{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T11:29:29Z","timestamp":1763810969129,"version":"3.37.3"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T00:00:00Z","timestamp":1667433600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T00:00:00Z","timestamp":1667433600000},"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":["61773330"],"award-info":[{"award-number":["61773330"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s10489-022-04107-w","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T06:04:38Z","timestamp":1667455478000},"page":"14775-14791","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Adversarial learning based intermediate feature refinement for semantic segmentation"],"prefix":"10.1007","volume":"53","author":[{"given":"Dongli","family":"Wang","sequence":"first","affiliation":[]},{"given":"Zhitian","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Wanli","family":"Ouyang","sequence":"additional","affiliation":[]},{"given":"Baopu","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2372-4947","authenticated-orcid":false,"given":"Yan","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"issue":"4","key":"4107_CR1","first-page":"1","volume":"51","author":"X Dai","year":"2021","unstructured":"Dai X, Yuan X, Wei X (2021) Tirnet: object detection in thermal infrared images for autonomous driving. Appl Intell 51(4):1\u201318","journal-title":"Appl Intell"},{"key":"4107_CR2","doi-asserted-by":"crossref","unstructured":"Wang K, Liu M (2021) Yolov3-mt: a yolov3 using multi-target tracking for vehicle visual detection. Applied Intelligence (3)","DOI":"10.1007\/s10489-021-02491-3"},{"key":"4107_CR3","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"},{"key":"4107_CR4","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, pp 801\u2013818","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"4107_CR5","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"1","key":"4107_CR6","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1109\/TITS.2017.2750080","volume":"19","author":"E Romera","year":"2017","unstructured":"Romera E, Alvarez JM, Bergasa LM, Arroyo R (2017) Erfnet: efficient residual factorized convnet for real-time semantic segmentation. IEEE Trans Intell Transp Syst 19(1):263\u2013272","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"4107_CR7","first-page":"1135","volume":"28","author":"S Han","year":"2015","unstructured":"Han S, Pool J, Tran J, Dally W (2015) Learning both weights and connections for efficient neural network. Adv Neur Inform Process Syst 28:1135\u20131143","journal-title":"Adv Neur Inform Process Syst"},{"key":"4107_CR8","first-page":"3123","volume":"28","author":"M Courbariaux","year":"2015","unstructured":"Courbariaux M, Bengio Y, David J-P (2015) Binaryconnect: training deep neural networks with binary weights during propagations. Adv Neur Inform Process Syst 28:3123\u20133131","journal-title":"Adv Neur Inform Process Syst"},{"key":"4107_CR9","doi-asserted-by":"crossref","unstructured":"Rastegari M, Ordonez V, Redmon J, Farhadi A (2016) Xnor-net: imagenet classification using binary convolutional neural networks. In: European conference on computer vision, pp 525\u2013542. Springer","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"4107_CR10","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. arXiv:1503.02531"},{"key":"4107_CR11","unstructured":"Romero A, Ballas N, Kahou SE, Chassang A, Gatta C, Bengio Y (2014) Fitnets: hints for thin deep nets. arXiv:1412.6550"},{"key":"4107_CR12","unstructured":"Zagoruyko S, Komodakis N (2016) Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. arXiv:1612.03928"},{"key":"4107_CR13","doi-asserted-by":"crossref","unstructured":"Michieli U, Zanuttigh P (2019) Incremental learning techniques for semantic segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision workshops, pp 0\u20130","DOI":"10.1109\/ICCVW.2019.00400"},{"key":"4107_CR14","unstructured":"Huang Z, Hao W, Wang X, Tao M, Huang J, Liu W, Hua X-S (2021) Half-real half-fake distillation for class-incremental semantic segmentation. arXiv:2104.00875"},{"issue":"1","key":"4107_CR15","first-page":"226","volume":"17","author":"\u00c7 G\u00fcl\u00e7ehre","year":"2016","unstructured":"G\u00fcl\u00e7ehre \u00c7, Bengio Y (2016) Knowledge matters: importance of prior information for optimization. J Mach Learn Res 17(1):226\u2013257","journal-title":"J Mach Learn Res"},{"key":"4107_CR16","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"4107_CR17","unstructured":"Lee C-Y, Xie S, Gallagher P, Zhang