What-Meets-Where: Unified Learning of Action and Contact Localization in a New Dataset

• URL: http://arxiv.org/abs/2508.09428v1
• Date: Wed, 13 Aug 2025 02:06:33 GMT
• Title: What-Meets-Where: Unified Learning of Action and Contact Localization in a New Dataset
• Authors: Yuxiao Wang, Yu Lei, Wolin Liang, Weiying Xue, Zhenao Wei, Nan Zhuang, Qi Liu,
• Abstract summary: We introduce a novel vision task that simultaneously predicts high-level action semantics and fine-grained body-part contact regions.<n>We present PaIR (Part-aware Interaction Representation), a comprehensive dataset containing 13,979 images that encompass 654 actions, 80 object categories, and 17 body parts.
• Score: 6.6946566008924036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: People control their bodies to establish contact with the environment. To comprehensively understand actions across diverse visual contexts, it is essential to simultaneously consider \textbf{what} action is occurring and \textbf{where} it is happening. Current methodologies, however, often inadequately capture this duality, typically failing to jointly model both action semantics and their spatial contextualization within scenes. To bridge this gap, we introduce a novel vision task that simultaneously predicts high-level action semantics and fine-grained body-part contact regions. Our proposed framework, PaIR-Net, comprises three key components: the Contact Prior Aware Module (CPAM) for identifying contact-relevant body parts, the Prior-Guided Concat Segmenter (PGCS) for pixel-wise contact segmentation, and the Interaction Inference Module (IIM) responsible for integrating global interaction relationships. To facilitate this task, we present PaIR (Part-aware Interaction Representation), a comprehensive dataset containing 13,979 images that encompass 654 actions, 80 object categories, and 17 body parts. Experimental evaluation demonstrates that PaIR-Net significantly outperforms baseline approaches, while ablation studies confirm the efficacy of each architectural component. The code and dataset will be released upon publication.

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