Visual-Semantic Graph Reasoning for Pedestrian Attribute Recognition | |
Li QZ(李乔哲); Zhao X(赵鑫); He R(赫然); Huang KQ(黄凯奇) | |
2019-07 | |
会议日期 | 2019-1-27 |
会议地点 | 夏威夷,美国 |
英文摘要 | Pedestrian attribute recognition in surveillance is a challenging task due to poor image quality, significant appearance variations and diverse spatial distribution of different attributes. This paper treats pedestrian attribute recognition as a sequential attribute prediction problem and proposes a novel visual-semantic graph reasoning framework to address this problem. Our framework contains a spatial graph and a directed semantic graph. By performing reasoning using the Graph Convolutional Network (GCN), one graph captures spatial relations between regions and the other learns potential semantic relations between attributes. An end-to-end architecture is presented to perform mutual embedding between these two graphs to guide the relational learning for each other. We verify the proposed framework on three large scale pedestrian attribute datasets including PETA, RAP, and PA100k. Experiments show superiority of the proposed method over state-of-the-art methods and effectiveness of our joint GCN structures for sequential attribute prediction. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/28374] |
专题 | 中国科学院自动化研究所 |
作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Li QZ,Zhao X,He R,et al. Visual-Semantic Graph Reasoning for Pedestrian Attribute Recognition[C]. 见:. 夏威夷,美国. 2019-1-27. |
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