{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:05:12Z","timestamp":1776884712919,"version":"3.51.2"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61802336"],"award-info":[{"award-number":["61802336"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61802336"],"award-info":[{"award-number":["61802336"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61802336"],"award-info":[{"award-number":["61802336"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61802336"],"award-info":[{"award-number":["61802336"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61802336"],"award-info":[{"award-number":["61802336"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61802336"],"award-info":[{"award-number":["61802336"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Yangzhou University \u201dQinglan Project\u201d"},{"name":"Yangzhou University Science and Technology innovation venture Fund"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s00371-024-03295-5","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T17:02:12Z","timestamp":1708966932000},"page":"9051-9062","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Boundary-aware dichotomous image segmentation"],"prefix":"10.1007","volume":"40","author":[{"given":"Haonan","family":"Tang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuhan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeyu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuelong","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,26]]},"reference":[{"key":"3295_CR1","first-page":"1","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1 (2012)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3295_CR2","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464\u20137475 (2023)","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"3295_CR3","first-page":"1","volume":"1","author":"X Lin","year":"2021","unstructured":"Lin, X., Sun, S., Huang, W., Sheng, B., Li, P., Feng, D.D.: Eapt: efficient attention pyramid transformer for image processing. IEEE Trans. Multimed. 1, 1 (2021)","journal-title":"IEEE Trans. Multimed."},{"issue":"2","key":"3295_CR4","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1007\/s13042-021-01496-1","volume":"14","author":"F Li","year":"2023","unstructured":"Li, F., Gao, D., Yang, Y., Zhu, J.: Small target deep convolution recognition algorithm based on improved yolov4. Int. J. Mach. Learn. Cybern. 14(2), 387\u2013394 (2023)","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"3295_CR5","doi-asserted-by":"crossref","unstructured":"Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M., Jagersand, M.: Basnet: boundary-aware salient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 7479\u20137489 (2019)","DOI":"10.1109\/CVPR.2019.00766"},{"issue":"12","key":"3295_CR6","doi-asserted-by":"publisher","first-page":"25419","DOI":"10.1109\/TITS.2022.3141107","volume":"23","author":"Y Li","year":"2022","unstructured":"Li, Y., Sun, J., Li, Y.: Weakly-supervised semantic segmentation network with iterative DCRF. IEEE Trans. Intell. Transp. Syst. 23(12), 25419\u201325426 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"1","key":"3295_CR7","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1109\/TII.2021.3085669","volume":"18","author":"J Li","year":"2021","unstructured":"Li, J., Chen, J., Sheng, B., Li, P., Yang, P., Feng, D.D., Qi, J.: Automatic detection and classification system of domestic waste via multimodel cascaded convolutional neural network. IEEE Trans. Industr. Inf. 18(1), 163\u2013173 (2021)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"3295_CR8","first-page":"1","volume":"1","author":"S Sun","year":"2023","unstructured":"Sun, S., Zhi, S., Liao, Q., Heikkil\u00e4, J., Liu, L.: Unbiased scene graph generation via two-stage causal modeling. IEEE Trans. Pattern Anal. Mach. Intell. 1, 1 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3295_CR9","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Zhou, Y., Najibi, M., Sun, P., Vo, K., Deng, B., Anguelov, D.: Offboard 3d object detection from point cloud sequences. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6134\u20136144 (2021)","DOI":"10.1109\/CVPR46437.2021.00607"},{"key":"3295_CR10","doi-asserted-by":"crossref","unstructured":"Hu, Q., Chen, Y., Xiao, J., Sun, S., Chen, J., Yuille, A.L., Zhou, Z.: Label-free liver tumor segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7422\u20137432 (2023)","DOI":"10.1109\/CVPR52729.2023.00717"},{"issue":"1","key":"3295_CR11","doi-asserted-by":"publisher","first-page":"7600","DOI":"10.1038\/s41598-023-34379-2","volume":"13","author":"X Wang","year":"2023","unstructured":"Wang, X., Hu, Z., Shi, S., Hou, M., Xu, L., Zhang, X.: A deep learning method for optimizing semantic segmentation accuracy of remote sensing images based on improved unet. Sci. Rep. 13(1), 7600 (2023)","journal-title":"Sci. Rep."},{"key":"3295_CR12","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/s41095-019-0149-9","volume":"5","author":"A Borji","year":"2019","unstructured":"Borji, A., Cheng, M.-M., Hou, Q., Jiang, H., Li, J.: Salient object detection: a survey. Comput. Vis. Med. 5, 117\u2013150 (2019)","journal-title":"Comput. Vis. Med."},{"key":"3295_CR13","doi-asserted-by":"crossref","unstructured":"Fan, D.-P., Ji, G.-P., Sun, G., Cheng, M.-M., Shen, J., Shao, L.: Camouflaged object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2777\u20132787 (2020)","DOI":"10.1109\/CVPR42600.2020.00285"},{"key":"3295_CR14","doi-asserted-by":"crossref","unstructured":"Qin, X., Dai, H., Hu, X., Fan, D.-P., Shao, L., Van\u00a0Gool, L.: Highly accurate dichotomous image segmentation. In: European Conference on Computer Vision, pp. 38\u201356. Springer (2022)","DOI":"10.1007\/978-3-031-19797-0_3"},{"key":"3295_CR15","doi-asserted-by":"crossref","unstructured":"Xie, C., Xia, C., Ma, M., Zhao, Z., Chen, X., Li, J.: Pyramid grafting network for one-stage high resolution saliency detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 11717\u201311726 (2022)","DOI":"10.1109\/CVPR52688.2022.01142"},{"key":"3295_CR16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"3295_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Iision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"3295_CR18","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, S., Chen, C., Xiang, T.-Z.: Boundary-guided camouflaged object detection. arXiv preprint arXiv:2207.00794 (2022)","DOI":"10.24963\/ijcai.2022\/186"},{"key":"3295_CR19","doi-asserted-by":"crossref","unstructured":"Zhai, Q., Li, X., Yang, F., Chen, C., Cheng, H., Fan, D.-P.: Mutual graph learning for camouflaged object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12997\u201313007 (2021)","DOI":"10.1109\/CVPR46437.2021.01280"},{"key":"3295_CR20","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. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2160\u20132170 (2022)","DOI":"10.1109\/CVPR52688.2022.00220"},{"key":"3295_CR21","unstructured":"Yang, C., Wang, Y., Zhang, J., Zhang, H., Lin, Z., Yuille, A.: Meticulous object segmentation. arXiv preprint arXiv:2012.07181 (2020)"},{"key":"3295_CR22","doi-asserted-by":"crossref","unstructured":"Shen, T., Zhang, Y., Qi, L., Kuen, J., Xie, X., Wu, J., Lin, Z., Jia, J.: High quality segmentation for ultra high-resolution images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1310\u20131319 (2022)","DOI":"10.1109\/CVPR52688.2022.00137"},{"key":"3295_CR23","doi-asserted-by":"crossref","unstructured":"Liew, J.H., Cohen, S., Price, B., Mai, L., Feng, J.: Deep interactive thin object selection. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 305\u2013314 (2021)","DOI":"10.1109\/WACV48630.2021.00035"},{"key":"3295_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., Zaiane, O.R., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection. Pattern Recogn. 106, 107404 (2020)","journal-title":"Pattern Recogn."},{"key":"3295_CR25","doi-asserted-by":"crossref","unstructured":"Zhao, X., Pang, Y., Zhang, L., Lu, H., Zhang, L.: Suppress and balance: A simple gated network for salient object detection. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part II 16, pp. 35\u201351. Springer (2020)","DOI":"10.1007\/978-3-030-58536-5_3"},{"key":"3295_CR26","doi-asserted-by":"crossref","unstructured":"Wei, J., Wang, S., Huang, Q.: F$$^3$$net: fusion, feedback and focus for salient object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, Vol.\u00a034, pp. 12321\u201312328 (2020)","DOI":"10.1609\/aaai.v34i07.6916"},{"key":"3295_CR27","doi-asserted-by":"crossref","unstructured":"Pei, J., Zhou, Z., Jin, Y., Tang, H.,Heng, P.-A.: Unite-divide-unite: Joint boosting trunk and structure for high-accuracy dichotomous image segmentation. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 2139\u20132147 (2023)","DOI":"10.1145\/3581783.3611811"},{"key":"3295_CR28","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Dong, B., Wu, Y., Zhu, W., Chen, G., Zhang, Y.: Dichotomous image segmentation with frequency priors. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, pp. 1822\u20131830 (2023)","DOI":"10.24963\/ijcai.2023\/202"},{"key":"3295_CR29","unstructured":"Tang, L., Li, B., Zhong, Y., Ding, S., Song, M.: Disentangled high quality salient object detection. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 3580\u20133590 (2021)"},{"key":"3295_CR30","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Zhang, P., Zhang, J., Lin, Z., Lu, H.: Towards high-resolution salient object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7234\u20137243 (2019)","DOI":"10.1109\/ICCV.2019.00733"},{"key":"3295_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, G., Lu, X., Tan, J., Li, J., Zhang, Z., Li, Q., Hu, X.: Refinemask: Towards high-quality instance segmentation with fine-grained features. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6861\u20136869 (2021)","DOI":"10.1109\/CVPR46437.2021.00679"},{"issue":"11","key":"3295_CR32","doi-asserted-by":"publisher","first-page":"2571","DOI":"10.1007\/s11263-022-01662-0","volume":"130","author":"X Hu","year":"2022","unstructured":"Hu, X., Tang, C., Chen, H., Li, X., Li, J., Zhang, Z.: Improving image segmentation with boundary patch refinement. Int. J. Comput. Vision 130(11), 2571\u20132589 (2022)","journal-title":"Int. J. Comput. Vision"},{"issue":"3","key":"3295_CR33","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1109\/TMI.2019.2935018","volume":"39","author":"Q Zhu","year":"2019","unstructured":"Zhu, Q., Du, B., Yan, P.: Boundary-weighted domain adaptive neural network for prostate mr image segmentation. IEEE Trans. Med. Imaging 39(3), 753\u2013763 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3295_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 286\u2013301 (2018)","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"3295_CR35","doi-asserted-by":"crossref","unstructured":"Hussain, T., Anwar, A., Anwar, S., Petersson, L.,Baik, S.W.: Pyramidal attention for saliency detection. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2877\u20132887. IEEE (2022)","DOI":"10.1109\/CVPRW56347.2022.00325"},{"key":"3295_CR36","unstructured":"Xie, Z., Zhang, W., Sheng, B., Li, P., Chen, C.P.: Bagfn: broad attentive graph fusion network for high-order feature interactions. IEEE Trans. Neural Netw. Learn. Syst. (2021)"},{"key":"3295_CR37","doi-asserted-by":"publisher","first-page":"3763","DOI":"10.1109\/TIP.2020.2965989","volume":"29","author":"S Chen","year":"2020","unstructured":"Chen, S., Tan, X., Wang, B., Lu, H., Hu, X., Fu, Y.: Reverse attention-based residual network for salient object detection. IEEE Trans. Image Process. 29, 3763\u20133776 (2020)","journal-title":"IEEE Trans. Image Process."},{"issue":"10","key":"3295_CR38","doi-asserted-by":"publisher","first-page":"6024","DOI":"10.1109\/TPAMI.2021.3085766","volume":"44","author":"D-P Fan","year":"2021","unstructured":"Fan, D.-P., Ji, G.-P., Cheng, M.-M., Shao, L.: Concealed object detection. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6024\u20136042 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3295_CR39","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S.,Hammerla, N.Y., Kainz, B., et\u00a0al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"},{"key":"3295_CR40","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., Lu, H.: Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3146\u20133154 (2019)","DOI":"10.1109\/CVPR.2019.00326"},{"key":"3295_CR41","first-page":"1","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141, Polosukhin, I.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 1 (2017)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"3295_CR42","doi-asserted-by":"crossref","unstructured":"Yang, M., Yu, K., Zhang, C., Li, Z., Yang, K.: Denseaspp for semantic segmentation in street scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3684\u20133692 (2018)","DOI":"10.1109\/CVPR.2018.00388"},{"key":"3295_CR43","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"3295_CR44","unstructured":"Yuan, P., Lin, S., Cui, C., Du, Y., Guo, R., He, D., Ding, E., Han, S.: Hs-resnet: Hierarchical-split block on convolutional neural network. arXiv preprint arXiv:2010.07621 (2020)"},{"key":"3295_CR45","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","volume":"134","author":"P-T De Boer","year":"2005","unstructured":"De Boer, P.