{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T20:55:43Z","timestamp":1775249743666,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T00:00:00Z","timestamp":1735776000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T00:00:00Z","timestamp":1735776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s00521-024-10891-y","type":"journal-article","created":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T00:26:20Z","timestamp":1735863980000},"page":"5481-5497","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An efficient and scalable semi-supervised framework for semantic segmentation"],"prefix":"10.1007","volume":"37","author":[{"given":"Huazheng","family":"Hao","sequence":"first","affiliation":[]},{"given":"Hui","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Junjie","family":"Xiong","sequence":"additional","affiliation":[]},{"given":"Li","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Diqun","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Dongtai","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Jiayan","family":"Zhuang","sequence":"additional","affiliation":[]},{"given":"Chengbin","family":"Peng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,2]]},"reference":[{"key":"10891_CR1","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/s13735-017-0141-z","volume":"7","author":"Y Guo","year":"2018","unstructured":"Guo Y, Liu Y, Georgiou T, Lew MS (2018) A review of semantic segmentation using deep neural networks. Int J Multimed Inf Retr 7:87\u201393","journal-title":"Int J Multimed Inf Retr"},{"key":"10891_CR2","unstructured":"Pel\u00e1ez-Vegas A, Mesejo P, Luengo J (2023) A survey on semi-supervised semantic segmentation. arXiv preprint arXiv:2302.09899"},{"key":"10891_CR3","unstructured":"Berthelot D, Carlini N, Goodfellow I, Papernot N, Oliver A, Raffel CA (2019) Mixmatch: a holistic approach to semi-supervised learning. Adv Neural Inf Process Syst 32:5049\u20135059"},{"key":"10891_CR4","first-page":"596","volume":"33","author":"K Sohn","year":"2020","unstructured":"Sohn K, Berthelot D, Carlini N, Zhang Z, Zhang H, Raffel CA, Cubuk ED, Kurakin A, Li C-L (2020) Fixmatch: simplifying semi-supervised learning with consistency and confidence. Adv Neural Inf Process Syst 33:596\u2013608","journal-title":"Adv Neural Inf Process Syst"},{"key":"10891_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110166","volume":"260","author":"H Xu","year":"2023","unstructured":"Xu H, Xiao H, Hao H, Dong L, Qiu X, Peng C (2023) Semi-supervised learning with pseudo-negative labels for image classification. Knowl-Based Syst 260:110166","journal-title":"Knowl-Based Syst"},{"key":"10891_CR6","first-page":"1","volume":"53","author":"P Liu","year":"2022","unstructured":"Liu P, Qian W, Cao J, Xu D (2022) Semi-supervised medical image classification via increasing prediction diversity. Appl Intell 53:1\u201314","journal-title":"Appl Intell"},{"key":"10891_CR7","unstructured":"Lee D-H, et al (2013) Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on challenges in representation learning, ICML, vol 3. Atlanta, p 896"},{"key":"10891_CR8","doi-asserted-by":"crossref","unstructured":"Zhang Y, Xiang T, Hospedales TM, Lu H (2018) Deep mutual learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4320\u20134328","DOI":"10.1109\/CVPR.2018.00454"},{"key":"10891_CR9","unstructured":"French G, Aila T, Laine S, Mackiewicz M, Finlayson G (2019) Semi-supervised semantic segmentation needs strong, high-dimensional perturbations [Online]. Available: https:\/\/openreview.net\/forum?id=B1eBoJStwr"},{"key":"10891_CR10","doi-asserted-by":"crossref","unstructured":"Yun S, Han D, Oh SJ, Chun S, Choe J, Yoo Y (2019) Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6023\u20136032","DOI":"10.1109\/ICCV.2019.00612"},{"key":"10891_CR11","doi-asserted-by":"crossref","unstructured":"Olsson V, Tranheden W, Pinto J, Svensson L (2021) Classmix: segmentation-based data augmentation for semi-supervised learning. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 1369\u20131378","DOI":"10.1109\/WACV48630.2021.00141"},{"key":"10891_CR12","doi-asserted-by":"crossref","unstructured":"Ouali Y, Hudelot C, Tami M (2020) Semi-supervised semantic segmentation with cross-consistency training. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12674\u201312684","DOI":"10.1109\/CVPR42600.2020.01269"},{"key":"10891_CR13","doi-asserted-by":"crossref","unstructured":"Chen X, Yuan Y, Zeng G, Wang J (2021) Semi-supervised semantic segmentation with cross pseudo supervision. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2613\u20132622","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"10891_CR14","doi-asserted-by":"crossref","unstructured":"Ke Z, Qiu D, Li K, Yan Q, Lau RW (2020) Guided collaborative training for pixel-wise semi-supervised learning. In: European conference on computer vision. Springer, pp 429\u2013445","DOI":"10.1007\/978-3-030-58601-0_26"},{"key":"10891_CR15","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.neucom.2022.08.052","volume":"508","author":"H Xiao","year":"2022","unstructured":"Xiao H, Li D, Xu H, Fu S, Yan D, Song K, Peng C (2022) Semi-supervised semantic segmentation with cross teacher training. Neurocomputing 508:36\u201346","journal-title":"Neurocomputing"},{"key":"10891_CR16","doi-asserted-by":"crossref","unstructured":"Ke Z, Wang D, Yan Q, Ren J, Lau RW (2019) Dual student: Breaking the limits of the teacher in semi-supervised learning. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6728\u20136736","DOI":"10.1109\/ICCV.2019.00683"},{"key":"10891_CR17","unstructured":"Tarvainen A, Valpola H (2017) Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. Adv Neural Inf Process Syst 30:1195\u20131204"},{"key":"10891_CR18","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"10891_CR19","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10891_CR20","doi-asserted-by":"crossref","unstructured":"Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2881\u20132890","DOI":"10.1109\/CVPR.2017.660"},{"issue":"4","key":"10891_CR21","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2017","unstructured":"Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10891_CR22","doi-asserted-by":"crossref","unstructured":"Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp 801\u2013818","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"10891_CR23","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3146\u20133154","DOI":"10.1109\/CVPR.2019.00326"},{"issue":"15","key":"10891_CR24","doi-asserted-by":"publisher","first-page":"18167","DOI":"10.1007\/s10489-022-03401-x","volume":"52","author":"T Gao","year":"2022","unstructured":"Gao T, Wei W, Cai Z, Fan Z, Xie SQ, Wang X, Yu Q (2022) Ci-net: a joint depth estimation and semantic segmentation network using contextual information. Appl Intell 52(15):18167\u201386","journal-title":"Appl Intell"},{"issue":"10","key":"10891_CR25","doi-asserted-by":"publisher","first-page":"11918","DOI":"10.1007\/s10489-022-04085-z","volume":"53","author":"W Yuan","year":"2023","unstructured":"Yuan W, Lu X, Zhang R, Liu Y (2023) Cross-supervision-based equilibrated fusion mechanism of local and global attention for semantic segmentation. Appl Intell 53(10):11918\u201311933","journal-title":"Appl Intell"},{"key":"10891_CR26","doi-asserted-by":"crossref","unstructured":"Takikawa T, Acuna D, Jampani V, Fidler S (2019) Gated-scnn: gated shape cnns for semantic segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 5229\u20135238","DOI":"10.1109\/ICCV.2019.00533"},{"key":"10891_CR27","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"10891_CR28","doi-asserted-by":"crossref","unstructured":"Liu Y, Tian Y, Chen Y, Liu F, Belagiannis V, Carneiro G (2022) Perturbed and strict teachers for semi-supervised semantic segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4258\u20134267","DOI":"10.1109\/CVPR52688.2022.00422"},{"key":"10891_CR29","doi-asserted-by":"crossref","unstructured":"Feng Z, Zhou Q, Gu Q, Tan X, Cheng G, Lu X, Shi J, Ma L (2022) Dmt: dynamic mutual training for semi-supervised learning. Pattern Recognition 130:108777","DOI":"10.1016\/j.patcog.2022.108777"},{"key":"10891_CR30","doi-asserted-by":"crossref","unstructured":"Zhong Y, Yuan B, Wu H, Yuan Z, Peng J, Wang Y-X (2021) Pixel contrastive-consistent semi-supervised semantic segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 7273\u20137282","DOI":"10.1109\/ICCV48922.2021.00718"},{"key":"10891_CR31","doi-asserted-by":"crossref","unstructured":"Zhou Y, Xu H, Zhang W, Gao B, Heng P-A (2021) C3-semiseg: contrastive semi-supervised segmentation via cross-set learning and dynamic class-balancing. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 7036\u20137045","DOI":"10.1109\/ICCV48922.2021.00695"},{"issue":"1","key":"10891_CR32","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21\u201327","journal-title":"IEEE Trans Inf Theory"},{"key":"10891_CR33","unstructured":"Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. Adv Neural Inf Process Syst 30:4080\u20134090"},{"key":"10891_CR34","doi-asserted-by":"crossref","unstructured":"Jetley S, Romera-Paredes B, Jayasumana S, Torr P (2015) Prototypical priors: From improving classification to zero-shot learning. arXiv preprint arXiv:1512.01192","DOI":"10.5244\/C.29.120"},{"key":"10891_CR35","doi-asserted-by":"crossref","unstructured":"Wu Z, Xiong Y, Yu SX, Lin D (2018) Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3733\u20133742","DOI":"10.1109\/CVPR.2018.00393"},{"key":"10891_CR36","unstructured":"Mettes P, Pol E, Snoek C (2019) Hyperspherical prototype networks. Adv Neural Inf Process Syst 32:1487\u20131497"},{"key":"10891_CR37","doi-asserted-by":"crossref","unstructured":"Biehl M, Hammer B, Villmann T (2013) Distance measures for prototype