Confidence-guided Outlier Refinement and Collaborative Embedding for Unsupervised Person Re-Identification

Confidence-guided Outlier Refinement and Collaborative Embedding for Unsupervised Person Re-Identification | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Confidence-guided Outlier Refinement and Collaborative Embedding for Unsupervised Person Re-Identification Jun Zhang, Shuli Cheng, Anyu Du, Haixu Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8466314/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Unsupervised person re-identification techniques have developed rapidly in recent years. Nonetheless, they still face challenges such as unstable pseudo-label quality and insufficient feature representation, particularly when handling outlier points and complex backgrounds. To address these issues, this paper proposes a joint optimization algorithm of Multi-level Confidence Outlier Refinement (MLCOR) and Collaborative Embedding Method (CEM), which aims to improve the discriminative nature of the embedding space and optimize the accuracy of pseudo-labels. Specifically, Multi-level Confidence Outlier Refinement evaluates the confidence level of outlier points by analyzing the distance relationship between outlier points and their neighboring samples, and classifies them into multiple confidence levels. We design a weighted voting strategy for low-confidence samples to correct the pseudo-labeling of low-confidence points by using the label distribution of neighboring samples, thus reducing noise interference and clustering errors and improving the accuracy of pseudo-labeling. Meanwhile, the Collaborative Embedding Method jointly optimizes global and local features, establishing an effective synergy between global category differentiation and local fine-grained feature learning. By integrating multi-level similarity relationships, this approach not only strengthens the model’s ability to capture subtle differences between samples but also significantly enhances the model's boundary awareness. Experimental results demonstrate that the proposed method achieves outstanding performance on multiple standard datasets, significantly improving both clustering accuracy and pseudo-label precision, while also exhibiting strong domain generalization and robustness in complex environments. Physical sciences/Engineering Physical sciences/Mathematics and computing Unsupervised person Re-ID Contrastive learning Clustering algorithm Outlier Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 03 Apr, 2026 Reviews received at journal 02 Apr, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviews received at journal 29 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers invited by journal 28 Jan, 2026 Editor assigned by journal 28 Jan, 2026 Editor invited by journal 01 Jan, 2026 Submission checks completed at journal 30 Dec, 2025 First submitted to journal 30 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8466314","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":581813903,"identity":"01e2fcf6-8bf6-40aa-9d54-b8ea3678982d","order_by":0,"name":"Jun Zhang","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Zhang","suffix":""},{"id":581813904,"identity":"b6a8e6d3-2a0f-4b74-855e-d231f197edca","order_by":1,"name":"Shuli Cheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYFAC5gYGCQYbBsYGIJuHOC2MIC1ppGphYDgMYROlhb//YJuEZdv5xOYZCYwP3rYxyJsT0iJxI7HZQLLttjHjjARmw7ltDIY7GwhoMZBgbHwA1CIH1MImzdvGkGBwgJAW/oMNByTbzvEAtbD/Jk4LQyLIlgNgW5iJ0gL2i8S5ZGPGnofNknPOSRhuIKSFv//wMWmJMrvEje3JBz+8KbORJ2gLCDBLAAnDBnAESRChHggYPwAJeeLUjoJRMApGwUgEAHqFPHHgrULkAAAAAElFTkSuQmCC","orcid":"","institution":"Xinjiang University","correspondingAuthor":true,"prefix":"","firstName":"Shuli","middleName":"","lastName":"Cheng","suffix":""},{"id":581813905,"identity":"e5b14f78-896d-4496-bb8b-5b1666a6fdc6","order_by":2,"name":"Anyu Du","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Anyu","middleName":"","lastName":"Du","suffix":""},{"id":581813906,"identity":"24ba0bef-a2a2-47da-bcb7-28a75fa23d72","order_by":3,"name":"Haixu Yang","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Haixu","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-12-28 14:24:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8466314/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8466314/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101471516,"identity":"cccdda85-c8ef-42f5-b5ce-b5b67c9a7fa7","added_by":"auto","created_at":"2026-01-30 05:41:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1263510,"visible":true,"origin":"","legend":"","description":"","filename":"confidencesc.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8466314/v1_covered_f55271bd-22cc-48b8-9099-2aebb33ec42a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Confidence-guided Outlier Refinement and Collaborative Embedding for Unsupervised Person Re-Identification","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Unsupervised person Re-ID, Contrastive learning, Clustering algorithm, Outlier","lastPublishedDoi":"10.21203/rs.3.rs-8466314/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8466314/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Unsupervised person re-identification techniques have developed rapidly in recent years. 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