{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T05:57:25Z","timestamp":1757311045112,"version":"3.37.3"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,4,15]],"date-time":"2023-04-15T00:00:00Z","timestamp":1681516800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,4,15]],"date-time":"2023-04-15T00:00:00Z","timestamp":1681516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100021856","name":"Ministero dell\u2019Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","award":["Law 232\/2016"],"award-info":[{"award-number":["Law 232\/2016"]}],"id":[{"id":"10.13039\/501100021856","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003500","name":"Universit\u00e0 degli Studi di Padova","doi-asserted-by":"publisher","award":["SID2020 Semantic Segmentation in the Wild"],"award-info":[{"award-number":["SID2020 Semantic Segmentation in the Wild"]}],"id":[{"id":"10.13039\/501100003500","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Deep learning models obtain impressive accuracy in road scene understanding; however, they need a large number of labeled samples for their training. Additionally, such models do not generalize well to environments where the statistical properties of data do not perfectly match those of training scenes, and this can be a significant problem for intelligent vehicles. Hence, domain adaptation approaches have been introduced to transfer knowledge acquired on a label-abundant source domain to a related label-scarce target domain. In this work, we design and carefully analyze multiple latent space-shaping regularization strategies that work together to reduce the domain shift. More in detail, we devise a feature clustering strategy to increase domain alignment, a feature perpendicularity constraint to space apart features belonging to different semantic classes, including those not present in the current batch, and a feature norm alignment strategy to separate active and inactive channels. In addition, we propose a novel evaluation metric to capture the relative performance of an adapted model with respect to supervised training. We validate our framework in driving scenarios, considering both synthetic-to-real and real-to-real adaptation, outperforming previous feature-level state-of-the-art methods on multiple road scenes benchmarks.<\/jats:p>","DOI":"10.1007\/s00371-023-02818-w","type":"journal-article","created":{"date-parts":[[2023,4,15]],"date-time":"2023-04-15T05:02:26Z","timestamp":1681534946000},"page":"811-830","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Road scenes segmentation across different domains by disentangling latent representations"],"prefix":"10.1007","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9893-5813","authenticated-orcid":false,"given":"Francesco","family":"Barbato","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Umberto","family":"Michieli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Toldo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pietro","family":"Zanuttigh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,15]]},"reference":[{"issue":"4","key":"2818_CR1","doi-asserted-by":"publisher","first-page":"90","DOI":"10.3390\/technologies10040090","volume":"10","author":"G Rizzoli","year":"2022","unstructured":"Rizzoli, G., Barbato, F., Zanuttigh, P.: Multimodal semantic segmentation in autonomous driving: a review of current approaches and future perspectives. Technologies 10(4), 90 (2022). https:\/\/doi.org\/10.3390\/technologies10040090","journal-title":"Technologies"},{"issue":"2","key":"2818_CR2","doi-asserted-by":"publisher","first-page":"35","DOI":"10.3390\/technologies8020035","volume":"8","author":"M Toldo","year":"2020","unstructured":"Toldo, M., Maracani, A., Michieli, U., Zanuttigh, P.: Unsupervised domain adaptation in semantic segmentation: a review. Technologies 8(2), 35 (2020)","journal-title":"Technologies"},{"issue":"8","key":"2818_CR3","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798\u20131828 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2818_CR4","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"2818_CR5","doi-asserted-by":"crossref","unstructured":"Barbato, F., Toldo, M., Michieli, U., Zanuttigh, P.: Latent space regularization for unsupervised domain adaptation in semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2021)","DOI":"10.1109\/CVPRW53098.2021.00318"},{"key":"2818_CR6","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, pp. 3213\u20133223 (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"2818_CR7","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":"2818_CR8","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, Springer, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2818_CR9","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, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"2818_CR10","unstructured":"Chen, L., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)"},{"key":"2818_CR11","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2018","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, 834\u2013848 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2818_CR12","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision, pp. 833\u2013851 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"2818_CR13","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"2818_CR14","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. arXiv preprint arXiv:2103.14030 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"2818_CR15","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":"2818_CR16","doi-asserted-by":"crossref","unstructured":"Neuhold, G., Ollmann, T., Rota\u00a0Bulo, S., Kontschieder, P.: The Mapillary vistas dataset for semantic understanding of street scenes. In: Proceedings of the International Conference on Computer Vision, pp. 4990\u20134999 (2017)","DOI":"10.1109\/ICCV.2017.534"},{"key":"2818_CR17","doi-asserted-by":"crossref","unstructured":"Varma, G., Subramanian, A., Namboodiri, A., Chandraker, M., Jawahar, C.: IDD: A dataset for exploring problems of autonomous navigation in unconstrained environments. In: Proceedings of the Winter Conference on Applications of Computer Vision, IEEE, pp. 1743\u20131751 (2019)","DOI":"10.1109\/WACV.2019.00190"},{"key":"2818_CR18","doi-asserted-by":"crossref","unstructured":"Chen, Y., Chen, W., Chen, Y., Tsai, B., Wang, Y.F., Sun, M.: No more discrimination: cross city adaptation of road scene segmenters. In: Proceedings of the International Conference on Computer Vision, pp. 2011\u20132020 (2017)","DOI":"10.1109\/ICCV.2017.220"},{"issue":"2","key":"2818_CR19","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.patrec.2008.04.005","volume":"30","author":"GJ Brostow","year":"2009","unstructured":"Brostow, G.J., Fauqueur, J., Cipolla, R.: Semantic object classes in video: a high-definition ground truth database. Pattern Recogn. Lett. 30(2), 88\u201397 (2009)","journal-title":"Pattern Recogn. Lett."},{"key":"2818_CR20","doi-asserted-by":"crossref","unstructured":"Richter, S.R., Vineet, V., Roth, S., Koltun, V.: Playing for data: ground truth from computer games. In: Proceedings of the European Conference on Computer Vision, pp. 102\u2013118 (2016)","DOI":"10.1007\/978-3-319-46475-6_7"},{"key":"2818_CR21","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, pp. 3234\u20133243 (2016)","DOI":"10.1109\/CVPR.2016.352"},{"key":"2818_CR22","doi-asserted-by":"crossref","unstructured":"Testolina, P., Barbato, F., Michieli, U., Giordani, M., Zanuttigh, P., Zorzi, M.: Selma: Semantic large-scale multimodal acquisitions in variable weather, daytime and viewpoints. arXiv preprint arXiv:2204.09788 (2022)","DOI":"10.1109\/TITS.2023.3257086"},{"key":"2818_CR23","doi-asserted-by":"crossref","unstructured":"Sun, T., Segu, M., Postels, J., Wang, Y., Van\u00a0Gool, L., Schiele, B., Tombari, F., Yu, F.: SHIFT: a synthetic driving dataset for continuous multi-task domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 21371\u201321382 (2022)","DOI":"10.1109\/CVPR52688.2022.02068"},{"issue":"2","key":"2818_CR24","first-page":"1","volume":"39","author":"Z Lin","year":"2022","unstructured":"Lin, Z., Sun, W., Tang, B., Li, J., Yao, X., Li, Y.: Semantic segmentation network with multi-path structure, attention reweighting and multi-scale encoding. Vis. Comput. 39(2), 1\u201312 (2022)","journal-title":"Vis. Comput."},{"issue":"7","key":"2818_CR25","doi-asserted-by":"publisher","first-page":"2329","DOI":"10.1007\/s00371-021-02115-4","volume":"38","author":"K Wang","year":"2022","unstructured":"Wang, K., Yang, J., Yuan, S., Li, M.: A lightweight network with attention decoder for real-time semantic segmentation. Vis. Comput. 38(7), 2329\u20132339 (2022)","journal-title":"Vis. Comput."},{"key":"2818_CR26","first-page":"1","volume":"24","author":"C Jiqing","year":"2022","unstructured":"Jiqing, C., Depeng, W., Teng, L., Tian, L., Huabin, W.: All-weather road drivable area segmentation method based on cycleGAN. Vis. Comput. 24, 1\u201317 (2022)","journal-title":"Vis. Comput."