{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T00:23:15Z","timestamp":1783124595014,"version":"3.54.6"},"reference-count":77,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004767","name":"University of Science and Technology Liaoning","doi-asserted-by":"publisher","award":["2024JH2\/102600091"],"award-info":[{"award-number":["2024JH2\/102600091"]}],"id":[{"id":"10.13039\/501100004767","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62472067"],"award-info":[{"award-number":["62472067"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.knosys.2026.116281","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T16:12:54Z","timestamp":1779725574000},"page":"116281","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["SPMNet: Self-prompt mask-guided network for Camouflaged Object Detection"],"prefix":"10.1016","volume":"347","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6749-9982","authenticated-orcid":false,"given":"Bo","family":"Cai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6171-698X","authenticated-orcid":false,"given":"Yanping","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1985-7026","authenticated-orcid":false,"given":"Mengyin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3150-447X","authenticated-orcid":false,"given":"Jin","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9901-9842","authenticated-orcid":false,"given":"Fenglin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7530-3343","authenticated-orcid":false,"given":"Houjie","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.knosys.2026.116281_b1","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.aspen.2019.11.006","article-title":"Application of an image and environmental sensor network for automated greenhouse insect pest monitoring","volume":"23","author":"Rustia","year":"2020","journal-title":"J. Asia-Pac. \u00c8ntomol."},{"key":"10.1016\/j.knosys.2026.116281_b2","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"263","article-title":"Pranet: Parallel reverse attention network for polyp segmentation","author":"Fan","year":"2020"},{"key":"10.1016\/j.knosys.2026.116281_b3","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part VI 23","first-page":"253","article-title":"Adaptive context selection for polyp segmentation","author":"Zhang","year":"2020"},{"key":"10.1016\/j.knosys.2026.116281_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107474","article-title":"A novel hybrid approach for crack detection","volume":"107","author":"Fang","year":"2020","journal-title":"Pattern Recognit."},{"issue":"10","key":"10.1016\/j.knosys.2026.116281_b5","doi-asserted-by":"crossref","first-page":"6024","DOI":"10.1109\/TPAMI.2021.3085766","article-title":"Concealed object detection","volume":"44","author":"Fan","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2026.116281_b6","series-title":"Context-aware cross-level fusion network for camouflaged object detection","author":"Sun","year":"2021"},{"key":"10.1016\/j.knosys.2026.116281_b7","doi-asserted-by":"crossref","unstructured":"M. Song, H. Wang, G. Zhong, Self-prompt mechanism for few-shot image recognition, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38, 2024, pp. 4934\u20134942.","DOI":"10.1609\/aaai.v38i5.28297"},{"issue":"1","key":"10.1016\/j.knosys.2026.116281_b8","doi-asserted-by":"crossref","DOI":"10.1049\/cvi2.70009","article-title":"Foundation model based camouflaged object detection","volume":"19","author":"Chen","year":"2025","journal-title":"IET Comput. Vis."},{"key":"10.1016\/j.knosys.2026.116281_b9","series-title":"European Conference on Computer Vision","first-page":"315","article-title":"Unlocking attributes\u2019 contribution to successful camouflage: A combined textual and visual analysis strategy","author":"Zhang","year":"2024"},{"key":"10.1016\/j.knosys.2026.116281_b10","doi-asserted-by":"crossref","unstructured":"X. Liu, S. Huang, R. Wu, H. Zhao, D. Xu, X. Wei, J. Han, S. Liu, Reference Prompted Model Adaptation for Referring Camouflaged Object Detection, in: 2024 IEEE International Conference on Multimedia and Expo, ICME, 2024, pp. 1\u20136.","DOI":"10.1109\/ICME57554.2024.10687557"},{"key":"10.1016\/j.knosys.2026.116281_b11","doi-asserted-by":"crossref","unstructured":"Z. He, C. Xia, S. Qiao, J. Li, Text-prompt Camouflaged Instance Segmentation with Graduated Camouflage Learning, in: Proceedings of the 32nd ACM International Conference on Multimedia, 2024, pp. 5584\u20135593.","DOI":"10.1145\/3664647.3681132"},{"key":"10.1016\/j.knosys.2026.116281_b12","series-title":"Large model based referring camouflaged object detection","author":"Cheng","year":"2023"},{"key":"10.1016\/j.knosys.2026.116281_b13","doi-asserted-by":"crossref","unstructured":"D.