From 83accf664ea5ed08009a670392b1e60d8165ea74 Mon Sep 17 00:00:00 2001 From: Adam Harvey Date: Tue, 4 Jun 2019 15:08:15 -0500 Subject: change trade routes to supply chain, add updates page --- site/datasets/verified/adience.csv | 26 ++++++++++++ site/datasets/verified/duke_mtmc.csv | 76 ++++++++++++++++++++++++++++++++++++ site/datasets/verified/imdb_face.csv | 5 +++ site/datasets/verified/megaface.csv | 5 +++ site/datasets/verified/morph_nc.csv | 2 +- site/datasets/verified/msceleb.csv | 29 ++++++++++++++ site/datasets/verified/pipa.csv | 7 ++++ site/datasets/verified/uccs.csv | 3 ++ 8 files changed, 152 insertions(+), 1 deletion(-) (limited to 'site/datasets') diff --git a/site/datasets/verified/adience.csv b/site/datasets/verified/adience.csv index f6e229b6..f46d4483 100644 --- a/site/datasets/verified/adience.csv +++ b/site/datasets/verified/adience.csv @@ -138,3 +138,29 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,t 136,Malaysia,Adience,adience,3.12267405,101.65356103,"University of Malaya, Kuala Lumpur",edu,d4d1ac1cfb2ca703c4db8cc9a1c7c7531fa940f9,citation,,"Gender estimation based on supervised HOG, Action Units and unsupervised CNN feature extraction",2017 137,United Kingdom,Adience,adience,51.5247272,-0.03931035,Queen Mary University of London,edu,d7fd3dedb6b260702ed5e4b9175127815286e8da,citation,,Knowledge sharing: From atomic to parametrised context and shallow to deep models,2017 138,Taiwan,Adience,adience,25.0421852,121.6145477,"Academia Sinica, Taipei, Taiwan",edu,aa6f7c3daed31d331ef626758e990cbc04632852,citation,,Merging Deep Neural Networks for Mobile Devices,2018 +139,China,Adience,adience,22.4162632,114.2109318,Chinese University of Hong Kong,edu,aaa2b45153051e23d5a35ccf9af8ecabc0fe24cd,citation,https://pdfs.semanticscholar.org/aaa2/b45153051e23d5a35ccf9af8ecabc0fe24cd.pdf,1 How Good can Human Predict Facial Age ?,2017 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Learning an Optimal Data Augmentation Strategy,2017 +148,Taiwan,Adience,adience,25.0410728,121.6147562,Institute of Information Science,edu,39539b7fcf1c637b04de84b23dc9c85a8b2f9f40,citation,https://arxiv.org/pdf/1805.04980.pdf,Unifying and Merging Well-trained Deep Neural Networks for Inference Stage,2018 +149,Taiwan,Adience,adience,25.021321,121.5360683,MOST Joint Research Center for AI Technology and All Vista Healthcare,company,39539b7fcf1c637b04de84b23dc9c85a8b2f9f40,citation,https://arxiv.org/pdf/1805.04980.pdf,Unifying and Merging Well-trained Deep Neural Networks for Inference Stage,2018 +150,India,Adience,adience,28.5449756,77.1926284,"IIT Delhi, India",edu,0dc61f199539cd15f847b688740be49b39e3d520,citation,https://pdfs.semanticscholar.org/0dc6/1f199539cd15f847b688740be49b39e3d520.pdf,Age Group Determination from Face Using Texture Classification based on Probabilistic Non-Extensive Entropy,2017 +151,Spain,Adience,adience,41.5008957,2.111553,Autonomous University of Barcelona,edu,d7f6eaa5caa0d187cd1fe51d5bc27343921e7539,citation,https://arxiv.org/pdf/1807.07320.pdf,Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery,2018 +152,Singapore,Adience,adience,1.2962018,103.77689944,National University of Singapore,edu,3b50a85ba29f0f7eb49fb275be86e6c2b4f8fa4b,citation,https://pdfs.semanticscholar.org/3b50/a85ba29f0f7eb49fb275be86e6c2b4f8fa4b.pdf,Image ordinal classification with deep multi-view learning,2018 +153,China,Adience,adience,28.874513,105.431827,"Sichuan Police College, Luzhou, China",gov,3b50a85ba29f0f7eb49fb275be86e6c2b4f8fa4b,citation,https://pdfs.semanticscholar.org/3b50/a85ba29f0f7eb49fb275be86e6c2b4f8fa4b.pdf,Image ordinal classification with deep multi-view learning,2018 +154,China,Adience,adience,30.788537,103.888902,"UESTC, Chengdu, China",edu,3b50a85ba29f0f7eb49fb275be86e6c2b4f8fa4b,citation,https://pdfs.semanticscholar.org/3b50/a85ba29f0f7eb49fb275be86e6c2b4f8fa4b.pdf,Image ordinal classification with deep multi-view learning,2018 +155,United States,Adience,adience,32.8536333,-117.2035286,Kyung Hee University,edu,73b83ef7ee5f929be51a91096b57c098008f384e,citation,https://pdfs.semanticscholar.org/73b8/3ef7ee5f929be51a91096b57c098008f384e.pdf,Mining Wrinkle-Patterns with Local EdgePrototypic Pattern (LEPP) Descriptor for the