A Unified Visual Architecture for Few-shot Learning

A Unified Visual Architecture for Few-shot Learning | 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Article A Unified Visual Architecture for Few-shot Learning Dequan Jin, Nan Xiang, Da Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7634782/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Few-shot learning is a human’s remarkable cognitive capability. The core object recognition is a typical few-shot learning behavior where the ventral visual stream plays a crucial role. The ventral visual stream consists of the cortical areas V1, V2, V4, andthe inferior temporal (IT) lobe. Lots of research discussed their mechanisms and functions in cognition but seldom considered them as an integrative system, leading to the difficulty of understanding human learning mechanisms. To this end, in this paper, we propose a unified computational model to simulate the ventral visual stream. It consists of an encoder and a classifier. These modules reproduce the former areas V1 and V2 and the latter regions V4 and the IT, respectively. The primary function of the V1 and V2 is feature extraction. We replicate it using deep neural networks. The V4 is responsible for complex feature representation, and the IT for object recognition. We describe them by two coupled neural fields. With some biologically plausible strategies, the proposed model provides a biologically realistic and unified architecture for understanding human visual cognition and possesses excellent few-shot learning performance, outperforming the state-of-the-art few-shot learning algorithms and achieving outstanding results in the tests on real-world image datasets. Few-shot learning Visual cognition Deep learning Neural model Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 20 Oct, 2025 Reviews received at journal 14 Oct, 2025 Reviews received at journal 04 Oct, 2025 Reviewers agreed at journal 25 Sep, 2025 Reviewers agreed at journal 23 Sep, 2025 Reviewers invited by journal 23 Sep, 2025 Editor assigned by journal 22 Sep, 2025 Submission checks completed at journal 17 Sep, 2025 First submitted to journal 16 Sep, 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. 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-7634782","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524528405,"identity":"88bb796b-bb72-432b-8273-53878b88685d","order_by":0,"name":"Dequan 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Learning","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":"international-journal-of-machine-learning-and-cybernetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmlc","sideBox":"Learn more about [International Journal of Machine Learning and Cybernetics](http://actavetscand.biomedcentral.com/)","snPcode":"13042","submissionUrl":"https://submission.nature.com/new-submission/13042/3","title":"International Journal of Machine Learning and 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The core object recognition is a typical few-shot learning behavior where the ventral visual stream plays a crucial role. The ventral visual stream consists of the cortical areas V1, V2, V4, andthe inferior temporal (IT) lobe. Lots of research discussed their mechanisms and functions in cognition but seldom considered them as an integrative system, leading to the difficulty of understanding human learning mechanisms. To this end, in this paper, we propose a unified computational model to simulate the ventral visual stream. It consists of an encoder and a classifier. These modules reproduce the former areas V1 and V2 and the latter regions V4 and the IT, respectively. The primary function of the V1 and V2 is feature extraction. We replicate it using deep neural networks. The V4 is responsible for complex feature representation, and the IT for object recognition. We describe them by two coupled neural fields. With some biologically plausible strategies, the proposed model provides a biologically realistic and unified architecture for understanding human visual cognition and possesses excellent few-shot learning performance, outperforming the state-of-the-art few-shot learning algorithms and achieving outstanding results in the tests on real-world image datasets.","manuscriptTitle":"A Unified Visual Architecture for Few-shot Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 06:39:34","doi":"10.21203/rs.3.rs-7634782/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-20T07:19:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-14T14:15:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-04T05:12:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272331943646880991520310605415764271428","date":"2025-09-25T13:17:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"324282953271666330320478638633033408390","date":"2025-09-23T12:42:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-23T11:47:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-22T08:04:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-17T11:29:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Machine Learning and Cybernetics","date":"2025-09-17T02:17:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-machine-learning-and-cybernetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmlc","sideBox":"Learn more about [International Journal of Machine Learning and Cybernetics](http://actavetscand.biomedcentral.com/)","snPcode":"13042","submissionUrl":"https://submission.nature.com/new-submission/13042/3","title":"International Journal of Machine Learning and Cybernetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"77549b1d-b1d9-4561-a230-76bd83f46075","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T07:23:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 06:39:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7634782","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7634782","identity":"rs-7634782","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}