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Here, we shed light on how to combine the two models to learn more discriminative pedestrian descriptors. Specifically, we propose a Siamese network that simultaneously computes the identification loss and verification loss. Given a pair of training images, the network predicts the identities of the two input images and whether they belong to the same identity. Our network learns a discriminative embedding and a similarity measurement at the same time, thus taking full usage of the re-ID annotations. Our method can be easily applied on different pretrained networks. Albeit simple, the learned embedding improves the state-of-the-art performance on two public person re-ID benchmarks. Further, we show that our architecture can also be applied to image retrieval. The code is available at https:\/\/github.com\/layumi\/2016_person_re-ID.<\/jats:p>","DOI":"10.1145\/3159171","type":"journal-article","created":{"date-parts":[[2017,12,20]],"date-time":"2017-12-20T09:54:00Z","timestamp":1513763640000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":533,"title":["A Discriminatively Learned CNN Embedding for Person Reidentification"],"prefix":"10.1145","volume":"14","author":[{"given":"Zhedong","family":"Zheng","sequence":"first","affiliation":[{"name":"University of Technology Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Zheng","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Yang","sequence":"additional","affiliation":[{"name":"University of Technology Sydney and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Zhong Guan Cun, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2017,12,13]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the Conference on Computer Vision and Pattern Recognition. 3908--3916","author":"Ahmed Ejaz","unstructured":"Ejaz Ahmed , Michael Jones , and Tim K. 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