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Visual relationship detection aims to capture interactions between pairs ofobjects in images. Relationships between objects and humans represent aparticularly important subset of this problem, with implications for challengessuch as understanding human behaviour, and identifying affordances, amongstothers. In addressing this problem we first construct a large-scalehuman-centric visual relationship detection dataset (HCVRD), which providesmany more types of relationship annotation (nearly 10K categories) than theprevious released datasets.  This large label space better reflects the reality of human-objectinteractions, but gives rise to a long-tail distribution problem, which in turndemands a zero-shot approach to labels appearing only in the test set. This isthe first time this issue has been addressed. We propose a webly-supervisedapproach to these problems and demonstrate that the proposed model provides astrong baseline on our HCVRD dataset.

Care about you: towards large-scale human-centric visual relationship detection