Generative Human-Object Interaction Detection via Differentiable Cognitive Steering of Multi-modal LLMs

• URL: http://arxiv.org/abs/2512.17640v1
• Date: Fri, 19 Dec 2025 14:41:50 GMT
• Title: Generative Human-Object Interaction Detection via Differentiable Cognitive Steering of Multi-modal LLMs
• Authors: Zhaolin Cai, Huiyu Duan, Zitong Xu, Fan Li, Zhi Liu, Jing Liu, Wei Shen, Xiongkuo Min, Guangtao Zhai,
• Abstract summary: Human-object interaction (HOI) detection aims to localize human-object pairs and the interactions between them.<n>Existing methods operate under a closed-world assumption, treating the task as a classification problem over a small, predefined verb set.<n>We propose GRASP-HO, a novel Generative Reasoning And Steerable Perception framework that reformulates HOI detection from the closed-set classification task to the open-vocabulary generation problem.
• Score: 85.69785384599827
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Human-object interaction (HOI) detection aims to localize human-object pairs and the interactions between them. Existing methods operate under a closed-world assumption, treating the task as a classification problem over a small, predefined verb set, which struggles to generalize to the long-tail of unseen or ambiguous interactions in the wild. While recent multi-modal large language models (MLLMs) possess the rich world knowledge required for open-vocabulary understanding, they remain decoupled from existing HOI detectors since fine-tuning them is computationally prohibitive. To address these constraints, we propose \GRASP-HO}, a novel Generative Reasoning And Steerable Perception framework that reformulates HOI detection from the closed-set classification task to the open-vocabulary generation problem. To bridge the vision and cognitive, we first extract hybrid interaction representations, then design a lightweight learnable cognitive steering conduit (CSC) module to inject the fine-grained visual evidence into a frozen MLLM for effective reasoning. To address the supervision mismatch between classification-based HOI datasets and open-vocabulary generative models, we introduce a hybrid guidance strategy that coupling the language modeling loss and auxiliary classification loss, enabling discriminative grounding without sacrificing generative flexibility. Experiments demonstrate state-of-the-art closed-set performance and strong zero-shot generalization, achieving a unified paradigm that seamlessly bridges discriminative perception and generative reasoning for open-world HOI detection.

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