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Detecting Visual Relationships with Deep Relational Networks

2017-04-11CVPR 2017Code Available0· sign in to hype

Bo Dai, Yuqi Zhang, Dahua Lin

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Abstract

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 dependencies between objects and their relationships. On two large datasets, the proposed method achieves substantial improvement over state-of-the-art.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
VRD Phrase DetectionDai et. al [[Dai, Zhang, and Lin2017]]R@10023.45Unverified
VRD Predicate DetectionDai et. al [[Dai, Zhang, and Lin2017]]R@10081.9Unverified
VRD Relationship DetectionDai et. al [[Dai, Zhang, and Lin2017]]R@10020.88Unverified

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