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Sernet-former: semantic segmentation by efficient residual network with attention-boosting gates and attention-fusion networks. arXiv preprint arXiv:2401.15741. 2024.","DOI":"10.1109\/CVMI61877.2024.10782648"},{"key":"ref2","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"14408","article-title":"Internimage: exploring large-scale vision foundation models with deformable convolutions","author":"Wang","year":"2023"},{"key":"ref3","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"2989","article-title":"Oneformer: one transformer to rule universal image segmentation","author":"Jain","year":"2023"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"30 073","DOI":"10.1007\/s11042-023-16782-z","article-title":"MFFLNet: lightweight semantic segmentation network based on multi-scale feature fusion","volume":"83","author":"Depeng","year":"2024","journal-title":"Multimed Tools Appl"},{"key":"ref5","series-title":"2020 International Symposium on Autonomous Systems (ISAS)","first-page":"249","article-title":"A real-time semantic segmentation algorithm based on improved lightweight network","author":"Liu","year":"2020"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"013 008","DOI":"10.1117\/1.JEI.33.1.013008","article-title":"Elanet: an efficiently lightweight asymmetrical network for real-time semantic segmentation","volume":"33","author":"Chen","year":"2024","journal-title":"J Electron Imaging"},{"key":"ref7","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"ref8","series-title":"2019 IEEE International Conference On Image Processing (ICIP)","first-page":"1860","article-title":"Lednet: a lightweight encoder-decoder network for real-time semantic segmentation","author":"Wang","year":"2019"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.1007\/s40747-023-01304-z","article-title":"An improved DeepLabv3+ lightweight network for remote-sensing image semantic segmentation","volume":"10","author":"Chen","year":"2024","journal-title":"Comp Intell Syst"},{"key":"ref10","first-page":"1","article-title":"Edge detection guide network for semantic segmentation of remote-sensing images","volume":"20","author":"Jin","year":"2023","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"ref11","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014."},{"key":"ref12","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference;","first-page":"234","article-title":"U-Net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015 Oct 5\u20139"},{"key":"ref13","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: a deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"Chen L-C, Papandreou G, Schroff F, Adam H. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587. 2017.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref17","series-title":"Proceedings of the European Conference on Computer Vision (ECCV)","first-page":"801","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","author":"Chen","year":"2018"},{"key":"ref18","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"7262","article-title":"Segmenter: transformer for semantic segmentation","author":"Strudel","year":"2021"},{"key":"ref19","first-page":"12 077","article-title":"Segformer: simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv Neural Inform Process Syst"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"2429","DOI":"10.3788\/OPE.20192711.2429","article-title":"Autonomous driving semantic segmentation with convolution neural networks","volume":"27","author":"Wang","year":"2019","journal-title":"Opt Precis Eng"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"2219","DOI":"10.1364\/AO.449589","article-title":"Maffnet: real-time multi-level attention feature fusion network with RGB-D semantic segmentation for autonomous driving","volume":"61","author":"Lv","year":"2022","journal-title":"Appl Opt"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"117420","DOI":"10.1016\/j.eswa.2022.117420","article-title":"DSRD-Net: dual-stream residual dense network for semantic segmentation of instruments in robot-assisted surgery","volume":"202","author":"Mahmood","year":"2022","journal-title":"Expert Syst Appl"},{"key":"ref23","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, et al. MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861. 2017."},{"key":"ref24","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"4510","article-title":"MobileNetV2: inverted residuals and linear bottlenecks","author":"Sandler","year":"2018"},{"key":"ref25","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"6848","article-title":"ShuffleNet: an extremely efficient convolutional neural network for mobile devices","author":"Zhang","year":"2018"},{"key":"ref26","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1251","article-title":"Xception: deep learning with depthwise separable convolutions","author":"Chollet","year":"2017"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"Gamal M, Siam M, Abdel-Razek M. ShuffleSeg: real-time semantic segmentation network. arXiv preprint arXiv:1803.03816. 