Detecting Visual Relationships with Deep Relational Networks
Refereed conference paper presented and published in conference proceedings
Officially Accepted for Publication

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AbstractRelationships among objects play a crucial role in image understanding. Despite the great success of deep learning techniques in recognizing individual objects, reasoning about the relationships among objects remains a challenging task. Previous methods often treat this as a classification problem, considering each type of relationship (e.g. “ride”) or each distinct visual phrase (e.g. “person- ride-horse”) as a category. Such approaches are faced with significant difficulties caused by the high diversity of visual appearance for each kind of relationships or the large number of distinct visual phrases. We propose an integrated framework to tackle this problem. At the heart of this framework is the Deep Relational Network, a novel formulation designed specifically for exploiting the statistical depen- dencies between objects and their relationships. On two large data sets, the proposed method achieves substantial improvement over state-of-the-art.
All Author(s) ListBo Dai, Yuqi Zhang, Dahua Lin
Name of ConferenceIEEE Conference on Computer Vision and Pattern Recognition
Start Date of Conference21/07/2017
End Date of Conference26/07/2017
Place of ConferenceHonolulu, Hawaii
Country/Region of ConferenceUnited States of America
LanguagesEnglish-United States

Last updated on 2018-20-01 at 18:58