A Lightweight Multi-Level Relation Network for Few-shot Action Recognition

Few-shot (FS) action recognition classifies new actions with limited training samples. Most existing works focus on the variability between actions/videos by designing for either feature extraction methods or training strategies. However, they ignore the relations for a same action at different time clips, which is crucial to improve class-specific discriminability. In this paper, we propose a lightweight multi-level relation network (MLRN) that considers the variability of an action that inner- and cross-video, based on episodic training strategies. Furthermore, a query-support similarity classifier is introduced to improve the class identifiability by enhancing the feature utilisation at different levels. Experiments on three challenging benchmarks demonstrate that the proposed MLRN outperforms state-of-the-art methods while using approximately 50% fewer trainable parameters.