{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:14:05Z","timestamp":1775326445238,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T00:00:00Z","timestamp":1720137600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T00:00:00Z","timestamp":1720137600000},"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":["Multimedia Systems"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s00530-024-01397-7","type":"journal-article","created":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T17:01:41Z","timestamp":1720198901000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Context-aware adaptive network for UDA semantic segmentation"],"prefix":"10.1007","volume":"30","author":[{"given":"Yu","family":"Yuan","sequence":"first","affiliation":[]},{"given":"Jinlong","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Shu","sequence":"additional","affiliation":[]},{"given":"Qiang","family":"Qian","sequence":"additional","affiliation":[]},{"given":"Yunna","family":"Song","sequence":"additional","affiliation":[]},{"given":"Zhen","family":"Ou","sequence":"additional","affiliation":[]},{"given":"Dan","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Zuo","sequence":"additional","affiliation":[]},{"given":"YueCheng","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Yunhan","family":"Sun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,5]]},"reference":[{"key":"1397_CR1","doi-asserted-by":"publisher","first-page":"3822","DOI":"10.1109\/TCSVT.2023.3243402","volume":"33","author":"Y Cao","year":"2023","unstructured":"Cao, Y., Zhang, H., Lu, X., Chen, Y., Xiao, Z., Wang, Y.: Adaptive refining-aggregation-separation framework for unsupervised domain adaptation semantic segmentation. IEEE Trans. Circuits Syst. Video Technol. 33, 3822\u20133832 (2023)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1397_CR2","doi-asserted-by":"publisher","first-page":"12707","DOI":"10.1109\/TPAMI.2023.3289308","volume":"45","author":"C-H Chao","year":"2023","unstructured":"Chao, C.-H., Cheng, B.-W., Wang, T.-W., Liao, H.-R., Lee, C.-Y.: Rainbow uda: combining domain adaptive models for semantic segmentation tasks. IEEE Trans. Pattern Anal. Mach. Intell. 45, 12707\u201312713 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"1397_CR3","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, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4), 834\u2013848 (2017)","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1397_CR4","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder\u2013decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"1397_CR5","first-page":"15105","volume":"35","author":"L Chen","year":"2022","unstructured":"Chen, L., Wei, Z., Jin, X., Chen, H., Zheng, M., Chen, K., Jin, Y.: Deliberated domain bridging for domain adaptive semantic segmentation. Adv. Neural Inf. Process. Syst. 35, 15105\u201315118 (2022)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1397_CR6","doi-asserted-by":"publisher","first-page":"9339","DOI":"10.1109\/TPAMI.2023.3248294","volume":"45","author":"Y Cheng","year":"2023","unstructured":"Cheng, Y., Wei, F., Bao, J., Chen, D., Zhang, W.: Adpl: adaptive dual path learning for domain adaptation of semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 45, 9339\u20139356 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1397_CR7","doi-asserted-by":"crossref","unstructured":"Colomer, M.B., Dovesi, P.L., Panagiotakopoulos, T., Carvalho, J.F., H\u00e4renstam-Nielsen, L., Azizpour, H., Kjellstr\u00f6m, H., Cremers, D., Poggi, M.: To adapt or not to adapt? Real-time adaptation for semantic segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision (CVPR), pp. 16548\u201316559 (2023)","DOI":"10.1109\/ICCV51070.2023.01517"},{"key":"1397_CR8","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 (CVPR), pp. 3213\u20133223 (2016)","DOI":"10.1109\/CVPR.2016.350"},{"issue":"1","key":"1397_CR9","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","volume":"35","author":"A Creswell","year":"2018","unstructured":"Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: an overview. IEEE Signal Process. Mag. 35(1), 53\u201365 (2018)","journal-title":"IEEE Signal Process. Mag."