Interact as you intend: Intention-driven human-object interaction detection

Bingjie Xu, Junnan Li, Yongkang Wong, Qi Zhao, Mohan S. Kankanhalli

Research output: Contribution to journalArticlepeer-review

70 Scopus citations


The recent advances in instance-level detection tasks lay strong foundation for genuine comprehension of the visual scenes. However, the ability to fully comprehend a social scene is still in its preliminary stage. In this work, we focus on detecting human-object interactions (HOIs) in social scene images, which is demanding in terms of research and increasingly useful for practical applications. To undertake social tasks interacting with objects, humans direct their attention and move their body based on their intention. Based on this observation, we provide a unique computational perspective to explore human intention in HOI detection. Specifically, the proposed human intention-driven HOI detection (iHOI) framework models human pose with the relative distances from body joints to the object instances. It also utilizes human gaze to guide the attended contextual regions in a weakly-supervised setting. In addition, we propose a hard negative sampling strategy to address the problem of mis-grouping. We perform extensive experiments on two benchmark datasets, namely V-COCO and HICO-DET. The efficacy of each proposed component has also been validated.

Original languageEnglish (US)
Article number8848601
Pages (from-to)1423-1432
Number of pages10
JournalIEEE Transactions on Multimedia
Issue number6
StatePublished - Jun 2020

Bibliographical note

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  • Human-Object Interactions (HOIs)
  • Intention-Driven Analysis
  • Visual Relationships


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