-P. Fan, G.-P. Ji, G. Sun, M.-M. Cheng, J. Shen, L. Shao, Camouflaged object detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 2777\u20132787.","DOI":"10.1109\/CVPR42600.2020.00285"},{"key":"10.1016\/j.knosys.2026.116281_b14","doi-asserted-by":"crossref","unstructured":"X. Hu, S. Wang, X. Qin, H. Dai, W. Ren, D. Luo, Y. Tai, L. Shao, High-resolution iterative feedback network for camouflaged object detection, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, 2023, pp. 881\u2013889.","DOI":"10.1609\/aaai.v37i1.25167"},{"issue":"11","key":"10.1016\/j.knosys.2026.116281_b15","doi-asserted-by":"crossref","first-page":"15993","DOI":"10.1109\/TNNLS.2023.3291595","article-title":"Camouflaged object segmentation based on Matching\u2013Recognition\u2013Refinement network","volume":"35","author":"Yan","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.knosys.2026.116281_b16","doi-asserted-by":"crossref","unstructured":"Z. Huang, H. Dai, T.-Z. Xiang, S. Wang, H.-X. Chen, J. Qin, H. Xiong, Feature shrinkage pyramid for camouflaged object detection with transformers, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 5557\u20135566.","DOI":"10.1109\/CVPR52729.2023.00538"},{"issue":"9","key":"10.1016\/j.knosys.2026.116281_b17","doi-asserted-by":"crossref","first-page":"4934","DOI":"10.1109\/TCSVT.2023.3245883","article-title":"MSCAF-Net: A general framework for camouflaged object detection via learning multi-scale context-aware features","volume":"33","author":"Liu","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"2","key":"10.1016\/j.knosys.2026.116281_b18","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1108\/JM2-09-2023-0207","article-title":"Thermal coal futures trading volume predictions through the neural network","volume":"20","author":"Jin","year":"2025","journal-title":"J. Model. Manag."},{"issue":"3","key":"10.1016\/j.knosys.2026.116281_b19","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1108\/FS-01-2023-0016","article-title":"Peanut oil price change forecasts through the neural network","volume":"27","author":"Jin","year":"2025","journal-title":"Foresight"},{"key":"10.1016\/j.knosys.2026.116281_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106120","article-title":"Corn cash price forecasting with neural networks","volume":"184","author":"Xu","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.knosys.2026.116281_b21","article-title":"Individual time series and composite forecasting of the Chinese stock index","volume":"5","author":"Xu","year":"2021","journal-title":"Mach. Learn. Appl."},{"key":"10.1016\/j.knosys.2026.116281_b22","series-title":"CGCOD: Class-guided camouflaged object detection","author":"Zhang","year":"2024"},{"key":"10.1016\/j.knosys.2026.116281_b23","series-title":"2024 IEEE International Conference on Multimedia and Expo","first-page":"1","article-title":"Reference prompted model adaptation for referring camouflaged object detection","author":"Liu","year":"2024"},{"key":"10.1016\/j.knosys.2026.116281_b24","series-title":"Boundary-guided camouflaged object detection","author":"Sun","year":"2022"},{"key":"10.1016\/j.knosys.2026.116281_b25","doi-asserted-by":"crossref","unstructured":"C. He, K. Li, Y. Zhang, L. Tang, Y. Zhang, Z. Guo, X. Li, Camouflaged object detection with feature decomposition and edge reconstruction, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 22046\u201322055.","DOI":"10.1109\/CVPR52729.2023.02111"},{"key":"10.1016\/j.knosys.2026.116281_b26","doi-asserted-by":"crossref","first-page":"3580","DOI":"10.1109\/TIP.2023.3287137","article-title":"Predictive uncertainty estimation for camouflaged object detection","volume":"32","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.knosys.2026.116281_b27","doi-asserted-by":"crossref","first-page":"1897","DOI":"10.1109\/TIP.2022.3223216","article-title":"MGL: Mutual graph learning for camouflaged object detection","volume":"32","author":"Zhai","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.knosys.2026.116281_b28","series-title":"2023 IEEE International Conference on Multimedia and Expo","first-page":"2441","article-title":"CFANet: A cross-layer feature aggregation network for camouflaged object detection","author":"Zhang","year":"2023"},{"key":"10.1016\/j.knosys.2026.116281_b29","doi-asserted-by":"crossref","unstructured":"C. Xie, C. Xia, T. Yu, J. Li, Frequency representation integration for camouflaged