Exploring Predicate Visual Context in Detecting of Human-Object Interactions

Frederic Z. Zhang*, Yuhui Yuan, Dylan Campbell, Zhuoyao Zhong, Stephen Gould

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    3 Citations (Scopus)

    Abstract

    Recently, the DETR framework has emerged as the dominant approach for human-object interaction (HOI) research. In particular, two-stage transformer-based HOI detectors are amongst the most performant and training-efficient approaches. However, these often condition HOI classification on object features that lack fine-grained contextual information, eschewing pose and orientation information in favour of visual cues about object identity and box extremities. This naturally hinders the recognition of complex or ambiguous interactions. In this work, we study these issues through visualisations and carefully designed experiments. Accordingly, we investigate how best to re-introduce image features via cross-attention. With an improved query design, extensive exploration of keys and values, and box pair positional embeddings as spatial guidance, our model with enhanced predicate visual context (PViC) outperforms state-of-the-art methods on the HICO-DET and V-COCO benchmarks, while maintaining low training cost.

    Original languageEnglish
    Title of host publicationProceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages10377-10387
    Number of pages11
    ISBN (Electronic)9798350307184
    DOIs
    Publication statusPublished - 2023
    Event2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023 - Paris, France
    Duration: 2 Oct 20236 Oct 2023

    Publication series

    NameProceedings of the IEEE International Conference on Computer Vision
    ISSN (Print)1550-5499

    Conference

    Conference2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
    Country/TerritoryFrance
    CityParis
    Period2/10/236/10/23

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