Recognition of Human Age-groups,2018 +156,India,Adience,adience,10.9365094,76.9562405,"Sri Krishna College of Engineering and Technology, Coimbatore, India",edu,ece46e3a126953f639149fc233bddcd44d8afad1,citation,https://pdfs.semanticscholar.org/ece4/6e3a126953f639149fc233bddcd44d8afad1.pdf,Semantic-Based Facial Image-Retrieval System with Aid of Adaptive Particle Swarm Optimization and Squared Euclidian Distance,2015 +157,Canada,Adience,adience,45.5010087,-73.6157778,University of Montreal,edu,3540625bc996601a9d04c4027169b7fcad1b9eae,citation,https://pdfs.semanticscholar.org/3540/625bc996601a9d04c4027169b7fcad1b9eae.pdf,TECHNIQUES IN ORDINAL CLASSIFICATION AND IMAGE-TO-IMAGE TRANSLATION,2018 +158,Canada,Adience,adience,45.5307147,-73.6135931,"Institute for Learning, Algorithms Montreal, Canada",edu,e8d0eb3c3bf64b38ec04e982745147428459e2d2,citation,https://arxiv.org/pdf/1705.05278.pdf,Unimodal Probability Distributions for Deep Ordinal Classification,2017 +159,China,Adience,adience,45.7413921,126.62552755,Harbin Institute of Technology,edu,09e353946fb6adf1621f33041853c58aecfd183b,citation,,Deep convolutional neural networks-based age and gender classification with facial images,2017 +160,China,Adience,adience,26.085573,119.372442,Fujian University of Technology,edu,09e353946fb6adf1621f33041853c58aecfd183b,citation,,Deep convolutional neural networks-based age and gender classification with facial images,2017 +161,China,Adience,adience,25.28164,110.337304,Guilin University of Electronic Technology,edu,09e353946fb6adf1621f33041853c58aecfd183b,citation,,Deep convolutional neural networks-based age and gender classification with facial images,2017 +162,Israel,Adience,adience,32.77824165,34.99565673,Open University of Israel,edu,1be498d4bbc30c3bfd0029114c784bc2114d67c0,citation,,Age and Gender Estimation of Unfiltered Faces,2014 +163,South Korea,Adience,adience,37.5509442,126.9410023,Sogang University,edu,0deea943ac4dc1be822c02f97d0c6c97e201ba8d,citation,,Age category estimation using matching convolutional neural network,2018 +164,Taiwan,Adience,adience,25.0411727,121.6146518,"Insititute of Information Science, Academia Sinica, Taipei, Taiwan",edu,3d0444be5be1d19d93e91519e48e314b3035e4cf,citation,,Joint Estimation of Age and Gender from Unconstrained Face Images Using Lightweight Multi-Task CNN for Mobile Applications,2018 diff --git a/site/datasets/verified/duke_mtmc.csv b/site/datasets/verified/duke_mtmc.csv index b85d9458..5ede8ed5 100644 --- a/site/datasets/verified/duke_mtmc.csv +++ b/site/datasets/verified/duke_mtmc.csv @@ -223,3 +223,79 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,t 221,China,Duke MTMC,duke_mtmc,31.2284923,121.40211389,East China Normal University,edu,0353fe24ecd237f4d9ae4dbc277a6a67a69ce8ed,citation,https://pdfs.semanticscholar.org/0353/fe24ecd237f4d9ae4dbc277a6a67a69ce8ed.pdf,Discriminative Feature Representation for Person Re-identification by Batch-contrastive Loss,2018 222,China,Duke MTMC,duke_mtmc,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,fd2bc4833c19a60d3646368952dcf35dbda007f3,citation,,Improving Person Re-Identification by Adaptive Hard Sample Mining,2018 223,China,Duke MTMC,duke_mtmc,30.60903415,114.3514284,Wuhan University of Technology,edu,fd2bc4833c19a60d3646368952dcf35dbda007f3,citation,,Improving Person Re-Identification by Adaptive Hard Sample Mining,2018 +224,China,Duke MTMC,duke_mtmc,30.19331415,120.11930822,Zhejiang University,edu,b350b567b13ab2b7ba94159767a41917fc38a2cb,citation,https://arxiv.org/pdf/1903.07071.pdf,Bag of Tricks and A Strong Baseline for Deep Person Re-identification,2019 +225,China,Duke MTMC,duke_mtmc,32.035225,118.855317,PLA Army Engineering University,mil,c8ac121e9c4eb9964be9c5713f22a95c1c3b57e9,citation,https://arxiv.org/pdf/1901.05798.pdf,Ensemble Feature for Person Re-Identification,2019 +226,China,Duke MTMC,duke_mtmc,22.4162632,114.2109318,Chinese University of Hong Kong,edu,0c769c19d894e0dbd6eb314781dc1db3c626df57,citation,https://arxiv.org/pdf/1604.01850.pdf,Joint Detection and Identification Feature Learning for Person Search,2017 +227,China,Duke MTMC,duke_mtmc,39.993008,116.329882,SenseTime,company,0c769c19d894e0dbd6eb314781dc1db3c626df57,citation,https://arxiv.org/pdf/1604.01850.pdf,Joint