{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T09:59:47Z","timestamp":1766138387037,"version":"3.37.3"},"reference-count":77,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T00:00:00Z","timestamp":1689724800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T00:00:00Z","timestamp":1689724800000},"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":["62132017"],"award-info":[{"award-number":["62132017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["226-2022-00235"],"award-info":[{"award-number":["226-2022-00235"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s00530-023-01131-9","type":"journal-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T16:02:02Z","timestamp":1689782522000},"page":"2633-2650","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Pull and concentrate: improving unsupervised semantic segmentation adaptation with cross- and intra-domain consistencies"],"prefix":"10.1007","volume":"29","author":[{"given":"Jian-Wei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,19]]},"reference":[{"key":"1131_CR1","unstructured":"Hoffman, J., Wang, D., Yu, F., Darrell, T.: FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation. arXiv:1612.02649 (2016)"},{"key":"1131_CR2","doi-asserted-by":"crossref","unstructured":"Tsai, Y.-H., Hung, W.-C., Schulter, S., Sohn, K., Yang, M.-H., Chandraker, M.: Learning to Adapt Structured Output Space for Semantic Segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7472\u20137481 (2018)","DOI":"10.1109\/CVPR.2018.00780"},{"key":"1131_CR3","unstructured":"Hoffman, J., Tzeng, E., Park, T., Zhu, J.-Y., Isola, P., Saenko, K., Efros, A., Darrell, T.: Cycada: Cycle-consistent adversarial domain adaptation. In: International Conference on Machine Learning, pp. 1989\u20131998 (2018). PMLR"},{"key":"1131_CR4","doi-asserted-by":"crossref","unstructured":"Wu, Z., Han, X., Lin, Y.-L., Uzunbas, M.G., Goldstein, T., Lim, S.N., Davis, L.S.: DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation. In: ECCV, pp. 518\u2013534 (2018)","DOI":"10.1007\/978-3-030-01228-1_32"},{"key":"1131_CR5","doi-asserted-by":"crossref","unstructured":"Luo, Y., Liu, P., Guan, T., Yu, J., Yang, Y.: Significance-Aware Information Bottleneck for Domain Adaptive Semantic Segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 6778\u20136787 (2019)","DOI":"10.1109\/ICCV.2019.00688"},{"key":"1131_CR6","doi-asserted-by":"crossref","unstructured":"Yang, Y., Soatto, S.: FDA: Fourier Domain Adaptation for Semantic Segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4085\u20134095 (2020)","DOI":"10.1109\/CVPR42600.2020.00414"},{"key":"1131_CR7","doi-asserted-by":"crossref","unstructured":"Zou, Y., Yu, Z., Vijaya\u00a0Kumar, B.V.K., Wang, J.: Unsupervised domain adaptation for semantic segmentation via class-balanced self-training. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01219-9_18"},{"key":"1131_CR8","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zheng, L., Guan, T., Yu, J., Yang, Y.: Taking a Closer Look at Domain Shift: Category-Level Adversaries for Semantics Consistent Domain Adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2507\u20132516 (2019)","DOI":"10.1109\/CVPR.2019.00261"},{"key":"1131_CR9","unstructured":"Zhang, Q., Zhang, J., Liu, W., Tao, D.: Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation. Advances in Neural Information Processing Systems 32 (2019)"},{"key":"1131_CR10","doi-asserted-by":"crossref","unstructured":"Mei, K., Zhu, C., Zou, J., Zhang, S.: Instance Adaptive Self-training for Unsupervised Domain Adaptation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV. Lecture Notes in Computer Science, pp. 415\u2013430. Springer International Publishing, Cham (2020)","DOI":"10.1007\/978-3-030-58574-7_25"},{"key":"1131_CR11","doi-asserted-by":"crossref","unstructured":"Wang, H., Shen, T., Zhang, W., Duan, L.