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Title: Object Detection Meets Knowledge Graphs
Other Titles: IJCAI
Authors: Fang, Yuan
Kuan, Kingsley
Lin, Jie
Tan, Cheston
Chandrasekhar, Vijay
Keywords: Subjects::Computing Science
Issue Date: Aug-2017
Abstract: Object detection in images is a crucial task in computer vision, with important applications ranging from security surveillance to autonomous vehicles. Existing state-of-the-art algorithms, including deep neural networks, only focus on utilizing features within an image itself, largely neglecting the vast amount of background knowledge about the real world. In this paper, we propose a novel framework of knowledge-aware object detection, which enables the integration of external knowledge such as knowledge graphs into any object detection algorithm. The framework employs the notion of semantic consistency to quantify and generalize knowledge, which improves object detection through a re-optimization process to achieve better consistency with background knowledge. Finally, empirical evaluation on two benchmark datasets show that our approach can significantly increase recall by up to 6.3 points without compromising mean average precision, when compared to the state-of-the-art baseline.
Appears in Collections:Institute for Infocomm Research

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