Detection and Identification Feature Learning for Person Search,2017 +228,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,0c769c19d894e0dbd6eb314781dc1db3c626df57,citation,https://arxiv.org/pdf/1604.01850.pdf,Joint Detection and Identification Feature Learning for Person Search,2017 +229,United States,Duke MTMC,duke_mtmc,22.5447154,113.9357164,Tencent,company,57c144f668d11ef7e2c89fdfcf67341a4733dd64,citation,https://pdfs.semanticscholar.org/57c1/44f668d11ef7e2c89fdfcf67341a4733dd64.pdf,Unlabeled images Auxiliary reference person images Backbone ResNet ‐ 50 Reference learning,2019 +230,United Kingdom,Duke MTMC,duke_mtmc,51.5247272,-0.03931035,Queen Mary University of London,edu,57c144f668d11ef7e2c89fdfcf67341a4733dd64,citation,https://pdfs.semanticscholar.org/57c1/44f668d11ef7e2c89fdfcf67341a4733dd64.pdf,Unlabeled images Auxiliary reference person images Backbone ResNet ‐ 50 Reference learning,2019 +231,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,57c144f668d11ef7e2c89fdfcf67341a4733dd64,citation,https://pdfs.semanticscholar.org/57c1/44f668d11ef7e2c89fdfcf67341a4733dd64.pdf,Unlabeled images Auxiliary reference person images Backbone ResNet ‐ 50 Reference learning,2019 +232,China,Duke MTMC,duke_mtmc,31.83907195,117.26420748,University of Science and Technology of China,edu,59a4cec1afb2804eeff1774c4eb315701443af76,citation,https://arxiv.org/pdf/1904.02998.pdf,Relation-Aware Global Attention,2019 +233,United States,Duke MTMC,duke_mtmc,42.3614256,-71.0812092,Microsoft Research Asia,company,59a4cec1afb2804eeff1774c4eb315701443af76,citation,https://arxiv.org/pdf/1904.02998.pdf,Relation-Aware Global Attention,2019 +234,China,Duke MTMC,duke_mtmc,32.0565957,118.77408833,Nanjing University,edu,9a433055551c1f5c670f2a69a57f6aad3a5810d9,citation,https://arxiv.org/pdf/1904.03425.pdf,A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification,2019 +235,Australia,Duke MTMC,duke_mtmc,-34.40505545,150.87834655,University of Wollongong,edu,9a433055551c1f5c670f2a69a57f6aad3a5810d9,citation,https://arxiv.org/pdf/1904.03425.pdf,A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification,2019 +236,Australia,Duke MTMC,duke_mtmc,-33.88890695,151.18943366,University of Sydney,edu,9a433055551c1f5c670f2a69a57f6aad3a5810d9,citation,https://arxiv.org/pdf/1904.03425.pdf,A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification,2019 +237,China,Duke MTMC,duke_mtmc,24.4399419,118.09301781,Xiamen University,edu,b9cc54c5f94371cfc8e79179c20ec559a1a43cbb,citation,https://arxiv.org/pdf/1904.01990.pdf,Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification,2019 +238,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,b9cc54c5f94371cfc8e79179c20ec559a1a43cbb,citation,https://arxiv.org/pdf/1904.01990.pdf,Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification,2019 +239,Australia,Duke MTMC,duke_mtmc,-35.2776999,149.118527,Australian National University,edu,b9cc54c5f94371cfc8e79179c20ec559a1a43cbb,citation,https://arxiv.org/pdf/1904.01990.pdf,Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification,2019 +240,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,123478b496a3fa39a9043ccaa660e81c473a14e9,citation,https://pdfs.semanticscholar.org/1234/78b496a3fa39a9043ccaa660e81c473a14e9.pdf,A Bottom-Up Clustering Approach to Unsupervised Person Re-identification,2019 +241,United States,Duke MTMC,duke_mtmc,29.888411,-97.938351,Texas State University,edu,123478b496a3fa39a9043ccaa660e81c473a14e9,citation,https://pdfs.semanticscholar.org/1234/78b496a3fa39a9043ccaa660e81c473a14e9.pdf,A Bottom-Up Clustering Approach to Unsupervised Person Re-identification,2019 +242,United States,Duke MTMC,duke_mtmc,42.3383668,-71.08793524,Northeastern University,edu,78fde57462fb68530a49f913c89343da5727580d,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w17/papers/Gou_DukeMTMC4ReID_A_Large-Scale_CVPR_2017_paper.pdf,DukeMTMC4ReID: A Large-Scale Multi-camera Person Re-identification Dataset,2017 +243,United States,Duke MTMC,duke_mtmc,42.7298459,-73.67950216,Rensselaer Polytechnic Institute,edu,78fde57462fb68530a49f913c89343da5727580d,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w17/papers/Gou_DukeMTMC4ReID_A_Large-Scale_CVPR_2017_paper.pdf,DukeMTMC4ReID: