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Res."],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper introduces deep gradient network (DGNet), a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD). It decouples the task into two connected branches, i.e., a context and a texture encoder. The essential connection is the gradient-induced transition, representing a soft grouping between context and texture features. Benefiting from the simple but efficient framework, DGNet outperforms existing state-of-the-art COD models by a large margin. Notably, our efficient version, DGNet-S, runs in real-time (80 fps) and achieves comparable results to the cutting-edge model JCSOD-CVPR21 with only 6.82% parameters. The application results also show that the proposed DGNet performs well in the polyp segmentation, defect detection, and transparent object segmentation tasks. The code will be made available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/GewelsJI\/DGNet\">https:\/\/github.com\/GewelsJI\/DGNet<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s11633-022-1365-9","type":"journal-article","created":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T17:04:43Z","timestamp":1673370283000},"page":"92-108","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":238,"title":["Deep Gradient Learning for Efficient Camouflaged Object Detection"],"prefix":"10.1007","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7092-2877","authenticated-orcid":false,"given":"Ge-Peng","family":"Ji","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5245-7518","authenticated-orcid":false,"given":"Deng-Ping","family":"Fan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9334-2899","authenticated-orcid":false,"given":"Yu-Cheng","family":"Chou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5440-9678","authenticated-orcid":false,"given":"Dengxin","family":"Dai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7858-7900","authenticated-orcid":false,"given":"Alexander","family":"Liniger","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3445-5711","authenticated-orcid":false,"given":"Luc","family":"Van Gool","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,10]]},"reference":[{"key":"1365_CR1","doi-asserted-by":"publisher","first-page":"2774","DOI":"10.1109\/CVPR42600.2020.00285","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"D P Fan","year":"2020","unstructured":"D. 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