# Towards Zero-shot Human-Object Interaction Detection via Vision-Language Integration

Source: [Towards Zero-shot Human-Object Interaction Detection via Vision-Language Integration](<https://www.sota2.com/research/paper/68fa727f1e209131cea56c2d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:27:04.021Z

Authors: Weiying Xue, Qi Liu, Qiwei Xiong, Yuxiao Wang, Zhenao Wei, Xiaofen Xing, Xiangmin Xu

Published: 2024-03-12T02:07:23.000Z

## Abstract

Human-object interaction (HOI) detection aims to locate human-object pairs and identify their interaction categories in images. Most existing methods primarily focus on supervised learning, which relies on extensive manual HOI annotations. In this paper, we propose a novel framework, termed Knowledge Integration to HOI (KI2HOI), that effectively integrates the knowledge of visual-language model to improve zero-shot HOI detection. Specifically, the verb feature learning module is designed based on visual semantics, by employing the verb extraction decoder to convert corresponding verb queries into interaction-specific category representations. We develop an effective additive self-attention mechanism to generate more comprehensive visual representations. Moreover, the innovative interaction representation decoder effectively extracts informative regions by integrating spatial and visual feature information through a cross-attention mechanism. To deal with zero-shot learning in low-data, we leverage a priori knowledge from the CLIP text encoder to initialize the linear classifier for enhanced interaction understanding. Extensive experiments conducted on the mainstream HICO-DET and V-COCO datasets demonstrate that our model outperforms the previous methods in various zero-shot and full-supervised settings.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.07246>)

[PDF](<https://arxiv.org/pdf/2403.07246.pdf>)
