Paper Info
Title
Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization
Abstract
We introduce a novel representation learning method to disentangle pose-dependent as well as view-dependent factors from 2D human poses. The method trains a network using cross-view mutual information maximization (CV-MIM) which maximizes mutual information of the same pose performed from different viewpoints in a contrastive learning manner. We further propose two regularization terms to ensure disentanglement and smoothness of the learned representations. The resulting pose representations can be used for cross-view action recognition. To evaluate the power of the learned representations, in addition to the conventional fully-supervised action recognition settings, we introduce a novel task called single-shot cross-view action recognition. This task trains models with actions from only one single viewpoint while models are evaluated on poses captured from all possible viewpoints. We evaluate the learned representations on standard benchmarks for action recognition, and show that (i) CV-MIM performs competitively compared with the state-of-the-art models in the fully-supervised scenarios; (ii) CV-MIM outperforms other competing methods by a large margin in the single-shot cross-view setting; (iii) and the learned representations can significantly boost the performance when reducing the amount of supervised training data. Our code is made publicly available at https://github.com/google-research/google-research/tree/master/poem.
Year
DOI
Venue
2021
10.1109/CVPR46437.2021.01260
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
DocType
ISSN
Citations 
Conference
1063-6919
0
PageRank 
References 
Authors
0.34
0
10
Name
Order
Citations
PageRank
Long Zhao141.42
Yuxiao Wang200.68
Jiaping Zhao3615.94
Liangzhe Yuan401.01
Jennifer J. Sun500.34
Florian Schroff675732.72
Hartwig Adam7132642.50
Xi Peng812313.67
Dimitris N. Metaxas98834952.25
Ting Liu10304.08