object detection, in: Proceedings of the 31st ACM International Conference on Multimedia, 2023, pp. 1789\u20131797.","DOI":"10.1145\/3581783.3611773"},{"key":"10.1016\/j.knosys.2026.116281_b30","series-title":"2023 IEEE International Conference on Multimedia and Expo","first-page":"1421","article-title":"Oaformer: Occlusion aware transformer for camouflaged object detection","author":"Yang","year":"2023"},{"key":"10.1016\/j.knosys.2026.116281_b31","doi-asserted-by":"crossref","unstructured":"L. Wang, J. Yang, Y. Zhang, F. Wang, F. Zheng, Depth-aware concealed crop detection in dense agricultural scenes, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 17201\u201317211.","DOI":"10.1109\/CVPR52733.2024.01628"},{"key":"10.1016\/j.knosys.2026.116281_b32","doi-asserted-by":"crossref","DOI":"10.1109\/TIP.2024.3449574","article-title":"IdeNet: Making neural network identify camouflaged objects like creatures","author":"Guan","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.knosys.2026.116281_b33","series-title":"European Conference on Computer Vision","first-page":"343","article-title":"Frequency-spatial entanglement learning for camouflaged object detection","author":"Sun","year":"2024"},{"key":"10.1016\/j.knosys.2026.116281_b34","doi-asserted-by":"crossref","unstructured":"Z. Chen, K. Sun, X. Lin, CamoDiffusion: Camouflaged object detection via conditional diffusion models, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38, 2024, pp. 1272\u20131280.","DOI":"10.1609\/aaai.v38i2.27890"},{"issue":"12","key":"10.1016\/j.knosys.2026.116281_b35","doi-asserted-by":"crossref","first-page":"9205","DOI":"10.1109\/TPAMI.2024.3417329","article-title":"ZoomNeXt: A unified collaborative pyramid network for camouflaged object detection","volume":"46","author":"Pang","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"12","key":"10.1016\/j.knosys.2026.116281_b36","doi-asserted-by":"crossref","first-page":"10362","DOI":"10.1109\/TPAMI.2024.3438565","article-title":"CamoFormer: Masked separable attention for camouflaged object detection","volume":"46","author":"Yin","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2026.116281_b37","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/TMM.2024.3521681","article-title":"Frequency-guided spatial adaptation for camouflaged object detection","volume":"27","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Multimed."},{"issue":"11","key":"10.1016\/j.knosys.2026.116281_b38","doi-asserted-by":"crossref","first-page":"12871","DOI":"10.1109\/TII.2024.3426979","article-title":"Camouflaged object detection via complementary information-selected network based on visual and semantic separation","volume":"20","author":"Yin","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.knosys.2026.116281_b39","article-title":"Uncertainty-guided diffusion model for camouflaged object detection","author":"Yang","year":"2025","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.knosys.2026.116281_b40","article-title":"Progressive region-to-boundary exploration network for camouflaged object detection","author":"Yue","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.knosys.2026.116281_b41","doi-asserted-by":"crossref","unstructured":"Z. Yu, X. Zhang, L. Zhao, Y. Bin, G. Xiao, Exploring deeper! segment anything model with depth perception for camouflaged object detection, in: Proceedings of the 32nd ACM International Conference on Multimedia, 2024, pp. 4322\u20134330.","DOI":"10.1145\/3664647.3681119"},{"key":"10.1016\/j.knosys.2026.116281_b42","series-title":"Promoting SAM for camouflaged object detection via selective key point-based guidance","author":"Liang","year":"2025"},{"key":"10.1016\/j.knosys.2026.116281_b43","doi-asserted-by":"crossref","unstructured":"Y. Pang, X. Zhao, T.-Z. Xiang, L. Zhang, H. Lu, Zoom in and out: A mixed-scale triplet network for camouflaged object detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 2160\u20132170.","DOI":"10.1109\/CVPR52688.2022.00220"},{"key":"10.1016\/j.knosys.2026.116281_b44","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108414","article-title":"Fast camouflaged object detection via edge-based reversible re-calibration network","volume":"123","author":"Ji","year":"2022","journal-title":"Pattern Recognit."},{"issue":"1","key":"10.1016\/j.knosys.2026.116281_b45","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1007\/s11633-022-1365-9","article-title":"Deep gradient learning for efficient camouflaged object detection","volume":"20","author":"Ji","year":"2023","journal-title":"Mach. Intell. Res."