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diff --git a/site/datasets/verified/lfpw.csv b/site/datasets/verified/lfpw.csv index a2b6a265..ac34778e 100644 --- a/site/datasets/verified/lfpw.csv +++ b/site/datasets/verified/lfpw.csv @@ -1,2 +1,232 @@ 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 +0,,LFPW,lfpw,0.0,0.0,,,,main,,Localizing Parts of Faces Using a Consensus of Exemplars,2011 +1,China,LFPW,lfpw,28.2290209,112.99483204,"National University of Defense Technology, China",mil,ac51d9ddbd462d023ec60818bac6cdae83b66992,citation,http://downloads.hindawi.com/journals/cin/2015/709072.pdf,An Efficient Robust Eye Localization by Learning the Convolution Distribution Using Eye Template,2015 +2,United Kingdom,LFPW,lfpw,52.9387428,-1.20029569,University of Nottingham,edu,529b1f33aed49dbe025a99ac1d211c777ad881ec,citation,http://eprints.eemcs.utwente.nl/26776/01/Pantic_Fast_and_Exact_Bi-Directional_Fitting.pdf,Fast and exact bi-directional fitting of active appearance models,2015 +3,Netherlands,LFPW,lfpw,52.2380139,6.8566761,University of Twente,edu,529b1f33aed49dbe025a99ac1d211c777ad881ec,citation,http://eprints.eemcs.utwente.nl/26776/01/Pantic_Fast_and_Exact_Bi-Directional_Fitting.pdf,Fast and exact bi-directional fitting of active appearance models,2015 +4,China,LFPW,lfpw,31.32235655,121.38400941,Shanghai University,edu,63fd7a159e58add133b9c71c4b1b37b899dd646f,citation,http://wei-shen.weebly.com/uploads/2/3/8/2/23825939/posecorrection.pdf,Exemplar-Based Human Action Pose Correction,2014 +5,China,LFPW,lfpw,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,63fd7a159e58add133b9c71c4b1b37b899dd646f,citation,http://wei-shen.weebly.com/uploads/2/3/8/2/23825939/posecorrection.pdf,Exemplar-Based Human Action Pose Correction,2014 +6,United States,LFPW,lfpw,47.6423318,-122.1369302,Microsoft,company,63fd7a159e58add133b9c71c4b1b37b899dd646f,citation,http://wei-shen.weebly.com/uploads/2/3/8/2/23825939/posecorrection.pdf,Exemplar-Based Human Action Pose Correction,2014 +7,United States,LFPW,lfpw,42.3614256,-71.0812092,Microsoft Research Asia,company,63fd7a159e58add133b9c71c4b1b37b899dd646f,citation,http://wei-shen.weebly.com/uploads/2/3/8/2/23825939/posecorrection.pdf,Exemplar-Based Human Action Pose Correction,2014 +8,China,LFPW,lfpw,22.4162632,114.2109318,Chinese University of Hong Kong,edu,57ebeff9273dea933e2a75c306849baf43081a8c,citation,http://mmlab.ie.cuhk.edu.hk/archive/CNN/data/CNN_FacePoint.pdf,Deep Convolutional Network Cascade for Facial Point Detection,2013 +9,China,LFPW,lfpw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,57ebeff9273dea933e2a75c306849baf43081a8c,citation,http://mmlab.ie.cuhk.edu.hk/archive/CNN/data/CNN_FacePoint.pdf,Deep Convolutional Network Cascade for Facial Point Detection,2013 +10,Canada,LFPW,lfpw,43.0095971,-81.2737336,University of Western Ontario,edu,f7ae38a073be7c9cd1b92359131b9c8374579b13,citation,http://www.digitalimaginggroup.ca/members/Shuo/07487053.pdf,Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression,2017 +11,Canada,LFPW,lfpw,42.960348,-81.226628,"London Healthcare Sciences Centre, Ontario, Canada",edu,f7ae38a073be7c9cd1b92359131b9c8374579b13,citation,http://www.digitalimaginggroup.ca/members/Shuo/07487053.pdf,Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression,2017 +12,United Kingdom,LFPW,lfpw,55.0030632,-1.57463231,Northumbria University,edu,f7ae38a073be7c9cd1b92359131b9c8374579b13,citation,http://www.digitalimaginggroup.ca/members/Shuo/07487053.pdf,Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression,2017 +13,Canada,LFPW,lfpw,43.0012953,-81.2550455,"St. Joseph's Health