Representation modeling learning with multi-domain decoupling for unsupervised skeleton-based action recognition

Skeleton-based action recognition is one of the basic researches in computer vision. In recent years, the unsupervised contrastive learning paradigm has achieved great success in skeleton-based action recognition. However, previous work often treated input skeleton sequences as a whole when performing comparisons, lacking fine-grained representation contrast learning. Therefore, we propose a contrastive learning method for Representation Modeling with Multi-domain Decoupling (RMMD), which extracts the most significant representations from input skeleton sequences in the temporal domain, spatial domain and frequency domain, respectively. Specifically, in the temporal and spatial domains, we propose a multi-level spatiotemporal mining reconstruction module (STMR) that iteratively reconstructs the original input skeleton sequences to highlight spatiotemporal representations under different actions. At the same time, we introduce position encoding and a global adaptive attention matrix, balancing both global and local information, and effectively modeling the spatiotemporal dependencies between joints. In the frequency domain, we use the discrete cosine transform (DCT) to achieve temporal-frequency conversion, discard part of the interference information, and use the frequency self-attention (FSA) and multi-level aggregation perceptron (MLAP) to deeply explore the frequency domain representation. The fusion of the temporal domain, spatial domain and frequency domain representations makes our model more discriminative in representing different actions. Besides, we verify the effectiveness of the model on the NTU RGB+D and PKU-MMD datasets. Extensive experiments show that our method outperforms existing unsupervised methods and achieves significant performance improvements in downstream tasks such as action recognition and action retrieval.