},{"issue":"10","key":"10.1016\/j.knosys.2026.116281_b46","doi-asserted-by":"crossref","first-page":"5444","DOI":"10.1109\/TCSVT.2023.3255304","article-title":"Go closer to see better: Camouflaged object detection via object area amplification and figure-ground conversion","volume":"33","author":"Xing","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.knosys.2026.116281_b47","doi-asserted-by":"crossref","unstructured":"R. Cong, M. Sun, S. Zhang, X. Zhou, W. Zhang, Y. Zhao, Frequency perception network for camouflaged object detection, in: Proceedings of the 31st ACM International Conference on Multimedia, 2023, pp. 1179\u20131189.","DOI":"10.1145\/3581783.3612083"},{"key":"10.1016\/j.knosys.2026.116281_b48","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112051","article-title":"SDRNet: Camouflaged object detection with independent reconstruction of structure and detail","volume":"299","author":"Guan","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116281_b49","doi-asserted-by":"crossref","first-page":"4050","DOI":"10.1109\/TMM.2023.3295095","article-title":"UEDG: uncertainty-edge dual guided camouflage object detection","volume":"26","author":"Lyu","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.knosys.2026.116281_b50","doi-asserted-by":"crossref","first-page":"7114","DOI":"10.1109\/TMM.2024.3360710","article-title":"Decoupling and integration network for camouflaged object detection","volume":"26","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.knosys.2026.116281_b51","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113056","article-title":"ESNet: An efficient skeleton-guided network for camouflaged object detection","volume":"311","author":"Ren","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116281_b52","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.cviu.2019.04.006","article-title":"Anabranch network for camouflaged object segmentation","volume":"184","author":"Le","year":"2019","journal-title":"Comput. Vis. Image Underst."},{"key":"10.1016\/j.knosys.2026.116281_b53","doi-asserted-by":"crossref","unstructured":"Y. Lv, J. Zhang, Y. Dai, A. Li, B. Liu, N. Barnes, D.-P. Fan, Simultaneously Localize, Segment and Rank the Camouflaged Objects, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 11591\u201311601.","DOI":"10.1109\/CVPR46437.2021.01142"},{"issue":"2","key":"10.1016\/j.knosys.2026.116281_b54","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","article-title":"Res2net: A new multi-scale backbone architecture","volume":"43","author":"Gao","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"7","key":"10.1016\/j.knosys.2026.116281_b55","doi-asserted-by":"crossref","first-page":"5452","DOI":"10.1109\/TCSVT.2023.3349209","article-title":"Efficient camouflaged object detection network based on global localization perception and local guidance refinement","volume":"34","author":"Hu","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.knosys.2026.116281_b56","doi-asserted-by":"crossref","unstructured":"D.-P. Fan, M.-M. Cheng, Y. Liu, T. Li, A. Borji, Structure-measure: A new way to evaluate foreground maps, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 4548\u20134557.","DOI":"10.1109\/ICCV.2017.487"},{"key":"10.1016\/j.knosys.2026.116281_b57","series-title":"Enhanced-alignment measure for binary foreground map evaluation","author":"Fan","year":"2018"},{"key":"10.1016\/j.knosys.2026.116281_b58","series-title":"2009 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1597","article-title":"Frequency-tuned salient region detection","author":"Achanta","year":"2009"},{"key":"10.1016\/j.knosys.2026.116281_b59","doi-asserted-by":"crossref","unstructured":"R. Margolin, L. Zelnik-Manor, A. Tal, How to evaluate foreground maps?, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 248\u2013255.","DOI":"10.1109\/CVPR.2014.39"},{"key":"10.1016\/j.knosys.2026.116281_b60","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.127050","article-title":"A systematic review of image-level camouflaged object detection with deep learning","volume":"566","author":"Liang","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2026.116281_b61","series-title":"Pytorch: An imperative style, high-performance deep learning library","author":"Paszke","year":"2019"},{"key":"10.1016\/j.knosys.2026.116281_b62","series-title":"Dinov2: Learning robust visual features without supervision","author":"Oquab","year":"2023"},{"key":"10.1016\/j.knosys.2026.116281_b63","doi-asserted-by":"crossref","unstructured":"B.-W. Yin, J.-L. Cao, M.