{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T06:39:08Z","timestamp":1776926348360,"version":"3.51.2"},"reference-count":36,"publisher":"Tech Science Press","issue":"3","license":[{"start":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T00:00:00Z","timestamp":1754179200000},"content-version":"vor","delay-in-days":214,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.064537","type":"journal-article","created":{"date-parts":[[2025,7,6]],"date-time":"2025-07-06T23:52:18Z","timestamp":1751845938000},"page":"5811-5829","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["CGMISeg: Context-Guided Multi-Scale Interactive for Efficient Semantic Segmentation"],"prefix":"10.32604","volume":"84","author":[{"given":"Ze","family":"Wang","sequence":"first","affiliation":[]},{"given":"Jin","family":"Qin","sequence":"additional","affiliation":[]},{"given":"Chuhua","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Yongjun","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Long","year":"2017","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","author":"Chen","year":"2018","journal-title":"Computer Vision\u2013ECCV 2018 (ECCV 2018)"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","article-title":"Deep high-resolution representation learning for visual recognition","volume":"43","author":"Wang","year":"2020","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref4","first-page":"17864","article-title":"Per-pixel classification is not all you need for semantic segmentation","volume":"34","author":"Cheng","year":"2021","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"106669","DOI":"10.1016\/j.engappai.2023.106669","article-title":"Semantic segmentation using vision transformers: a survey","volume":"126","author":"Thisanke","year":"2023","journal-title":"Eng Appl Artif Intell"},{"key":"ref6","series-title":"2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"19529","article-title":"PIDNet: a real-time semantic segmentation network inspired by PID controllers","author":"Xu","year":"2023"},{"key":"ref7","first-page":"2263","article-title":"FeedFormer: revisiting transformer decoder for efficient semantic segmentation","volume":"37","author":"Shim","year":"2023","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref8","series-title":"NIPS\u201922: 36th International Conference on Neural Information Processing Systems","first-page":"1140","article-title":"SegNeXt: rethinking convolutional attention design for semantic segmentation","author":"Guo","year":"2022 Nov 28\u2013Dec 9"},{"key":"ref9","unstructured":"Chen Liang-Chieh. Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587. 2017. doi:10.48550\/arXiv.1706.05587."},{"key":"ref10","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"6230","article-title":"Pyramid scene parsing network","author":"Zhao","year":"2017"},{"key":"ref11","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"13062","article-title":"Squeeze-and-attention networks for semantic segmentation","author":"Zhong","year":"2020"},{"key":"ref12","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"603","article-title":"CCNet: criss-cross attention for semantic segmentation","author":"Huang","year":"2019"},{"key":"ref13","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"6877","article-title":"Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers","author":"Zheng","year":"2021"},{"key":"ref14","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"1280","article-title":"Masked-attention mask transformer for universal image segmentation","author":"Cheng","year":"2022"},{"key":"ref15","series-title":"NIPS\u201922: 36th International Conference on Neural Information Processing Systems","first-page":"7423","article-title":"RTFormer: efficient design for real-time semantic segmentation with transformer","author":"Wang","year":"2022 Nov 28\u2013Dec 9"},{"key":"ref16","first-page":"7281","article-title":"HRFormer: high-resolution vision transformer for dense predict","volume":"34","author":"Yuan","year":"2021","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1007\/978-3-030-01261-8_20","author":"Yu","year":"2018","journal-title":"Computer Vision\u2013ECCV 2018 (ECCV 2018)"},{"key":"ref18","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":"ref19","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9711","article-title":"Rethinking bisenet for real-time semantic segmentation","author":"Fan","year":"2021"},{"key":"ref20","series-title":"Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9514","article-title":"DFANet: deep feature aggregation for real-time semantic segmentation","author":"Li","year":"2019"},{"key":"ref21","series-title":"The Eleventh International Conference on Learning Representations","article-title":"SeaFormer: squeeze-enhanced axial transformer for mobile semantic segmentation","author":"Wan","year":"2023 May 1\u20135"},{"key":"ref22","first-page":"12077","article-title":"SegFormer: simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref23","series-title":"Proceedings of the 40th International Conference on MachineLearning, ICML\u201923","first-page":"20787","article-title":"Clustseg: clustering for universal segmentation","author":"Liang","year":"2023 Jul 23\u201329"},{"key":"ref24","series-title":"Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision","first-page":"7283","article-title":"Exploring cross-image pixel contrast for semantic segmentation","author":"Wang","year":"2021"},{"key":"ref25","series-title":"Computer Vision-ECCV 2020: 16th European Conference","first-page":"173","article-title":"Object-contextual representations for semantic segmentation","author":"Yuan","year":"2020 Aug 23\u201328"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"7401","DOI":"10.1109\/TITS.2023.3348631","article-title":"Lightweight context-aware network using partial-channel transformation for real-time semantic segmentation","volume":"25","author":"Shi","year":"2024","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"ref27","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops","first-page":"1971","article-title":"Gcnet: non-local networks meet squeeze-excitation networks and beyond","author":"Cao","year":"2019"},{"key":"ref28","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"13713","article-title":"Coordinate attention for efficient mobile network design","author":"Hou","year":"2021"},{"key":"ref29","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"5122","article-title":"Scene parsing through ADE20K dataset","author":"Zhou","year":"2017"},{"key":"ref30","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":"ref31","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":"ref32","unstructured":"Contributors MMS. MMSegmentation: OpenMMLab semantic segmentation toolbox and Benchmark. 2020.\u201d 2023 [Internet] [cited 2025 May 18]. Available from: https:\/\/github.com\/open-mmlab\/mmsegmentation."},{"key":"ref33","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"16843","article-title":"Rethinking vision transformers for mobilenet size and speed","author":"Li","year":"2023"},{"key":"ref34","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"15804","article-title":"PEM: prototype-based efficient maskformer for image segmentation","author":"Cavagnero","year":"2024"},{"key":"ref35","unstructured":"Wu Y, Zhang S, Liu Y, Zhang L, Zhan X, Zhou D, et al. Low-resolution self-attention for semantic segmentation. arXiv:2310.05026. 2023. doi:10.48550\/arXiv.2310.05026."},{"key":"ref36","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"}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-84-3\/TSP_CMC_64537\/TSP_CMC_64537.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T05:45:34Z","timestamp":1776923134000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v84n3\/63137"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":36,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.064537","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2025-02-18","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-19","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-30","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}