{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T05:31:50Z","timestamp":1768800710438,"version":"3.49.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T00:00:00Z","timestamp":1680566400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T00:00:00Z","timestamp":1680566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s11432-022-3592-8","type":"journal-article","created":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T17:03:04Z","timestamp":1681405384000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Double-branch fusion network with a parallel attention selection mechanism for camouflaged object detection"],"prefix":"10.1007","volume":"66","author":[{"given":"Junjiang","family":"Xiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengrong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songnian","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuwen","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,4]]},"reference":[{"key":"3592_CR1","doi-asserted-by":"crossref","unstructured":"Fan D P, Ji G P, Sun G, et al. Camouflaged object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020. 2777\u20132787","DOI":"10.1109\/CVPR42600.2020.00285"},{"key":"3592_CR2","doi-asserted-by":"publisher","unstructured":"Fan D P, Ji G P, Cheng M M, et al. Concealed object detection. IEEE Trans Pattern Anal Mach Intell, 2021. doi: https:\/\/doi.org\/10.1109\/TPAMI.2021.3085766","DOI":"10.1109\/TPAMI.2021.3085766"},{"key":"3592_CR3","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1109\/LSP.2018.2825959","volume":"26","author":"Y Zheng","year":"2018","unstructured":"Zheng Y, Zhang X, Wang F, et al. Detection of people with camouflage pattern via dense deconvolution network. IEEE Signal Process Lett, 2018, 26: 29\u201333","journal-title":"IEEE Signal Process Lett"},{"key":"3592_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1778765.1778788","volume":"29","author":"H K Chu","year":"2010","unstructured":"Chu H K, Hsu W H, Mitra N J, et al. Camouflage images. ACM Trans Graph, 2010, 29: 1\u20138","journal-title":"ACM Trans Graph"},{"key":"3592_CR5","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s41095-017-0102-8","volume":"4","author":"S Ge","year":"2018","unstructured":"Ge S, Jin X, Ye Q, et al. Image editing by object-aware optimal boundary searching and mixed-domain composition. Comp Visual Media, 2018, 4: 71\u201382","journal-title":"Comp Visual Media"},{"key":"3592_CR6","doi-asserted-by":"crossref","unstructured":"Fan D P, Ji G P, Zhou T, et al. PraNet: parallel reverse attention network for polyp segmentation. In: Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020. 263\u2013273","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"3592_CR7","doi-asserted-by":"publisher","unstructured":"Ren J, Hu X, Zhu L, et al. Deep texture-aware features for camouflaged object detection. IEEE Trans Circuits Syst Video Technol, 2021. doi: https:\/\/doi.org\/10.1109\/TCSVT.2021.3126591","DOI":"10.1109\/TCSVT.2021.3126591"},{"key":"3592_CR8","doi-asserted-by":"crossref","unstructured":"Zhai Q, Li X, Yang F, et al. Mutual graph learning for camouflaged object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021. 12997\u201313007","DOI":"10.1109\/CVPR46437.2021.01280"},{"key":"3592_CR9","doi-asserted-by":"crossref","unstructured":"Mei H, Ji G P, Wei Z, et al. Camouflaged object segmentation with distraction mining. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021. 8772\u20138781","DOI":"10.1109\/CVPR46437.2021.00866"},{"key":"3592_CR10","doi-asserted-by":"crossref","unstructured":"Li A, Zhang J, Lv Y, et al. Uncertainty-aware joint salient object and camouflaged object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021. 10071\u201310081","DOI":"10.1109\/CVPR46437.2021.00994"},{"key":"3592_CR11","doi-asserted-by":"crossref","unstructured":"Lv Y, Zhang J, Dai Y, et al. Simultaneously localize, segment and rank the camouflaged objects. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021. 11591\u201311601","DOI":"10.1109\/CVPR46437.2021.01142"},{"key":"3592_CR12","doi-asserted-by":"crossref","unstructured":"Feng M, Lu H, Ding E. Attentive feedback network for boundary-aware salient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019. 1623\u20131632","DOI":"10.1109\/CVPR.2019.00172"},{"key":"3592_CR13","doi-asserted-by":"crossref","unstructured":"Qin X, Zhang Z, Huang C, et al. BASNet: boundary-aware salient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019. 7479\u20137489","DOI":"10.1109\/CVPR.2019.00766"},{"key":"3592_CR14","doi-asserted-by":"crossref","unstructured":"Chen Z, Xu Q, Cong R, et al. Global context-aware progressive aggregation network for salient object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2020. 34: 10599\u201310606","DOI":"10.1609\/aaai.v34i07.6633"},{"key":"3592_CR15","unstructured":"Liu S, Huang D, Wang Y. Learning spatial fusion for single-shot object detection. 2019. ArXiv:1911.09516"},{"key":"3592_CR16","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J Y, et al. CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), 2018. 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"3592_CR17","doi-asserted-by":"crossref","unstructured":"Chen L, Zhang H, Xiao J, et al. SCA-CNN: spatial and channel-wise attention in convolutional networks for image captioning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017. 5659\u20135667","DOI":"10.1109\/CVPR.2017.667"},{"key":"3592_CR18","doi-asserted-by":"crossref","unstructured":"Wang Q, Wu B, Zhu P, et al. ECA-Net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020. 11531\u201311539","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"3592_CR19","doi-asserted-by":"crossref","unstructured":"Zhao T, Wu X. Pyramid feature attention network for saliency detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019. 