Enhancing Cross-Modal Retrieval via Label Graph Optimization and Hybrid Loss Functions

Enhancing Cross-Modal Retrieval via Label Graph Optimization and Hybrid Loss Functions | 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 Enhancing Cross-Modal Retrieval via Label Graph Optimization and Hybrid Loss Functions Lin Wang, Chenchen Wang, Simin Peng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7931660/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Cross-modal retrieval, particularly image-text matching, is crucial in multimedia analysis and artificial intelligence, with applications in intelligent search and human-computer interaction. Current methods often overlook the rich semantic relationships between labels, leading to limited discriminability. We introduce a Two-Layer Graph Convolutional Network (L2-GCN) to model label correlations and a hybrid loss function, Circle-Soft, to enhance alignment and discriminability. Our approach, evaluated on NUS-WIDE, MIRFlickr, and MS-COCO datasets, achieves state-of-the-art performance, demonstrating its effectiveness and robustness. The source code is accessible via https://github.com/buzzcut619/L2-GCN-CIRCLE-SOFT Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Physical sciences/Mathematics and computing Cross-modal retrieval L2-GCN Circle-Soft Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 15 Dec, 2025 Reviews received at journal 15 Dec, 2025 Reviewers agreed at journal 29 Nov, 2025 Reviewers agreed at journal 27 Nov, 2025 Reviews received at journal 25 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviews received at journal 07 Nov, 2025 Reviewers agreed at journal 01 Nov, 2025 Reviewers invited by journal 30 Oct, 2025 Editor assigned by journal 30 Oct, 2025 Editor invited by journal 30 Oct, 2025 Submission checks completed at journal 28 Oct, 2025 First submitted to journal 28 Oct, 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-7931660","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":542273034,"identity":"3b9f0697-d550-4b63-b376-7f6f40924bdc","order_by":0,"name":"Lin 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Current methods often overlook the rich semantic relationships between labels, leading to limited discriminability. We introduce a Two-Layer Graph Convolutional Network (L2-GCN) to model label correlations and a hybrid loss function, Circle-Soft, to enhance alignment and discriminability. Our approach, evaluated on NUS-WIDE, MIRFlickr, and MS-COCO datasets, achieves state-of-the-art performance, demonstrating its effectiveness and robustness. The source code is accessible via https://github.com/buzzcut619/L2-GCN-CIRCLE-SOFT\u003c/p\u003e","manuscriptTitle":"Enhancing Cross-Modal Retrieval via Label Graph Optimization and Hybrid Loss Functions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 17:04:41","doi":"10.21203/rs.3.rs-7931660/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-15T12:41:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-15T09:18:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117211065140336116050219599064013417057","date":"2025-11-29T15:04:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16574918422773246805631131628385658073","date":"2025-11-27T09:31:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-25T13:29:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89391543479181852946481649153544780684","date":"2025-11-25T09:28:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-07T15:36:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202824395642484606115920707431503901237","date":"2025-11-02T03:00:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-30T20:38:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-30T20:31:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-30T19:35:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-28T11:34:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-28T11:31:12+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"bcf0a28e-0c0c-4554-8417-de1f1c89fe32","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":57685493,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":57685494,"name":"Physical sciences/Engineering"},{"id":57685495,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-02-02T16:07:56+00:00","versionOfRecord":{"articleIdentity":"rs-7931660","link":"https://doi.org/10.1038/s41598-026-37525-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-27 15:58:46","publishedOnDateReadable":"January 27th, 2026"},"versionCreatedAt":"2025-11-11 17:04:41","video":"","vorDoi":"10.1038/s41598-026-37525-8","vorDoiUrl":"https://doi.org/10.1038/s41598-026-37525-8","workflowStages":[]},"version":"v1","identity":"rs-7931660","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7931660","identity":"rs-7931660","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}