Care, Ontario, Canada",edu,f7ae38a073be7c9cd1b92359131b9c8374579b13,citation,http://www.digitalimaginggroup.ca/members/Shuo/07487053.pdf,Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression,2017 +14,United States,LFPW,lfpw,37.3936717,-122.0807262,Facebook,company,dcd2ac544a8336d73e4d3d80b158477c783e1e50,citation,https://arxiv.org/pdf/1709.01591.pdf,Improving Landmark Localization with Semi-Supervised Learning,2018 +15,United States,LFPW,lfpw,37.3706254,-121.9671894,NVIDIA,company,dcd2ac544a8336d73e4d3d80b158477c783e1e50,citation,https://arxiv.org/pdf/1709.01591.pdf,Improving Landmark Localization with Semi-Supervised Learning,2018 +16,Canada,LFPW,lfpw,45.5010087,-73.6157778,University of Montreal,edu,dcd2ac544a8336d73e4d3d80b158477c783e1e50,citation,https://arxiv.org/pdf/1709.01591.pdf,Improving Landmark Localization with Semi-Supervised Learning,2018 +17,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,034b3f3bac663fb814336a69a9fd3514ca0082b9,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/alabort_cvpr2015.pdf,Unifying holistic and Parts-Based Deformable Model fitting,2015 +18,China,LFPW,lfpw,31.83907195,117.26420748,University of Science and Technology of China,edu,084bd02d171e36458f108f07265386f22b34a1ae,citation,http://7xrqgw.com1.z0.glb.clouddn.com/3000fps.pdf,Face Alignment at 3000 FPS via Regressing Local Binary Features,2014 +19,United States,LFPW,lfpw,47.6423318,-122.1369302,Microsoft,company,084bd02d171e36458f108f07265386f22b34a1ae,citation,http://7xrqgw.com1.z0.glb.clouddn.com/3000fps.pdf,Face Alignment at 3000 FPS via Regressing Local Binary Features,2014 +20,United Kingdom,LFPW,lfpw,51.24303255,-0.59001382,University of Surrey,edu,2d2e1d1f50645fe20c051339e9a0fca7b176422a,citation,https://arxiv.org/pdf/1803.05536.pdf,Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild,2018 +21,United Kingdom,LFPW,lfpw,56.1454119,-3.9205713,University of Stirling,edu,2d2e1d1f50645fe20c051339e9a0fca7b176422a,citation,https://arxiv.org/pdf/1803.05536.pdf,Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild,2018 +22,China,LFPW,lfpw,31.4854255,120.2739581,Jiangnan University,edu,2d2e1d1f50645fe20c051339e9a0fca7b176422a,citation,https://arxiv.org/pdf/1803.05536.pdf,Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild,2018 +23,China,LFPW,lfpw,30.642769,104.06751175,"Sichuan University, Chengdu",edu,2d2e1d1f50645fe20c051339e9a0fca7b176422a,citation,https://arxiv.org/pdf/1803.05536.pdf,Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild,2018 +24,Germany,LFPW,lfpw,48.48187645,9.18682404,Reutlingen University,edu,2d2e1d1f50645fe20c051339e9a0fca7b176422a,citation,https://arxiv.org/pdf/1803.05536.pdf,Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild,2018 +25,United States,LFPW,lfpw,45.57022705,-122.63709346,Concordia University,edu,266ed43dcea2e7db9f968b164ca08897539ca8dd,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/3B_037.pdf,Beyond Principal Components: Deep Boltzmann Machines for face modeling,2015 +26,United States,LFPW,lfpw,40.4441619,-79.94272826,Carnegie Mellon University,edu,266ed43dcea2e7db9f968b164ca08897539ca8dd,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/3B_037.pdf,Beyond Principal Components: Deep Boltzmann Machines for face modeling,2015 +27,United States,LFPW,lfpw,40.47913175,-74.43168868,Rutgers University,edu,3b470b76045745c0ef5321e0f1e0e6a4b1821339,citation,https://pdfs.semanticscholar.org/8e72/fa02f2d90ba31f31e0a7aa96a6d3e10a66fc.pdf,Consensus of Regression for Occlusion-Robust Facial Feature Localization,2014 +28,United States,LFPW,lfpw,37.3309307,-121.8940485,"Adobe Research, San Jose, CA",company,3b470b76045745c0ef5321e0f1e0e6a4b1821339,citation,https://pdfs.semanticscholar.org/8e72/fa02f2d90ba31f31e0a7aa96a6d3e10a66fc.pdf,Consensus