Developing Feature Extraction and Clustering Approaches for Temporal-Relation-Based Action Prediction

Developing Feature Extraction and Clustering Approaches for Temporal-Relation-Based Action Prediction | 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 Developing Feature Extraction and Clustering Approaches for Temporal-Relation-Based Action Prediction Duc Minh Ha Vu, Minh Nguyen Xuan, Tu Anh Le, Thai Dinh Kim, Hai Xuan Le This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8899525/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 In this work, we propose a weakly supervised framework for action segmentation that automatically discovers temporal structures without predefined cluster numbers. The method combines DP-Means to estimate temporal anchors, Pseudo-Label Ensembling (PLE) to generate reliable pseudo-labels from complementary clustering strategies, Iterative Clustering (IC) to refine boundaries and propagate labels to uncertain regions. This design enhances temporal consistency, boundary precision, robustness under sparse supervision. Experiments on GTEA and 50Salads show that the proposed approach consistently outperforms individual clustering methods and conventional pseudo-labeling techniques while substantially reducing annotation costs. Action segmentation Weakly supervised learning Timestamp supervision Human action recognition Temporal relation 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. 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-8899525","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":597446155,"identity":"5434d6ba-18d5-4a17-a9e9-8844f3883028","order_by":0,"name":"Duc Minh Ha Vu","email":"","orcid":"","institution":"VNU University of Science","correspondingAuthor":false,"prefix":"","firstName":"Duc","middleName":"Minh Ha","lastName":"Vu","suffix":""},{"id":597446157,"identity":"eb7625f0-0968-47de-84a9-828369689c64","order_by":1,"name":"Minh Nguyen Xuan","email":"","orcid":"","institution":"Vietnam National University, Hanoi","correspondingAuthor":false,"prefix":"","firstName":"Minh","middleName":"Nguyen","lastName":"Xuan","suffix":""},{"id":597446159,"identity":"007d61c7-1acf-4f51-964a-a24aec2686ae","order_by":2,"name":"Tu Anh Le","email":"","orcid":"","institution":"Vietnam National University, Hanoi","correspondingAuthor":false,"prefix":"","firstName":"Tu","middleName":"Anh","lastName":"Le","suffix":""},{"id":597446160,"identity":"b14b9e4a-3444-43ff-8375-6dd9cf2eec4c","order_by":3,"name":"Thai Dinh Kim","email":"","orcid":"","institution":"Vietnam National University, Hanoi","correspondingAuthor":false,"prefix":"","firstName":"Thai","middleName":"Dinh","lastName":"Kim","suffix":""},{"id":597446162,"identity":"2a909dbc-b1e4-4e11-a9fc-07ba702d39c2","order_by":4,"name":"Hai Xuan Le","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYBACAwbGxgMMFRCOBLFaGg4wnCFNCwPDAcY2UrSYsx9uOMw773CewQHmg7d5GA7nEdRi2ZPYcHDmtsPFBgfYkq2BWooJO+xAYsOBj9vSEjcc4DGT5mFIS2wgqOX8w4YDiXNAWvi/EanlBsiWBhuQLWxALTbEaHnYcHDGMZvEmYfZjC3nGBCj5Xz6w8c8NRKJfcebH954UyFBWAsCMINNIF79KBgFo2AUjAI8AAAIQEEgeUl2PwAAAABJRU5ErkJggg==","orcid":"","institution":"Vietnam National University, Hanoi","correspondingAuthor":true,"prefix":"","firstName":"Hai","middleName":"Xuan","lastName":"Le","suffix":""}],"badges":[],"createdAt":"2026-02-17 09:23:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8899525/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8899525/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105515836,"identity":"edf5d58e-1371-4b22-b01d-9a2caa7d1ff0","added_by":"auto","created_at":"2026-03-26 23:54:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":323632,"visible":true,"origin":"","legend":"","description":"","filename":"ManuScript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8899525/v1_covered_7cf690fb-c9dc-44eb-8194-aa43ceb596e3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Developing Feature Extraction and Clustering Approaches for Temporal-Relation-Based Action Prediction","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":"Action segmentation, Weakly supervised learning, Timestamp supervision, Human action recognition, Temporal relation","lastPublishedDoi":"10.21203/rs.3.rs-8899525/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8899525/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In this work, we propose a weakly supervised framework for action segmentation that automatically discovers temporal structures without predefined cluster numbers. The method combines DP-Means to estimate temporal anchors, Pseudo-Label Ensembling (PLE) to generate reliable pseudo-labels from complementary clustering strategies, Iterative Clustering (IC) to refine boundaries and propagate labels to uncertain regions. This design enhances temporal consistency, boundary precision, robustness under sparse supervision. Experiments on GTEA and 50Salads show that the proposed approach consistently outperforms individual clustering methods and conventional pseudo-labeling techniques while substantially reducing annotation costs.","manuscriptTitle":"Developing Feature Extraction and Clustering Approaches for Temporal-Relation-Based Action Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-02 06:17:29","doi":"10.21203/rs.3.rs-8899525/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"b5bcd8fa-6d03-4803-837f-a813b603c9e9","owner":[],"postedDate":"March 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-26T23:53:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-02 06:17:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8899525","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8899525","identity":"rs-8899525","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}