[2604.02071] Mining Instance-Centric Vision-Language Contexts for Human-Object Interaction Detection

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.02071 (cs) [Submitted on 2 Apr 2026] Title:Mining Instance-Centric Vision-Language Contexts for Human-Object Interaction Detection Authors:Soo Won Seo, KyungChae Lee, Hyungchan Cho, Taein Son, Nam Ik Cho, Jun Won Choi View a PDF of the paper titled Mining Instance-Centric Vision-Language Contexts for Human-Object Interaction Detection, by Soo Won Seo and 5 other authors View PDF HTML (experimental) Abstract:Human-Object Interaction (HOI) detection aims to localize human-object pairs and classify their interactions from a single image, a task that demands strong visual understanding and nuanced contextual reasoning. Recent approaches have leveraged Vision-Language Models (VLMs) to introduce semantic priors, significantly improving HOI detection performance. However, existing methods often fail to fully capitalize on the diverse contextual cues distributed across the entire scene. To overcome these limitations, we propose the Instance-centric Context Mining Network (InCoM-Net)-a novel framework that effectively integrates rich semantic knowledge extracted from VLMs with instance-specific features produced by an object detector. This design enables deeper interaction reasoning by modeling relationships not only within each detected instance but also across instances and their surrounding scene context. InCoM-Net comprises two core components: Instancecentric Context Refinement (ICR), which separately extrac...