based classification. In: International workshop on brain-inspired computing. Springer, pp 100\u2013116","DOI":"10.1007\/978-3-319-12084-3_9"},{"key":"10891_CR38","doi-asserted-by":"crossref","unstructured":"Jiang Z, Li Y, Yang C, Gao P, Wang Y, Tai Y, Wang C (2022) Prototypical contrast adaptation for domain adaptive semantic segmentation. In: European conference on computer vision. Springer, pp 36\u201354","DOI":"10.1007\/978-3-031-19830-4_3"},{"key":"10891_CR39","doi-asserted-by":"crossref","unstructured":"Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B (2016) The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3213\u20133223","DOI":"10.1109\/CVPR.2016.350"},{"issue":"1","key":"10891_CR40","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"M Everingham","year":"2015","unstructured":"Everingham M, Eslami S, Van Gool L, Williams CK, Winn J, Zisserman A (2015) The pascal visual object classes challenge: a retrospective. Int J Comput Vision 111(1):98\u2013136","journal-title":"Int J Comput Vision"},{"key":"10891_CR41","unstructured":"Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in PyTorch, NeurIPS-W"},{"key":"10891_CR42","doi-asserted-by":"crossref","unstructured":"Zheng Z, Yang Y (2019) Unsupervised scene adaptation with memory regularization in vivo. arXiv preprint arXiv:1912.11164","DOI":"10.24963\/ijcai.2020\/150"},{"key":"10891_CR43","doi-asserted-by":"crossref","unstructured":"Kwon D, Kwak S (2022) Semi-supervised semantic segmentation with error localization network. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9957\u20139967","DOI":"10.1109\/CVPR52688.2022.00972"},{"issue":"4","key":"10891_CR44","doi-asserted-by":"publisher","first-page":"1369","DOI":"10.1109\/TPAMI.2019.2960224","volume":"43","author":"S Mittal","year":"2019","unstructured":"Mittal S, Tatarchenko M, Brox T (2019) Semi-supervised semantic segmentation with high-and low-level consistency. IEEE Trans Pattern Anal Mach Intell 43(4):1369\u20131379","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10891_CR45","doi-asserted-by":"crossref","unstructured":"Mendel R, Souza LAd, Rauber D, Papa JP, Palm C (2020) Semi-supervised segmentation based on error-correcting supervision. In: European conference on computer vision. Springer, pp 141\u2013157","DOI":"10.1007\/978-3-030-58526-6_9"},{"key":"10891_CR46","doi-asserted-by":"crossref","unstructured":"Alonso I, Sabater A, Ferstl D, Montesano L, Murillo AC (2021) Semi-supervised semantic segmentation with pixel-level contrastive learning from a class-wise memory bank. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 8219\u20138228","DOI":"10.1109\/ICCV48922.2021.00811"},{"key":"10891_CR47","unstructured":"Hung W-C, Tsai Y-H, Liou Y-T, Lin Y-Y, Yang M-H (2018) Adversarial learning for semi-supervised semantic segmentation. arXiv preprint arXiv:1802.07934"},{"key":"10891_CR48","unstructured":"Zou Y, Zhang Z, Zhang H, Li C-L, Bian X, Huang J-B, Pfister T (2020) Pseudoseg: designing pseudo labels for semantic segmentation. arXiv preprint arXiv:2010.09713"},{"key":"10891_CR49","doi-asserted-by":"crossref","unstructured":"Zhang J, Wu T, Ding C, Zhao H, Guo G (2022) Region-level contrastive and consistency learning for semi-supervised semantic segmentation. arXiv preprint arXiv:2204.13314","DOI":"10.24963\/ijcai.2022\/226"},{"key":"10891_CR50","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/j.cag.2023.07.009","volume":"115","author":"H Huang","year":"2023","unstructured":"Huang H, Luo X, Xu S, Li Y (2023) Twin pseudo-training for semi-supervised semantic segmentation. Comput Graph 115:348\u2013358","journal-title":"Comput Graph"},{"key":"10891_CR51","doi-asserted-by":"crossref","unstructured":"Lai X, Tian Z, Jiang L, Liu S, Zhao H, Wang L, Jia J (2021) Semi-supervised semantic segmentation with directional context-aware consistency. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1205\u20131214","DOI":"10.1109\/CVPR46437.2021.00126"},{"key":"10891_CR52","doi-asserted-by":"crossref","unstructured":"Student: The probable error of a mean. Biometrika, 1\u201325 (1908)","DOI":"10.2307\/2331554"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10891-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10891-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10891-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T21:46:59Z","timestamp":1740779219000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10891-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,2]]},"references-count":52,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["10891"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10891-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,2]]},"assertion":[{"value":"11 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2025","order":3,"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 they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}