of Regression for Occlusion-Robust Facial Feature Localization,2014 +29,China,LFPW,lfpw,40.0044795,116.370238,Chinese Academy of Sciences,edu,2a4153655ad1169d482e22c468d67f3bc2c49f12,citation,http://cseweb.ucsd.edu/~mkchandraker/classes/CSE291/Winter2018/Lectures/FaceAlignment.pdf,Face Alignment Across Large Poses: A 3D Solution,2016 +30,United States,LFPW,lfpw,42.718568,-84.47791571,Michigan State University,edu,2a4153655ad1169d482e22c468d67f3bc2c49f12,citation,http://cseweb.ucsd.edu/~mkchandraker/classes/CSE291/Winter2018/Lectures/FaceAlignment.pdf,Face Alignment Across Large Poses: A 3D Solution,2016 +31,United Kingdom,LFPW,lfpw,53.22853665,-0.54873472,University of Lincoln,edu,232b6e2391c064d483546b9ee3aafe0ba48ca519,citation,http://doc.utwente.nl/89696/1/Pantic_Optimization_problems_for_fast_AAM_fitting.pdf,Optimization Problems for Fast AAM Fitting in-the-Wild,2013 +32,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,232b6e2391c064d483546b9ee3aafe0ba48ca519,citation,http://doc.utwente.nl/89696/1/Pantic_Optimization_problems_for_fast_AAM_fitting.pdf,Optimization Problems for Fast AAM Fitting in-the-Wild,2013 +33,United Kingdom,LFPW,lfpw,52.9387428,-1.20029569,University of Nottingham,edu,75fd9acf5e5b7ed17c658cc84090c4659e5de01d,citation,http://eprints.nottingham.ac.uk/31442/1/tzimiro_CVPR15.pdf,Project-Out Cascaded Regression with an application to face alignment,2015 +34,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,788a7b59ea72e23ef4f86dc9abb4450efefeca41,citation,http://eprints.eemcs.utwente.nl/26840/01/Pantic_Robust_Statistical_Face_Frontalization.pdf,Robust Statistical Face Frontalization,2015 +35,Netherlands,LFPW,lfpw,52.2380139,6.8566761,University of Twente,edu,788a7b59ea72e23ef4f86dc9abb4450efefeca41,citation,http://eprints.eemcs.utwente.nl/26840/01/Pantic_Robust_Statistical_Face_Frontalization.pdf,Robust Statistical Face Frontalization,2015 +36,China,LFPW,lfpw,39.9041999,116.4073963,Key Lab of Intelligent Information Processing of Chinese Academy of Sciences,edu,090ff8f992dc71a1125636c1adffc0634155b450,citation,https://pdfs.semanticscholar.org/090f/f8f992dc71a1125636c1adffc0634155b450.pdf,Topic-Aware Deep Auto-Encoders (TDA) for Face Alignment,2014 +37,China,LFPW,lfpw,40.0044795,116.370238,Chinese Academy of Sciences,edu,090ff8f992dc71a1125636c1adffc0634155b450,citation,https://pdfs.semanticscholar.org/090f/f8f992dc71a1125636c1adffc0634155b450.pdf,Topic-Aware Deep Auto-Encoders (TDA) for Face Alignment,2014 +38,China,LFPW,lfpw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,090ff8f992dc71a1125636c1adffc0634155b450,citation,https://pdfs.semanticscholar.org/090f/f8f992dc71a1125636c1adffc0634155b450.pdf,Topic-Aware Deep Auto-Encoders (TDA) for Face Alignment,2014 +39,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,090ff8f992dc71a1125636c1adffc0634155b450,citation,https://pdfs.semanticscholar.org/090f/f8f992dc71a1125636c1adffc0634155b450.pdf,Topic-Aware Deep Auto-Encoders (TDA) for Face Alignment,2014 +40,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,e4754afaa15b1b53e70743880484b8d0736990ff,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/1-s2.0-s0262885616000147-main.pdf,300 Faces In-The-Wild Challenge: database and results,2016 +41,United Kingdom,LFPW,lfpw,52.9387428,-1.20029569,University of Nottingham,edu,e4754afaa15b1b53e70743880484b8d0736990ff,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/1-s2.0-s0262885616000147-main.pdf,300 Faces In-The-Wild Challenge: database and results,2016 +42,Netherlands,LFPW,lfpw,52.2380139,6.8566761,University of