CV-MIM
Contrastive Cross-View Mutual Information Maximization
**CV-MIM**, or **Contrastive Cross-View Mutual Information Maximization**, is a 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, which maximizes mutual information of the same pose performed from different viewpoints in a contrastive learning manner. It further utilizes two regularization terms to ensure disentanglement and smoothness of the learned representations.
Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization
https://paperswithcode.com/paper/learning-view-disentangled-human-pose
Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization