Methods › General › Representation Learning › CV-MIM
Contrastive Cross-View Mutual Information Maximization
CV-MIM
Introduced by Long Zhao et al. in Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
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.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Masked Autoencoders Are Scalable Vision Learners 11 Nov 2021 · 58 repositories · arXiv:2111.06377Syntology ran 71 of 137 samples · 66 unverified · 73 pointer-only (licence)
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Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization 2 Dec 2020 · 1 repository · arXiv:2012.01405
Tasks archive 2025-07-28
12 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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