  In recent years, self-supervised representation learning for skeleton-based
action recognition has been developed with the advance of contrastive learning
methods. The existing contrastive learning methods use normal augmentations to
construct similar positive samples, which limits the ability to explore novel
movement patterns. In this paper, to make better use of the movement patterns
introduced by extreme augmentations, a Contrastive Learning framework utilizing
Abundant Information Mining for self-supervised action Representation (AimCLR)
is proposed. First, the extreme augmentations and the Energy-based
Attention-guided Drop Module (EADM) are proposed to obtain diverse positive
samples, which bring novel movement patterns to improve the universality of the
learned representations. Second, since directly using extreme augmentations may
not be able to boost the performance due to the drastic changes in original
identity, the Dual Distributional Divergence Minimization Loss (D$^3$M Loss) is
proposed to minimize the distribution divergence in a more gentle way. Third,
the Nearest Neighbors Mining (NNM) is proposed to further expand positive
samples to make the abundant information mining process more reasonable.
Exhaustive experiments on NTU RGB+D 60, PKU-MMD, NTU RGB+D 120 datasets have
verified that our AimCLR can significantly perform favorably against
state-of-the-art methods under a variety of evaluation protocols with observed
higher quality action representations. Our code is available at
https://github.com/Levigty/AimCLR.
