{"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/see-finer-see-more-implicit-modality","title":"See Finer, See More: Implicit Modality Alignment for Text-based Person Retrieval","arxiv_id":"2208.08608","date":"2022-08-18","proceeding":null,"authors":["Xiujun Shu","Wei Wen","Haoqian Wu","Keyu Chen","Yiran Song","Ruizhi Qiao","Bo Ren","Xiao Wang"],"abstract":"Text-based person retrieval aims to find the query person based on a textual description. The key is to learn a common latent space mapping between visual-textual modalities. To achieve this goal, existing works employ segmentation to obtain explicitly cross-modal alignments or utilize attention to explore salient alignments. These methods have two shortcomings: 1) Labeling cross-modal alignments are time-consuming. 2) Attention methods can explore salient cross-modal alignments but may ignore some subtle and valuable pairs. To relieve these issues, we introduce an Implicit Visual-Textual (IVT) framework for text-based person retrieval. Different from previous models, IVT utilizes a single network to learn representation for both modalities, which contributes to the visual-textual interaction. To explore the fine-grained alignment, we further propose two implicit semantic alignment paradigms: multi-level alignment (MLA) and bidirectional mask modeling (BMM). The MLA module explores finer matching at sentence, phrase, and word levels, while the BMM module aims to mine \\textbf{more} semantic alignments between visual and textual modalities. Extensive experiments are carried out to evaluate the proposed IVT on public datasets, i.e., CUHK-PEDES, RSTPReID, and ICFG-PEDES. Even without explicit body part alignment, our approach still achieves state-of-the-art performance. Code is available at: https://github.com/TencentYoutuResearch/PersonRetrieval-IVT.","url_abs":"https://arxiv.org/abs/2208.08608v2","url_pdf":"https://arxiv.org/pdf/2208.08608v2.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":"see-finer-see-more-implicit-modality","repo_url":"https://github.com/tencentyouturesearch/personretrieval-ivt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"person-retrieval","task_name":"Person Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"nlp-based-person-retrival","task_name":"Text based Person Retrieval"},{"task_slug":"text-based-person-retrieval","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/text-based-person-retrieval-with-noisy","task":"Text-based Person Retrieval with Noisy Correspondence","dataset":"CUHK-PEDES","model":"IVT","rank_in_archive_order":5,"of":6,"metrics":{"Rank 10":"85.61","Rank-1":"58.59","Rank-5":"78.51","mAP":"57.19","mINP":"45.78"},"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":"IVT","rank_in_archive_order":5,"of":6,"metrics":{"Rank 1":"50.21","Rank-10":"76.18","Rank-5":"69.14","mAP":"34.72","mINP":"8.77"},"uses_additional_data":false},{"leaderboard":"/sota/text-based-person-retrieval-with-noisy-2","task":"Text-based Person Retrieval with Noisy Correspondence","dataset":"RSTPReid","model":"IVT","rank_in_archive_order":5,"of":6,"metrics":{"Rank 1":"43.65","Rank 10":"75.70","Rank 5":"66.50","mAP":"37.22","mINP":"20.47"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2208.08608","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}