Learning View-Disentangled Human Pose Representation by Contrastive
Cross-View Mutual Information Maximization
- URL: http://arxiv.org/abs/2012.01405v2
- Date: Fri, 26 Mar 2021 04:05:44 GMT
- Title: Learning View-Disentangled Human Pose Representation by Contrastive
Cross-View Mutual Information Maximization
- Authors: Long Zhao, Yuxiao Wang, Jiaping Zhao, Liangzhe Yuan, Jennifer J. Sun,
Florian Schroff, Hartwig Adam, Xi Peng, Dimitris Metaxas, Ting Liu
- Abstract summary: 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 (CV-MIM) which maximizes mutual information of the same pose performed from different viewpoints.
CV-MIM outperforms other competing methods by a large margin in the single-shot cross-view setting.
- Score: 33.36330493757669
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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
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