/datasets/verified/voc.csv b/site/datasets/verified/voc.csv new file mode 100644 index 00000000..89a14200 --- /dev/null +++ b/site/datasets/verified/voc.csv @@ -0,0 +1,2 @@ +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 diff --git a/site/datasets/verified/who_goes_there.csv b/site/datasets/verified/who_goes_there.csv new file mode 100644 index 00000000..8ff8ff9a --- /dev/null +++ b/site/datasets/verified/who_goes_there.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,WGT,who_goes_there,0.0,0.0,,,,main,,Who goes there?: approaches to mapping facial appearance diversity,2016 diff --git a/site/datasets/verified/wider.csv b/site/datasets/verified/wider.csv new file mode 100644 index 00000000..bfabc75b --- /dev/null +++ b/site/datasets/verified/wider.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,WIDER,wider,0.0,0.0,,,,main,,Recognize complex events from static images by fusing deep channels,2015 diff --git a/site/datasets/verified/wider_attribute.csv b/site/datasets/verified/wider_attribute.csv new file mode 100644 index 00000000..29165936 --- /dev/null +++ b/site/datasets/verified/wider_attribute.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,WIDER Attribute,wider_attribute,0.0,0.0,,,,main,,Human Attribute Recognition by Deep Hierarchical Contexts,2016 diff --git a/site/datasets/verified/wider_face.csv b/site/datasets/verified/wider_face.csv new file mode 100644 index 00000000..86c470e4 --- /dev/null +++ b/site/datasets/verified/wider_face.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,WIDER FACE,wider_face,0.0,0.0,,,,main,,WIDER FACE: A Face Detection Benchmark,2016 diff --git a/site/datasets/verified/wildtrack.csv b/site/datasets/verified/wildtrack.csv new file mode 100644 index 00000000..e6329a56 --- /dev/null +++ b/site/datasets/verified/wildtrack.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,WildTrack,wildtrack,0.0,0.0,,,,main,,WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection,2018 diff --git a/site/datasets/verified/yale_faces.csv b/site/datasets/verified/yale_faces.csv new file mode 100644 index 00000000..fd43e5cf --- /dev/null +++ b/site/datasets/verified/yale_faces.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,YaleFaces,yale_faces,0.0,0.0,,,,main,,From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose,2001 diff --git a/site/datasets/verified/yfcc_100m.csv b/site/datasets/verified/yfcc_100m.csv new file mode 100644 index 00000000..c7b3cd1f --- /dev/null +++ b/site/datasets/verified/yfcc_100m.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,YFCC100M,yfcc_100m,0.0,0.0,,,,main,,YFCC100M: the new data in multimedia research,2016 diff --git a/site/datasets/verified/youtube_celebrities.csv b/site/datasets/verified/youtube_celebrities.csv new file mode 100644 index 00000000..a3b08ee1 --- /dev/null +++ b/site/datasets/verified/youtube_celebrities.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,YouTube Celebrities,youtube_celebrities,0.0,0.0,,,,main,,Face tracking and recognition with visual constraints in real-world videos,2008 diff --git a/site/datasets/verified/youtube_faces.csv b/site/datasets/verified/youtube_faces.csv new file mode 100644 index 00000000..32356450 --- /dev/null +++ b/site/datasets/verified/youtube_faces.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,YouTubeFaces,youtube_faces,0.0,0.0,,,,main,,Face recognition in unconstrained videos with matched background similarity,2011 diff --git a/site/datasets/verified/youtube_makeup.csv b/site/datasets/verified/youtube_makeup.csv new file mode 100644 index 00000000..9ea99ac9 --- /dev/null +++ b/site/datasets/verified/youtube_makeup.csv @@ -0,0 +1,2 @@ +id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,,YMU,youtube_makeup,0.0,0.0,,,,main,,Can facial cosmetics affect the matching accuracy of face recognition systems?,2012 diff --git a/site/datasets/verified/youtube_poses.csv b/site/datasets/verified/youtube_poses.csv new file mode 100644 index 00000000..5298f596 --- /dev/null +++ b/site/datasets/verified/youtube_poses.csv @@ -0,0 +1,2 @@ +id,country,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,,,,main,,Personalizing Human Video Pose Estimation,2016 |