CMFF_VS：A Video Summarization Extraction Model based on Cross-modal Feature Fusion

CMFF_VS：A Video Summarization Extraction Model based on Cross-modal Feature Fusion | 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 CMFF_VS：A Video Summarization Extraction Model based on Cross-modal Feature Fusion Chaoqun Xin, Mingyang Wang, Xianhao Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5004063/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 Video summarization aims to present the most relevant and important information in the video stream in the form of a summary. Most existing researches focus on the selection process of keyframes, determining the importance of video frames by obtaining dependency information between them. However, these works overlook the feature extraction process of video frames. In fact, rich and reliable video frame features are an important basis for determining whether video frames can be selected correctly. This article proposes a cross-modal video summarization extraction model CMFF_VS by extracting deep semantic information from video frames. CMFF_VS model utilizes the mutual enhancement of video modality and text modality to extract richer semantic information of video frames, thereby providing necessary features for the subsequent video frame selection process. To solve the alignment problem between semantic information of two modalities, CMFF_VS introduces a cross-modal attention mechanism, which utilizes the semantic correlation of modalities to achieve cross-modal semantic fusion. At the same time, CMFF_VS introduces the ASPP module to extract and fuse multi-scale semantic features of individual modalities, enriching the capture of advanced semantic information for each modality. The experimental results show that compared with the state-of-art unimodal and multimodal video summarization models, CMFF-VS achieves the best performance, indicating that the cross-modal deep feature extraction and fusion strategy proposed in CMFF-VS is reasonable and effective. Video summarization Cross-modal Multimodal semantic alignment ASPP 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. 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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-5004063","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":357079699,"identity":"08264a13-34c7-4568-a676-5cfa97a2a82d","order_by":0,"name":"Chaoqun Xin","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Chaoqun","middleName":"","lastName":"Xin","suffix":""},{"id":357079700,"identity":"17a9dde4-2328-451e-a9b8-e9a80a554026","order_by":1,"name":"Mingyang Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYNACAyj9gYGBH0RLEK2FcQYDg2QDcVqggJmHGC0Gx88efnWj4I7dBiDjte0OGwmDA8wHb/Mw2OXh1HImL806x+BZ8gYQI/dMGlALW7I1D0NyMS4tZgdyzIxzDA4nG4AYuW2H6wwO8JhJ8zAcSGzApeX8G6gWEMOy7T/QFv5v+LXcyDF+DNRiZwBiMLYdAGrhYcOrxf7GGzNmoJYESSCDsbctWULyMJux5RyDZJxaJPtzjD/n/Dlsz3c+x/jDzzY7Cb7jzQ9vvKmww6kFCNhAsZC44ACEAYwdEGGAWz1IyQeQA+UbIIxRMApGwSgYBRgAALzPXErIN0pgAAAAAElFTkSuQmCC","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Mingyang","middleName":"","lastName":"Wang","suffix":""},{"id":357079701,"identity":"75d78db3-cbf6-4606-b6b4-c78be695480f","order_by":2,"name":"Xianhao Zhao","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Xianhao","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-08-30 12:51:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5004063/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5004063/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65134447,"identity":"b7523f46-f3e8-4043-b469-fe7ce26967fe","added_by":"auto","created_at":"2024-09-24 03:27:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":442973,"visible":true,"origin":"","legend":"","description":"","filename":"CMFFVSAVideoSummarizationExtractionModelbasedonCrossmodalFeatureFusion.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5004063/v1_covered_e97e4f64-415a-4d09-9d26-da59cdc190ef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CMFF_VS：A Video Summarization Extraction Model based on Cross-modal Feature Fusion","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Video summarization, Cross-modal, Multimodal semantic alignment, ASPP","lastPublishedDoi":"10.21203/rs.3.rs-5004063/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5004063/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Video summarization aims to present the most relevant and important information in the video stream in the form of a summary. 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