{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T04:12:24Z","timestamp":1759983144374,"version":"build-2065373602"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Fundamental Research Funds for the Central Universities","award":["24CX02030A","24CX02030A","24CX02030A"],"award-info":[{"award-number":["24CX02030A","24CX02030A","24CX02030A"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s11760-025-04740-1","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T14:23:50Z","timestamp":1757600630000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Semantic segmentation of marine animal image by U-Net based on multi-cognitive visual adapter and dual-attention fusion mechanism"],"prefix":"10.1007","volume":"19","author":[{"given":"Hongtao","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengyue","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cai","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,11]]},"reference":[{"key":"4740_CR1","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1007\/s11760-025-03829-x","volume":"19","author":"Y Li","year":"2025","unstructured":"Li, Y., Zhao, Z., Li, R.: Dual-domain feature aggregation transformer network for underwater image enhancement. SIViP 19, 248 (2025)","journal-title":"SIViP"},{"issue":"6","key":"4740_CR2","doi-asserted-by":"publisher","first-page":"1653","DOI":"10.1093\/icb\/icz066","volume":"59","author":"LE Bagge","year":"2019","unstructured":"Bagge, L.E.: Not as clear as it may appear: Challenges associated with transparent camouflage in the ocean. Integr. Comp. Biol. 59(6), 1653\u20131663 (2019)","journal-title":"Integr. Comp. Biol."},{"key":"4740_CR3","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1007\/s11760-025-04390-3","volume":"19","author":"B Wang","year":"2025","unstructured":"Wang, B., Zhu, H., Cao, P., Zhang, L., Shen, A.: A novel multi-feature fusion network for semantic segmentation. SIViP 19, 785 (2025)","journal-title":"SIViP"},{"key":"4740_CR4","doi-asserted-by":"crossref","unstructured":"Hu, X., Li, P., Karimi, H. R., Jiang, L., & Zhang, D.: Open-set marine object instance segmentation with prototype learning. Signal, Image and Video Processing, 18, 6055\u20136062 (2024). Open access","DOI":"10.1007\/s11760-024-03293-z"},{"key":"4740_CR5","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., VanDerMaaten, L., Weinberger, K Q.: Densely connected convolutional networks. IEEE Conference on Computer Vision and Pattern Recognition, 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"4740_CR6","unstructured":"Ravi, N., Gabeur, V., Hu, Y.T., Hu, R., Ryali, C., Ma, T., Khedr, H., R\u00e4dle, R., Rolland, C., Gustafson, L., et al.: Sam 2: Segment anything in images and videos. arXiv preprint, arXiv:2408.00714 (2024)"},{"key":"4740_CR7","doi-asserted-by":"crossref","unstructured":"Zhang, P., Yan, T., Liu, Y., Lu, H.: Fantastic animals and where to find them: Segment any marine animal with dual SAM. Computer Vision and Pattern Recognition, 2578\u20132577 (2024)","DOI":"10.1109\/CVPR52733.2024.00249"},{"issue":"4","key":"4740_CR8","doi-asserted-by":"publisher","first-page":"2303","DOI":"10.1109\/TCSVT.2021.3093890","volume":"32","author":"L Li","year":"2022","unstructured":"Li, L., Dong, B., Rigall, E., Zhou, T., Dong, J., Chen, G.: Marine animal segmentation. IEEE Trans. Circuits Syst. Video Technol. 32(4), 2303\u20132314 (2022)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"4740_CR9","doi-asserted-by":"publisher","first-page":"1811","DOI":"10.1007\/s00371-024-03494-0","volume":"41","author":"J Chen","year":"2025","unstructured":"Chen, J., Su, W., Ge, M., He, Y., Yu, J.: To-Former: semantic segmentation of transparent object with edge-enhanced transformer. Vis. Comput. 41, 1811\u20131825 (2025)","journal-title":"Vis. Comput."},{"key":"4740_CR10","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1007\/978-981-97-8692-3_25","volume":"15039","author":"J Chen","year":"2024","unstructured":"Chen, J., Su, W.: EG-Trans: Transparent Object Segmentation with Edge Enhanced and Global Integrated Transformers. Pattern Recognition and Computer Vision (PRCV 2024). Lect. Notes Comput. Sci. 15039, 349\u2013363 (2024)","journal-title":"Lect. Notes Comput. Sci."