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
this https URL

本研究提出了一种使用多视角补充监督信号的交叉视图对比学习框架，该框架涵盖了单视图对比学习和交叉视图一致性知识挖掘模块，能够在无监督设置下实现 NTU-60 和 NTU-120 数据集上卓越的动作识别结果。