{"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/pose-driven-deep-convolutional-model-for","title":"Pose-driven Deep Convolutional Model for Person Re-identification","arxiv_id":"1709.08325","date":"2017-09-25","proceeding":"ICCV 2017 10","authors":["Chi Su","Jianing Li","Shiliang Zhang","Junliang Xing","Wen Gao","Qi Tian"],"abstract":"Feature extraction and matching are two crucial components in person\nRe-Identification (ReID). The large pose deformations and the complex view\nvariations exhibited by the captured person images significantly increase the\ndifficulty of learning and matching of the features from person images. To\novercome these difficulties, in this work we propose a Pose-driven Deep\nConvolutional (PDC) model to learn improved feature extraction and matching\nmodels from end to end. Our deep architecture explicitly leverages the human\npart cues to alleviate the pose variations and learn robust feature\nrepresentations from both the global image and different local parts. To match\nthe features from global human body and local body parts, a pose driven feature\nweighting sub-network is further designed to learn adaptive feature fusions.\nExtensive experimental analyses and results on three popular datasets\ndemonstrate significant performance improvements of our model over all\npublished state-of-the-art methods.","url_abs":"http://arxiv.org/abs/1709.08325v1","url_pdf":"http://arxiv.org/pdf/1709.08325v1.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":[],"tasks":[{"task_slug":"person-re-identification","task_name":"Person Re-Identification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/person-re-identification-on-market-1501","task":"Person Re-Identification","dataset":"Market-1501","model":"PDF","rank_in_archive_order":107,"of":135,"metrics":{"Rank-1":"84.14","mAP":"63.41"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1709.08325","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}