  Self-supervised learning has demonstrated remarkable capability in
representation learning for skeleton-based action recognition. Existing methods
mainly focus on applying global data augmentation to generate different views
of the skeleton sequence for contrastive learning. However, due to the rich
action clues in the skeleton sequences, existing methods may only take a global
perspective to learn to discriminate different skeletons without thoroughly
leveraging the local relationship between different skeleton joints and video
frames, which is essential for real-world applications. In this work, we
propose a Partial Spatio-Temporal Learning (PSTL) framework to exploit the
local relationship from a partial skeleton sequences built by a unique
spatio-temporal masking strategy. Specifically, we construct a
negative-sample-free triplet steam structure that is composed of an anchor
stream without any masking, a spatial masking stream with Central Spatial
Masking (CSM), and a temporal masking stream with Motion Attention Temporal
Masking (MATM). The feature cross-correlation matrix is measured between the
anchor stream and the other two masking streams, respectively. (1) Central
Spatial Masking discards selected joints from the feature calculation process,
where the joints with a higher degree of centrality have a higher possibility
of being selected. (2) Motion Attention Temporal Masking leverages the motion
of action and remove frames that move faster with a higher possibility. Our
method achieves SOTA performance on NTU-60, NTU-120 and PKU-MMD under various
downstream tasks. A practical evaluation is performed where some skeleton
joints are lost in downstream tasks. In contrast to previous methods that
suffer from large performance drops, our PSTL can still achieve remarkable
results, validating the robustness of our method. Code:
https://github.com/YujieOuO/PSTL.git.