-Y., Mei, T.: Classes Matter: A Fine-Grained Adversarial Approach to Cross-Domain Semantic Segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV. Lecture Notes in Computer Science, pp. 642\u2013659. Springer International Publishing, Cham (2020)","DOI":"10.1007\/978-3-030-58568-6_38"},{"key":"1131_CR12","doi-asserted-by":"crossref","unstructured":"Zhang, P., Zhang, B., Zhang, T., Chen, D., Wang, Y., Wen, F.: Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12414\u201312424 (2021)","DOI":"10.1109\/CVPR46437.2021.01223"},{"key":"1131_CR13","doi-asserted-by":"crossref","unstructured":"Araslanov, N., Roth, S.: Self-Supervised Augmentation Consistency for Adapting Semantic Segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15384\u201315394 (2021)","DOI":"10.1109\/CVPR46437.2021.01513"},{"key":"1131_CR14","doi-asserted-by":"crossref","unstructured":"Zou, Y., Yu, Z., Liu, X., Kumar, B.V.K.V., 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"},{"key":"1131_CR15","doi-asserted-by":"crossref","unstructured":"Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 1501\u20131510 (2017)","DOI":"10.1109\/ICCV.2017.167"},{"key":"1131_CR16","unstructured":"Zhou, K., Yang, Y., Qiao, Y., Xiang, T.: Domain generalization with MixStyle. In: ICLR (2021)"},{"key":"1131_CR17","first-page":"21271","volume":"33","author":"J-B Grill","year":"2020","unstructured":"Grill, J.-B., Strub, F., Altch\u00e9, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., Piot, B., Kavukcuoglu, K., Munos, R., Valko, M.: Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning. Adv. Neural. Inf. Process. Syst. 33, 21271\u201321284 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1131_CR18","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum Contrast for Unsupervised Visual Representation Learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"1131_CR19","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhang, R., Shen, C., Kong, T., Li, L.: Dense Contrastive Learning for Self-Supervised Visual Pre-Training. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3024\u20133033 (2021)","DOI":"10.1109\/CVPR46437.2021.00304"},{"key":"1131_CR20","unstructured":"Wei, C., Shen, K., Chen, Y., Ma, T.: Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data. In: ICLR (2020)"},{"key":"1131_CR21","doi-asserted-by":"crossref","unstructured":"Chapelle, O., Scholkopf, B., Zien, A. Eds.: Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews]. IEEE Transactions on Neural Networks 20(3), 542\u2013542 (2009)","DOI":"10.1109\/TNN.2009.2015974"},{"key":"1131_CR22","unstructured":"Amini, M.-R., Feofanov, V., Pauletto, L., Devijver, E., Maximov, Y.: Self-Training: A Survey. arXiv"},{"issue":"4","key":"1131_CR23","doi-asserted-by":"publisher","first-page":"1106","DOI":"10.1007\/s11263-020-01395-y","volume":"129","author":"Z Zheng","year":"2021","unstructured":"Zheng, Z., Yang, Y.: Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation. IJCV 129(4), 1106\u20131120 (2021)","journal-title":"IJCV"},{"key":"1131_CR24","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Wei, F., Bao, J., Chen, D., Wen, F., Zhang, W.: Dual Path Learning for Domain Adaptation of Semantic Segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9082\u20139091 (2021)","DOI":"10.1109\/ICCV48922.2021.00895"},{"key":"1131_CR25","doi-asserted-by":"crossref","unstructured":"Li, W., Yang, X., Li, Z.: Mlcb-net: a multi-level class balancing network for domain adaptive semantic segmentation. Multimedia Systems, 1\u201312 (2023)","DOI":"10.1007\/s00530-023-01055-4"},{"key":"1131_CR26","doi-asserted-by":"crossref","unstructured":"Melas-Kyriazi, L., Manrai, A.K.: PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12435\u201312445 (2021)","DOI":"10.1109\/CVPR46437.2021.01225"},{"key":"1131_CR27","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yu, M., Wei, Y., Feris, R., Xiong, J., Hwu, W.