{"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/semantically-self-aligned-network-for-text-to","title":"Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification","arxiv_id":"2107.12666","date":"2021-07-27","proceeding":null,"authors":["Zefeng Ding","Changxing Ding","Zhiyin Shao","DaCheng Tao"],"abstract":"Text-to-image person re-identification (ReID) aims to search for images containing a person of interest using textual descriptions. However, due to the significant modality gap and the large intra-class variance in textual descriptions, text-to-image ReID remains a challenging problem. Accordingly, in this paper, we propose a Semantically Self-Aligned Network (SSAN) to handle the above problems. First, we propose a novel method that automatically extracts semantically aligned part-level features from the two modalities. Second, we design a multi-view non-local network that captures the relationships between body parts, thereby establishing better correspondences between body parts and noun phrases. Third, we introduce a Compound Ranking (CR) loss that makes use of textual descriptions for other images of the same identity to provide extra supervision, thereby effectively reducing the intra-class variance in textual features. Finally, to expedite future research in text-to-image ReID, we build a new database named ICFG-PEDES. Extensive experiments demonstrate that SSAN outperforms state-of-the-art approaches by significant margins. Both the new ICFG-PEDES database and the SSAN code are available at https://github.com/zifyloo/SSAN.","url_abs":"https://arxiv.org/abs/2107.12666v2","url_pdf":"https://arxiv.org/pdf/2107.12666v2.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":"semantically-self-aligned-network-for-text-to","repo_url":"https://github.com/zifyloo/SSAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"person-re-identification","task_name":"Person Re-Identification"},{"task_slug":"nlp-based-person-retrival","task_name":"Text based Person Retrieval"},{"task_slug":"text-based-person-retrieval-with-noisy","task_name":"Text-based Person Retrieval with Noisy Correspondence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-retrieval-on-icfg-pedes","task":"Image Retrieval","dataset":"ICFG-PEDES","model":"SSAN","rank_in_archive_order":1,"of":1,"metrics":{"rank-1":"54.23"},"uses_additional_data":false},{"leaderboard":"/sota/nlp-based-person-retrival-on-cuhk-pedes","task":"Text based Person Retrieval","dataset":"CUHK-PEDES","model":"SSAN","rank_in_archive_order":11,"of":21,"metrics":{"R@1":"61.37","R@10":"86.73","R@5":"80.15"},"uses_additional_data":false},{"leaderboard":"/sota/text-based-person-retrieval-on-icfg-pedes","task":"Text based Person Retrieval","dataset":"ICFG-PEDES","model":"SSAN","rank_in_archive_order":11,"of":12,"metrics":{"R@1":"54.23"},"uses_additional_data":false},{"leaderboard":"/sota/text-based-person-retrieval-with-noisy","task":"Text-based Person Retrieval with Noisy Correspondence","dataset":"CUHK-PEDES","model":"SSAN","rank_in_archive_order":6,"of":6,"metrics":{"Rank 10":"77.42","Rank-1":"46.52","Rank-5":"68.36","mAP":"42.49","mINP":"28.13"},"uses_additional_data":false},{"leaderboard":"/sota/text-based-person-retrieval-with-noisy-1","task":"Text-based Person Retrieval with Noisy Correspondence","dataset":"ICFG-PEDES","model":"SSAN","rank_in_archive_order":6,"of":6,"metrics":{"Rank 1":"40.57","Rank-10":"71.53","Rank-5":"62.58","mAP":"20.93","mINP":"2.22"},"uses_additional_data":false},{"leaderboard":"/sota/text-based-person-retrieval-with-noisy-2","task":"Text-based Person Retrieval with Noisy Correspondence","dataset":"RSTPReid","model":"SSAN","rank_in_archive_order":6,"of":6,"metrics":{"Rank 1":"35.10","Rank 10":"71.45","Rank 5":"60.00","mAP":"28.90","mINP":"12.08"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2107.12666","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}