MA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition

• URL: http://arxiv.org/abs/2308.01532v2
• Date: Fri, 04 Oct 2024 06:43:47 GMT
• Title: MA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition
• Authors: Jiazheng Xing, Chao Xu, Mengmeng Wang, Guang Dai, Baigui Sun, Yong Liu, Jingdong Wang, Jian Zhao,
• Abstract summary: We introduce MA-FSAR, a framework that employs the Fine-Tuning (PEFT) technique to enhance the CLIP visual encoder in terms of action-related temporal and semantic representations. In addition to these token-level designs, we propose a prototype-level text-guided construction module to further enrich the temporal and semantic characteristics of video prototypes.
• Score: 41.78245303513613
• License:
• Abstract: Applying large-scale vision-language pre-trained models like CLIP to few-shot action recognition (FSAR) can significantly enhance both performance and efficiency. While several studies have recognized this advantage, most of them resort to full-parameter fine-tuning to make CLIP's visual encoder adapt to the FSAR data, which not only costs high computations but also overlooks the potential of the visual encoder to engage in temporal modeling and focus on targeted semantics directly. To tackle these issues, we introduce MA-FSAR, a framework that employs the Parameter-Efficient Fine-Tuning (PEFT) technique to enhance the CLIP visual encoder in terms of action-related temporal and semantic representations. Our solution involves a Fine-grained Multimodal Adaptation, which is different from the previous attempts of PEFT in regular action recognition. Specifically, we first insert a Global Temporal Adaptation that only receives the class token to capture global motion cues efficiently. Then these outputs integrate with visual tokens to enhance local temporal dynamics by a Local Multimodal Adaptation, which incorporates text features unique to the FSAR support set branch to highlight fine-grained semantics related to actions. In addition to these token-level designs, we propose a prototype-level text-guided construction module to further enrich the temporal and semantic characteristics of video prototypes. Extensive experiments demonstrate our superior performance in various tasks using minor trainable parameters.

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