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Recent contributions have demonstrated that it is possible to recognize the pose of humans densely and accurately given a large dataset of poses annotated in detail.
Clustered pose and nonlinear appearance models for human pose estimation
Sam Johnson and Mark Everingham · 2010
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Learning effective human pose estimation from inaccurate annotation
Sam Johnson and Mark Everingham · 2011
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From actemes to action: A strongly-supervised representation for detailed action understanding
Weiyu Zhang, Menglong Zhu, and Konstantinos G Derpanis · 2013
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2d human pose estimation: New benchmark and state of the art analysis
Mykhaylo Andriluka, Leonid Pishchulin, Peter Gehler, and Bernt Schiele · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Shot: Unique signatures of histograms for surface and texture description
S. Salti, F. Tombari, and L. Di Stefano · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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SMPL: A skinned multi- person linear model
M. Loper, N. Mahmood, J. Romero, G. Pons-Moll, and M. J. Black and · 2015
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Smpl: A skinned multi-person linear model
Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black · 2015
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Human and sheep facial landmarks localisation by triplet interpolated features
Heng Yang, Renqiao Zhang, and Peter Robinson · 2015
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Cliquecnn: Deep unsupervised exemplar learning
Miguel A Bautista, Artsiom Sanakoyeu, Ekaterina Tikhoncheva, and Björn Ommer · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Continuous semantic description of 3d meshes
V. Leon, N. Bonneel, G. Lavoue, and J.-P. Vandeborre · 2016
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Stacked hourglass networks for human pose estimation
Alejandro Newell, Kaiyu Yang, and Jia Deng · 2016
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Convolutional pose machines
Shih-En Wei, Varun Ramakrishna, Takeo Kanade, and Yaser Sheikh · 2016
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Realtime multi-person 2d pose estimation using part affinity fields
Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh · 2017
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Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
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Mask R-CNN
K. He, G. Gkioxari, and P. Dollár and. R. Girshick · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
A. Kendall and Y. Gal · 2017
Cited alongside, same era.
Interspecies knowledge transfer for facial keypoint detection
Maheen Rashid, Xiuye Gu, and Yong Jae Lee · 2017
Cited alongside, same era.
Unsupervised learning of object landmarks by factorized spatial embeddings
J. Thewlis, H. Bilen, and A. Vedaldi · 2017
Cited alongside, same era.
Unsupervised object learning from dense invariant image labelling
J. Thewlis, H. Bilen, and A. Vedaldi · 2017
Cited alongside, same era.
3d menagerie: Modeling the 3d shape and pose of animals
Silvia Zuffi, Angjoo Kanazawa, David W. Jacobs, and Michael J. Black · 2017
Cited alongside, same era.
PoseTrack: A benchmark for human pose estimation and tracking
M. Andriluka, U. Iqbal, E. Ensafutdinov, L. Pishchulin, A. Milan, J. Gall, and Schiele B · 2018
Lions and tigers and bears: Capturing non-rigid, 3d, articulated shape from images
Silvia Zuffi, Angjoo Kanazawa, and Michael J. Black · 2018
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Semi-supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks
Yauhen Babakhin, Artsiom Sanakoyeu, and Hirotoshi Kitamura · 2019
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Cross-domain adaptation for animal pose estimation
Jinkun Cao, Hongyuang Tang, Fang Hao-Shu, Xiaoyong Shen, Cewu Lu, and Yu-Wing Tai · 2019
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Deepfly3d, a deep learning-based approach for 3d limb and appendage tracking in tethered, adult drosophila
Semih Günel, Helge Rhodin, Daniel Morales, João H. Campagnolo, Pavan Ramdya, and Pascal Fua · 2019
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Self-supervised learning of dense shape correspondence
Oshri Halimi, Or Litany, Emanuele Rodola, Alex Bronstein, and Ron Kimmel · 2019
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Cited alongside, same era.
Creatures great and smal: Recovering the shape and motion of animals from video
Benjamin Biggs, Thomas Roddick, Andrew Fitzgibbon, and Roberto Cipolla · 2018
Cited alongside, same era.
Encoder-decoder with atrous separable convlution for semantic image segmentation
L. Chen, G. Papandreou, F. Schroff, and H. Adam · 2018
Cited alongside, same era.
Densepose: Dense human pose estimation in the wild
Rıza Alp Güler, Natalia Neverova, and Iasonas Kokkinos · 2018
Cited alongside, same era.
Unsupervised learning of object landmarks through conditional image generation
Tomas Jakab, Ankush Gupta, Hakan Bilen, and Andrea Vedaldi · 2018
Cited alongside, same era.
End-to-end recovery of human shape and pose
Angjoo Kanazawa, Michael J. Black, David W. Jacobs, and Jitendra Malik · 2018
Cited alongside, same era.
Deeplabcut: markerless pose estimation of user-defined body parts with deep learning
Alexander Mathis, Pranav Mamidanna, Kevin M. Cury, Abe Taiga, Venkatesh N. Murthy, Mackenzie Weygandt Mathis, and Matthias Bethge · 2018
Cited alongside, same era.
Learning 3d human dynamics from video
Angjoo Kanazawa, Jason Y. Zhang, Panna Felsen, and Jitendra Malik · 2019
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lambdaloop/anipose: v0.5.0
Pierre Karashchuk · 2019
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Panoptic feature pyramid networks
Alexander Kirillov, Ross Girshick, Kaiming He, and Piotr Dollár · 2019
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Amur tiger re-identification in the wild
Shuyuan Li, Jianguo Li, Weiyao Lin, and Hanlin Tang · 2019
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Unsupervised part-based disentangling of object shape and appearance
Dominik Lorenz, Leonard Bereska, Timo Milbich, and Björn Ommer · 2019
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Deep learning tools for the measurement of animal behavior in neuroscience
Machenzie Weygandt Mathis and Alexander Mathis · 2019
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Using deeplab-cut for 3d markerless pose estimation across species and behaviors
Tanmay Nath, Alexander Mathis, An Chi Chen, Amir Patel, and Mackenzie W. Bethge, Matthias andd Mathis · 2019
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Slim DensePose: Thrifty learning from sparse annotations and motion cues
Natalia Neverova, James Thewlis, RIza Alp Güler, Iasonas Kokkinos, and Andrea Vedaldi · 2019
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C3DPO: Canonical 3d pose networks for non-rigid structure from motion
David Novotny, Nikhila Ravi, Benjamin Graham, Natalia Neverova, and Andrea Vedaldi · 2019
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Unsupervised learning of landmarks by descriptor vector exchange
James Thewlis, Samuel Albanie, Hakan Bilen, and Andrea Vedaldi · 2019
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Billion-scale semi-supervised learning for image classification
I. Zeki Yalniz, Hervé Jegou, Kan Chen, Manohar Paluri, and Dhruv Mahajan · 2019
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Three-d safari: Learning to estimate zebra pose, shape, and texture from images ”in the wild”
Silvia Zuffi, Angjoo Kanazawa, Tanya Berger-Wolf, and Michael J. Black · 2019
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