},{"key":"1397_CR10","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1397_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106399","volume":"177","author":"W Dong","year":"2024","unstructured":"Dong, W., Liang, Z., Wang, L., Tian, G., Long, Q.: Unsupervised domain adaptive segmentation algorithm based on two-level category alignment. Neural Netw. 177, 106399 (2024)","journal-title":"Neural Netw."},{"key":"1397_CR12","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"1397_CR13","doi-asserted-by":"crossref","unstructured":"Gao, L., Zhang, J., Zhang, L., Tao, D.: Dsp: dual soft-paste for unsupervised domain adaptive semantic segmentation. In: Proceedings of the 29th ACM International Conference on Multimedia (ACM MM), pp. 2825\u20132833 (2021)","DOI":"10.1145\/3474085.3475186"},{"issue":"2","key":"1397_CR14","doi-asserted-by":"publisher","first-page":"621","DOI":"10.3390\/s23020621","volume":"23","author":"JL G\u00f3mez","year":"2023","unstructured":"G\u00f3mez, J.L., Villalonga, G., L\u00f3pez, A.M.: Co-training for unsupervised domain adaptation of semantic segmentation models. Sensors 23(2), 621 (2023)","journal-title":"Sensors"},{"key":"1397_CR15","doi-asserted-by":"crossref","unstructured":"He, J., Deng, Z., Qiao, Y.: Dynamic multi-scale filters for semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision(CVPR), pp. 3562\u20133572 (2019)","DOI":"10.1109\/ICCV.2019.00366"},{"key":"1397_CR16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1397_CR17","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhang, L., Cheng, M.-M., Feng, J.: Strip pooling: Rethinking spatial pooling for scene parsing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4003\u20134012 (2020)","DOI":"10.1109\/CVPR42600.2020.00406"},{"key":"1397_CR18","doi-asserted-by":"crossref","unstructured":"Hoyer, L., Dai, D., Van Gool, L.: Daformer: improving network architectures and training strategies for domain-adaptive semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022).","DOI":"10.1109\/CVPR52688.2022.00969"},{"key":"1397_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109299","volume":"137","author":"S Hu","year":"2023","unstructured":"Hu, S., Bonardi, F., Bouchafa, S., Sidib\u00e9, D.: Multi-modal unsupervised domain adaptation for semantic image segmentation. Pattern Recognit. 137, 109299 (2023)","journal-title":"Pattern Recognit."},{"key":"1397_CR20","doi-asserted-by":"publisher","unstructured":"Huo, X., Xie, L., Hu, H. et al. Domain-Agnostic Priors for Semantic Segmentation Under Unsupervised Domain Adaptation and Domain Generalization. Int J Comput Vis (2024). https:\/\/doi.org\/10.1007\/s11263-024-02041-7","DOI":"10.1007\/s11263-024-02041-7"},{"key":"1397_CR21","doi-asserted-by":"publisher","unstructured":"Li, J., Wang, Z., Gao, Y., Hu, X.: Exploring high-quality target domain information for unsupervised domain adaptive semantic segmentation. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 5237\u20135245 (2022). https:\/\/doi.org\/10.1145\/3503161.354811","DOI":"10.1145\/3503161.354811"},{"key":"1397_CR22","doi-asserted-by":"crossref","unstructured":"Liu, P., Ge, Y., Duan, L., Li, W., Luo, H., Lv, F.: Transferring multi-modal domain knowledge to uni-modal domain for urban scene segmentation. IEEE Trans. Intell. Transp. Syst. 1\u201314 (2024)","DOI":"10.1109\/TITS.2024.3382880"},{"key":"1397_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2021.108418","volume":"193","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Duan, Y., Zeng, T.: Learning multi-level structural information for small organ segmentation. Signal Process. 193, 108418 (2022)","journal-title":"Signal Process."},{"key":"1397_CR24","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (CVPR), pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1397_CR25","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"1397_CR26","first-page":"9","volume":"31","author":"M Long","year":"2018","unstructured":"Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. Adv. Neural Inf. Process. Syst. 31, 9 (2018)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1397_CR27","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"issue":"1","key":"1397_CR28","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/s11263-023-01863-1","volume":"132","author":"X Luo","year":"2024","unstructured":"Luo, X., Chen, W., Liang, Z., Yang, L., Wang, S., Li, C.: Crots: cross-domain teacher-student learning for source-free domain adaptive semantic segmentation. Int. J. Comput. Vis. 132(1), 20\u201339 (2024)","journal-title":"Int. J. Comput. Vis."