-m., Huang, T.S., Shi, H.: Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12635\u201312644 (2020)","DOI":"10.1109\/CVPR42600.2020.01265"},{"key":"1131_CR28","doi-asserted-by":"crossref","unstructured":"Guo, X., Yang, C., Li, B., Yuan, Y.: MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3927\u20133936 (2021)","DOI":"10.1109\/CVPR46437.2021.00392"},{"key":"1131_CR29","doi-asserted-by":"crossref","unstructured":"Li, R., Li, S., He, C., Zhang, Y., Jia, X., Zhang, L.: Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation. arXiv:2203.09744 [cs] (2022)","DOI":"10.1109\/CVPR52688.2022.01130"},{"issue":"7","key":"1131_CR30","first-page":"9004","volume":"45","author":"B Xie","year":"2023","unstructured":"Xie, B., Li, S., Li, M., Liu, C.H., Huang, G., Wang, G.: SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 45(7), 9004\u20139021 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1131_CR31","doi-asserted-by":"crossref","unstructured":"Li, T., Roy, S., Zhou, H., Lu, H., Lathuili\u00e8re, S.: Contrast, Stylize and Adapt: Unsupervised Contrastive Learning Framework for Domain Adaptive Semantic Segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4868\u20134878 (2023)","DOI":"10.1109\/CVPRW59228.2023.00515"},{"key":"1131_CR32","doi-asserted-by":"crossref","unstructured":"Hoyer, L., Dai, D., Van\u00a0Gool, 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":"1131_CR33","doi-asserted-by":"crossref","unstructured":"Hoyer, L., Dai, D., Van\u00a0Gool, L.: HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation. arXiv:2204.13132 [cs] (2022)","DOI":"10.1007\/978-3-031-20056-4_22"},{"key":"1131_CR34","doi-asserted-by":"crossref","unstructured":"Gong, R., Wang, Q., Danelljan, M., Dai, D., Van\u00a0Gool, L.: Continuous Pseudo-Label Rectified Domain Adaptive Semantic Segmentation With Implicit Neural Representations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7225\u20137235 (2023)","DOI":"10.1109\/CVPR52729.2023.00698"},{"key":"1131_CR35","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems 30 (2017)"},{"key":"1131_CR36","unstructured":"Laine, S., Aila, T.: Temporal Ensembling for Semi-Supervised Learning. arXiv:1610.02242 (2017)"},{"key":"1131_CR37","doi-asserted-by":"crossref","unstructured":"Gong, C., Wang, D., Liu, Q.: AlphaMatch: Improving Consistency for Semi-Supervised Learning With Alpha-Divergence. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13683\u201313692 (2021)","DOI":"10.1109\/CVPR46437.2021.01347"},{"key":"1131_CR38","unstructured":"Hyun, M., Jeong, J., Kwak, N.: Class-Imbalanced Semi-Supervised Learning. arXiv:2002.06815 (2020)"},{"key":"1131_CR39","first-page":"596","volume":"33","author":"K Sohn","year":"2020","unstructured":"Sohn, K., Berthelot, D., Carlini, N., Zhang, Z., Zhang, H., Raffel, C.A., Cubuk, E.D., Kurakin, A., Li, C.-L.: Fixmatch: Simplifying semi-supervised learning with consistency and confidence. Adv. Neural. Inf. Process. Syst. 33, 596\u2013608 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1131_CR40","unstructured":"Ghosh, A., Thiery, A.H.: On Data-Augmentation and Consistency-Based Semi-Supervised Learning. In: ICLR (2020)"},{"key":"1131_CR41","doi-asserted-by":"crossref","unstructured":"Lai, X., Tian, Z., Jiang, L., Liu, S., Zhao, H., Wang, L., Jia, J.: Semi-Supervised Semantic Segmentation With Directional Context-Aware Consistency. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1205\u20131214 (2021)","DOI":"10.1109\/CVPR46437.2021.00126"},{"key":"1131_CR42","doi-asserted-by":"crossref","unstructured":"Wu, Y., Liu, C., Chen, L., Zhao, D., Zheng, Q., Zhou, H.: Perturbation consistency and mutual information regularization for semi-supervised semantic segmentation. Multimedia Systems, 1\u201313 (2022)","DOI":"10.1007\/s00530-022-00931-9"},{"key":"1131_CR43","doi-asserted-by":"crossref","unstructured":"Xie, Z., Lin, Y., Zhang, Z., Cao, Y., Lin, S., Hu, H.: Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16684\u201316693 (2021)","DOI":"10.1109\/CVPR46437.2021.01641"},{"key":"1131_CR44","doi-asserted-by":"crossref","unstructured":"Wang, W., Zhou, T., Yu, F., Dai, J., Konukoglu, E., Van\u00a0Gool, L.: Exploring Cross-Image Pixel Contrast for Semantic Segmentation. arXiv:2101.11939 (2021)","DOI":"10.1109\/ICCV48922.2021.00721"},{"key":"1131_CR45","unstructured":"Liang, X., Wu, L., Li, J., Wang, Y., Meng, Q., Qin, T., Chen, W., Zhang, M., Liu, T.