Deep Contextual Attention for Human-Object Interaction Detection

Tiancai Wang, Rao Muhammad Anwer, Muhammad Haris Khan, Fahad Shahbaz Khan, Yanwei Pang, Ling Shao, Jorma Laaksonen

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

1 Citation (Scopus)
21 Downloads (Pure)

Abstract

This work proposes to combine neural networks with the compositional hierarchy of human bodies for efficient and complete human parsing. We formulate the approach as a neural information fusion framework. Our model assembles the information from three inference processes over the hierarchy: direct inference (directly predicting each part of a human body using image information), bottom-up inference (assembling knowledge from constituent parts), and top-down inference (leveraging context from parent nodes). The bottom-up and top-down inferences explicitly model the compositional and decompositional relations in human bodies, respectively. In addition, the fusion of multi-source information is conditioned on the inputs, i.e., by estimating and considering the confidence of the sources. The whole model is end-to-end differentiable, explicitly modeling information flows and structures. Our approach is extensively evaluated on four popular datasets, outperforming the state-of-the-arts in all cases, with a fast processing speed of 23fps. Our code and results have been released to help ease future research in this direction.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Computer Vision (ICCV2019)
PublisherIEEE
Pages5694-5702
ISBN (Electronic)9781728148038
DOIs
Publication statusPublished - Feb 2020
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Computer Vision - Seoul, Korea, Republic of
Duration: 27 Oct 20192 Nov 2019
http://iccv2019.thecvf.com/

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2019-October
ISSN (Electronic)1550-5499

Conference

ConferenceIEEE International Conference on Computer Vision
Abbreviated titleICCV
CountryKorea, Republic of
CitySeoul
Period27/10/201902/11/2019
Internet address

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  • Projects

    MeMAD Laaksonen

    Laaksonen, J., Sjöberg, M., Laria Mantecon, H. & Pehlivan Tort, S.

    01/01/201831/12/2020

    Project: EU: Framework programmes funding

    Deep neural networks in scene graph generation for perception of visual multimedia semantics

    Laaksonen, J., Sjöberg, M., Anwer, R., Pehlivan Tort, S. & Wang, T.

    01/01/201831/12/2019

    Project: Academy of Finland: Other research funding

    Equipment

    Science-IT

    Mikko Hakala (Manager)

    School of Science

    Facility/equipment: Facility

  • Cite this

    Wang, T., Anwer, R. M., Khan, M. H., Khan, F. S., Pang, Y., Shao, L., & Laaksonen, J. (2020). Deep Contextual Attention for Human-Object Interaction Detection. In Proceedings of the International Conference on Computer Vision (ICCV2019) (pp. 5694-5702). (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2019-October). IEEE. https://doi.org/10.1109/ICCV.2019.00579