AI RESEARCH
Knowledge is Power: Advancing Few-shot Action Recognition with Multimodal Semantics from MLLMs
arXiv CS.CV
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ArXi:2603.26033v1 Announce Type: new Multimodal Large Language Models (MLLMs) have propelled the field of few-shot action recognition (FSAR). However, preliminary explorations in this area primarily focus on generating captions to form a suboptimal feature->caption->feature pipeline and adopt metric learning solely within the visual space. In this paper, we propose FSAR-LLaVA, the first end-to-end method to leverage MLLMs (such as Video-LLaVA) as a multimodal knowledge base for directly enhancing