Prompt-guided Representation Disentanglement for Action Recognition

• URL: http://arxiv.org/abs/2509.21783v3
• Date: Tue, 14 Oct 2025 04:00:07 GMT
• Title: Prompt-guided Representation Disentanglement for Action Recognition
• Authors: Tianci Wu, Guangming Zhu, Jiang Lu, Siyuan Wang, Ning Wang, Nuoye Xiong, Zhang Liang,
• Abstract summary: We propose Prompt-guided Disentangled Representation for Action Recognition (ProDA)<n>ProDA disentangles any specified actions from a multi-action scene.<n>We design a video-adapted Graph Parsing Neural Network (GPNN) that aggregates information using dynamic weights.
• Score: 16.990362901681948
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Action recognition is a fundamental task in video understanding. Existing methods typically extract unified features to process all actions in one video, which makes it challenging to model the interactions between different objects in multi-action scenarios. To alleviate this issue, we explore disentangling any specified actions from complex scenes as an effective solution. In this paper, we propose Prompt-guided Disentangled Representation for Action Recognition (ProDA), a novel framework that disentangles any specified actions from a multi-action scene. ProDA leverages Spatio-temporal Scene Graphs (SSGs) and introduces Dynamic Prompt Module (DPM) to guide a Graph Parsing Neural Network (GPNN) in generating action-specific representations. Furthermore, we design a video-adapted GPNN that aggregates information using dynamic weights. Experiments in video action recognition demonstrate the effectiveness of our approach when compared with the state-of-the-art methods. Our code can be found in https://github.com/iamsnaping/ProDA.git

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