A method based on difference guide and feature self-enhancement for clothes-changing person re-identification

A method based on difference guide and feature self-enhancement for clothes-changing 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 Research Article A method based on difference guide and feature self-enhancement for clothes-changing person re-identification Bin Ge, Yang Lu, Chenxin Xia, Junming Guan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4010457/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Due to the effect of clothing change on person re-identification models, some researchers have car-ried out in-depth studies on clothes-changing person re-identification(CC-ReID). However, there are some problem of the loss of edge identity information in the semantic guidance process in current methods. In this work, we propose a dual-stream network model, named GFSAnet, which consists of both global and face streams. This model is capable of retaining edge identity information while reinforcing the weight of fine-grained discriminative information. Firstly, in the global stream, we de-sign a difference guide model (DGM) and a feature self-augmentation model (FSAM). The differential features are learned through the difference guide module to preserve the edge identity information of the boundary between background and foreground, while the weights of the local information in the network are optimized through the feature self-augmentation module. Secondly, in the face stream, the multi-scale structure design of pyramid residual network is used to learn the facial features fusing coarse and fine granularity. Finally, the contributions of global and facial features are dynamically adjusted to work together in the inference by setting the hyperparameter α. Extensive experiments show that the method in this paper achieves better performance on the PRCC, Celeb-ReID and Celeb-Light datasets. CC-ReID Loss of edge identity information GFSAnet Pyramid residual network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4010457","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276159697,"identity":"914981a6-4739-497d-9991-d50524f44a96","order_by":0,"name":"Bin Ge","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYBACAwYGxgcINpFamEFKJUjSwiZBmhZz/sXHKn621dUxsDdvk2CouUNYi+WMZ2k3e9sOSzDwHCuTYDj2jAiH3ThjdoO37YAEg0SOmQRjw2FitJz/Vvi3rU6CQf4NsVrO97Ax87YxA23hIVKL5Qw2Y2mZc4cl23jSii0SjhGhxZz/8MOPb8rq+PnZD2+88aGGCC0MEgkQmg1EJBChgYGB/wBRykbBKBgFo2AkAwDrqjWTPXjJKQAAAABJRU5ErkJggg==","orcid":"","institution":"Anhui University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Ge","suffix":""},{"id":276159698,"identity":"8c7eec03-fa40-4a34-96b2-0a0be999699e","order_by":1,"name":"Yang Lu","email":"","orcid":"","institution":"Anhui University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Lu","suffix":""},{"id":276159699,"identity":"4e681ae9-3022-475f-b255-fbc3c2f3e19b","order_by":2,"name":"Chenxin Xia","email":"","orcid":"","institution":"Anhui University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Chenxin","middleName":"","lastName":"Xia","suffix":""},{"id":276159700,"identity":"cb9db708-2720-4329-ae0d-d2b43b40179c","order_by":3,"name":"Junming Guan","email":"","orcid":"","institution":"Huang Shan University","correspondingAuthor":false,"prefix":"","firstName":"Junming","middleName":"","lastName":"Guan","suffix":""}],"badges":[],"createdAt":"2024-03-04 04:02:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4010457/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4010457/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53076071,"identity":"80bb057a-38fa-4b16-a1a2-33e5385f7dd9","added_by":"auto","created_at":"2024-03-20 09:43:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":767534,"visible":true,"origin":"","legend":"","description":"","filename":"Amethodofpersonreidentification.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4010457/v1_covered_fe4f95f9-9b99-4b7e-a61e-f4d27c4cfb42.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A method based on difference guide and feature self-enhancement for clothes-changing person re-identification","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CC-ReID, Loss of edge identity information, GFSAnet, Pyramid residual network","lastPublishedDoi":"10.21203/rs.3.rs-4010457/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4010457/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDue to the effect of clothing change on person re-identification models, some researchers have car-ried out in-depth studies on clothes-changing person re-identification(CC-ReID). However, there are some problem of the loss of edge identity information in the semantic guidance process in current methods. In this work, we propose a dual-stream network model, named GFSAnet, which consists of both global and face streams. This model is capable of retaining edge identity information while reinforcing the weight of fine-grained discriminative information. Firstly, in the global stream, we de-sign a difference guide model (DGM) and a feature self-augmentation model (FSAM). The differential features are learned through the difference guide module to preserve the edge identity information of the boundary between background and foreground, while the weights of the local information in the network are optimized through the feature self-augmentation module. Secondly, in the face stream, the multi-scale structure design of pyramid residual network is used to learn the facial features fusing coarse and fine granularity. Finally, the contributions of global and facial features are dynamically adjusted to work together in the inference by setting the hyperparameter α. 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