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keyword

open-category HOI

Open-category human-object interaction refers to a computer vision paradigm in which a system detects, localizes, and recognizes relationships between people and objects without being restricted to a fixed, predefined set of interaction classes. In standard human-object interaction tasks, models are limited to identifying specific human, action, and object triplets encountered during training. By contrast, open-category approaches enable the detection of novel, unseen, or open-vocabulary interactions in unconstrained environments by leveraging multimodal representations and natural language descriptions, such as phrases or interpretive sentences, facilitating zero-shot recognition and broader generalization in real-world visual understanding.

1 item

Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models

Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models

Yichao Cao, Qingfei Tang, Xiu Su, Song Chen, Shan You, Xiaobo Lu, Chang Xu

OrganizationsNanjing Enbo Tech.SenseTimeSoutheast UniversityUniversity of Sydney

Why you should read this

Proposes UniHOI, a framework that integrates vision-language foundation models with language-model-generated knowledge via spatial prompt learning to advance open-world and zero-shot human-object interaction detection.

Human-object interaction (HOI) detection aims to comprehend the intricate relationships between humans and objects, predicting < human, action, object > triplets, and serving as the foundation for numerous computer vision tasks. The complexity and diversity of human-object interactions in the real world, however, pose significant challenges for both annotation and recognition, particularly in recognizing interactions within an open world context. This study explores the universal interaction recognition in an open-world setting through the use of Vision-Language (VL) foundation models and large language models (LLMs). The proposed method is dubbed as UniHOI. We conduct a deep analysis of the three hierarchical features inherent in visual HOI detectors and propose a method for high-level relation extraction aimed at VL foundation models, which we call HO prompt-based learning. Our design includes an HO Prompt-guided Decoder (HOPD), facilitates the association of high-level relation representations in the foundation model with various HO pairs within the image. Furthermore, we utilize a LLM (i.e. GPT) for interaction interpretation, generating a richer linguistic understanding for complex HOIs. For open-category interaction recognition, our method supports either of two input types: interaction phrase or interpretive sentence. Our efficient architecture design and learning methods effectively unleash the potential of the VL foundation models and LLMs, allowing UniHOI to surpass all existing methods with a substantial margin, under both supervised and zero-shot settings. The code and pre-trained weights are available at: https://github.com/Caoyichao/UniHOI.

Added

2026-09-26