-Y.: R-Drop: Regularized Dropout for Neural Networks. arXiv:2106.14448 (2021)"},{"key":"1131_CR46","unstructured":"Huang, T., Sun, Y., Wang, X., Yao, H., Zhang, C.: Spatial Ensemble: A Novel Model Smoothing Mechanism for Student-Teacher Framework. In: Advances in Neural Information Processing Systems, vol. 34, pp. 15957\u201315968. Curran Associates, Inc"},{"key":"1131_CR47","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580 (2012)"},{"issue":"12","key":"1131_CR48","doi-asserted-by":"publisher","first-page":"1551","DOI":"10.1631\/FITEE.2100463","volume":"22","author":"Y Yang","year":"2021","unstructured":"Yang, Y., Zhuang, Y., Pan, Y.: Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies. Frontiers of Information Technology & Electronic Engineering 22(12), 1551\u20131558 (2021)","journal-title":"Frontiers of Information Technology & Electronic Engineering"},{"key":"1131_CR49","doi-asserted-by":"crossref","unstructured":"Gatys, L.A., Ecker, A.S., Bethge, M.: Image Style Transfer Using Convolutional Neural Networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2414\u20132423 (2016)","DOI":"10.1109\/CVPR.2016.265"},{"key":"1131_CR50","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: The missing ingredient for fast stylization. arXiv:1607.08022 (2016)"},{"key":"1131_CR51","doi-asserted-by":"crossref","unstructured":"Peng, D., Lei, Y., Liu, L., Zhang, P., Liu, J.: Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentation 30, 6594\u20136608","DOI":"10.1109\/TIP.2021.3096334"},{"key":"1131_CR52","unstructured":"Zhao, Y., Zhong, Z., Luo, Z., Lee, G.H., Sebe, N.: Source-Free Open Compound Domain Adaptation in Semantic Segmentation, 1\u20131"},{"key":"1131_CR53","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhu, L., Zheng, Z., Xu, M., Yang, Y.: Align and tell: Boosting text-video retrieval with local alignment and fine-grained supervision. IEEE Transactions on Multimedia (2022)","DOI":"10.1109\/TMM.2022.3204444"},{"key":"1131_CR54","doi-asserted-by":"crossref","unstructured":"Li, Y., Yuan, L., Vasconcelos, N.: Bidirectional Learning for Domain Adaptation of Semantic Segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6936\u20136945 (2019)","DOI":"10.1109\/CVPR.2019.00710"},{"key":"1131_CR55","doi-asserted-by":"crossref","unstructured":"Yang, J., An, W., Wang, S., Zhu, X., Yan, C., Huang, J.: Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV. Lecture Notes in Computer Science, pp. 480\u2013498. Springer International Publishing, Cham (2020)","DOI":"10.1007\/978-3-030-58583-9_29"},{"key":"1131_CR56","unstructured":"Musto, L., Zinelli, A.: Semantically Adaptive Image-to-image Translation for Domain Adaptation of Semantic Segmentation. arXiv:2009.01166 (2020)"},{"key":"1131_CR57","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Bach, F.R., Blei, D.M. (eds.) Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015. JMLR Workshop and Conference Proceedings, vol. 37, pp. 448\u2013456. JMLR.org"},{"key":"1131_CR58","unstructured":"French, G., Mackiewicz, M., Fisher, M.: Self-ensembling for visual domain adaptation. In: ICLR (2018)"},{"key":"1131_CR59","doi-asserted-by":"crossref","unstructured":"Richter, S.R., Vineet, V., Roth, S., Koltun, V.: Playing for data: Ground truth from computer games. In: ECCV, pp. 102\u2013118 (2016). Springer","DOI":"10.1007\/978-3-319-46475-6_7"},{"key":"1131_CR60","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\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3213\u20133223 (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"1131_CR61","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\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3234\u20133243 (2016)","DOI":"10.1109\/CVPR.2016.352"},{"issue":"4","key":"1131_CR62","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L Chen","year":"2018","unstructured":"Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE TPAMI 40(4), 834\u2013848 (2018)","journal-title":"IEEE TPAMI"},{"key":"1131_CR63","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1131_CR64","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A simple way to prevent neural networks from overfitting 15(56), 1929\u20131958"},{"key":"1131_CR65","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., Adam, H.: Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv:1706.05587 (2017)"},{"key":"1131_CR66","doi-asserted-by":"crossref","unstructured":"Chen, X., He, K.: Exploring Simple Siamese Representation Learning. arXiv:2011.10566 (2020)","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"1131_CR67","doi-asserted-by":"crossref","unstructured":"Tranheden, W., Olsson, V., Pinto, J., Svensson, L.: DACS: Domain Adaptation via Cross-Domain Mixed Sampling. In: WACV, pp. 1379\u20131389 (2021)","DOI":"10.1109\/WACV48630.2021.00142"},{"key":"1131_CR68","doi-asserted-by":"crossref","unstructured":"Vu, T.