-M. Cheng, Q. Hou, Dformerv2: Geometry self-attention for rgbd semantic segmentation, in: Proceedings of the Computer Vision and Pattern Recognition Conference, 2025, pp. 19345\u201319355.","DOI":"10.1109\/CVPR52734.2025.01802"},{"key":"10.1016\/j.knosys.2026.116281_b64","doi-asserted-by":"crossref","unstructured":"Z. Wu, D.P. Paudel, D.-P. Fan, J. Wang, S. Wang, C. Demonceaux, R. Timofte, L. Van Gool, Source-free depth for object pop-out, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2023, pp. 1032\u20131042.","DOI":"10.1109\/ICCV51070.2023.00101"},{"key":"10.1016\/j.knosys.2026.116281_b65","doi-asserted-by":"crossref","DOI":"10.1109\/TIP.2024.3475219","article-title":"Hierarchical graph interaction transformer with dynamic token clustering for camouflaged object detection","author":"Yao","year":"2024","journal-title":"IEEE Trans. Image Process."},{"issue":"12","key":"10.1016\/j.knosys.2026.116281_b66","doi-asserted-by":"crossref","first-page":"13601","DOI":"10.1007\/s11227-023-05207-1","article-title":"Regional steel price index forecasts with neural networks: evidence from east, south, north, central south, northeast, southwest, and northwest China: X. Xu, y. Zhang","volume":"79","author":"Xu","year":"2023","journal-title":"J. Supercomput."},{"key":"10.1016\/j.knosys.2026.116281_b67","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","article-title":"WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians","volume":"43","author":"Bernal","year":"2015","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.knosys.2026.116281_b68","series-title":"International Conference on Multimedia Modeling","first-page":"451","article-title":"Kvasir-seg: A segmented polyp dataset","author":"Jha","year":"2019"},{"issue":"9","key":"10.1016\/j.knosys.2026.116281_b69","doi-asserted-by":"crossref","first-page":"3166","DOI":"10.1016\/j.patcog.2012.03.002","article-title":"Towards automatic polyp detection with a polyp appearance model","volume":"45","author":"Bernal","year":"2012","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.knosys.2026.116281_b70","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.knosys.2026.116281_b71","series-title":"International Workshop on Deep Learning in Medical Image Analysis","first-page":"3","article-title":"Unet++: A nested u-net architecture for medical image segmentation","author":"Zhou","year":"2018"},{"key":"10.1016\/j.knosys.2026.116281_b72","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"99","article-title":"Lesion-aware dynamic kernel for polyp segmentation","author":"Zhang","year":"2022"},{"key":"10.1016\/j.knosys.2026.116281_b73","doi-asserted-by":"crossref","unstructured":"W. Yue, J. Zhang, K. Hu, Y. Xia, J. Luo, Z. Wang, Surgicalsam: Efficient class promptable surgical instrument segmentation, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38, 2024, pp. 6890\u20136898.","DOI":"10.1609\/aaai.v38i7.28514"},{"key":"10.1016\/j.knosys.2026.116281_b74","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"118","article-title":"Asps: Augmented segment anything model for polyp segmentation","author":"Li","year":"2024"},{"key":"10.1016\/j.knosys.2026.116281_b75","series-title":"Polyp-sam++: Can a text guided sam perform better for polyp segmentation?","author":"Biswas","year":"2023"},{"key":"10.1016\/j.knosys.2026.116281_b76","series-title":"Proceedings of the ACM International Conference on Multimedia (MM \u201925)","article-title":"ST-SAM: SAM-driven self-training framework for semi-supervised camouflaged object detection","author":"Hu","year":"2025"},{"key":"10.1016\/j.knosys.2026.116281_b77","doi-asserted-by":"crossref","unstructured":"P. Ren, T. Bai, J. Sun, F. Sun, Seeing the Unseen: A Semantic Alignment and Context-Aware Prompt Framework for Open-Vocabulary Camouflaged Object Segmentation, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV, 2025.","DOI":"10.1109\/ICCV51701.2025.02196"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126010075?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126010075?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T00:05:11Z","timestamp":1783123511000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126010075"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":77,"alternative-id":["S0950705126010075"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116281","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"SPMNet: Self-prompt mask-guided network for Camouflaged Object Detection","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116281","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"116281"}}