CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Identity-Guided Human Semantic Parsing for Person Re-Identification
Zhu Kuan1,2; Guo Haiyun1; Liu Zhiwei1,2; Tang Ming1; Wang Jinqiao1
Conference NameEuropean Conference on Computer Vision
Conference Date2020-8
Conference Placeonline

Existing alignment-based methods have to employ the pretrained human parsing models to achieve the pixel-level alignment, and cannot identify the personal belongings (e.g., backpacks and reticule) which are crucial to person re-ID. In this paper, we propose the identityguided human semantic parsing approach (ISP) to locate both the human body parts and personal belongings at pixel-level for aligned person reID only with person identity labels. We design the cascaded clustering on feature maps to generate the pseudo-labels of human parts. Specifically, for the pixels of all images of a person, we first group them to foreground or background and then group the foreground pixels to human parts. The cluster assignments are subsequently used as pseudo-labels of human parts to supervise the part estimation and ISP iteratively learns the feature maps and groups them. Finally, local features of both human body parts and personal belongings are obtained according to the selflearned part estimation, and only features of visible parts are utilized for the retrieval. Extensive experiments on three widely used datasets validate the superiority of ISP over lots of state-of-the-art methods. Our code is available at

Indexed ByEI
Document Type会议论文
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.School of Artificial Intelligence, University of Chinese Academy of Sciences
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhu Kuan,Guo Haiyun,Liu Zhiwei,et al. Identity-Guided Human Semantic Parsing for Person Re-Identification[C],2020.
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