{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-discriminatively-learned-cnn-embedding-for","title":"A Discriminatively Learned CNN Embedding for Person Re-identification","arxiv_id":"1611.05666","date":"2016-11-17","proceeding":null,"authors":["Zhedong Zheng","Liang Zheng","Yi Yang"],"abstract":"We revisit two popular convolutional neural networks (CNN) in person\nre-identification (re-ID), i.e, verification and classification models. The two\nmodels have their respective advantages and limitations due to different loss\nfunctions. In this paper, we shed light on how to combine the two models to\nlearn more discriminative pedestrian descriptors. Specifically, we propose a\nnew siamese network that simultaneously computes identification loss and\nverification loss. Given a pair of training images, the network predicts the\nidentities of the two images and whether they belong to the same identity. Our\nnetwork learns a discriminative embedding and a similarity measurement at the\nsame time, thus making full usage of the annotations. Albeit simple, the\nlearned embedding improves the state-of-the-art performance on two public\nperson re-ID benchmarks. Further, we show our architecture can also be applied\nin image retrieval.","url_abs":"http://arxiv.org/abs/1611.05666v2","url_pdf":"http://arxiv.org/pdf/1611.05666v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-discriminatively-learned-cnn-embedding-for","repo_url":"https://github.com/layumi/2016_person_re-ID","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"a-discriminatively-learned-cnn-embedding-for","repo_url":"https://github.com/LDVC124/2016_person_re-ID","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"a-discriminatively-learned-cnn-embedding-for","repo_url":"https://github.com/layumi/Person-reID-verification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"a-discriminatively-learned-cnn-embedding-for","repo_url":"https://github.com/ahangchen/rank-reid","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"person-re-identification","task_name":"Person Re-Identification"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[{"method_slug":"siamese-network","method_name":"Siamese Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-retrieval-on-oxford5k","task":"Image Retrieval","dataset":"Oxford5k","model":"Identification+Verification","rank_in_archive_order":2,"of":2,"metrics":{"mAP":"76.4"},"uses_additional_data":false},{"leaderboard":"/sota/person-re-identification-on-cuhk03","task":"Person Re-Identification","dataset":"CUHK03","model":"DLCE","rank_in_archive_order":5,"of":19,"metrics":{"MAP":"86.4","Rank-1":"83.4"},"uses_additional_data":false},{"leaderboard":"/sota/person-re-identification-on-dukemtmc-reid","task":"Person Re-Identification","dataset":"DukeMTMC-reID","model":"DLCE","rank_in_archive_order":82,"of":94,"metrics":{"Rank-1":"68.9","mAP":"49.3"},"uses_additional_data":false},{"leaderboard":"/sota/person-re-identification-on-msmt17","task":"Person Re-Identification","dataset":"MSMT17","model":"DLCE","rank_in_archive_order":39,"of":43,"metrics":{"Rank-1":"60.48","mAP":"31.58"},"uses_additional_data":false},{"leaderboard":"/sota/person-re-identification-on-market-1501","task":"Person Re-Identification","dataset":"Market-1501","model":"DLCE","rank_in_archive_order":114,"of":135,"metrics":{"Rank-1":"79.51","mAP":"59.87"},"uses_additional_data":false},{"leaderboard":"/sota/person-re-identification-on-market-1501-500k","task":"Person Re-Identification","dataset":"Market-1501+500k","model":"DLCE","rank_in_archive_order":1,"of":1,"metrics":{"MAP":"45.24","Rank-1":"68.26"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1611.05666","atlas_url":"https://app.syntology.ai/?focus=1611.05666","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}