Skeleton-Based Few-Shot Action Recognition via Fine-Grained Information Capture and Adaptive Metric Aggregation

Recognizing novel actions based on a few labeled skeleton sequence samples is a promising field. Existing works primarily focus on global representations of similar actions and designing novel metric functions. This approach overlooks fine-grained and unique characteristics within samples, which are crucial for distinguishing subtle differences between actions. Moreover, as the field advances, designing increasingly complex metric functions yields diminishing returns in performance improvements. To address these issues, we propose the fine-grained information capture and adaptive metric aggregation (FICAMA) framework for skeleton-based few-shot action recognition (FSAR). This framework enhances the self-information of support samples to facilitate the capture of fine-grained representations. The combination of fine-grained representations and coarse-grained global representations contributes to better action matching. We then employ the skeletal motion fusion module to spatially combine coarse-grained and fine-grained representations within samples and introduce temporal context information through positional encoding. In addition, we design an adaptive multimetric distance aggregation module (AMA) capable of simultaneously utilizing multiple metric functions to aggregate the results of different metric functions in a task-adaptive manner and achieve high-precision skeleton sequence matching. Experiments on the NTU RGB+D 120 and Kinetics datasets demonstrate the effectiveness of our approach. Code is available at https://github.com/jinjinggu00/FICAMA.