3085\u20133094","DOI":"10.1109\/CVPR.2019.00320"},{"key":"3592_CR20","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","volume":"43","author":"S H Gao","year":"2019","unstructured":"Gao S H, Cheng M M, Zhao K, et al. Res2Net: a new multi-scale backbone architecture. IEEE Trans Pattern Anal Mach Intell, 2019, 43: 652\u2013662","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3592_CR21","doi-asserted-by":"crossref","unstructured":"Yang M, Yu K, Zhang C, et al. DenseASPP for semantic segmentation in street scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018. 3684\u20133692","DOI":"10.1109\/CVPR.2018.00388"},{"key":"3592_CR22","doi-asserted-by":"crossref","unstructured":"Basak H, Kundu R, Agarwal A, et al. Single image super-resolution using residual channel attention network. In: Proceedings of 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS), 2020. 219\u2013224","DOI":"10.1109\/ICIIS51140.2020.9342688"},{"key":"3592_CR23","doi-asserted-by":"crossref","unstructured":"Liu S, Huang D. Receptive field block net for accurate and fast object detection. In: Proceedings of the European Conference on Computer Vision (ECCV), 2018. 385\u2013400","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"3592_CR24","unstructured":"Skurowski P, Abdulameer H, B\u0142aszczyk J, et al. Animal camouflage analysis: CHAMELEON database. http:\/\/kgwisc.aei.polsl.pl\/datasets\/CamouflageBase\/animals.7z"},{"key":"3592_CR25","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.cviu.2019.04.006","volume":"184","author":"T N Le","year":"2019","unstructured":"Le T N, Nguyen T V, Nie Z, et al. Anabranch network for camouflaged object segmentation. Comput Vision Image Understanding, 2019, 184: 45\u201356","journal-title":"Comput Vision Image Understanding"},{"key":"3592_CR26","doi-asserted-by":"crossref","unstructured":"Margolin R, Zelnik-Manor L, Tal A. How to evaluate foreground maps? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014. 248\u2013255","DOI":"10.1109\/CVPR.2014.39"},{"key":"3592_CR27","doi-asserted-by":"crossref","unstructured":"Fan D P, Cheng M M, Liu Y, et al. Structure-measure: a new way to evaluate foreground maps. In: Proceedings of the IEEE International Conference on Computer Vision, 2017. 4548\u20134557","DOI":"10.1109\/ICCV.2017.487"},{"key":"3592_CR28","doi-asserted-by":"crossref","unstructured":"Fan D P, Gong C, Cao Y, et al. Enhanced-alignment measure for binary foreground map evaluation. In: Proceedings of International Joint Conference on Artificial Intelligence, 2018. 698\u2013704","DOI":"10.24963\/ijcai.2018\/97"},{"key":"3592_CR29","doi-asserted-by":"crossref","unstructured":"Lin T Y, Dollar P, Girshick R, et al. Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017. 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"3592_CR30","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou Z, Siddiquee M M R, Tajbakhsh N, et al. UNet++: a nested U-Net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Cham: Springer, 2018. 3\u201311"},{"key":"3592_CR31","doi-asserted-by":"crossref","unstructured":"Liu J J, Hou Q, Cheng M M, et al. A simple pooling-based design for real-time salient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019. 3917\u20133926","DOI":"10.1109\/CVPR.2019.00404"},{"key":"3592_CR32","doi-asserted-by":"crossref","unstructured":"Wu Z, Su L, Huang Q. Cascaded partial decoder for fast and accurate salient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019. 3907\u20133916","DOI":"10.1109\/CVPR.2019.00403"},{"key":"3592_CR33","doi-asserted-by":"crossref","unstructured":"Zhao J X, Liu J J, Fan D P, et al. EGNet: edge guidance network for salient object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019. 8779\u20138788","DOI":"10.1109\/ICCV.2019.00887"},{"key":"3592_CR34","unstructured":"Dong B, Zhuge M, Wang Y, et al. Towards accurate camouflaged object detection with mixture convolution and interactive fusion. 2021. ArXiv:2101.05687"},{"key":"3592_CR35","doi-asserted-by":"crossref","unstructured":"Zhao H, Shi J, Qi X, et al. Pyramid scene parsing network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2017. 6230\u20136239","DOI":"10.1109\/CVPR.2017.660"},{"key":"3592_CR36","doi-asserted-by":"crossref","unstructured":"Liu N, Han J, Yang M-H. PiCANet: learning pixel-wise contextual attention for saliency detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2018. 3089\u20133098","DOI":"10.1109\/CVPR.2018.00326"},{"key":"3592_CR37","doi-asserted-by":"crossref","unstructured":"Huang Z, Huang L, Gong Y, et al. Mask scoring R-CNN. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019. 6409\u20136418","DOI":"10.1109\/CVPR.2019.00657"},{"key":"3592_CR38","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Dollar P, et al. Mask R-CNN, In: Proceedings of the IEEE International Conference on Computer Vision, 2017. 2961\u20132969","DOI":"10.1109\/ICCV.2017.322"},{"key":"3592_CR39","unstructured":"Mao Y, Zhang J, Wan Z, et al. Generative transformer for accurate and reliable salient object detection. 2022. ArXiv:2104.10127"},{"key":"3592_CR40","doi-asserted-by":"publisher","first-page":"162201","DOI":"10.1007\/s11432-020-3027-0","volume":"65","author":"F Y Hou","year":"2022","unstructured":"Hou F Y, Sun J, Yang Q L, et al. Deep reinforcement learning for optimal denial-of-service attacks scheduling. Sci China Inf Sci, 2022, 65: 162201","journal-title":"Sci China Inf Sci"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-022-3592-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-022-3592-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-022-3592-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T20:22:00Z","timestamp":1721420520000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-022-3592-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,4]]},"references-count":40,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["3592"],"URL":"https:\/\/doi.org\/10.1007\/s11432-022-3592-8","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,4]]},"assertion":[{"value":"7 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 September 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"162403"}}