  Self-supervised skeleton-based action recognition enjoys a rapid growth along
with the development of contrastive learning. The existing methods rely on
imposing invariance to augmentations of 3D skeleton within a single data
stream, which merely leverages the easy positive pairs and limits the ability
to explore the complicated movement patterns. In this paper, we advocate that
the defect of single-stream contrast and the lack of necessary feature
transformation are responsible for easy positives, and therefore propose a
Cross-Stream Contrastive Learning framework for skeleton-based action
Representation learning (CSCLR). Specifically, the proposed CSCLR not only
utilizes intra-stream contrast pairs, but introduces inter-stream contrast
pairs as hard samples to formulate a better representation learning. Besides,
to further exploit the potential of positive pairs and increase the robustness
of self-supervised representation learning, we propose a Positive Feature
Transformation (PFT) strategy which adopts feature-level manipulation to
increase the variance of positive pairs. To validate the effectiveness of our
method, we conduct extensive experiments on three benchmark datasets NTU-RGB+D
60, NTU-RGB+D 120 and PKU-MMD. Experimental results show that our proposed
CSCLR exceeds the state-of-the-art methods on a diverse range of evaluation
protocols.
