Towards Zero-shot Human-Object Interaction Detection via Vision-Language Integration

• URL: http://arxiv.org/abs/2403.07246v1
• Date: Tue, 12 Mar 2024 02:07:23 GMT
• Title: Towards Zero-shot Human-Object Interaction Detection via Vision-Language Integration
• Authors: Weiying Xue, Qi Liu, Qiwei Xiong, Yuxiao Wang, Zhenao Wei, Xiaofen Xing, Xiangmin Xu
• Abstract summary: We propose a novel framework, termed Knowledge Integration to HOI (KI2HOI), that effectively integrates the knowledge of visual-language model to improve zero-shot HOI detection. We develop an effective additive self-attention mechanism to generate more comprehensive visual representations. Our model outperforms the previous methods in various zero-shot and full-supervised settings.
• Score: 14.678931157058363
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human-object interaction (HOI) detection aims to locate human-object pairs and identify their interaction categories in images. Most existing methods primarily focus on supervised learning, which relies on extensive manual HOI annotations. In this paper, we propose a novel framework, termed Knowledge Integration to HOI (KI2HOI), that effectively integrates the knowledge of visual-language model to improve zero-shot HOI detection. Specifically, the verb feature learning module is designed based on visual semantics, by employing the verb extraction decoder to convert corresponding verb queries into interaction-specific category representations. We develop an effective additive self-attention mechanism to generate more comprehensive visual representations. Moreover, the innovative interaction representation decoder effectively extracts informative regions by integrating spatial and visual feature information through a cross-attention mechanism. To deal with zero-shot learning in low-data, we leverage a priori knowledge from the CLIP text encoder to initialize the linear classifier for enhanced interaction understanding. Extensive experiments conducted on the mainstream HICO-DET and V-COCO datasets demonstrate that our model outperforms the previous methods in various zero-shot and full-supervised settings.

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