  In this work, we propose a Cross-view Contrastive Learning framework for
unsupervised 3D skeleton-based action Representation (CrosSCLR), by leveraging
multi-view complementary supervision signal. CrosSCLR consists of both
single-view contrastive learning (SkeletonCLR) and cross-view consistent
knowledge mining (CVC-KM) modules, integrated in a collaborative learning
manner. It is noted that CVC-KM works in such a way that high-confidence
positive/negative samples and their distributions are exchanged among views
according to their embedding similarity, ensuring cross-view consistency in
terms of contrastive context, i.e., similar distributions. Extensive
experiments show that CrosSCLR achieves remarkable action recognition results
on NTU-60 and NTU-120 datasets under unsupervised settings, with observed
higher-quality action representations. Our code is available at
https://github.com/LinguoLi/CrosSCLR.
