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The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people.
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Clustered pose and nonlinear appearance models for human pose estimation
Johnson, S., Everingham, M.:
Cited in the paper.
2d human pose estimation: New benchmark and state of the art analysis
Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.:
Cited in the paper.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.:
Cited in the paper.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.:
Cited in the paper.
Discriminative appearance models for pictorial structures
Andriluka, M., Roth, S., Schiele, B.:
Cited in the paper.
Articulated human detection with flexible mixtures of parts
Yang, Y., Ramanan, D.:
Cited in the paper.
Poselet conditioned pictorial structures
Pishchulin, L., Andriluka, M., Gehler, P., Schiele, B.:
Cited in the paper.
Joint training of a convolutional network and a graphical model for human pose estimation
Tompson, J.J., Jain, A., LeCun, Y., Bregler, C.:
Cited in the paper.
Articulated pose estimation by a graphical model with image dependent pairwise relations
Chen, X., Yuille, A.:
Cited in the paper.
Deepcut: Joint subset partition and labeling for multi person pose estimation
Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P., Schiele, B.:
Cited in the paper.
Convolutional pose machines
Wei, S.E., Ramakrishna, V., Kanade, T., Sheikh, Y.:
Cited in the paper.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.:
Cited in the paper.
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