Z, Tu Z (2015) Deeply-supervised nets. In: Artificial intelligence and statistics, pp 562\u2013570. PMLR"},{"key":"4107_CR18","unstructured":"Liu Y, Shu C, Wang J, Shen C (2020) Structured knowledge distillation for dense prediction. IEEE Trans Pattern Anal Mach Intell, 1\u20131"},{"key":"4107_CR19","unstructured":"Xie J, Shuai B, Hu J-F, Lin J, Zheng W-S (2018) Improving fast segmentation with teacher-student learning. arXiv:1810.08476"},{"key":"4107_CR20","first-page":"2672","volume":"27","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neur Inform Process Syst 27:2672\u20132680","journal-title":"Adv Neur Inform Process Syst"},{"issue":"1","key":"4107_CR21","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1007\/s10489-021-02427-x","volume":"52","author":"Y Wang","year":"2022","unstructured":"Wang Y, Ye H, Cao F (2022) A novel multi-discriminator deep network for image segmentation. Appl Intell 52(1):1092\u20131109","journal-title":"Appl Intell"},{"key":"4107_CR22","doi-asserted-by":"crossref","unstructured":"Shen K, Quan H, Han J, Wu M (2022) Uro-gan: an untrustworthy region optimization approach for adipose tissue segmentation based on adversarial learning. Appl Intell, 1\u201323","DOI":"10.1007\/s10489-021-02976-1"},{"issue":"7","key":"4107_CR23","doi-asserted-by":"publisher","first-page":"5146","DOI":"10.1007\/s10489-020-01966-z","volume":"51","author":"H Tong","year":"2021","unstructured":"Tong H, Fang Z, Wei Z, Cai Q, Gao Y (2021) Sat-net: a side attention network for retinal image segmentation. Appl Intell 51(7):5146\u20135156","journal-title":"Appl Intell"},{"key":"4107_CR24","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":"4107_CR25","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"4107_CR26","unstructured":"Yuan Y, Wang J (2018) Ocnet: object context network for scene parsing. arXiv:1809.00916"},{"key":"4107_CR27","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, So Kweon I (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision, pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"4107_CR28","unstructured":"Tao A, Sapra K, Catanzaro B (2020) Hierarchical multi-scale attention for semantic segmentation. arXiv:2005.10821"},{"key":"4107_CR29","unstructured":"Paszke A, Chaurasia A, Kim S, Culurciello E (2016) Enet: a deep neural network architecture for real-time semantic segmentation. arXiv:1606.02147"},{"issue":"12","key":"4107_CR30","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 (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4107_CR31","doi-asserted-by":"crossref","unstructured":"Yu C, Wang J, Peng C, Gao C, Yu G, Sang N (2018) Bisenet: bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European conference on computer vision, pp 325\u2013341","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"4107_CR32","doi-asserted-by":"crossref","unstructured":"Mehta S, Rastegari M, Caspi A, Shapiro L, Hajishirzi H (2018) Espnet: efficient spatial pyramid of dilated convolutions for semantic segmentation. In: Proceedings of the European conference on computer vision, pp 552\u2013568","DOI":"10.1007\/978-3-030-01249-6_34"},{"key":"4107_CR33","doi-asserted-by":"crossref","unstructured":"Michieli U, Zanuttigh P (2021) Continual semantic segmentation via repulsion-attraction of sparse and disentangled latent representations. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1114\u20131124","DOI":"10.1109\/CVPR46437.2021.00117"},{"key":"4107_CR34","doi-asserted-by":"crossref","unstructured":"He T, Shen C, Tian Z, Gong D, Sun C, Yan Y (2019) Knowledge adaptation for efficient semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 578\u2013587","DOI":"10.1109\/CVPR.2019.00067"},{"key":"4107_CR35","unstructured":"Shu C, Liu Y, Gao J, Xu L, Shen C (2020) Channel-wise distillation for semantic segmentation. arXiv e-prints 2011"},{"key":"4107_CR36","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhou W, Jiang T, Bai X, Xu Y (2020) Intra-class feature variation distillation for semantic segmentation. In: European Conference on computer vision, pp 346\u2013362. Springer","DOI":"10.1007\/978-3-030-58571-6_21"},{"key":"4107_CR37","doi-asserted-by":"crossref","unstructured":"Wang H, Qin Z, Wan T (2018) Text generation based on generative adversarial nets with latent variables. In: Pacific-Asia conference on knowledge discovery and data mining, pp 92\u2013103. Springer","DOI":"10.1007\/978-3-319-93037-4_8"},{"key":"4107_CR38","unstructured":"Mirza M, Osindero S (2014) Conditional generative adversarial nets. Computer Science, 