-H., Jain, H., Bucher, M., Cord, M., Perez, P.: ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2517\u20132526 (2019)","DOI":"10.1109\/CVPR.2019.00262"},{"issue":"07","key":"1131_CR69","doi-asserted-by":"publisher","first-page":"12613","DOI":"10.1609\/aaai.v34i07.6952","volume":"34","author":"J Yang","year":"2020","unstructured":"Yang, J., Xu, R., Li, R., Qi, X., Shen, X., Li, G., Lin, L.: An Adversarial Perturbation Oriented Domain Adaptation Approach for Semantic Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence 34(07), 12613\u201312620 (2020)","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"1131_CR70","doi-asserted-by":"crossref","unstructured":"Tsai, Y.-H., Sohn, K., Schulter, S., Chandraker, M.: Domain Adaptation for Structured Output via Discriminative Patch Representations. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 1456\u20131465 (2019)","DOI":"10.1109\/ICCV.2019.00154"},{"key":"1131_CR71","doi-asserted-by":"crossref","unstructured":"Truong, T.-D., Duong, C.N., Le, N., Phung, S.L., Rainwater, C., Luu, K.: BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 8548\u20138557 (2021)","DOI":"10.1109\/ICCV48922.2021.00843"},{"key":"1131_CR72","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Qiu, Z., Yao, T., Ngo, C.-W., Liu, D., Mei, T.: Transferring and Regularizing Prediction for Semantic Segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9621\u20139630 (2020)","DOI":"10.1109\/CVPR42600.2020.00964"},{"key":"1131_CR73","doi-asserted-by":"crossref","unstructured":"Lian, Q., Lv, F., Duan, L., Gong, B.: Constructing Self-Motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6758\u20136767 (2019)","DOI":"10.1109\/ICCV.2019.00686"},{"key":"1131_CR74","doi-asserted-by":"crossref","unstructured":"Ma, H., Lin, X., Wu, Z., Yu, Y.: Coarse-To-Fine Domain Adaptive Semantic Segmentation With Photometric Alignment and Category-Center Regularization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4051\u20134060 (2021)","DOI":"10.1109\/CVPR46437.2021.00404"},{"key":"1131_CR75","doi-asserted-by":"crossref","unstructured":"Liu, Y., Deng, J., Gao, X., Li, W., Duan, L.: BAPA-Net: Boundary Adaptation and Prototype Alignment for Cross-Domain Semantic Segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 8801\u20138811 (2021)","DOI":"10.1109\/ICCV48922.2021.00868"},{"key":"1131_CR76","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A Simple Framework for Contrastive Learning of Visual Representations. ICML 1 (2020)"},{"key":"1131_CR77","unstructured":"McInnes, L., Healy, J., Melville, J.: UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426 (2020)"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-023-01131-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-023-01131-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-023-01131-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T13:11:25Z","timestamp":1694783485000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-023-01131-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,19]]},"references-count":77,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["1131"],"URL":"https:\/\/doi.org\/10.1007\/s00530-023-01131-9","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"type":"print","value":"0942-4962"},{"type":"electronic","value":"1432-1882"}],"subject":[],"published":{"date-parts":[[2023,7,19]]},"assertion":[{"value":"8 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2023","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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}