},{"issue":"8","key":"1397_CR29","first-page":"3940","volume":"44","author":"Y Luo","year":"2021","unstructured":"Luo, Y., Liu, P., Zheng, L., Guan, T., Junqing, Yu., Yang, Y.: Category-level adversarial adaptation for semantic segmentation using purified features. IEEE Trans. Pattern Anal. Mach. Intell. 44(8), 3940\u20133956 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1397_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.127641","volume":"586","author":"D Ren","year":"2024","unstructured":"Ren, D., Wang, S., Zhang, Z., Yang, W., Ren, M., Zhang, H.: Unsupervised cross domain semantic segmentation with mutual refinement and information distillation. Neurocomputing 586, 127641 (2024)","journal-title":"Neurocomputing"},{"key":"1397_CR31","doi-asserted-by":"crossref","unstructured":"Richter, S.R., Vineet, V., Roth, S., Koltun, V.: Playing for data: Ground truth from computer games. In: Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part II 14, pp. 102\u2013118. Springer (2016)","DOI":"10.1007\/978-3-319-46475-6_7"},{"key":"1397_CR32","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: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1397_CR33","doi-asserted-by":"crossref","unstructured":"Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.M.: The synthia dataset: a large collection of synthetic images for semantic segmentation of urban scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3234\u20133243 (2016)","DOI":"10.1109\/CVPR.2016.352"},{"key":"1397_CR34","doi-asserted-by":"crossref","unstructured":"Shi, W., Xu, J., Gao, P.: Ssformer: a lightweight transformer for semantic segmentation. In: 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP), pp. 1\u20135. IEEE (2022)","DOI":"10.1109\/MMSP55362.2022.9949177"},{"key":"1397_CR35","first-page":"17194","volume":"34","author":"K Tanwisuth","year":"2021","unstructured":"Tanwisuth, K., Fan, X., Zheng, H., Zhang, S., Zhang, H., Chen, B., Zhou, M.: A prototype-oriented framework for unsupervised domain adaptation. Adv. Neural Inf. Process. Syst. 34, 17194\u201317208 (2021)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1397_CR36","doi-asserted-by":"crossref","unstructured":"Tranheden, W., Olsson, V., Pinto, J., Svensson, L.: Dacs: domain adaptation via cross-domain mixed sampling. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (CVPR), pp. 1379\u20131389 (2021)","DOI":"10.1109\/WACV48630.2021.00142"},{"issue":"11","key":"1397_CR37","first-page":"2579","volume":"9","author":"L der Maaten","year":"2008","unstructured":"der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(11), 2579-2605 (2008)","journal-title":"J. Mach. Learn. Res"},{"key":"1397_CR38","unstructured":"Vayyat, M., Kasi, J., Bhattacharya, A., Ahmed, S., Tallamraju, R.: Cluda: contrastive learning in unsupervised domain adaptation for semantic segmentation. arXiv preprint arXiv:2208.14227 (2022)"},{"key":"1397_CR39","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11534\u201311542 (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"1397_CR40","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11534\u201311542 (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"1397_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2024.104055","volume":"98","author":"Y Wang","year":"2024","unstructured":"Wang, Y., Zhou, Y., Hao, W., Liu, X., Zhai, X., Sun, K., Tian, C., Zhao, H., Li, T., Jia, W., et al.: Mfcanet: a road scene segmentation network based on multi-scale feature fusion and context information aggregation. J. Vis. Commun. Image Represent. 