Towards Zero-shot Human-Object Interaction Detection via Vision-Language Integration
<|reference_start|>Towards Zero-shot Human-Object Interaction Detection via Vision-Language Integration: 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.<|reference_end|>
@article{xue2024towards,
    title={Towards Zero-shot Human-Object Interaction Detection via Vision-Language
  Integration},
    author={Weiying Xue, Qi Liu, Qiwei Xiong, Yuxiao Wang, Zhenao Wei, Xiaofen
  Xing, Xiangmin Xu},
    journal={arXiv preprint arXiv:2403.07246},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07246},
    primaryClass={cs.CV}
}