},{"key":"4740_CR11","unstructured":"Xiong, X., Wu, Z., Tan, S., Li, W., Tang, F., Chen, Y., Li, S., Ma, J., Li, G.: SAM2-UNet: Segment Anything 2 makes strong encoder for natural and medical image segmentation. arXiv preprint, arXiv:2408.08870 (2024)"},{"key":"4740_CR12","doi-asserted-by":"crossref","unstructured":"Wang, X., Li, J., Zhu, L., Zhang, Z., Chen, Z., Li, X.: VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows. IEEE (2023)","DOI":"10.1109\/TCYB.2023.3318601"},{"key":"4740_CR13","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., et al.: Segment anything. ICCV, 4015\u20134026 (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"4740_CR14","unstructured":"Ryali, C., Hu, Y.T., Bolya, D., Wei, C., Fan, H., Huang, P.Y., Aggarwal, V., Chowdhury, A., Poursaeed, O., Hoffman, J., et al.: Hiera: A hierarchical vision transformer without the bells-and-whistles. ICML, 29441\u201329454 (2023)"},{"key":"4740_CR15","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Ji, G.P., Zhou, T., Chen, G., Fu, H., Shen, J., Shao, L.: PraNet: Parallel reverse attention network for polyp segmentation. MICCAI, 263\u2013273 (2020)","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"4740_CR16","doi-asserted-by":"crossref","unstructured":"Chen, J., Mei, J., Li, X., Lu, Y., Yu, Q., Wei, Q., Luo, X., Xie, Y., Adeli, E., Wang, Y., et al.: TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers. Medical Image Analysis, 103280 (2024)","DOI":"10.1016\/j.media.2024.103280"},{"issue":"4","key":"4740_CR17","doi-asserted-by":"publisher","first-page":"2303","DOI":"10.1109\/TCSVT.2021.3093890","volume":"32","author":"L Li","year":"2022","unstructured":"Li, L., Dong, B., Rigall, E., Zhou, T., Dong, J., Chen, G.: Marine animal segmentation. IEEE Trans. Circuits Syst. Video Technol. 32(4), 2303\u20132314 (2022)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"4740_CR18","doi-asserted-by":"crossref","unstructured":"Fu, Z., Chen, R., Huang, Y., Cheng, E., Ding, X., Ma, K.K.: Masnet: A robust deep marine animal segmentation network. IEEE Journal of Oceanic Engineering (2023)","DOI":"10.1109\/JOE.2023.3252760"},{"key":"4740_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13173-021-00117-7","volume":"27","author":"P Drews-Jr","year":"2021","unstructured":"Drews-Jr, P., de Souza, I., Maurell, I.P., Protas, E.V., Botelho, S.S.C.: Underwater image segmentation in the wild using deep learning. J. Braz. Comput. Soc. 27, 1\u201314 (2021)","journal-title":"J. Braz. Comput. Soc."},{"key":"4740_CR20","unstructured":"Islam, M.J., Luo, P., Sattar, J.: Simultaneous enhancement and super-resolution of underwater imagery for improved visual perception. arXiv preprint, arXiv:2002.01155 (2020)"},{"key":"4740_CR21","doi-asserted-by":"crossref","unstructured":"Wu, Z., Su, L., Huang, Q.: Stacked cross refinement network for edge-aware salient object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), 7263\u20137272 (2019)","DOI":"10.1109\/ICCV.2019.00736"},{"key":"4740_CR22","doi-asserted-by":"crossref","unstructured":"Zhao, T., Wu, X.: Pyramid feature attention network for saliency detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 3085\u20133094 (2019)","DOI":"10.1109\/CVPR.2019.00320"},{"issue":"6","key":"4740_CR23","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2020","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: UNet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856\u20131867 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4740_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107404","volume":"106","author":"X Qin","year":"2020","unstructured":"Qin, X., Zhang, Z., Huang, C., Dehghan, M., Za\u00efane, O.R., J\u00e4gersand, M.: U2-Net: Going deeper with nested U-structure for salient object detection. Pattern Recogn. 106, 107404 (2020)","journal-title":"Pattern Recogn."},{"key":"4740_CR25","doi-asserted-by":"crossref","unstructured":"Fan, D.-P., Ji, G.-P., Sun, G., Cheng, M.-M., Shen, J., Shao, L.: Camouflaged object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2774\u20132784 (2020)","DOI":"10.1109\/CVPR42600.2020.00285"},{"key":"4740_CR26","doi-asserted-by":"crossref","unstructured":"Piao, Y., Wang, J., Zhang, M., Lu, H.: MFNet: Multi-filter directive network for weakly supervised salient object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), 4116\u20134125 (2021)","DOI":"10.1109\/ICCV48922.2021.00410"},{"key":"4740_CR27","doi-asserted-by":"crossref","unstructured":"Mei, H., Ji, G.-P., Wei, Z., Yang, X., Wei, X., Fan, D.-P.: Camouflaged object segmentation with distraction mining. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 8772\u20138781 (2021)","DOI":"10.1109\/CVPR46437.2021.00866"},{"key":"4740_CR28","doi-asserted-by":"crossref","unstructured":"Lv, Y., Zhang, J., Dai, Y., Li, A., Liu, B., Barnes, N., Fan, D.-P.: Simultaneously localize, segment and rank the camouflaged objects. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 11591\u201311601 (2021)","DOI":"10.1109\/CVPR46437.2021.01142"},{"key":"4740_CR29","doi-asserted-by":"crossref","unstructured":"Sun, Y., Chen, G., Zhou, T., Zhang, Y., Liu, N.: Context-aware cross-level fusion network for camouflaged object detection. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 1025\u20131031 (2021)","DOI":"10.24963\/ijcai.2021\/142"},{"key":"4740_CR30","doi-asserted-by":"crossref","unstructured":"Liu, J., Zhang, J., Barnes, N.: Modeling aleatoric uncertainty for camouflaged object detection. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), 2613\u20132622 (2022)","DOI":"10.1109\/WACV51458.2022.00267"},{"key":"4740_CR31","doi-asserted-by":"crossref","unstructured":"Pang, Y., Zhao, X., Xiang, T.-Z., Zhang, L., Lu, H.: Zoom in and out: A mixed-scale triplet network for camouflaged object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2150\u20132160 (2022)","DOI":"10.1109\/CVPR52688.2022.00220"},{"issue":"3","key":"4740_CR32","doi-asserted-by":"publisher","first-page":"1104","DOI":"10.1109\/JOE.2023.3252760","volume":"49","author":"Z Fu","year":"2024","unstructured":"Fu, Z., Chen, R., Huang, Y., Cheng, E., Ding, X., Ma, K.-K.: Masnet: A robust deep marine animal segmentation network. IEEE J. Oceanic Eng. 49(3), 1104\u20131115 (2024)","journal-title":"IEEE J. Oceanic Eng."},{"key":"4740_CR33","doi-asserted-by":"crossref","unstructured":"Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P.H.S., Zhang, L.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 6881\u20136890 (2021)","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"4740_CR34","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: TransUNet: Transformers make strong encoders for medical image segmentation. arXiv preprint, arXiv:2102.04306 (2021)"},{"issue":"9","key":"4740_CR35","doi-asserted-by":"publisher","first-page":"2763","DOI":"10.1109\/TMI.2023.3264513","volume":"42","author":"A He","year":"2023","unstructured":"He, A., Wang, K., Li, T., Du, C., Xia, S., Fu, H.: H2Former: An efficient hierarchical hybrid transformer for medical image segmentation. IEEE Trans. Med. Imaging 42(9), 2763\u20132775 (2023)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4740_CR36","unstructured":"Wu, J., Fu, R., Fang, H., Liu, Y., Wang, Z., Xu, Y., Jin, Y., Arbel, T.: Medical SAM adapter: Adapting segment anything model for medical image segmentation. arXiv preprint, arXiv:2304.12620 (2023)"},{"key":"4740_CR37","doi-asserted-by":"crossref","unstructured":"Chen, T., Zhu, L., Ding, C., Cao, R., Wang, Y., Li, Z., Sun, L., Mao, P., Zang, Y.: SAM Fails to Segment Anything? - SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, and More. arXiv preprint, arXiv:2304.09148 (2023)","DOI":"10.1109\/ICCVW60793.2023.00361"},{"key":"4740_CR38","first-page":"180","volume":"14463","author":"Y Lai","year":"2023","unstructured":"Lai, Y., Luo, Z., Yu, Z.: Detect Any Deepfakes: Segment Anything Meets Face Forgery Detection and Localization. CCBR 14463, 180\u2013190 (2023)","journal-title":"CCBR"},{"key":"4740_CR39","doi-asserted-by":"crossref","unstructured":"Wei, X., Cao, J., Jin, Y., Lu, M., Wang, G., Zhang, S.: I-MedSAM: Implicit medical image segmentation with segment anything. Computer Vision \u2013 ECCV 2024 15068, 90\u2013107 (2024)","DOI":"10.1007\/978-3-031-72684-2_6"},{"key":"4740_CR40","unstructured":"Yan, T., Wan, Z., Deng, X., Zhang, P., Liu, Y., Lu, H.: MAS-SAM: segment any marine animal with aggregated features. International Joint Conference on Artificial Intelligence, 6886\u20136894 (2024)"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04740-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-04740-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04740-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T03:29:01Z","timestamp":1759980541000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-04740-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,11]]},"references-count":40,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["4740"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-04740-1","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"type":"print","value":"1863-1703"},{"type":"electronic","value":"1863-1711"}],"subject":[],"published":{"date-parts":[[2025,9,11]]},"assertion":[{"value":"9 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 August 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 September 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 September 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or nonfinancial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1138"}}