Construct a Powerful Discriminative Relationship for Few-Shot Action Recognition

Learning discriminative features from very few labeled samples has gradually become a hot issue in the task of human skeleton action recognition. Most of the existing works follow the paradigms of meta-learning or contrastive learning. However, we argue that the discriminative relationship established in this way is rather simple, because the temporal and spatial features are complex and it is difficult to find the relationships of the same category. In this paper, we propose a Multi-Level Semantic Prompting Joint Contrastive Learning Head (MSJCL-Head), which consists of Joint Hard-Soft Contrastive Learning module (JCL) and Action Semantic Prompts (ASP), to obtain the discriminative representations of texts and skeletons with effect strength and discover and calibrate ambiguous samples in the feature space. A large number of experiments have been conducted on the NTU-T, NTU-S and Kinetics datasets, and the results show that our model has achieved competitive results in few-shot tasks.