{"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/swap-path-network-for-robust-person-search","title":"Swap Path Network for Robust Person Search Pre-training","arxiv_id":"2412.05433","date":"2024-12-06","proceeding":null,"authors":["Lucas Jaffe","Avideh Zakhor"],"abstract":"In person search, we detect and rank matches to a query person image within a set of gallery scenes. Most person search models make use of a feature extraction backbone, followed by separate heads for detection and re-identification. While pre-training methods for vision backbones are well-established, pre-training additional modules for the person search task has not been previously examined. In this work, we present the first framework for end-to-end person search pre-training. Our framework splits person search into object-centric and query-centric methodologies, and we show that the query-centric framing is robust to label noise, and trainable using only weakly-labeled person bounding boxes. Further, we provide a novel model dubbed Swap Path Net (SPNet) which implements both query-centric and object-centric training objectives, and can swap between the two while using the same weights. Using SPNet, we show that query-centric pre-training, followed by object-centric fine-tuning, achieves state-of-the-art results on the standard PRW and CUHK-SYSU person search benchmarks, with 96.4% mAP on CUHK-SYSU and 61.2% mAP on PRW. In addition, we show that our method is more effective, efficient, and robust for person search pre-training than recent backbone-only pre-training alternatives.","url_abs":"https://arxiv.org/abs/2412.05433v1","url_pdf":"https://arxiv.org/pdf/2412.05433v1.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":"swap-path-network-for-robust-person-search","repo_url":"https://github.com/llnl/spnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"person-search","task_name":"Person Search"}],"methods":[{"method_slug":"set","method_name":"SET"},{"method_slug":"spnet","method_name":"SPNet"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/person-search-on-cuhk-sysu","task":"Person Search","dataset":"CUHK-SYSU","model":"SPNet","rank_in_archive_order":2,"of":16,"metrics":{"MAP":"96.4","Top-1":"97.0"},"uses_additional_data":false},{"leaderboard":"/sota/person-search-on-prw","task":"Person Search","dataset":"PRW","model":"SPNet","rank_in_archive_order":1,"of":15,"metrics":{"Top-1":"90.9","mAP":"61.2"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}