2018.","DOI":"10.1109\/ICIP.2018.8451495"},{"key":"ref28","first-page":"1929","article-title":"A lightweight road scene semantic segmentation algorithm","volume":"77","author":"Jiansheng Peng","year":"2023","journal-title":"Comput Mater Contin"},{"key":"ref29","series-title":"Proceedings of the European Conference on Computer Vision (ECCV)","first-page":"325","article-title":"BiSeNet: bilateral segmentation network for real-time semantic segmentation","author":"Yu","year":"2018"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.1007\/s11263-021-01515-2","article-title":"BiSeNet V2: bilateral network with guided aggregation for real-time semantic segmentation","volume":"129","author":"Yu","year":"2021","journal-title":"Int J Comput Vis"},{"key":"ref31","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9716","article-title":"Rethinking bisenet for real-time semantic segmentation","author":"Fan","year":"2021"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/LSP.2021.3124186","article-title":"BiAttnNet: bilateral attention for improving real-time semantic segmentation","volume":"29","author":"Li","year":"2021","journal-title":"IEEE Signal Process Lett"},{"key":"ref33","unstructured":"Poudel RP, Liwicki S, Cipolla R. Fast-SCNN: fast semantic segmentation network. arXiv preprint arXiv:1902.04502. 2019."},{"key":"ref34","unstructured":"Hong Y, Pan H, Sun W, Jia Y. Deep dual-resolution networks for real-time and accurate semantic segmentation of road scenes. arXiv preprint arXiv:2101.06085. 2021."},{"key":"ref35","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"19 529","article-title":"Pidnet: a real-time semantic segmentation network inspired by pid controllers","author":"Xu","year":"2023"},{"key":"ref36","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2881","article-title":"Pyramid scene parsing network","author":"Zhao","year":"2017"},{"key":"ref37","series-title":"31st Conference on Neural Information Processing Systems (NIPS 2017)","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"ref38","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"3213","article-title":"The cityscapes dataset for semantic urban scene understanding","author":"Cordts","year":"2016"},{"key":"ref39","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1209","article-title":"COCO-Stuff: thing and stuff classes in context","author":"Caesar","year":"2018"},{"key":"ref40","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.patrec.2008.04.005","article-title":"Semantic object classes in video: a high-definition ground truth database","volume":"30","author":"Brostow","year":"2009","journal-title":"Pattern Recognit Lett"},{"key":"ref41","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9145","article-title":"Partial order pruning: for best speed\/accuracy trade-off in neural architecture search","author":"Li","year":"2019"},{"key":"ref42","series-title":"Computer Vision-ECCV 2020: 16th European Conference","first-page":"775","article-title":"Semantic flow for fast and accurate scene parsing","author":"Li","year":"2020 Aug 23\u201328"},{"key":"ref43","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"4675","article-title":"Rethinking dilated convolution for real-time semantic segmentation","author":"Gao","year":"2023"},{"key":"ref44","series-title":"2021 IEEE International Conference on Robotics and Automation (ICRA)","first-page":"13 517","article-title":"Cabinet: efficient context aggregation network for low-latency semantic segmentation","author":"Kumaar","year":"2021"},{"key":"ref45","series-title":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"4060","article-title":"Hyperseg: patch-wise hypernetwork for real-time semantic segmentation","author":"Nirkin","year":"2020"},{"key":"ref46","series-title":"Proceedings of the European Conference on Computer Vision (ECCV)","first-page":"405","article-title":"Icnet for real-time semantic segmentation on high-resolution images","author":"Zhao","year":"2018"},{"key":"ref47","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"8818","article-title":"Temporally distributed networks for fast video semantic segmentation","author":"Hu","year":"2020"},{"key":"ref48","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"6596","article-title":"Dense decoder shortcut connections for single-pass semantic segmentation","author":"Bilinski","year":"2018"},{"key":"ref49","series-title":"Proceedings of the IEEE conference on Computer Vision and Pattern Recognition","first-page":"8915","article-title":"Deep spatio-temporal random fields for efficient video segmentation","author":"Chandra","year":"2018"},{"key":"ref50","unstructured":"Pei M. Msfnet: multi-scale features network for monocular depth estimation. arXiv preprint arXiv:2107.06445. 2021."},{"key":"ref51","unstructured":"Paszke A, Chaurasia A, Kim S, Culurciello E. ENet: a deep neural network architecture for real-time semantic segmentation. arXiv preprint arXiv:1606.02147. 2016."},{"key":"ref52","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"1296","article-title":"Swiftnet: real-time video object segmentation","author":"Wang","year":"2021"},{"key":"ref53","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.neucom.2021.12.003","article-title":"RELAXNet: residual efficient learning and attention expected fusion network for real-time semantic segmentation","volume":"474","author":"Liu","year":"2022","journal-title":"Neurocomputing"}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-83-1\/TSP_CMC_60244\/TSP_CMC_60244.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T06:35:58Z","timestamp":1763102158000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v83n1\/60091"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.060244","relation":{},"ISSN":["1546-2226"],"issn-type":[{"type":"electronic","value":"1546-2226"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2024-10-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-30","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-03-26","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}