Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection

Yupeng Hu; Changxing Ding; Chang Sun; Shaoli Huang; Xiangmin Xu

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arXiv · 2507.06510

Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection

Yupeng Hu·Changxing Ding·Chang Sun·Shaoli Huang·Xiangmin Xu

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Abstract

Open vocabulary Human-Object Interaction (HOI) detection is a challenging task that detects all triplets of interest in an image, even those that are not pre-defined in the training set. Existing approaches typically rely on output features generated by large Vision-Language Models (VLMs) to enhance the generalization ability of interaction representations. However, the visual features produced by VLMs are holistic and coarse-grained, which contradicts the nature of detection tasks. To address this issue, we propose a novel Bilateral Collaboration framework for open vocabulary HOI detection (BC-HOI). This framework includes an Attention Bias Guidance (ABG) component, which guides the VLM to produce fine-grained instance-level interaction features according to the attention bias provided by the HOI detector. It also includes a Large Language Model (LLM)-based Supervision Guidance (LSG) component, which provides fine-grained token-level supervision for the HOI detector by the LLM component of the VLM. LSG enhances the ability of ABG to generate high-quality attention bias. We conduct extensive experiments on two popular benchmarks: HICO-DET and V-COCO, consistently achieving superior performance in the open vocabulary and closed settings. The code will be released in Github.

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BibTeX RIS

Yupeng Hu, Changxing Ding, Chang Sun, Shaoli Huang, Xiangmin Xu. 2025-07-09. Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection. https://arxiv.org/abs/2507.06510

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