SinCount:Fourier Transform-Based Single Domain Generalization for Crowd Counting

SinCount:Fourier Transform-Based Single Domain Generalization for Crowd Counting | 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 SinCount:Fourier Transform-Based Single Domain Generalization for Crowd Counting Lei Song, Tong Li, Zhaoyu Cai, Jinliang Guo, Jingxi He, Junfeng Xie, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7578116/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 16 You are reading this latest preprint version Abstract Crowd counting plays a vital role in public safety and urban management. However, existing models often fail to generalize to unseen scenarios due to domain shifts. Domain Generalization (DG) can alleviate this issue, but most studies focus on classifification, while crowd counting under the Single-source DG (SDG) setting remains largely unexplored. In this paper, we propose SinCount, a novel SDG framework for crowd counting that integrates frequency-aware attention into an existing dual-branch architecture. Specififically, we extract high-frequency features from the density feature maps to generate spatial attention, which is applied back to the density branch to enhance fifine-grained details and domain-invariant representations. Meanwhile, low-frequency features are extracted from the classifification feature maps to produce channel attention, guiding the classifification branch to focus on semantic consistency and class-aware discrimination. We evaluate our method on multiple benchmark datasets and demonstrate that it achieves competitive results compared to state-of-the-art DG approaches. Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 20 Nov, 2025 Reviews received at journal 16 Nov, 2025 Reviews received at journal 16 Nov, 2025 Reviews received at journal 11 Nov, 2025 Reviews received at journal 05 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviews received at journal 04 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers agreed at journal 13 Oct, 2025 Reviewers invited by journal 11 Oct, 2025 Editor assigned by journal 10 Oct, 2025 Editor invited by journal 19 Sep, 2025 Submission checks completed at journal 13 Sep, 2025 First submitted to journal 13 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. 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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-7578116","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":532972511,"identity":"b1911a22-311e-4b8a-bd3b-179e66016bd0","order_by":0,"name":"Lei Song","email":"","orcid":"","institution":"College of Mathematics and Computer, Guangdong Ocean University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Song","suffix":""},{"id":532972512,"identity":"96b0522e-f30c-482b-8064-8972a36598c4","order_by":1,"name":"Tong Li","email":"","orcid":"","institution":"College of Mathematics and Computer, Guangdong Ocean 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