{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:23:37Z","timestamp":1740108217369,"version":"3.37.3"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62303449"],"award-info":[{"award-number":["62303449"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005047","name":"Natural Science Foundation of Liaoning Province","doi-asserted-by":"publisher","award":["2023-BS-029"],"award-info":[{"award-number":["2023-BS-029"]}],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["3072022TS0604"],"award-info":[{"award-number":["3072022TS0604"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFC3320302"],"award-info":[{"award-number":["2021YFC3320302"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s00138-024-01540-4","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T17:01:51Z","timestamp":1714410111000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Robust semantic segmentation method of urban scenes in snowy environment"],"prefix":"10.1007","volume":"35","author":[{"given":"Hanqi","family":"Yin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guisheng","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiming","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liguo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ye","family":"Tian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,29]]},"reference":[{"key":"1540_CR1","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. arXiv preprint arXiv:2304.02643 (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"issue":"4","key":"1540_CR2","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1007\/s00138-010-0279-7","volume":"22","author":"E Kim","year":"2011","unstructured":"Kim, E., Medioni, G.: Urban scene understanding from aerial and ground lidar data. Mach. Vis. Appl. 22(4), 691\u2013703 (2011)","journal-title":"Mach. Vis. Appl."},{"issue":"2","key":"1540_CR3","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/s00138-021-01168-8","volume":"32","author":"S Gupta","year":"2021","unstructured":"Gupta, S., Dileep, A.D., Thenkanidiyoor, V.: Recognition of varying size scene images using semantic analysis of deep activation maps. Mach. Vis. Appl. 32(2), 52 (2021)","journal-title":"Mach. Vis. Appl."},{"key":"1540_CR4","doi-asserted-by":"publisher","first-page":"9085","DOI":"10.1109\/TIP.2021.3122004","volume":"30","author":"X Tan","year":"2021","unstructured":"Tan, X., Xu, K., Cao, Y., Zhang, Y., Ma, L., Lau, R.W.: Night-time scene parsing with a large real dataset. IEEE Trans. Image Process. 30, 9085\u20139098 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"1540_CR5","doi-asserted-by":"publisher","first-page":"2386","DOI":"10.1109\/TIP.2023.3267044","volume":"32","author":"Z Xie","year":"2023","unstructured":"Xie, Z., Wang, S., Xu, K., Zhang, Z., Tan, X., Xie, Y., Ma, L.: Boosting night-time scene parsing with learnable frequency. IEEE Trans. Image Process. 32, 2386\u20132398 (2023)","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"1540_CR6","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1007\/s00138-023-01373-7","volume":"34","author":"H Yin","year":"2023","unstructured":"Yin, H., Xie, W., Zhang, J., Zhang, Y., Zhu, W., Gao, J., Shao, Y., Li, Y.: Dual context network for real-time semantic segmentation. Mach. Vis. Appl. 34(2), 22 (2023)","journal-title":"Mach. Vis. Appl."},{"issue":"4","key":"1540_CR7","doi-asserted-by":"publisher","first-page":"90","DOI":"10.3390\/technologies10040090","volume":"10","author":"G Rizzoli","year":"2022","unstructured":"Rizzoli, G., Barbato, F., Zanuttigh, P.: Multimodal semantic segmentation in autonomous driving: a review of current approaches and future perspectives. Technologies 10(4), 90 (2022)","journal-title":"Technologies"},{"issue":"4","key":"1540_CR8","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/s00138-022-01305-x","volume":"33","author":"J Gao","year":"2022","unstructured":"Gao, J., Yi, J., Murphey, Y.L.: Attention-based global context network for driving maneuvers prediction. Mach. Vis. Appl. 33(4), 53 (2022)","journal-title":"Mach. Vis. Appl."},{"issue":"3","key":"1540_CR9","first-page":"3492","volume":"45","author":"X Tan","year":"2022","unstructured":"Tan, X., Lin, J., Xu, K., Chen, P., Ma, L., Lau, R.W.: Mirror detection with the visual chirality cue. IEEE Trans. Pattern Anal. Mach. Intell. 