arXiv Computer Vision
CEZSAR: A Contrastive Embedding Method for Zero-Shot Action Recognition
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Few-Shot Video Recognition via Hierarchical Metric Learning
The paper introduces HML-FSAR, a hierarchical metric learning framework for few-shot action recognition. It incorporates a spatial‑enhanced module, temporal MHA, heterogeneous alignment, spatial‑temporal fusion, and dictionary learning to build a comprehensive feature pipeline. Progressive constraints—center, alignment, contrastive, dictionary, and prototype metrics—are applied from frame‑level representations to final prototypes, improving feature compactness, alignment, discriminability, and robustness.