Attention-guided mask learning for self-supervised 3D action recognition

Most existing 3D action recognition works rely on the supervised learning paradigm, yet the limited availability of annotated data limits the full potential of encoding networks. As a result, effective self-supervised pre-training strategies have been actively researched. In this paper, we target to explore a self-supervised learning approach for 3D action recognition, and propose the Attention-guided Mask Learning (AML) scheme. Specifically, the dropping mechanism is introduced into contrastive learning to develop Attention-guided Mask (AM) module as well as mask learning strategy, respectively. The AM module leverages the spatial and temporal attention to guide the corresponding features masking, so as to produce the masked contrastive object. The mask learning strategy enables the model to discriminate different actions even with important features masked, which makes action representation learning more discriminative. What’s more, to alleviate the strict positive constraint that would hinder representation learning, the positive-enhanced learning strategy is leveraged in the second-stage training. Extensive experiments on NTU-60, NTU-120, and PKU-MMD datasets show that the proposed AML scheme improves the performance in self-supervised 3D action recognition, achieving state-of-the-art results.