2672\u20132680"},{"key":"4107_CR39","doi-asserted-by":"crossref","unstructured":"Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: European conference on computer vision, pp 694\u2013711. Springer","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"4107_CR40","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.neucom.2018.05.045","volume":"311","author":"Y Liu","year":"2018","unstructured":"Liu Y, Qin Z, Wan T, Luo Z (2018) Auto-painter: cartoon image generation from sketch by using conditional wasserstein generative adversarial networks. Neurocomputing 311:78\u201387","journal-title":"Neurocomputing"},{"key":"4107_CR41","unstructured":"Luc P, Couprie C, Chintala S, Verbeek J (2016) Semantic segmentation using adversarial networks. arXiv:1611.08408"},{"key":"4107_CR42","unstructured":"Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017) Improved training of wasserstein gans. In: Advances in neural information processing systems, pp 5767\u20135777"},{"key":"4107_CR43","unstructured":"Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434"},{"key":"4107_CR44","unstructured":"Zhang H, Goodfellow I, Metaxas D, Odena A (2019) Self-attention generative adversarial networks. In: International conference on machine learning, pp 7354\u20137363. PMLR"},{"key":"4107_CR45","doi-asserted-by":"crossref","unstructured":"Huang Z, Wang X, Huang L, Huang C, Wei Y, Liu W (2019) Ccnet: criss-cross attention for semantic segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 603\u2013612","DOI":"10.1109\/ICCV.2019.00069"},{"key":"4107_CR46","unstructured":"Shen Z, Zhang M, Zhao H, Yi S, Li H (2021) Efficient attention: attention with linear complexities. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 3531\u20133539"},{"key":"4107_CR47","doi-asserted-by":"crossref","unstructured":"Cao Y, Xu J, Lin S, Wei F, Hu H (2019) Gcnet: non-local networks meet squeeze-excitation networks and beyond. In: 2019 IEEE\/CVF International conference on computer vision workshop (ICCVW), pp 0\u20130","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"4107_CR48","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":"2","key":"4107_CR49","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88(2):303\u2013338","journal-title":"Int J Comput Vis"},{"key":"4107_CR50","doi-asserted-by":"crossref","unstructured":"Hariharan B, Arbel\u00e1ez P, Bourdev L, Maji S, Malik J (2011) Semantic contours from inverse detectors. In: 2011 International conference on computer vision, pp 991\u2013998. IEEE","DOI":"10.1109\/ICCV.2011.6126343"},{"key":"4107_CR51","doi-asserted-by":"crossref","unstructured":"Brostow GJ, Shotton J, Fauqueur J, Cipolla R (2008) Segmentation and recognition using structure from motion point clouds. In: European conference on computer vision, pp 44\u201357. Springer","DOI":"10.1007\/978-3-540-88682-2_5"},{"key":"4107_CR52","unstructured":"Tan M, Le QV (2019) Efficientnet: rethinking model scaling for convolutional neural networks. arXiv:1905.11946"},{"key":"4107_CR53","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 9"},{"key":"4107_CR54","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Inverted residuals and linear bottlenecks: mobile networks for classification detection and segmentation","DOI":"10.1109\/CVPR.2018.00474"},{"key":"4107_CR55","doi-asserted-by":"crossref","unstructured":"Zhu Z, Xu M, Bai S, Huang T, Bai X (2019) Asymmetric non-local neural networks for semantic segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 593\u2013602","DOI":"10.1109\/ICCV.2019.00068"},{"key":"4107_CR56","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 conference on computer vision and pattern recognition, pp 3146\u20133154","DOI":"10.1109\/CVPR.2019.00326"},{"key":"4107_CR57","unstructured":"Chen L-C, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04107-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-04107-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04107-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T03:45:45Z","timestamp":1685591145000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-04107-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,3]]},"references-count":57,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["4107"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-04107-w","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2022,11,3]]},"assertion":[{"value":"23 August 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 2022","order":2,"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 there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}