45(3), 3492\u20133504 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"12","key":"1540_CR10","doi-asserted-by":"publisher","first-page":"15328","DOI":"10.1109\/TPAMI.2023.3319470","volume":"45","author":"X Tan","year":"2023","unstructured":"Tan, X., Ma, Q., Gong, J., Xu, J., Zhang, Z., Song, H., Qu, Y., Xie, Y., Ma, L.: Positive-negative receptive field reasoning for omni-supervised 3d segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 45(12), 15328\u201315344 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1540_CR11","doi-asserted-by":"crossref","unstructured":"Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., Cottrell, G.: Understanding convolution for semantic segmentatfion. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1451\u20131460 (2018)","DOI":"10.1109\/WACV.2018.00163"},{"issue":"1","key":"1540_CR12","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1007\/s00138-022-01360-4","volume":"34","author":"Z Yang","year":"2023","unstructured":"Yang, Z., Wang, Q., Zeng, J., Qin, P., Chai, R., Sun, D.: RAU-Net: U-Net network based on residual multi-scale fusion and attention skip layer for overall spine segmentation. Mach. Vis. Appl. 34(1), 10 (2023)","journal-title":"Mach. Vis. Appl."},{"key":"1540_CR13","doi-asserted-by":"crossref","unstructured":"Ha, Q., Watanabe, K., Karasawa, T., Ushiku, Y., Harada, T.: MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes. In: 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5108\u20135115 (2017)","DOI":"10.1109\/IROS.2017.8206396"},{"issue":"3","key":"1540_CR14","doi-asserted-by":"publisher","first-page":"2576","DOI":"10.1109\/LRA.2019.2904733","volume":"4","author":"Y Sun","year":"2019","unstructured":"Sun, Y., Zuo, W., Liu, M.: RTFNet: RGB-thermal fusion network for semantic segmentation of urban scenes. IEEE Robotics and Automation Letters 4(3), 2576\u20132583 (2019)","journal-title":"IEEE Robotics and Automation Letters"},{"issue":"4","key":"1540_CR15","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1007\/s00138-022-01314-w","volume":"33","author":"T Houben","year":"2022","unstructured":"Houben, T., Huisman, T., Pisarenco, M., Sommen, F., With, P.H.: Depth estimation from a single sem image using pixel-wise fine-tuning with multimodal data. Mach. Vis. Appl. 33(4), 56 (2022)","journal-title":"Mach. Vis. Appl."},{"issue":"17","key":"1540_CR16","doi-asserted-by":"publisher","first-page":"15435","DOI":"10.1109\/JIOT.2022.3176400","volume":"9","author":"P McEnroe","year":"2022","unstructured":"McEnroe, P., Wang, S., Liyanage, M.: A survey on the convergence of edge computing and AI for UAVs: opportunities and challenges. IEEE Internet Things J. 9(17), 15435\u201315459 (2022)","journal-title":"IEEE Internet Things J."},{"issue":"2","key":"1540_CR17","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1007\/s00138-022-01283-0","volume":"33","author":"H Carrillo","year":"2022","unstructured":"Carrillo, H., Quiroga, J., Zapata, L., Maldonado, E.: Automatic football video production system with edge processing. Mach. Vis. Appl. 33(2), 32 (2022)","journal-title":"Mach. Vis. Appl."},{"issue":"7\u20138","key":"1540_CR18","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1007\/s00138-019-01048-2","volume":"30","author":"K Asghar","year":"2019","unstructured":"Asghar, K., Sun, X., Rosin, P.L., Saddique, M., Hussain, M., Habib, Z.: Edge-texture feature-based image forgery detection with cross-dataset evaluation. Mach. Vis. Appl. 30(7\u20138), 1243\u20131262 (2019)","journal-title":"Mach. Vis. Appl."},{"key":"1540_CR19","unstructured":"Hu, C., Tiliwalidi, K.: Adversarial neon beam: Robust physical-world adversarial attack to DNNs. arXiv preprint arXiv:2204.00853 (2022)"},{"key":"1540_CR20","doi-asserted-by":"crossref","unstructured":"Duan, R., Mao, X., Qin, A.K., Chen, Y., Ye, S., He, Y., Yang, Y.: Adversarial laser beam: Effective physical-world attack to DNNs in a blink. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 16062\u201316071 (2021)","DOI":"10.1109\/CVPR46437.2021.01580"},{"issue":"2","key":"1540_CR21","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/s11263-020-01366-3","volume":"129","author":"M Tremblay","year":"2020","unstructured":"Tremblay, M., Halder, S.S., De Charette, R., Lalonde, J.