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We propose a new learning-based method for estimating 2D human pose from a single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN).
M. A. Fischler and R. A. Elschlager, “The representation and matching of pictorial structures,”
1973
Earlier work this paper cites.
Y. LeCun, B. Boser, J. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Handwritten digit recognition with a back-propagation network,” in
1990
Earlier work this paper cites.
Y. LeCun, F. J. Huang, and L. Bottou, “Learning methods for generic object recognition with invariance to pose and lighting,” in
2004
Earlier work this paper cites.
P. F. Felzenszwalb and D. P. Huttenlocher, “Pictorial structures for object recognition,”
2005
Earlier work this paper cites.
X. Ren, A. C. Berg, and J. Malik, “Recovering human body configurations using pairwise constraints between parts,” in
2005
Earlier work this paper cites.
Y. Wang and G. Mori, “Multiple tree models for occlusion and spatial constraints in human pose estimation,” in
2008
Earlier work this paper cites.
H. Jiang and D. R. Martin, “Global pose estimation using non-tree models,” in
2008
Earlier work this paper cites.
M. Andriluka, S. Roth, and B. Schiele, “Pictorial structures revisited: People detection and articulated pose estimation,” in
2009
Earlier work this paper cites.
M. Eichner and V. Ferrari, “Better appearance models for pictorial structures,” in
2009
Earlier work this paper cites.
K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. LeCun, “What is the best multi-stage architecture for object recognition?” in
2009
Earlier work this paper cites.
H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng, “Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations,” in
2009
Earlier work this paper cites.
S. Johnson and M. Everingham, “Clustered pose and nonlinear appearance models for human pose estimation,” in
2010
Earlier work this paper cites.
B. Sapp, A. Toshev, and B. Taskar, “Cascaded models for articulated pose estimation,” in
2010
Earlier work this paper cites.
V. K. Singh, R. Nevatia, and C. Huang, “Efficient inference with multiple heterogeneous part detectors for human pose estimation,” in
2010
Earlier work this paper cites.
T.-P. Tian and S. Sclaroff, “Fast globally optimal 2d human detection with loopy graph models,” in
2010
Cited alongside, same era.
M. Sun and S. Savarese, “Articulated part-based model for joint object detection and pose estimation,” in
2011
Cited alongside, same era.
Y. Yang and D. Ramanan, “Articulated pose estimation with flexible mixtures-of-parts,” in
2011
Cited alongside, same era.
S. Johnson and M. Everingham, “Learning effective human pose estimation from inaccurate annotation,” in
2011
Cited alongside, same era.
Y. Tian, C. L. Zitnick, and S. G. Narasimhan, “Exploring the spatial hierarchy of mixture models for human pose estimation,” in
2012
Cited alongside, same era.
M. Eichner and V. Ferrari, “Appearance sharing for collective human pose estimation,” in
B. Sapp and B. Taskar, “Modec: Multimodal decomposable models for human pose estimation,” in
2013
Later among the works it cites.
C. Szegedy, A. Toshev, and D. Erhan, “Deep neural networks for object detection,” in
2013
Later among the works it cites.
J. R. Uijlings, K. E. van de Sande, T. Gevers, and A. W. Smeulders, “Selective search for object recognition,”
2013
Later among the works it cites.
L. Pishchulin, M. Andriluka, P. Gehler, and B. Schiele, “Strong appearance and expressive spatial models for human pose estimation,” in
2013
Later among the works it cites.
A. Toshev and C. Szegedy, “Deeppose: Human pose estimation via deep neural networks,”
2014
Later among the works it cites.
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2012
Cited alongside, same era.
K. Duan, D. Batra, and D. J. Crandall, “A multi-layer composite model for human pose estimation.” in
2012
Cited alongside, same era.
L. Pishchulin, A. Jain, M. Andriluka, T. Thormahlen, and B. Schiele, “Articulated people detection and pose estimation: Reshaping the future,” in
2012
Cited alongside, same era.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in
2012
Cited alongside, same era.
M. Eichner, M. Marin-Jimenez, A. Zisserman, and V. Ferrari, “2d articulated human pose estimation and retrieval in (almost) unconstrained still images,”
2012
Cited alongside, same era.
F. Wang and Y. Li, “Beyond physical connections: Tree models in human pose estimation,” in
2013
Cited alongside, same era.
L. Pishchulin, M. Andriluka, P. Gehler, and B. Schiele, “Poselet conditioned pictorial structures,” in
2013
Cited alongside, same era.
2014
Later among the works it cites.
N. Zhang, J. Donahue, R. Girshick, and T. Darrell, “Part-based r-cnns for fine-grained category detection,” in
2014
Later among the works it cites.
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “Overfeat: Integrated recognition, localization and detection using convolutional networks,” in
2014
Later among the works it cites.
A. Jain, J. Tompson, M. Andriluka, G. W. Taylor, and C. Bregler, “Learning human pose estimation features with convolutional networks,”
2014
Later among the works it cites.
J. J. Tompson, A. Jain, Y. LeCun, and C. Bregler, “Joint training of a convolutional network and a graphical model for human pose estimation,” in
2014
Later among the works it cites.
C. L. Zitnick and P. Dollár, “Edge boxes: Locating object proposals from edges,” in
2014
Later among the works it cites.
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” in
2014
Later among the works it cites.
W. Ouyang, X. Chu, and X. Wang, “Multi-source deep learning for human pose estimation,” in
2014
Later among the works it cites.