  Recent contrastive based 3D action representation learning has made great
progress. However, the strict positive/negative constraint is yet to be relaxed
and the use of non-self positive is yet to be explored. In this paper, a
Contrastive Positive Mining (CPM) framework is proposed for unsupervised
skeleton 3D action representation learning. The CPM identifies non-self
positives in a contextual queue to boost learning. Specifically, the siamese
encoders are adopted and trained to match the similarity distributions of the
augmented instances in reference to all instances in the contextual queue. By
identifying the non-self positive instances in the queue, a positive-enhanced
learning strategy is proposed to leverage the knowledge of mined positives to
boost the robustness of the learned latent space against intra-class and
inter-class diversity. Experimental results have shown that the proposed CPM is
effective and outperforms the existing state-of-the-art unsupervised methods on
the challenging NTU and PKU-MMD datasets.
