Two-stream joint matching method based on contrastive learning for
  few-shot action recognition
Long Deng Ziqiang Li Bingxin Zhou Zhongming Chen Ao Li Yongxin Ge
Although few-shot action recognition based on metric learning paradigm has
achieved significant success, it fails to address the following issues: (1)
inadequate action relation modeling and underutilization of multi-modal
information; (2) challenges in handling video matching problems with different
lengths and speeds, and video matching problems with misalignment of video
sub-actions. To address these issues, we propose a Two-Stream Joint Matching
method 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 mutual
information relationships, thereby thoroughly extracting modal information to
enhance the modeling of action relationships. The JMM aims to simultaneously
address the aforementioned video matching problems. The effectiveness of the
proposed method is evaluated on two widely used few shot action recognition
datasets, namely, SSv2 and Kinetics. Comprehensive ablation experiments are
also conducted to substantiate the efficacy of our proposed approach.