UnionDet: Union-Level Detector Towards Real-Time Human-Object Interaction Detection

Bumsoo Kim, Taeho Choi, Jaewoo Kang, Hyunwoo J. Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

57 Citations (Scopus)


Recent advances in deep neural networks have achieved significant progress in detecting individual objects from an image. However, object detection is not sufficient to fully understand a visual scene. Towards a deeper visual understanding, the interactions between objects, especially humans and objects are essential. Most prior works have obtained this information with a bottom-up approach, where the objects are first detected and the interactions are predicted sequentially by pairing the objects. This is a major bottleneck in HOI detection inference time. To tackle this problem, we propose UnionDet, a one-stage meta-architecture for HOI detection powered by a novel union-level detector that eliminates this additional inference stage by directly capturing the region of interaction. Our one-stage detector for human-object interaction shows a significant reduction in interaction prediction time (4 × ∼ 14 ×) while outperforming state-of-the-art methods on two public datasets: V-COCO and HICO-DET.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings
EditorsAndrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
ISBN (Print)9783030585549
Publication statusPublished - 2020
Event16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom
Duration: 2020 Aug 232020 Aug 28

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12360 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th European Conference on Computer Vision, ECCV 2020
Country/TerritoryUnited Kingdom

Bibliographical note

Funding Information:
Acknowledgement. This work was supported by the National Research Council of Science & Technology (NST) grant by the Korea government (MSIT)(No.CAP-18-03-ETRI), National Research Foundation of Korea (NRF-2017M3C4A7065887), and Samsung Electronics, Co. Ltd.

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.


  • Human-object interaction detection
  • Object detection
  • Real-time detection
  • Visual relationships

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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