HCVRD: A benchmark for large-scale human-centered visual relationship detection
Date
2018
Authors
Zhuang, B.
Wu, Q.
Shen, C.
Reid, I.
Van Den Hengel, A.
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Conference paper
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Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2018, pp.7631-7638
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Bohan Zhuang, Qi Wu, Chunhua Shen, Ian Reid, Anton van den Hengel
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AAAI Conference on Artificial Intelligence (AAAI) (2 Feb 2018 - 7 Feb 2018 : New Orleans)
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.
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Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.