-F.: Rain rendering for evaluating and improving robustness to bad weather. Int. J. Comput. Vision 129(2), 341\u2013360 (2020)","journal-title":"Int. J. Comput. Vision"},{"key":"1540_CR22","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., Beijbom, O.: nuScenes: A multimodal dataset for autonomous driving. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 11621\u201311631 (2020)","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"1540_CR23","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3213\u20133223 (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"1540_CR24","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The kitti vision benchmark suite. In: 2012 IEEE conference on computer vision and pattern recognition, pp. 3354\u20133361 (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"1540_CR25","doi-asserted-by":"crossref","unstructured":"Choi, S., Jung, S., Yun, H., Kim, J.T., Kim, S., Choo, J.: RobustNet: Improving domain generalization in urban-scene segmentation via instance selective whitening. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 11580\u201311590 (2021)","DOI":"10.1109\/CVPR46437.2021.01141"},{"issue":"4\u20135","key":"1540_CR26","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1177\/0278364920979368","volume":"40","author":"M Pitropov","year":"2021","unstructured":"Pitropov, M., Garcia, D.E., Rebello, J., Smart, M., Wang, C., Czarnecki, K., Waslander, S.: Canadian adverse driving conditions dataset. Int. J. Robot. Res. 40(4\u20135), 681\u2013690 (2021)","journal-title":"Int. J. Robot. Res."},{"key":"1540_CR27","unstructured":"Liu, M.-Y., Tuzel, O.: Coupled generative adversarial networks. In: Advances in neural information processing systems, vol. 29 (2016)"},{"key":"1540_CR28","unstructured":"Liu, M.-Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation networks. In: Advances in neural information processing systems, vol. 30 (2017)"},{"key":"1540_CR29","doi-asserted-by":"crossref","unstructured":"Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., Krishnan, D.: Unsupervised pixel-level domain adaptation with generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3722\u20133731 (2017)","DOI":"10.1109\/CVPR.2017.18"},{"key":"1540_CR30","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., Webb, R.: Learning from simulated and unsupervised images through adversarial training. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2107\u20132116 (2017)","DOI":"10.1109\/CVPR.2017.241"},{"key":"1540_CR31","doi-asserted-by":"crossref","unstructured":"Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp. 2223\u20132232 (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"1540_CR32","doi-asserted-by":"crossref","unstructured":"Yi, Z., Zhang, H., Tan, P., Gong, M.: DualGAN: unsupervised dual learning for image-to-image translation. In: Proceedings of the IEEE international conference on computer vision, pp. 2849\u20132857 (2017)","DOI":"10.1109\/ICCV.2017.310"},{"key":"1540_CR33","doi-asserted-by":"crossref","unstructured":"Choi, Y., Uh, Y., Yoo, J., Ha, J.-W.: StarGAN v2: diverse image synthesis for multiple domains. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 8188\u20138197 (2020)","DOI":"10.1109\/CVPR42600.2020.00821"},{"key":"1540_CR34","doi-asserted-by":"crossref","unstructured":"Liu, M.-Y., Huang, X., Mallya, A., Karras, T., Aila, T., Lehtinen, J., Kautz, J.: Few-shot unsupervised image-to-image translation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 10551\u201310560 (2019)","DOI":"10.1109\/ICCV.2019.01065"},{"key":"1540_CR35","doi-asserted-by":"crossref","unstructured":"Pizzati, F., Charette, R.d., Zaccaria, M., Cerri, P.: Domain bridge for unpaired image-to-image translation and unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp. 2990\u20132998 (2020)","DOI":"10.1109\/WACV45572.2020.9093540"},{"key":"1540_CR36","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)"},{"key":"1540_CR37","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"key":"1540_CR38","doi-asserted-by":"crossref","unstructured":"Yi-de, M., Qing, L., Zhi-Bai, Q.: Automated image segmentation using improved PCNN model based on cross-entropy. In: Proceedings of 2004 international symposium on intelligent multimedia, video and speech