A Large-Scale Multi-camera Person Re-identification Dataset,2017 +244,United States,Duke MTMC,duke_mtmc,38.5336349,-121.79077264,"University of California, Davis",edu,79c959833ff49f860e20b6654dbf4d6acdee0230,citation,https://arxiv.org/pdf/1811.02545.pdf,Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond,2018 +245,China,Duke MTMC,duke_mtmc,30.19331415,120.11930822,Zhejiang University,edu,79c959833ff49f860e20b6654dbf4d6acdee0230,citation,https://arxiv.org/pdf/1811.02545.pdf,Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond,2018 +246,China,Duke MTMC,duke_mtmc,30.2931534,120.1620458,Zhejiang University of Technology,edu,8fbb73bc6fb74e119b5fdf02482fa90afb7e443e,citation,https://pdfs.semanticscholar.org/8fbb/73bc6fb74e119b5fdf02482fa90afb7e443e.pdf,Parts Semantic Segmentation Aware Representation Learning for Person Re-Identification,2019 +247,China,Duke MTMC,duke_mtmc,39.061004,117.142023,Tianjin University of Technology,edu,8fbb73bc6fb74e119b5fdf02482fa90afb7e443e,citation,https://pdfs.semanticscholar.org/8fbb/73bc6fb74e119b5fdf02482fa90afb7e443e.pdf,Parts Semantic Segmentation Aware Representation Learning for Person Re-Identification,2019 +248,China,Duke MTMC,duke_mtmc,27.712328,112.006373,Hunan University of Humanities,edu,2ff0f94f1a05fb4e6cb906f8b5aa59d50c9754be,citation,https://arxiv.org/pdf/1807.11042.pdf,Towards Good Practices on Building Effective CNN Baseline Model for Person Re-identification,2018 +249,Singapore,Duke MTMC,duke_mtmc,1.2988926,103.7873107,"A*STAR, Singapore",edu,2ff0f94f1a05fb4e6cb906f8b5aa59d50c9754be,citation,https://arxiv.org/pdf/1807.11042.pdf,Towards Good Practices on Building Effective CNN Baseline Model for Person Re-identification,2018 +250,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,5f12ca6b863b5bc28f58443ba2b70a102af965bd,citation,https://arxiv.org/pdf/1903.09776.pdf,Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification,2019 +251,Italy,Duke MTMC,duke_mtmc,46.0658836,11.1159894,University of Trento,edu,4c903009e7b963f1cd4f02482ea4b242d71e8058,citation,https://arxiv.org/pdf/1904.01308.pdf,Camera Adversarial Transfer for Unsupervised Person Re-Identification,2019 +252,United States,Duke MTMC,duke_mtmc,47.6543238,-122.30800894,University of Washington,edu,17829aec0f06dc8f45f417e667e3d92eeba923dc,citation,https://arxiv.org/pdf/1903.09254.pdf,CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification,2019 +253,China,Duke MTMC,duke_mtmc,40.00229045,116.32098908,Tsinghua University,edu,4f83ef534c164bd7fbd1e71fe6a3d09a30326b26,citation,https://arxiv.org/pdf/1810.10221.pdf,Cross-Resolution Person Re-identification with Deep Antithetical Learning,2018 +254,United States,Duke MTMC,duke_mtmc,28.59899755,-81.19712501,University of Central Florida,edu,427aee2aaf7d2d67738b046aea2782f9b8892c68,citation,https://arxiv.org/pdf/1904.11397.pdf,Deep Constrained Dominant Sets for Person Re-identification,2019 +255,China,Duke MTMC,duke_mtmc,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,07dead6b98379faac1cf0b2cb34a5db842ab9de9,citation,https://arxiv.org/pdf/1711.10658.pdf,Deep-Person: Learning Discriminative Deep Features for Person Re-Identification,2017 +256,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,19a0f34440c25323544b90d9d822a212bfed0eb5,citation,https://arxiv.org/pdf/1901.10100.pdf,Discovering Underlying Person Structure Pattern with Relative Local Distance for Person Re-identification,2019 +257,China,Duke MTMC,duke_mtmc,22.053565,113.39913285,Jilin University,edu,05f9d47bcc438ffcd4efcc5d77792a7b1984342a,citation,https://arxiv.org/pdf/1811.11510.pdf,Identity Preserving Generative Adversarial Network for Cross-Domain Person Re-identification,2018 +258,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,424cce55355f2fa4b3c020d56967e1f7b82b1de9,citation,https://pdfs.semanticscholar.org/424c/ce55355f2fa4b3c020d56967e1f7b82b1de9.pdf,M 2 M-GAN : Many-to-Many Generative Adversarial Transfer Learning for Person Re-Identification,2018 +259,China,Duke MTMC,duke_mtmc,23.09461185,113.28788994,Sun Yat-Sen University,edu,8824638e8077f62283d292804006ce94c92764bf,citation,https://arxiv.org/pdf/1811.03768.pdf,M2M-GAN: Many-to-Many Generative Adversarial Transfer