Twente,edu,e4754afaa15b1b53e70743880484b8d0736990ff,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/1-s2.0-s0262885616000147-main.pdf,300 Faces In-The-Wild Challenge: database and results,2016 +43,United States,LFPW,lfpw,38.2167565,-85.75725023,University of Louisville,edu,9a4c45e5c6e4f616771a7325629d167a38508691,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W02/papers/Mostafa_A_Facial_Features_2015_CVPR_paper.pdf,A facial features detector integrating holistic facial information and part-based model,2015 +44,Egypt,LFPW,lfpw,31.21051105,29.91314562,Alexandria University,edu,9a4c45e5c6e4f616771a7325629d167a38508691,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W02/papers/Mostafa_A_Facial_Features_2015_CVPR_paper.pdf,A facial features detector integrating holistic facial information and part-based model,2015 +45,Egypt,LFPW,lfpw,27.18794105,31.17009498,Assiut University,edu,9a4c45e5c6e4f616771a7325629d167a38508691,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W02/papers/Mostafa_A_Facial_Features_2015_CVPR_paper.pdf,A facial features detector integrating holistic facial information and part-based model,2015 +46,China,LFPW,lfpw,31.4854255,120.2739581,Jiangnan University,edu,60824ee635777b4ee30fcc2485ef1e103b8e7af9,citation,http://epubs.surrey.ac.uk/808177/1/Feng-TIP-2015.pdf,Cascaded Collaborative Regression for Robust Facial Landmark Detection Trained Using a Mixture of Synthetic and Real Images With Dynamic Weighting,2015 +47,United Kingdom,LFPW,lfpw,51.2421839,-0.5905421,University of Surrey Guildford,edu,60824ee635777b4ee30fcc2485ef1e103b8e7af9,citation,http://epubs.surrey.ac.uk/808177/1/Feng-TIP-2015.pdf,Cascaded Collaborative Regression for Robust Facial Landmark Detection Trained Using a Mixture of Synthetic and Real Images With Dynamic Weighting,2015 +48,China,LFPW,lfpw,39.9041999,116.4073963,Key Lab of Intelligent Information Processing of Chinese Academy of Sciences,edu,22e2066acfb795ac4db3f97d2ac176d6ca41836c,citation,https://pdfs.semanticscholar.org/26f5/3a1abb47b1f0ea1f213dc7811257775dc6e6.pdf,Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment,2014 +49,China,LFPW,lfpw,40.0044795,116.370238,Chinese Academy of Sciences,edu,22e2066acfb795ac4db3f97d2ac176d6ca41836c,citation,https://pdfs.semanticscholar.org/26f5/3a1abb47b1f0ea1f213dc7811257775dc6e6.pdf,Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment,2014 +50,China,LFPW,lfpw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,22e2066acfb795ac4db3f97d2ac176d6ca41836c,citation,https://pdfs.semanticscholar.org/26f5/3a1abb47b1f0ea1f213dc7811257775dc6e6.pdf,Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment,2014 +51,United States,LFPW,lfpw,42.3614256,-71.0812092,Microsoft Research Asia,company,63d865c66faaba68018defee0daf201db8ca79ed,citation,https://arxiv.org/pdf/1409.5230.pdf,Deep Regression for Face Alignment,2014 +52,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,5e9ec3b8daa95d45138e30c07321e386590f8ec7,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/eleftheriadis_tip.pdf,Discriminative Shared Gaussian Processes for Multiview and View-Invariant Facial Expression Recognition,2015 +53,United States,LFPW,lfpw,40.4441619,-79.94272826,Carnegie Mellon University,edu,35f921def890210dda4b72247849ad7ba7d35250,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Zhou_Exemplar-Based_Graph_Matching_2013_ICCV_paper.pdf,Exemplar-Based Graph Matching for Robust Facial Landmark Localization,2013 +54,China,LFPW,lfpw,35.86166,104.195397,"Megvii Inc. (Face++), China",company,1a8ccc23ed73db64748e31c61c69fe23c48a2bb1,citation,http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W11/papers/Zhou_Extensive_Facial_Landmark_2013_ICCV_paper.pdf,Extensive