processing, pp. 743\u2013746 (2004)","DOI":"10.1109\/ISIMP.2004.1434171"},{"key":"1540_CR39","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"1540_CR40","doi-asserted-by":"crossref","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge\u00a0Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Deep learning in medical image analysis and multimodal learning for clinical decision support, pp. 240\u2013248 (2017)","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"1540_CR41","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":"1540_CR42","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017)"},{"key":"1540_CR43","doi-asserted-by":"crossref","unstructured":"Fan, X., Wang, Q., Ke, J., Yang, F., Gong, B., Zhou, M.: Adversarially adaptive normalization for single domain generalization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 8208\u20138217 (2021)","DOI":"10.1109\/CVPR46437.2021.00811"},{"key":"1540_CR44","unstructured":"Volpi, R., Namkoong, H., Sener, O., Duchi, J.C., Murino, V., Savarese, S.: Generalizing to unseen domains via adversarial data augmentation. In: Advances in neural information processing systems, vol. 31 (2018)"},{"key":"1540_CR45","doi-asserted-by":"crossref","unstructured":"Qiao, F., Peng, X.: Uncertainty-guided model generalization to unseen domains. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 6790\u20136800 (2021)","DOI":"10.1109\/CVPR46437.2021.00672"},{"issue":"11","key":"1540_CR46","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"1540_CR47","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, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"12","key":"1540_CR48","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1540_CR49","doi-asserted-by":"crossref","unstructured":"Hazirbas, C., Ma, L., Domokos, C., Cremers, D.: FuseNet: Incorporating depth into semantic segmentation via fusion-based cnn architecture. In: Computer Vision\u2013ACCV 2016: 13th Asian conference on computer vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part I 13, pp. 213\u2013228 (2017)","DOI":"10.1007\/978-3-319-54181-5_14"},{"key":"1540_CR50","doi-asserted-by":"crossref","unstructured":"Deng, F., Feng, H., Liang, M., Wang, H., Yang, Y., Gao, Y., Chen, J., Hu, J., Guo, X., Lam, T.L.: FEANet: Feature-enhanced attention network for rgb-thermal real-time semantic segmentation. In: 2021 IEEE\/RSJ international conference on intelligent robots and systems (IROS), pp. 4467\u20134473 (2021)","DOI":"10.1109\/IROS51168.2021.9636084"},{"key":"1540_CR51","doi-asserted-by":"publisher","first-page":"7790","DOI":"10.1109\/TIP.2021.3109518","volume":"30","author":"W Zhou","year":"2021","unstructured":"Zhou, W., Liu, J., Lei, J., Yu, L., Hwang, J.-N.: GMNet: graded-feature multilabel-learning network for RGB-thermal urban scene semantic segmentation. IEEE Trans. Image Process. 30, 7790\u20137802 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"1540_CR52","doi-asserted-by":"publisher","first-page":"1329","DOI":"10.1109\/TIP.2023.3242775","volume":"32","author":"W Zhou","year":"2023","unstructured":"Zhou, W., Zhu, Y., Lei, J., Yang, R., Yu, L.: LSNet: lightweight spatial boosting network for detecting salient objects in RGB-thermal images. IEEE Trans. Image Process. 32, 1329\u20131340 (2023)","journal-title":"IEEE Trans. Image Process."}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-024-01540-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-024-01540-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-024-01540-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,17]],"date-time":"2024-11-17T07:38:20Z","timestamp":1731829100000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-024-01540-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,29]]},"references-count":52,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["1540"],"URL":"https:\/\/doi.org\/10.1007\/s00138-024-01540-4","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"type":"print","value":"0932-8092"},{"type":"electronic","value":"1432-1769"}],"subject":[],"published":{"date-parts":[[2024,4,29]]},"assertion":[{"value":"28 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2024","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 declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"59"}}