Learning for Person Re-Identification,2018 +260,China,Duke MTMC,duke_mtmc,31.28473925,121.49694909,Tongji University,edu,74e38dfeb5abc7ddf077abc01de90f4d0a49c142,citation,https://arxiv.org/pdf/1812.05319.pdf,Omni-directional Feature Learning for Person Re-identification,2018 +261,United States,Duke MTMC,duke_mtmc,40.1019523,-88.2271615,UIUC,edu,040c0612e0f006fa93f140ccb97b9738efcf74a5,citation,https://arxiv.org/pdf/1811.10144.pdf,One Shot Domain Adaptation for Person Re-Identification,2018 +262,Spain,Duke MTMC,duke_mtmc,41.5007811,2.11143663,Universitat Autònoma de Barcelona,edu,388b03244e7cdf28c750d7f6d4b4eb64219c3e7a,citation,https://arxiv.org/pdf/1812.02937.pdf,Optimizing Speed/Accuracy Trade-Off for Person Re-identification via Knowledge Distillation,2018 +263,China,Duke MTMC,duke_mtmc,22.53521465,113.9315911,Shenzhen University,edu,1e3cb57830fde3bb588acbe2784b01e922f031b0,citation,https://arxiv.org/pdf/1904.00355.pdf,Pedestrian re-identification based on Tree branch network with local and global learning,2019 +264,United States,Duke MTMC,duke_mtmc,43.0008093,-78.7889697,University at Buffalo,edu,1ba61a4fedc217f7bd052d1b2904567c9985dc44,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w6/papers/Narayan_Person_Re-Identification_for_CVPR_2017_paper.pdf,Person Re-identification for Improved Multi-person Multi-camera Tracking by Continuous Entity Association,2017 +265,United States,Duke MTMC,duke_mtmc,28.59899755,-81.19712501,University of Central Florida,edu,a1e97c4043d5cc9896dc60ae7ca135782d89e5fc,citation,https://arxiv.org/pdf/1612.02155.pdf,"Re-identification of Humans in Crowds using Personal, Social and Environmental Constraints",2016 +266,United States,Duke MTMC,duke_mtmc,42.7298459,-73.67950216,Rensselaer Polytechnic Institute,edu,24d6d3adf2176516ef0de2e943ce2084e27c4f94,citation,https://arxiv.org/pdf/1811.07487.pdf,Re-Identification with Consistent Attentive Siamese Networks,2018 +267,United States,Duke MTMC,duke_mtmc,42.7298459,-73.67950216,Rensselaer Polytechnic Institute,edu,afc01c33b7dd9de9e5c84c063aaccc4e0c839e74,citation,https://arxiv.org/pdf/1811.07487.pdf,Re-Identification with Consistent Attentive Siamese Networks,2018 +268,China,Duke MTMC,duke_mtmc,30.19331415,120.11930822,Zhejiang University,edu,74bfaacd4e86a1304d2b5e7340591cffb38d84dd,citation,https://arxiv.org/pdf/1807.00537.pdf,SphereReID: Deep Hypersphere Manifold Embedding for Person Re-Identification,2019 +269,United States,Duke MTMC,duke_mtmc,35.9990522,-78.9290629,Duke University,edu,0c0e26737fbc27d2dc7aab58783b155b009a06cf,citation,https://arxiv.org/pdf/1803.05872.pdf,Virtual CNN Branching: Efficient Feature Ensemble for Person Re-Identification,2018 +270,China,Duke MTMC,duke_mtmc,40.00229045,116.32098908,Tsinghua University,edu,753d2a35c9edf5dfcac4ef3a6adc993b657b01f0,citation,https://arxiv.org/pdf/1711.09349.pdf,Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline),2017 +271,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,753d2a35c9edf5dfcac4ef3a6adc993b657b01f0,citation,https://arxiv.org/pdf/1711.09349.pdf,Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline),2017 +272,United States,Duke MTMC,duke_mtmc,29.58333105,-98.61944505,University of Texas at San Antonio,edu,753d2a35c9edf5dfcac4ef3a6adc993b657b01f0,citation,https://arxiv.org/pdf/1711.09349.pdf,Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline),2017 +273,China,Duke MTMC,duke_mtmc,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,26ac3ee756d4a24ec31de918f54098012e17fd25,citation,https://arxiv.org/pdf/1711.10658.pdf,Deep-Person: Learning Discriminative Deep Features for Person Re-Identification,2017 +274,China,Duke MTMC,duke_mtmc,40.0044795,116.370238,Chinese Academy of Sciences,edu,3c89455d9a91560eb08e59237dbc4f9fac16ff09,citation,https://arxiv.org/pdf/1904.04975.pdf,Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification,2019 +275,Australia,Duke MTMC,duke_mtmc,-35.2776999,149.118527,Australian National University,edu,48b4b0bbbfee08604b68bb0246b295e357444ed1,citation,https://arxiv.org/pdf/1904.07223.pdf,Joint Discriminative and Generative Learning for Person Re-identification,2019 +276,United States,Duke