98, 104055 (2024)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"1397_CR42","doi-asserted-by":"crossref","unstructured":"Wang, Y., Liang, J., Xiao, J., Mei, S., Yang, Y., Zhang, Z.: Informative data mining for one-shot cross-domain semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (CVPR), pp. 1064\u20131074 (2023)","DOI":"10.1109\/ICCV51070.2023.00104"},{"key":"1397_CR43","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: simple and efficient design for semantic segmentation with transformers. Adv. Neural Inf. Process. Syst. 34, 12077\u201312090 (2021)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1397_CR44","doi-asserted-by":"crossref","unstructured":"Yang, L., Hoyer, L., Weber, M., Fischer, T., Dai, D., Leal-Taix\u00e9, L., Cremers, D., Pollefeys, M., Van Gool, L.: Micdrop: masking image and depth features via complementary dropout for domain-adaptive semantic segmentation. In: Synthetic Data for Computer Vision Workshop@ CVPR (2024)","DOI":"10.1007\/978-3-031-72933-1_19"},{"key":"1397_CR45","doi-asserted-by":"crossref","unstructured":"Yin, Y., Hu, W., Liu, Z., Wang, G., Xiang, S., Zimmermann, R.: Crossmatch: source-free domain adaptive semantic segmentation via cross-modal consistency training. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (CVPR), pp. 21786\u201321796 (2023)","DOI":"10.1109\/ICCV51070.2023.01991"},{"key":"1397_CR46","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (CVPR), pp. 6023\u20136032 (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"issue":"5","key":"1397_CR47","doi-asserted-by":"publisher","first-page":"2633","DOI":"10.1007\/s00530-023-01131-9","volume":"29","author":"J-W Zhang","year":"2023","unstructured":"Zhang, J.-W., Sun, Y., Chen, W.: Pull and concentrate: improving unsupervised semantic segmentation adaptation with cross-and intra-domain consistencies. Multimed. Syst. 29(5), 2633\u20132650 (2023)","journal-title":"Multimed. Syst."},{"key":"1397_CR48","doi-asserted-by":"crossref","unstructured":"Zhao, G., Cui, W., You, S., Jay K.C.-C.: Semst: semantically consistent multi-scale image translation via structure-texture alignment. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 7209\u20137219 (2024)","DOI":"10.1109\/WACV57701.2024.00704"},{"key":"1397_CR49","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"1397_CR50","doi-asserted-by":"publisher","unstructured":"Zhao, S., Wang, Y., Wu, X. et al. MAFNet: dual-branch fusion network with multiscale atrous pyramid pooling aggregate contextual features for real-time semantic segmentation. Complex Intell. Syst. (2024). https:\/\/doi.org\/10.1007\/s40747-024-01428-w","DOI":"10.1007\/s40747-024-01428-w"},{"key":"1397_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2022.103448","volume":"221","author":"Q Zhou","year":"2022","unstructured":"Zhou, Q., Feng, Z., Qiqi, G., Cheng, G., Xuequan, L., Shi, J., Ma, L.: Uncertainty-aware consistency regularization for cross-domain semantic segmentation. Comput. Vis. Image Underst. 221, 103448 (2022)","journal-title":"Comput. Vis. Image Underst."},{"issue":"2","key":"1397_CR52","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1007\/s00371-023-02846-6","volume":"40","author":"T Zhu","year":"2024","unstructured":"Zhu, T., Zhu, S., Zheng, T., Ding, H., Song, W., Li, C.: Heu-net: hybrid attention residual block-based network with external skip connections for metal corrosion semantic segmentation. Vis. Comput. 40(2), 1273\u20131287 (2024)","journal-title":"Vis. Comput."},{"key":"1397_CR53","doi-asserted-by":"crossref","unstructured":"Zou, Y., Yu, Z., Liu, X., Kumar, B.V.K., Wang, J.: Confidence regularized self-training. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 5982\u20135991 (2019)","DOI":"10.1109\/ICCV.2019.00608"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01397-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-024-01397-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01397-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T14:51:23Z","timestamp":1732373483000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-024-01397-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,5]]},"references-count":53,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["1397"],"URL":"https:\/\/doi.org\/10.1007\/s00530-024-01397-7","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,5]]},"assertion":[{"value":"15 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2024","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 no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"198"}}