Progressive Learning of Instance-Level Proxy Semantics for Few-Shot Action Recognition

Fang Peng, Xiaoshan Yang, Yaowei Wang, Changsheng Xu

Abstract: Few-shot action recognition is a crucial task for mitigating the challenges of data scarcity in video understanding. Recent advancements in large-scale pre-trained models have introduced the potential of incorporating semantic knowledge from multi-modal pre-trained models, such as CLIP, to alleviate these challenges. Although some progress have been made, existing methods still rely on class-level text embeddings that are inherently low in diversity, limiting their ability to generalize to unseen actions. To overcome this limitation, we propose a novel framework called Progressive Learning of Instance-Level Proxy Semantics (ProLIPS). ProLIPS integrates Proxy Semantic Diffusion (PSD) to generate rich, instance-level proxy semantic features with diverse semantic contents and temporal dynamics, utilizing a multi-step CLIP-guidance mechanism and a time-conditioned reverse diffusion process. Our approach preserves the diversity of semantic-aligned visual features, significantly improving the generalization and robustness of few-shot action recognition. Extensive experiments on five challenging benchmarks demonstrate the effectiveness of ProLIPS.

External IDs:doi:10.1109/tmm.2025.3632652