MTMC,duke_mtmc,37.3706254,-121.9671894,NVIDIA,company,48b4b0bbbfee08604b68bb0246b295e357444ed1,citation,https://arxiv.org/pdf/1904.07223.pdf,Joint Discriminative and Generative Learning for Person Re-identification,2019 +277,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,48b4b0bbbfee08604b68bb0246b295e357444ed1,citation,https://arxiv.org/pdf/1904.07223.pdf,Joint Discriminative and Generative Learning for Person Re-identification,2019 +278,China,Duke MTMC,duke_mtmc,35.86166,104.195397,"Megvii Inc. (Face++), China",company,10c20cf47d61063032dce4af73a4b8e350bf1128,citation,https://arxiv.org/pdf/1712.09531.pdf,"Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project",2017 +279,France,Duke MTMC,duke_mtmc,45.7833244,4.8781984,University of Lyon,edu,19650d66be1bf350fe784467da3ff7074c94c940,citation,https://pdfs.semanticscholar.org/1965/0d66be1bf350fe784467da3ff7074c94c940.pdf,Person re-identification in images with deep learning,2018 +280,Singapore,Duke MTMC,duke_mtmc,1.3392609,103.8916077,Panasonic Singapore,company,70ce1a17f257320fc718d61964b21e7aeabd8cd5,citation,https://arxiv.org/pdf/1803.10630.pdf,Person re-identification with fusion of hand-crafted and deep pose-based body region features,2018 +281,China,Duke MTMC,duke_mtmc,31.30104395,121.50045497,Fudan University,edu,66e4f5e354240a022789353798ce92e4ab68e109,citation,https://arxiv.org/pdf/1712.02225.pdf,Pose-Normalized Image Generation for Person Re-identification,2018 +282,Japan,Duke 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Belfast,edu,05c4eace439fcc011aaa70c8c00c7386a0cf479e,citation,https://pdfs.semanticscholar.org/05c4/eace439fcc011aaa70c8c00c7386a0cf479e.pdf,Video Person Re-Identification for Wide Area Tracking based on Recurrent Neural Networks,2017 +286,China,Duke MTMC,duke_mtmc,39.979203,116.33287,"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China",edu,f12e2888e6db23433166db72ff77c448cb6845e8,citation,,GLAD: Global–Local-Alignment Descriptor for Scalable Person Re-Identification,2018 +287,China,Duke MTMC,duke_mtmc,39.9922379,116.30393816,Peking University,edu,f12e2888e6db23433166db72ff77c448cb6845e8,citation,,GLAD: Global–Local-Alignment Descriptor for Scalable Person Re-Identification,2018 +288,Australia,Duke MTMC,duke_mtmc,-33.8809651,151.20107299,University of Technology Sydney,edu,a34f8768b10d928aa4f4105afb971819c26a2219,citation,,Multi-Pseudo Regularized Label for Generated Data in Person Re-Identification,2018 +289,China,Duke MTMC,duke_mtmc,40.0044795,116.370238,Chinese Academy of Sciences,edu,a34f8768b10d928aa4f4105afb971819c26a2219,citation,,Multi-Pseudo Regularized Label for Generated Data in Person Re-Identification,2018 +290,China,Duke MTMC,duke_mtmc,31.0252201,121.4337784,Shanghai Jiaotong University,edu,f8c4959ca67846d0c08f371ee884bb8a0845af1e,citation,,Enhancing Model Performance of Person Re-Indentification on Unknown Target Domain,2018 +291,China,Duke MTMC,duke_mtmc,31.83907195,117.26420748,University of Science and Technology of China,edu,f81f69570113e5171203ac121d1ec1d8b91df4a4,citation,,Local Convolutional Neural Networks for Person Re-Identification,2018 +292,China,Duke MTMC,duke_mtmc,34.1235825,108.83546,Xidian University,edu,03df42c643872aa664a7d6a8f5dbb12cbc3d09f3,citation,,An End-to-End Noise-Weakened Person Re-Identification and Tracking With Adaptive Partial Information,2019 +293,China,Duke MTMC,duke_mtmc,39.0607286,117.1256421,Tianjin Normal University,edu,59161bd01e739ad69a93f88303fa2b6e21f6ea98,citation,,Discrimination-Aware Integration for Person Re-Identification in Camera Networks,2019 +294,China,Duke MTMC,duke_mtmc,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,960cdda2dcd299ecdf64e867a7538e24ee4e2a99,citation,,Learning deep embedding with mini-cluster loss for person re-identification,2019 +295,China,Duke MTMC,duke_mtmc,22.8376,108.289839,Guangxi University,edu,aaca2ebcd26ed668788f364dd7af8b4615492b66,citation,,Omnidirectional Feature Learning for Person Re-Identification,2019 +296,China,Duke MTMC,duke_mtmc,31.28473925,121.49694909,Tongji University,edu,aaca2ebcd26ed668788f364dd7af8b4615492b66,citation,,Omnidirectional Feature Learning for Person Re-Identification,2019 +297,China,Duke MTMC,duke_mtmc,34.2469152,108.91061982,Northwestern Polytechnical