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Appl."],"published-print":{"date-parts":[[2025,12,31]]},"abstract":"<jats:p>\n                    In nature, certain objects exhibit patterns that closely resemble their backgrounds, a phenomenon commonly referred to as Camouflaged Object Detection (COD). We argue that existing COD approaches often suffer from insufficient discriminability for these objects, which we attribute to a lack of effective disentangling of foreground and background representations. To address this, we propose a novel Foreground-Background Disentanglement Network (FBD-Net) that enhances foreground-background disentanglement learning to improve discriminability. Specifically, we design an Edge-guided Foreground-Background Decoupling (EFBD) module, which facilitates the separated learning of foreground and background representations. Additionally, we introduce the Foreground-Background Representation Disentangling Head (DisHead) to further boost the discriminative power of the model. The DisHead consists of two objectives: the Edge Objective and the FoBa Objective. Furthermore, we propose three complementary modules: the Context Aggregation Module (CAM) for initial coarse object detection, the Scale-Interaction Enhanced Pyramid (SIEP) for multi-scale information extraction, and the Cross-Stage Adaptive Fusion (CSAF) module for subtle clue accumulation. Extensive experiments demonstrate that both our CNN-based and Transformer-based FBD-Nets outperform 26 state-of-the-art COD methods across four public datasets. Codes will be released on\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/TomorrowJW\/FBD-Net-COD\">https:\/\/github.com\/TomorrowJW\/FBD-Net-COD<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3768584","type":"journal-article","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T18:43:36Z","timestamp":1758221016000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Boosting Foreground-Background Disentanglement for Camouflaged Object Detection"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6941-3300","authenticated-orcid":false,"given":"Jiesheng","family":"Wu","sequence":"first","affiliation":[{"name":"School of Computer and Information, Anhui Normal University, Wuhu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7320-9423","authenticated-orcid":false,"given":"Fangwei","family":"Hao","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nankai University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8532-2241","authenticated-orcid":false,"given":"Jing","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nankai University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,21]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2015.02.007"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICIT.2006.34"},{"key":"e_1_3_1_5_2","unstructured":"Yuxuan Cai Yizhuang Zhou Qi Han Jianjian Sun Xiangwen Kong Jun Li and Xiangyu Zhang. 2022. 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