Disentangling and Reusing Interaction Cues for Zero-Shot HOI Detection

Zero-shot Human-Object Interaction (HOI) detection has achieved significant progress by leveraging the open-vocabulary capabilities of Vision-Language Models. However, the generalization of existing methods is severely restricted by a two-fold representational bottleneck. First, at the feature level, essential fine-grained interaction cues are frequently overshadowed by high-level global semantics. Second, at the decision level, models exhibit a strong background shortcut bias, often relying on scene co-occurrences rather than the actual humanobject interactions. To address these challenges, we propose a novel zeroshot HOI detection framework, DRIC, which is explicitly formulated for Disentangling and Reusing Interaction Cues. Specifically, we introduce the Context-Orthogonal Residual Disentangling (CORD) module, which projects features onto a global prototype subspace to decouple fine-grained residual details from global semantic biases, subsequently reintegrating them for enhanced local discriminability. Furthermore, the proposed Stratified Local Fusion (SLF) mechanism mitigates background shortcuts by aggregating multi-depth features from the image encoder of CLIP. This recovers essential contact-level details, guiding the model toward actual interactions. Extensive experiments demonstrate that DRIC consistently outperforms state-of-the-art methods across zero-shot evaluation settings, validating the robustness of the proposed framework in handling interaction ambiguity and cross-scene transfer.