Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/117898
Type: | Conference paper |
Title: | HCVRD: A benchmark for large-scale human-centered visual relationship detection |
Author: | Zhuang, B. Wu, Q. Shen, C. Reid, I. Van Den Hengel, A. |
Citation: | Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2018, pp.7631-7638 |
Publisher: | Association for the Advancement of Artificial Intelligence |
Issue Date: | 2018 |
Series/Report no.: | AAAI Conference on Artificial Intelligence |
ISBN: | 9781577358008 |
ISSN: | 2159-5399 2374-3468 |
Conference Name: | AAAI Conference on Artificial Intelligence (AAAI) (2 Feb 2018 - 7 Feb 2018 : New Orleans) |
Statement of Responsibility: | Bohan Zhuang, Qi Wu, Chunhua Shen, Ian Reid, Anton van den Hengel |
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. |
Rights: | Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. |
Appears in Collections: | Aurora harvest 8 Australian Institute for Machine Learning publications Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.