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

• URL: http://arxiv.org/abs/2507.06510v1
• Date: Wed, 09 Jul 2025 03:16:39 GMT
• Title: Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection
• Authors: Yupeng Hu, Changxing Ding, Chang Sun, Shaoli Huang, Xiangmin Xu,
• Abstract summary: Open vocabulary Human-Object Interaction (HOI) detection is a challenging task that detects all human, verb, object> triplets of interest in an image.<n>Existing approaches typically rely on output features generated by large Vision-Language Models (VLMs)<n>We propose a novel Bilateral Collaboration framework for open vocabulary HOI detection (BC-HOI)
• Score: 29.24483392547041
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open vocabulary Human-Object Interaction (HOI) detection is a challenging task that detects all <human, verb, object> 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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