{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:13:07Z","timestamp":1785420787359,"version":"3.56.0"},"reference-count":58,"publisher":"Association for Computing Machinery (ACM)","issue":"10","funder":[{"name":"Beijing Natural Science Foundation","award":["L244050 and L252018"],"award-info":[{"award-number":["L244050 and L252018"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,10,31]]},"abstract":"<jats:p>\n            Camouflaged Object Detection (COD) aims to accurately detect objects that blend in with their backgrounds. While deep learning-based methods have significantly improved detection accuracy, the persistent similarity in chromatic and textural characteristics between camouflaged objects and their environments continues to pose substantial challenges, particularly in complex scenarios. Existing COD methods still encounter two primary challenges: (1) the difficulty in effectively fusing detailed features with semantic representations, leading to suboptimal balance between local and global features in the fused features; (2) progressive information dilution in conventional top-down decoder architectures, resulting in imprecise boundary localization. To address these dual challenges of multi-scale feature enhancement and semantic information dilution, we propose a\n            <jats:italic toggle=\"yes\">D<\/jats:italic>\n            ual-Branch\n            <jats:italic toggle=\"yes\">C<\/jats:italic>\n            ross-Layer\n            <jats:italic toggle=\"yes\">I<\/jats:italic>\n            nformation\n            <jats:italic toggle=\"yes\">F<\/jats:italic>\n            low Network (DCIFNet) for COD in complex scenes. Specifically, our framework integrates a hybrid CNN-ViT architecture for parallel extraction of localized patterns and global contextual features. Subsequently, a Global Local Fusion (GLF) module is employed to iteratively and adaptively fuse the global and local features at each stage using gating weights. Additionally, a novel Dense Connection Decoder (DCD) module is designed to mitigate the information dilution problem associated with traditional top-down decoders. Extensive experiments show that DCIFNet outperforms other mainstream COD models in most performance metrics on four widely used benchmark datasets. On the COD10K dataset, compared with the latest SOTA work of PRNet, DCIFNet improves the metrics of\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\({S_{\\alpha}}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            ,\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\({E_{\\phi}}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            , and\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(F_{\\beta}^{w}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            by 0.11%, 0.11%, and 1.38%, respectively, demonstrating a significant performance enhancement. The code is available\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/WateverOk\/DCIFNet\">https:\/\/github.com\/WateverOk\/DCIFNet<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3764866","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T15:23:01Z","timestamp":1756826581000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Dual-Branch Cross-Layer Information Flow Network for Camouflaged Object Detection in Complex Scenes"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3827-371X","authenticated-orcid":false,"given":"Yuan","family":"Cao","sequence":"first","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0068-8824","authenticated-orcid":false,"given":"Dong","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.optcom.2010.06.059"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3178173"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i2.27890"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612083"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00360"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.487"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/97"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3085766"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00285"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s44267-023-00019-6"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2996645"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2938758"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3349209"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-72849-8_60"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108414"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00467"},{"issue":"12","key":"e_1_3_1_20_2","first-page":"240210","article-title":"A road extraction algorithm that fuses element multiplication and detail optimization","volume":"51","author":"Jin Zhang","year":"2024","unstructured":"Zhang Jin, Lv Minghai, Feng Yongan, and Zhang Ying. 2024. 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