{"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/exploring-fine-grained-representation-and","title":"Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification","arxiv_id":"2308.10692","date":"2023-08-21","proceeding":null,"authors":["Qizao Wang","Xuelin Qian","Bin Li","xiangyang xue","Yanwei Fu"],"abstract":"Cloth-changing person Re-IDentification (Re-ID) is a particularly challenging task, suffering from two limitations of inferior discriminative features and limited training samples. Existing methods mainly leverage auxiliary information to facilitate identity-relevant feature learning, including soft-biometrics features of shapes or gaits, and additional labels of clothing. However, this information may be unavailable in real-world applications. In this paper, we propose a novel FIne-grained Representation and Recomposition (FIRe$^{2}$) framework to tackle both limitations without any auxiliary annotation or data. Specifically, we first design a Fine-grained Feature Mining (FFM) module to separately cluster images of each person. Images with similar so-called fine-grained attributes (e.g., clothes and viewpoints) are encouraged to cluster together. An attribute-aware classification loss is introduced to perform fine-grained learning based on cluster labels, which are not shared among different people, promoting the model to learn identity-relevant features. Furthermore, to take full advantage of fine-grained attributes, we present a Fine-grained Attribute Recomposition (FAR) module by recomposing image features with different attributes in the latent space. It significantly enhances robust feature learning. Extensive experiments demonstrate that FIRe$^{2}$ can achieve state-of-the-art performance on five widely-used cloth-changing person Re-ID benchmarks. The code is available at https://github.com/QizaoWang/FIRe-CCReID.","url_abs":"https://arxiv.org/abs/2308.10692v2","url_pdf":"https://arxiv.org/pdf/2308.10692v2.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":"exploring-fine-grained-representation-and","repo_url":"https://github.com/qizaowang/fire-ccreid","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"cloth-changing-person-re-identification","task_name":"Cloth-Changing Person Re-Identification"},{"task_slug":"clothes-changing-person-re-identification","task_name":"Clothes Changing Person Re-Identification"},{"task_slug":"person-re-identification","task_name":"Person Re-Identification"},{"task_slug":"person-retrieval","task_name":"Person Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"erson-re-identification","task_name":"erson Re-Identification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/person-re-identification-on-ltcc","task":"Person Re-Identification","dataset":"LTCC","model":"FIRe2","rank_in_archive_order":4,"of":13,"metrics":{" Rank-1":"44.6"," mAP":"19.1"},"uses_additional_data":false},{"leaderboard":"/sota/person-re-identification-on-prcc","task":"Person Re-Identification","dataset":"PRCC","model":"FIRe2","rank_in_archive_order":5,"of":13,"metrics":{" Rank-1":"65.0","mAP":"63.1"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2308.10692","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}