-T., Kroese, D.P., Mannor, S., Rubinstein, R.Y.: A tutorial on the cross-entropy method. Ann. Oper. Res. 134, 19\u201367 (2005)","journal-title":"Ann. Oper. Res."},{"key":"3295_CR46","doi-asserted-by":"crossref","unstructured":"Yu, J., Jiang, Y., Wang, Z., Cao, Z., Huang, T.: Unitbox: an advanced object detection network. In: Proceedings of the 24th ACM International Conference on Multimedia, pp. 516\u2013520 (2016)","DOI":"10.1145\/2964284.2967274"},{"key":"3295_CR47","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. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8772\u20138781 (2021)","DOI":"10.1109\/CVPR46437.2021.00866"},{"key":"3295_CR48","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"3295_CR49","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"10","key":"3295_CR50","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","volume":"43","author":"J Wang","year":"2020","unstructured":"Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3349\u20133364 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3295_CR51","doi-asserted-by":"crossref","unstructured":"Zhao, H., Qi, X., Shen, X., Shi, J., Jia, J.: Icnet for real-time semantic segmentation on high-resolution images. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 405\u2013420 (2018)","DOI":"10.1007\/978-3-030-01219-9_25"},{"key":"3295_CR52","doi-asserted-by":"crossref","unstructured":"Nirkin, Y., Wolf, L., Hassner, T.: Hyperseg: Patch-wise hypernetwork for real-time semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4061\u20134070 (2021)","DOI":"10.1109\/CVPR46437.2021.00405"},{"issue":"12","key":"3295_CR53","doi-asserted-by":"publisher","first-page":"5706","DOI":"10.1109\/TIP.2015.2487833","volume":"24","author":"A Borji","year":"2015","unstructured":"Borji, A., Cheng, M.-M., Jiang, H., Li, J.: Salient object detection: a benchmark. IEEE Trans. Image Process. 24(12), 5706\u20135722 (2015)","journal-title":"IEEE Trans. Image Process."},{"key":"3295_CR54","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/j.patcog.2018.08.007","volume":"86","author":"H Chen","year":"2019","unstructured":"Chen, H., Li, Y., Su, D.: Multi-modal fusion network with multi-scale multi-path and cross-modal interactions for rgb-d salient object detection. Pattern Recogn. 86, 376\u2013385 (2019)","journal-title":"Pattern Recogn."},{"key":"3295_CR55","doi-asserted-by":"crossref","unstructured":"Zhao, J.-X., Cao, Y., Fan, D.-P., Cheng, M.-M., Li, X.-Y., Zhang, L.: Contrast prior and fluid pyramid integration for rgbd salient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer vision and Pattern Recognition, pp. 3927\u20133936 (2019)","DOI":"10.1109\/CVPR.2019.00405"},{"key":"3295_CR56","doi-asserted-by":"crossref","unstructured":"Fan, D.-P., Gong, C., Cao, Y., Ren, B., Cheng, M.-M., Borji, A.: Enhanced-alignment measure for binary foreground map evaluation. arXiv preprint arXiv:1805.10421 (2018)","DOI":"10.24963\/ijcai.2018\/97"},{"key":"3295_CR57","doi-asserted-by":"crossref","unstructured":"Freixenet, J., Munoz, X., Raba, D., Mart\u00ed, J., Cuf\u00ed, X.: Yet another survey on image segmentation: region and boundary information integration. In: Computer Vision-ECCV 2002: 7th European Conference on Computer Vision Copenhagen, Denmark, May 28\u201331, 2002 Proceedings, Part III 7, pp. 408\u2013422. Springer (2002)","DOI":"10.1007\/3-540-47977-5_27"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03295-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-024-03295-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03295-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T09:15:32Z","timestamp":1731402932000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-024-03295-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,26]]},"references-count":57,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["3295"],"URL":"https:\/\/doi.org\/10.1007\/s00371-024-03295-5","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,26]]},"assertion":[{"value":"25 January 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interests"}}]}}