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Recent state-of-the-art performance on human-body pose estimation has been achieved with Deep Convolutional Networks (ConvNets).
Signature verification using a “siamese” time delay neural network
J. Bromley, J. W. Bentz, L. Bottou, I. Guyon, Y. LeCun, C. Moore, E. Säckinger, and R. Shah · 1993
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
A discriminatively trained, multiscale, deformable part model
P. Felzenszwalb, D. McAllester, and D. Ramanan · 2008
Earlier work this paper cites.
Pictorial structures revisited: People detection and articulated pose estimation
M. Andriluka, S. Roth, and B. Schiele · 2009
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Poselets: Body part detectors trained using 3d human pose annotations
L. Bourdev and J. Malik · 2009
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Better appearance models for pictorial structures
M. Eichner and V. Ferrari · 2009
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Clustered pose and nonlinear appearance models for human pose estimation
S. Johnson and M. Everingham · 2010
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Torch7: A matlab-like environment for machine learning
R. Collobert, K. Kavukcuoglu, and C. Farabet · 2011
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Improving neural networks by preventing co-adaptation of feature detectors
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov · 2012
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Modec: Multimodal decomposable models for human pose estimation
B. Sapp and B. Taskar · 2013
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2d human pose estimation: New benchmark and state of the art analysis
M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele · 2014
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Articulated pose estimation by a graphical model with image dependent pairwise relations
Depth map prediction from a single image using a multi-scale deep network
C. P. David Eigen and R. Fergus · 2014
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Learning human pose estimation features with convolutional networks
A. Jain, J. Tompson, M. Andriluka, G. Taylor, and C. Bregler · 2014
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Modeep: A deep learning framework using motion features for human pose estimation
A. Jain, J. Tompson, Y. LeCun, and C. Bregler · 2014
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Join training of a convolutional network and a graphical model for human pose estimation
J. Tompson, A. Jain, Y. LeCun, and C. Bregler · 2014
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Deeppose: Human pose estimation via deep neural networks
A. Toshev and C. Szegedy · 2014
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X. Chen and A. Yuille · 2014
Cited alongside, same era.
Human pose estimation using body parts dependent joint regressors
M. Dantone, J. Gall, C. Leistner, and L. V. Gool
Cited in the paper.
Articulated pose estimation using discriminative armlet classifiers
G. Gkioxari, P. Arbelaez, L. Bourdev, and J. Malik
Cited in the paper.
Learning Effective Human Pose Estimation from Inaccurate Annotation
S. Johnson and M. Everingham
Cited in the paper.
Poselet conditioned pictorial structures
L. Pishchulin, M. Andriluka, P. Gehler, and B. Schiele
Cited in the paper.
Strong appearance and expressive spatial models for human pose estimation
L. Pishchulin, M. Andriluka, P. Gehler, and B. Schiele
Cited in the paper.
Articulated pose estimation with flexible mixtures-of-parts
Y. Yang and D. Ramanan
Cited in the paper.