University,edu,11cb49d8f19f0491e1930d9471988a3c07b70bb4,citation,,Person Re-Identification With Triplet Focal Loss,2018 +298,China,Duke MTMC,duke_mtmc,34.250803,108.983693,Xi’an Jiaotong University,edu,11cb49d8f19f0491e1930d9471988a3c07b70bb4,citation,,Person Re-Identification With Triplet Focal Loss,2018 +299,United States,Duke MTMC,duke_mtmc,42.0551164,-87.67581113,Northwestern University,edu,665b263ce030bcb3356fcd6e45b219c9184d09e1,citation,,Random linear interpolation data augmentation for person re-identification,2018 diff --git a/site/datasets/verified/imdb_face.csv b/site/datasets/verified/imdb_face.csv index 57609d4b..2154db39 100644 --- a/site/datasets/verified/imdb_face.csv +++ b/site/datasets/verified/imdb_face.csv @@ -1,2 +1,7 @@ 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 +1,China,IMDb Face,imdb_face,39.993008,116.329882,SenseTime,company,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +2,China,IMDb Face,imdb_face,28.2290209,112.99483204,"National University of Defense Technology, China",mil,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +3,China,IMDb Face,imdb_face,22.4162632,114.2109318,Chinese University of Hong Kong,edu,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +4,China,IMDb Face,imdb_face,39.9808333,116.34101249,Beihang University,edu,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +5,China,IMDb Face,imdb_face,40.00229045,116.32098908,Tsinghua University,edu,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 diff --git a/site/datasets/verified/megaface.csv b/site/datasets/verified/megaface.csv index 4c38af0b..77af9bc8 100644 --- a/site/datasets/verified/megaface.csv +++ b/site/datasets/verified/megaface.csv @@ -64,3 +64,8 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,t 62,United States,MegaFace,megaface,47.6543238,-122.30800894,University of Washington,edu,28d4e027c7e90b51b7d8908fce68128d1964668a,citation,,Level Playing Field for Million Scale Face Recognition,2017 63,China,MegaFace,megaface,31.30104395,121.50045497,Fudan University,edu,c5e37630d0672e4d44f7dee83ac2c1528be41c2e,citation,,Multi-task Deep Neural Network for Joint Face Recognition and Facial Attribute Prediction,2017 64,United States,MegaFace,megaface,39.65404635,-79.96475355,West Virginia University,edu,b1b7603a70860cbe5ff7b963976b5e6f780c88fc,citation,,A Deep Face Identification Network Enhanced by Facial Attributes Prediction,2018 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Recognition is in the Noise,2018 +108,United States,MsCeleb,msceleb,32.87935255,-117.23110049,"University of California, San Diego",edu,9e31e77f9543ab42474ba4e9330676e18c242e72,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018 +109,China,MsCeleb,msceleb,22.4162632,114.2109318,Chinese University of Hong Kong,edu,53840c83f7b6ae78d4310c5b84ab3fde1a33bc4f,citation,https://arxiv.org/pdf/1801.01687.pdf,Accelerated Training for Massive Classification via Dynamic Class Selection,2018 +110,China,MsCeleb,msceleb,39.993008,116.329882,SenseTime,company,53840c83f7b6ae78d4310c5b84ab3fde1a33bc4f,citation,https://arxiv.org/pdf/1801.01687.pdf,Accelerated Training for Massive Classification via Dynamic Class Selection,2018 +111,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 +112,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 +113,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 +114,United States,MsCeleb,msceleb,47.6423318,-122.1369302,Microsoft,company,291265db88023e92bb8c8e6390438e5da148e8f5,citation,https://arxiv.org/pdf/1607.08221.pdf,MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition,2016 +115,China,MsCeleb,msceleb,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,3c5ba48d25fbe24691ed060fa8f2099cc9eba14f,citation,https://arxiv.org/pdf/1812.00194.pdf,Racial Faces in-the-Wild: Reducing Racial Bias by Deep Unsupervised Domain Adaptation,2018 +116,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 +117,Spain,MsCeleb,msceleb,40.4167754,-3.7037902,"Computer Vision Group (www.vision4uav.com), Centro de Automática y Robótica (CAR) UPM-CSIC, Universidad Politécnica de Madrid, José Gutiérrez Abascal 2, 28006, Spain",edu,726f76f11e904d7fcb12736c276a0b00eb5cde49,citation,https://arxiv.org/pdf/1901.05903.pdf,A Performance Comparison of Loss