Constant-Invariant Information Guided Augmented Spatiotemporal Networks for Skeleton-Based Action Recognition

Contrastive learning has achieved remarkable success in unsupervised skeleton-based action recognition. For contrast learning, multiple contrast pairs have been found to enhance the learning quality of the model. However, existing methods often only mine invariant information within corresponding contrastive pairs, ignoring the constant-invariant information across different contrastive pairs. To address this limitation, this paper introduces a novel Constant-invariant Information Guided Augmented Spatiotemporal Networks (CI-AS), composed of constant-invariant contrastive learning and an augmented spatiotemporal network. The constant-invariant contrastive learning leverages the feature differences among multiple positive pairs, prompting the model to learn more constant-invariant information. It also significantly increases the number of negative samples without adding extra parameters. The augmented spatiotemporal network integrates two multi-contrastive strategies: different domains and varying augmentation levels, thus maximizing the potential of constant-invariant contrastive learning. Additionally, a domain augmentation module is introduced to strengthen the interaction between features of different augmentation strengths, enabling the model to incorporate information from diverse domains and augmentation levels during comparison. Experiments on three large-scale skeleton datasets demonstrate that our method outperforms several state-of-the-art approaches in multiple benchmarks. Our code is available at https://github.com/aoyuleo/CI-AS.