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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 +140,China,Adience,adience,39.993008,116.329882,SenseTime,company,aaa2b45153051e23d5a35ccf9af8ecabc0fe24cd,citation,https://pdfs.semanticscholar.org/aaa2/b45153051e23d5a35ccf9af8ecabc0fe24cd.pdf,1 How Good can Human Predict Facial Age ?,2017 +141,China,Adience,adience,39.993008,116.329882,SenseTime,company,8fee9b8c44626c4ac6b96ef183394bc4f36dc95f,citation,https://arxiv.org/pdf/1708.09687.pdf,Quantifying Facial Age by Posterior of Age Comparisons,2017 +142,China,Adience,adience,22.4162632,114.2109318,Chinese University of Hong Kong,edu,8fee9b8c44626c4ac6b96ef183394bc4f36dc95f,citation,https://arxiv.org/pdf/1708.09687.pdf,Quantifying Facial Age by Posterior of Age Comparisons,2017 +143,United States,Adience,adience,40.9153196,-73.1270626,Stony Brook University,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,https://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017 +144,United States,Adience,adience,35.93006535,-84.31240032,Oak Ridge National Laboratory,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,https://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017 +145,United States,Adience,adience,40.90826665,-73.11520891,Stony Brook University Hospital,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,https://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017 +146,India,Adience,adience,28.5456282,77.2731505,"IIIT Delhi, India",edu,c43d3ad956118ea1d26d39903097e2db86eae822,citation,https://arxiv.org/pdf/1904.01219.pdf,Deep Learning for Face Recognition: Pride or Prejudiced?,2019 +147,Ireland,Adience,adience,53.27639715,-9.05829961,National University of Ireland Galway,edu,e08038b14165536c52ffe950d90d0f43be9c8f15,citation,https://arxiv.org/pdf/1703.08383.pdf,Smart Augmentation 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 |