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Surv."],"published-print":{"date-parts":[[2026,10,31]]},"abstract":"<jats:p>\n                    Deep learning is developing rapidly and handling common computer vision tasks well. It is time to pay attention to more complex vision tasks, as model size, knowledge, and reasoning capabilities continue to improve. In this article, we introduce and review a family of complex tasks, termed Concealed Dense Prediction (CDP), which has great value in agriculture, industry, etc. CDP\u2019s intrinsic trait is that the targets are concealed in their surroundings, thus fully perceiving them requires fine-grained representations, prior knowledge, auxiliary reasoning, etc. The contributions of this review are three-fold: (i)\u00a0We introduce the scope, characteristics, and challenges specific to CDP tasks and emphasize their essential differences from generic vision tasks. (ii)\u00a0We develop a taxonomy based on concealment counteracting to summarize deep learning efforts in CDP through experiments on three tasks. We compare 25 state-of-the-art methods across 12 widely used concealed datasets. (iii)\u00a0We discuss the potential applications of CDP in the large model era and summarize 6 potential research directions. We offer perspectives for the future development of CDP by constructing a large-scale multimodal instruction fine-tuning dataset,\n                    <jats:monospace>CvpINST<\/jats:monospace>\n                    , and a concealed visual perception agent,\n                    <jats:monospace>CvpAgent<\/jats:monospace>\n                    . Our traceable resource links are\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/PanchengZhao\/Concealed-Dense-Prediction\">https:\/\/github.com\/PanchengZhao\/Concealed-Dense-Prediction<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3820770","type":"journal-article","created":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T21:03:06Z","timestamp":1781125386000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning in Concealed Dense Prediction"],"prefix":"10.1145","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6293-5876","authenticated-orcid":false,"given":"Pancheng","family":"Zhao","sequence":"first","affiliation":[{"name":"Nankai University","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5245-7518","authenticated-orcid":false,"given":"Deng-Ping","family":"Fan","sequence":"additional","affiliation":[{"name":"Nankai University","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5888-5129","authenticated-orcid":false,"given":"Shupeng","family":"Cheng","sequence":"additional","affiliation":[{"name":"Tsinghua University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9502-1749","authenticated-orcid":false,"given":"Salman","family":"Khan","sequence":"additional","affiliation":[{"name":"Mohamed bin Zayed University of Artificial Intelligence","place":["Masdar City, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4263-3143","authenticated-orcid":false,"given":"Fahad","family":"Khan","sequence":"additional","affiliation":[{"name":"Mohamed bin Zayed University of Artificial Intelligence","place":["Masdar City, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9848-8555","authenticated-orcid":false,"given":"David","family":"Clifton","sequence":"additional","affiliation":[{"name":"University of Oxford","place":["Oxford, United Kingdom of Great Britain and Northern Ireland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6431-1110","authenticated-orcid":false,"given":"Peng","family":"Xu","sequence":"additional","affiliation":[{"name":"Tsinghua University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0219-3443","authenticated-orcid":false,"given":"Jufeng","family":"Yang","sequence":"additional","affiliation":[{"name":"Nankai University","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,11]]},"reference":[{"issue":"1","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s00530-021-00813-6","article-title":"Camouflage design, assessment and breaking techniques: A survey","volume":"28","author":"Mondal A.","year":"2022","unstructured":"A. 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