Mutual Information Driven Equivariant Contrastive Learning for 3D Action Representation Learning.

Self-supervised contrastive learning has proven to be successful for skeleton-based action recognition. For contrastive learning, data transformations are found to fundamentally affect the learned representation quality. However, traditional invariant contrastive learning is detrimental to the performance on the downstream task if the transformation carries important information for the task. In this sense, it limits the application of many data transformations in the current contrastive learning pipeline. To address these issues, we propose to utilize equivariant contrastive learning, which extends invariant contrastive learning and preserves important information. By integrating equivariant and invariant contrastive learning into a hybrid approach, the model can better leverage the motion patterns exposed by data transformations and obtain a more discriminative representation space. Specifically, a self-distillation loss is first proposed for transformed data of different intensities to fully utilize invariant transformations, especially strong invariant transformations. For equivariant transformations, we explore the potential of skeleton mixing and temporal shuffling for equivariant contrastive learning. Meanwhile, we analyze the impacts of different data transformations on the feature space in terms of two novel metrics proposed in this paper, namely, consistency and diversity. In particular, we demonstrate that equivariant learning boosts performance by alleviating the dimensional collapse problem. Experimental results on several benchmarks indicate that our method outperforms existing state-of-the-art methods.