HCVRD: a benchmark for large-scale human-centered visual relationship detection

Bohan Zhuang, Qi Wu, Chunhua Shen, Ian Reid, Anton van den Hengel

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

14 Citations (Scopus)

Abstract

Visual relationship detection aims to capture interactions between pairs of objects in images. Relationships between objects and humans represent a particularly important subset of this problem, with implications for challenges such as understanding human behavior, and identifying affordances, amongst others. In addressing this problem we first construct a large-scale human-centric visual relationship detection dataset (HCVRD), which provides many more types of relationship annotations (nearly 10K categories) than the previous released datasets. This large label space better reflects the reality of human-object interactions, but gives rise to a long-tail distribution problem, which in turn demands a zero-shot approach to labels appearing only in the test set. This is the first time this issue has been addressed. We propose a webly-supervised approach to these problems and demonstrate that the proposed model provides a strong baseline on our HCVRD dataset.

Original languageEnglish
Title of host publicationThe Thirty-Second AAAI Conference on Artificial Intelligence
EditorsSheila McIlraith, Kilian Weinberger
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages7631-7638
Number of pages8
ISBN (Electronic)9781577358008
Publication statusPublished - 2018
Externally publishedYes
EventAAAI Conference on Artificial Intelligence 2018 - New Orleans, United States of America
Duration: 2 Feb 20187 Feb 2018
Conference number: 32nd
https://aaai.org/Conferences/AAAI-18/

Conference

ConferenceAAAI Conference on Artificial Intelligence 2018
Abbreviated titleAAAI 2018
Country/TerritoryUnited States of America
CityNew Orleans
Period2/02/187/02/18
Internet address

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