Visual Question Answering Based on Visual Contentand Query Enhancement

Visual Question Answering Based on Visual Contentand Query Enhancement | 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 Visual Question Answering Based on Visual Contentand Query Enhancement Longbao Wang, Yuxin Shao, Jinhao Zhang, Meng Ding, Hongmin Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8318402/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract With the rapid development of computer vision and natural language processing technologies, visual question answering (VQA), as an intersection of these two fields, is gradually becoming a research hotspot. Existing VQA models have achieved significant progress in general scenarios, but in data-biased scenarios, methods for mitigating bias still suffer from poor transferability. Meanwhile, in recent years, multimodal information enhancement techniques have made remarkable progress in exploring and integrating cross-modal semantics, offering powerful cross-modal semantic representation capabilities that provide new solutions for addressing issues caused by data bias. Therefore, this paper takes multimodal information enhancement as the entry point and designs a VQA method based on visual content and query enhancement to meet the demands of data-biased scenarios, aiming to improve the model’s reasoning capabilities and overall performance in such contexts. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 Mar, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers invited by journal 06 Feb, 2026 Editor assigned by journal 02 Feb, 2026 Editor invited by journal 09 Jan, 2026 Submission checks completed at journal 05 Jan, 2026 First submitted to journal 05 Jan, 2026 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8318402","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":586747061,"identity":"c2cda47a-eb4b-4afb-94a0-fe1284c862e7","order_by":0,"name":"Longbao Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAn0lEQVRIiWNgGAWjYDACdh7Gx4wNIFYCsVqYeZiNSdbCJk2aFoPDvMeqC3ccZuBnzzFg+LmDKC18abdnnjnMINnzxoCx9wwRWswO85jd5m07zGBwI8eAmbGNSC3FIC32JGlhBtsiQawW+8N8ydK8bek8EmeeFRzsJUaLZHvvwc+8bdZy/O3JGx/8JEYLDPCAiAMkaBgFo2AUjIJRgA8AANvJMEbgfvGJAAAAAElFTkSuQmCC","orcid":"","institution":"Hohai University","correspondingAuthor":true,"prefix":"","firstName":"Longbao","middleName":"","lastName":"Wang","suffix":""},{"id":586747062,"identity":"f591b24e-f250-444a-9437-6c03a54ff9fc","order_by":1,"name":"Yuxin Shao","email":"","orcid":"","institution":"Hohai University","correspondingAuthor":false,"prefix":"","firstName":"Yuxin","middleName":"","lastName":"Shao","suffix":""},{"id":586747064,"identity":"0c067af7-3da8-4c4f-8e53-f1fc61fc0a64","order_by":2,"name":"Jinhao Zhang","email":"","orcid":"","institution":"Hohai University","correspondingAuthor":false,"prefix":"","firstName":"Jinhao","middleName":"","lastName":"Zhang","suffix":""},{"id":586747066,"identity":"8fedf22d-ef69-48c2-a881-4ab1feffeac0","order_by":3,"name":"Meng Ding","email":"","orcid":"","institution":"Hohai University","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Ding","suffix":""},{"id":586747070,"identity":"28c0e68c-8142-4afd-9051-284500bd3680","order_by":4,"name":"Hongmin Gao","email":"","orcid":"","institution":"Hohai University","correspondingAuthor":false,"prefix":"","firstName":"Hongmin","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2025-12-09 13:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8318402/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8318402/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102298001,"identity":"d6ec25ab-36b3-4e94-997c-eaee6020da33","added_by":"auto","created_at":"2026-02-10 10:30:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1599367,"visible":true,"origin":"","legend":"","description":"","filename":"VisualQuestionAnsweringBasedonVisualContentandQueryEnhancement.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8318402/v1_covered_356833f2-d3ff-4ed1-bcaa-8029f2a4b7d5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Visual Question Answering Based on Visual Contentand Query Enhancement","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":"","lastPublishedDoi":"10.21203/rs.3.rs-8318402/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8318402/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the rapid development of computer vision and natural language processing technologies, visual question answering (VQA), as an intersection of these two fields, is gradually becoming a research hotspot. 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