Mining Instance-Centric Vision-Language Contexts for Human-Object Interaction Detection
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 featu...
| Authors | Soo Won Seo, KyungChae Lee, Hyungchan Cho et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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