End-to-End Learning of Deformable Mixture of Parts and Deep Convolutional Neural Networks for Human Pose Estimation

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 2016-December, Issue , 2016, Pages 3073-3082

  Yang, Wei ^a   Ouyang, Wanli ^a   Li, Hongsheng ^a   Wang, Xiaogang ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; COMPLEX NETWORKS; COMPUTER VISION; CONVOLUTION; DEFORMATION; GESTURE RECOGNITION; MIXTURES; NEURAL NETWORKS; PATTERN RECOGNITION;

CONTEXTUAL RELATIONSHIPS; CONVOLUTIONAL NEURAL NETWORK; FEATURE REPRESENTATION; GEOMETRIC RELATIONSHIPS; HUMAN POSE ESTIMATIONS; LEARNING PROCESS; STATE-OF-THE-ART APPROACH; TREE-STRUCTURED;

MOTION ESTIMATION;

EID: 84986295323     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.335     Document Type: Conference Paper

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