Facial Landmark Localization with Coarse-to-Fine Convolutional Network Cascade,2013 +55,United Kingdom,LFPW,lfpw,52.17638955,0.14308882,University of Cambridge,edu,023be757b1769ecb0db810c95c010310d7daf00b,citation,https://arxiv.org/pdf/1507.03148.pdf,Face Alignment Assisted by Head Pose Estimation,2015 +56,United Kingdom,LFPW,lfpw,51.5247272,-0.03931035,Queen Mary University of London,edu,023be757b1769ecb0db810c95c010310d7daf00b,citation,https://arxiv.org/pdf/1507.03148.pdf,Face Alignment Assisted by Head Pose Estimation,2015 +57,United States,LFPW,lfpw,42.36782045,-71.12666653,Harvard University,edu,023be757b1769ecb0db810c95c010310d7daf00b,citation,https://arxiv.org/pdf/1507.03148.pdf,Face Alignment Assisted by Head Pose Estimation,2015 +58,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,71b07c537a9e188b850192131bfe31ef206a39a0,citation,https://pdfs.semanticscholar.org/71b0/7c537a9e188b850192131bfe31ef206a39a0.pdf,Faces InThe-Wild Challenge : database and results,2016 +59,United Kingdom,LFPW,lfpw,52.9387428,-1.20029569,University of Nottingham,edu,71b07c537a9e188b850192131bfe31ef206a39a0,citation,https://pdfs.semanticscholar.org/71b0/7c537a9e188b850192131bfe31ef206a39a0.pdf,Faces InThe-Wild Challenge : database and results,2016 +60,Netherlands,LFPW,lfpw,52.2380139,6.8566761,University of Twente,edu,71b07c537a9e188b850192131bfe31ef206a39a0,citation,https://pdfs.semanticscholar.org/71b0/7c537a9e188b850192131bfe31ef206a39a0.pdf,Faces InThe-Wild Challenge : database and results,2016 +61,United Kingdom,LFPW,lfpw,53.22853665,-0.54873472,University of Lincoln,edu,624496296af19243d5f05e7505fd927db02fd0ce,citation,http://eprints.eemcs.utwente.nl/25815/01/Pantic_Gauss-Newton_Deformable_Part_Models.pdf,Gauss-Newton Deformable Part Models for Face Alignment In-the-Wild,2014 +62,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,624496296af19243d5f05e7505fd927db02fd0ce,citation,http://eprints.eemcs.utwente.nl/25815/01/Pantic_Gauss-Newton_Deformable_Part_Models.pdf,Gauss-Newton Deformable Part Models for Face Alignment In-the-Wild,2014 +63,United Kingdom,LFPW,lfpw,53.22853665,-0.54873472,University of Lincoln,edu,6a4ebd91c4d380e21da0efb2dee276897f56467a,citation,http://eprints.nottingham.ac.uk/31441/1/tzimiroICIP14b.pdf,HOG active appearance models,2014 +64,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,500b92578e4deff98ce20e6017124e6d2053b451,citation,http://eprints.eemcs.utwente.nl/25818/01/Pantic_Incremental_Face_Alignment_in_the_Wild.pdf,Incremental Face Alignment in the Wild,2014 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Informatics,edu,c17a332e59f03b77921942d487b4b102b1ee73b6,citation,https://pdfs.semanticscholar.org/c17a/332e59f03b77921942d487b4b102b1ee73b6.pdf,Learning an appearance-based gaze estimator from one million synthesised images,2016 +69,China,LFPW,lfpw,40.0044795,116.370238,Chinese Academy of Sciences,edu,a820941eaf03077d68536732a4d5f28d94b5864a,citation,http://openaccess.thecvf.com/content_iccv_2015/papers/Zhang_Leveraging_Datasets_With_ICCV_2015_paper.pdf,Leveraging Datasets with Varying Annotations for Face Alignment via Deep Regression Network,2015 +70,China,LFPW,lfpw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,a820941eaf03077d68536732a4d5f28d94b5864a,citation,http://openaccess.thecvf.com/content_iccv_2015/papers/Zhang_Leveraging_Datasets_With_ICCV_2015_paper.pdf,Leveraging Datasets with Varying Annotations for Face Alignment via Deep Regression Network,2015 +71,Sweden,LFPW,lfpw,59.34986645,18.07063213,"KTH Royal Institute of Technology, 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