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Although few-shot action recognition based on metric learning paradigm hasachieved significant success, it fails to address the following issues: (1)inadequate action relation modeling and underutilization of multi-modalinformation; (2) challenges in handling video matching problems with differentlengths and speeds, and video matching problems with misalignment of videosub-actions. To address these issues, we propose a Two-Stream Joint Matchingmethod based on contrastive learning (TSJM), which consists of two modules:Multi-modal Contrastive Learning Module (MCL) and Joint Matching Module (JMM).The objective of the MCL is to extensively investigate the inter-modal mutualinformation relationships, thereby thoroughly extracting modal information toenhance the modeling of action relationships. The JMM aims to simultaneouslyaddress the aforementioned video matching problems. The effectiveness of theproposed method is evaluated on two widely used few shot action recognitiondatasets, namely, SSv2 and Kinetics. Comprehensive ablation experiments arealso conducted to substantiate the efficacy of our proposed approach.

Two-stream joint matching method based on contrastive learning for few-shot action recognition