{"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/tipcb-a-simple-but-effective-part-based","title":"TIPCB: A Simple but Effective Part-based Convolutional Baseline for Text-based Person Search","arxiv_id":"2105.11628","date":"2021-05-25","proceeding":null,"authors":["Yuhao Chen","Guoqing Zhang","Yujiang Lu","zhenxing Wang","yuhui Zheng","Ruili Wang"],"abstract":"Text-based person search is a sub-task in the field of image retrieval, which aims to retrieve target person images according to a given textual description. The significant feature gap between two modalities makes this task very challenging. Many existing methods attempt to utilize local alignment to address this problem in the fine-grained level. However, most relevant methods introduce additional models or complicated training and evaluation strategies, which are hard to use in realistic scenarios. In order to facilitate the practical application, we propose a simple but effective end-to-end learning framework for text-based person search named TIPCB (i.e., Text-Image Part-based Convolutional Baseline). Firstly, a novel dual-path local alignment network structure is proposed to extract visual and textual local representations, in which images are segmented horizontally and texts are aligned adaptively. Then, we propose a multi-stage cross-modal matching strategy, which eliminates the modality gap from three feature levels, including low level, local level and global level. Extensive experiments are conducted on the widely-used benchmark dataset (CUHK-PEDES) and verify that our method outperforms the state-of-the-art methods by 3.69%, 2.95% and 2.31% in terms of Top-1, Top-5 and Top-10. Our code has been released in https://github.com/OrangeYHChen/TIPCB.","url_abs":"https://arxiv.org/abs/2105.11628v1","url_pdf":"https://arxiv.org/pdf/2105.11628v1.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":"tipcb-a-simple-but-effective-part-based","repo_url":"https://github.com/OrangeYHChen/TIPCB","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"person-search","task_name":"Person Search"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"nlp-based-person-retrival","task_name":"Text based Person Retrieval"},{"task_slug":"text-based-person-search","task_name":"Text based Person Search"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/nlp-based-person-retrival-on-cuhk-pedes","task":"Text based Person Retrieval","dataset":"CUHK-PEDES","model":"TIPCB","rank_in_archive_order":9,"of":21,"metrics":{"R@1":"63.63","R@10":"89.01","R@5":"82.82"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2105.11628","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}