{"type":"ScholarlyArticle","title":"Color-Sensitive Person Re-Identification","authors":[{"name":"Guan'an Wang","slug":"guan-an-wang","given":"Guan'an","family":"Wang","affiliation":"The State Key Laboratory for Management and Control of Complex Systems , Institute of Automation , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Yang Yang","slug":"yang-yang","given":"Yang","family":"Yang","affiliation":"National Laboratory of Pattern Recognition , Institute of Automation , Chinese Academy of Sciences , Beijing , China"},{"name":"Jian Cheng","slug":"jian-cheng","given":"Jian","family":"Cheng","affiliation":"National Laboratory of Pattern Recognition , Institute of Automation , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China; Center for Excellence in Brain Science and Intelligence Technology , Beijing , China"},{"name":"Jinqiao Wang","slug":"jinqiao-wang","given":"Jinqiao","family":"Wang","affiliation":"National Laboratory of Pattern Recognition , Institute of Automation , Chinese Academy of Sciences , Beijing , China"},{"name":"Zengguang Hou","slug":"zengguang-hou","given":"Zengguang","family":"Hou","affiliation":"The State Key Laboratory for Management and Control of Complex Systems , Institute of Automation , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China; Center for Excellence in Brain Science and Intelligence Technology , Beijing , China"}],"abstract":"Recent deep Re-ID models mainly focus on learning high-level semantic features, while failing to explicitly explore color information which is one of the most important cues for person Re-ID. In this paper, we propose a novel Color-Sensitive Re-ID to take full advantage of color information. On one hand, we train our model with real and fake images. By using the extra fake images, more color information can be exploited and it can avoid overfitting during training. On the other hand, we also train our model with images of the same person with different colors. By doing so, features can be forced to focus on the color difference in regions. To generate fake images with specified colors, we propose a novel Color Translation GAN (CTGAN) to learn mappings between different clothing colors and preserve identity consistency among the same clothing color. Extensive evaluations on two benchmark datasets show that our approach significantly outperforms state-of-the-art  Re-ID models.","doi":"10.24963/ijcai.2019/131","url":"https://ijcai.org/proceedings/2019/131","pdf":"https://ijcai.org/proceedings/2019/0131.pdf","markdown":"https://ijcai.org/proceedings/2019/131.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/131","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Computer Vision","track":"Main track","number":131,"pages":{"first":933,"last":939},"keywords":["Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation","Computer Vision: Computer Vision","Computer Vision: Biometrics, Face and Gesture Recognition"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2017] Yanbei Chen, Xiatian Zhu, and Shaogang Gong. 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