Functions for Deep Face Recognition,2019 +118,India,MsCeleb,msceleb,13.5568171,80.0261283,"Indian Institute of Information Technology, Sri City, India",edu,726f76f11e904d7fcb12736c276a0b00eb5cde49,citation,https://arxiv.org/pdf/1901.05903.pdf,A Performance Comparison of Loss Functions for Deep Face Recognition,2019 +119,China,MsCeleb,msceleb,39.98177,116.330086,National Laboratory of Pattern Recognition,edu,3a27d164e931c422d16481916a2fa6401b74bcef,citation,https://arxiv.org/pdf/1709.03654.pdf,Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification,2018 +120,China,MsCeleb,msceleb,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,3a27d164e931c422d16481916a2fa6401b74bcef,citation,https://arxiv.org/pdf/1709.03654.pdf,Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification,2018 +121,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 +122,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 diff --git a/site/datasets/verified/pipa.csv b/site/datasets/verified/pipa.csv index 1124eebc..6a5b1ef2 100644 --- a/site/datasets/verified/pipa.csv +++ b/site/datasets/verified/pipa.csv @@ -45,3 +45,10 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,t 43,Belgium,PIPA,pipa,50.8784802,4.4348624,"Toyota Motor Europe (TME), Brussels 1140, Belgium",edu,503906ca940fa3b01e39d05879c9b6a36524aaf5,citation,,Natural and Effective Obfuscation by Head Inpainting,2018 44,Singapore,PIPA,pipa,1.2966426,103.7763939,National University of Singapore & Qihoo 360 AI Institute,edu,af4759f5e636b5d9049010d5f0e2b0df2a69cd72,citation,,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018 45,Singapore,PIPA,pipa,1.2962018,103.77689944,National University of Singapore,edu,af4759f5e636b5d9049010d5f0e2b0df2a69cd72,citation,,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018 +46,China,PIPA,pipa,23.09461185,113.28788994,Sun Yat-Sen University,edu,2b7e18ecfa27cee95dbf8653b18d6d3cdbe80926,citation,https://arxiv.org/pdf/1807.00504.pdf,Deep Reasoning with Knowledge Graph for Social Relationship Understanding,2018 +47,China,PIPA,pipa,39.993008,116.329882,SenseTime,company,2b7e18ecfa27cee95dbf8653b18d6d3cdbe80926,citation,https://arxiv.org/pdf/1807.00504.pdf,Deep Reasoning with Knowledge Graph for Social Relationship Understanding,2018 +48,United States,PIPA,pipa,42.4505507,-76.4783513,Cornell University,edu,c8a4b38913153611652038a29c8f88ef1ddaa5a7,citation,https://arxiv.org/pdf/1805.04049.pdf,Exploiting Unintended Feature Leakage in Collaborative Learning,2018 +49,United Kingdom,PIPA,pipa,51.5247272,-0.03931035,Queen Mary University of London,edu,ccf8cc3b1ead41cbf4fddd77648a2e9538fb747a,citation,https://arxiv.org/pdf/1811.08965.pdf,Low-Resolution Face Recognition,2018 +50,China,PIPA,pipa,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,4e7942a35fedc6bcb8a973608809f798a8d182bf,citation,https://arxiv.org/pdf/1901.03067.pdf,Multi-Granularity Reasoning for Social Relation Recognition from Images,2019 +51,China,PIPA,pipa,22.4162632,114.2109318,Chinese University of Hong Kong,edu,1db5d63aaaa739d36d3dcb7fd17b2f0775ade681,citation,https://arxiv.org/pdf/1710.00870.pdf,Rethinking Feature Discrimination and Polymerization for Large-scale Recognition,2017 +52,Australia,PIPA,pipa,-34.9189226,138.60423668,University of Adelaide,edu,3d24b386d003bee176a942c26336dbe8f427aadd,citation,https://arxiv.org/pdf/1611.09967.pdf,Sequential Person Recognition in Photo Albums with a Recurrent Network,2017 diff --git a/site/datasets/verified/uccs.csv b/site/datasets/verified/uccs.csv index 1cbefd32..0ecea1af 100644 --- a/site/datasets/verified/uccs.csv +++ b/site/datasets/verified/uccs.csv @@ -5,3 +5,6 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,t 3,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 4,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 5,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 +6,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 +7,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 +8,United States,UCCS,uccs,41.70456775,-86.23822026,University of Notre Dame,edu,e94c2c9be4abd121d3d601bfff27edf35f3514ad,citation,https://arxiv.org/pdf/1805.11529.pdf,On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques,2019 -- cgit v1.2.3-70-g09d2