Self-supervised skeleton-based action recognition enjoys a rapid growth alongwith 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 NTURGB
+ 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.