{"type":"ScholarlyArticle","title":"Person Re-Identification by Deep Joint Learning of Multi-Loss Classification","authors":[{"name":"Wei Li","slug":"wei-li","given":"Wei","family":"Li","affiliation":"University of London , London E1 4NS , UK"},{"name":"Xiatian Zhu","slug":"xiatian-zhu","given":"Xiatian","family":"Zhu","affiliation":"University of London , London E1 4NS , UK"},{"name":"Shaogang Gong","slug":"shaogang-gong","given":"Shaogang","family":"Gong","affiliation":"University of London , London E1 4NS , UK"}],"abstract":"Existing person re-identification (re-id) methods rely mostly on either localised or global feature representation. This ignores their joint benefit and mutual complementary effects. In this work, we show the advantages of jointly learning local and global features in a Convolutional Neural Network (CNN) by aiming to discover correlated local and global features in different context. Specifically, we formulate a method for joint learning of local and global feature selection losses designed to optimise person re-id when using generic matching metrics such as the L2 distance. We design a novel CNN architecture for Jointly Learning Multi-Loss (JLML) of local and global discriminative feature optimisation subject concurrently to the same re-id labelled information. Extensive comparative evaluations demonstrate the advantages of this new JLML model for person re-id over a wide range of state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03, Market-1501).","doi":"10.24963/ijcai.2017/305","url":"https://ijcai.org/proceedings/2017/305","pdf":"https://ijcai.org/proceedings/2017/0305.pdf","markdown":"https://ijcai.org/proceedings/2017/305.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/305","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":305,"pages":{"first":2194,"last":2200},"keywords":["Multidisciplinary Topics and Applications: Validation and Verification","Robotics and Vision: Robotics and Vision","Machine Learning: Deep Learning"],"videos":[],"references":[{"position":1,"raw":"[Ahmed et al., 2015] Ejaz Ahmed, Michael Jones, and Tim K Marks. 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