Detecting Visual Relationships with Deep Relational Networks
Refereed conference paper presented and published in conference proceedings
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香港中文大學研究人員

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摘要Relationships 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.
著者Bo Dai, Yuqi Zhang, Dahua Lin
會議名稱IEEE Conference on Computer Vision and Pattern Recognition
會議開始日21.07.2017
會議完結日26.07.2017
會議地點Honolulu, Hawaii
會議國家/地區美國
出版年份2017
月份7
語言美式英語

上次更新時間 2018-20-01 於 18:58