},{"key":"2818_CR27","doi-asserted-by":"crossref","unstructured":"Barbato, F., Rizzoli, G., Zanuttigh, P.: Depthformer: Multimodal positional encodings and cross-input attention for transformer-based segmentation networks. arXiv preprint arXiv:2211.04188 (2022)","DOI":"10.1109\/ICASSP49357.2023.10096314"},{"issue":"11","key":"2818_CR28","doi-asserted-by":"publisher","first-page":"21405","DOI":"10.1109\/TITS.2022.3177615","volume":"23","author":"H Wang","year":"2022","unstructured":"Wang, H., Chen, Y., Cai, Y., Chen, L., Li, Y., Sotelo, M.A., Li, Z.: SFNet-N: An improved SFNet algorithm for semantic segmentation of low-light autonomous driving road scenes. IEEE Trans. Intell. Trans. Syst. 23(11), 21405\u201317 (2022)","journal-title":"IEEE Trans. Intell. Trans. Syst."},{"key":"2818_CR29","doi-asserted-by":"crossref","unstructured":"Lei, Y., Emaru, T., Ravankar, A.A., Kobayashi, Y., Wang, S.: Semantic image segmentation on snow driving scenarios. In: 2020 IEEE International Conference on Mechatronics and Automation, IEEE, pp. 1094\u20131100 (2020)","DOI":"10.1109\/ICMA49215.2020.9233538"},{"issue":"6","key":"2818_CR30","doi-asserted-by":"publisher","first-page":"1871","DOI":"10.1007\/s00371-021-02246-8","volume":"38","author":"Y Ding","year":"2022","unstructured":"Ding, Y., Duan, Z., Li, S.: Source-free unsupervised multi-source domain adaptation via proxy task for person re-identification. Vis. Comput. 38(6), 1871\u20131882 (2022)","journal-title":"Vis. Comput."},{"key":"2818_CR31","unstructured":"Toldo, M., Michieli, U., Zanuttigh, P.: Learning with style: continual semantic segmentation across tasks and domains. arXiv preprint arXiv:2210.07016 (2022)"},{"key":"2818_CR32","doi-asserted-by":"crossref","unstructured":"Chen, Y.-C., Lin, Y.-Y., Yang, M.-H., Huang, J.-B.: Crdoco: Pixel-level domain transfer with cross-domain consistency. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1791\u20131800 (2019)","DOI":"10.1109\/CVPR.2019.00189"},{"key":"2818_CR33","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: Proceedings of the International Conference on Machine Learning, pp. 1994\u20132003 (2018)"},{"key":"2818_CR34","unstructured":"Hoffman, J., Wang, D., Yu, F., Darrell, T.: FCNs in the wild: Pixel-level adversarial and constraint-based adaptation. arXiv preprint arXiv:1612.02649 (2016)"},{"key":"2818_CR35","doi-asserted-by":"crossref","unstructured":"Murez, Z., Kolouri, S., Kriegman, D.J., Ramamoorthi, R., Kim, K.: Image to image translation for domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4500\u20134509 (2018)","DOI":"10.1109\/CVPR.2018.00473"},{"key":"2818_CR36","doi-asserted-by":"publisher","first-page":"103889","DOI":"10.1016\/j.imavis.2020.103889","volume":"95","author":"M Toldo","year":"2020","unstructured":"Toldo, M., Michieli, U., Agresti, G., Zanuttigh, P.: Unsupervised domain adaptation for mobile semantic segmentation based on cycle consistency and feature alignment. Image Vis. Comput. 95, 103889 (2020)","journal-title":"Image Vis. Comput."},{"key":"2818_CR37","doi-asserted-by":"crossref","unstructured":"Pizzati, F., Charette, R.d., Zaccaria, M., Cerri, P.: Domain bridge for unpaired image-to-image translation and unsupervised domain adaptation. In: Proceedings of the Winter Conference on Applications of Computer Vision, pp. 2990\u20132998 (2020)","DOI":"10.1109\/WACV45572.2020.9093540"},{"key":"2818_CR38","unstructured":"Zhou, Q., Feng, Z., Gu, Q., Pang, J., Cheng, G., Lu, X., Shi, J., Ma, L.: Context-aware mixup for domain adaptive semantic segmentation. arXiv preprint arXiv:2108.03557 (2021)"},{"key":"2818_CR39","first-page":"1","volume":"23","author":"C Kunert","year":"2022","unstructured":"Kunert, C., Schwandt, T., Nadar, C.R., Broll, W.: Neural network adaption for depth sensor replication. Vis. Comput. 23, 1\u201311 (2022)","journal-title":"Vis. Comput."},{"key":"2818_CR40","doi-asserted-by":"crossref","unstructured":"Du, L., Tan, J., Yang, H., Feng, J., Xue, X., Zheng, Q., Ye, X., Zhang, X.: SSF-DAN: separated semantic feature based domain adaptation network for semantic segmentation. In: Proceedings of the International Conference on Computer Vision, pp. 982\u2013991 (2019)","DOI":"10.1109\/ICCV.2019.00107"},{"key":"2818_CR41","doi-asserted-by":"crossref","unstructured":"Sankaranarayanan, S., Balaji, Y., Jain, A., Nam\u00a0Lim, S., Chellappa, R.: Learning from synthetic data: addressing domain shift for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3752\u20133761 (2018)","DOI":"10.1109\/CVPR.2018.00395"},{"key":"2818_CR42","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 Conference on Computer Vision and Pattern Recognition, pp. 7472\u20137481 (2018)","DOI":"10.1109\/CVPR.2018.00780"},{"key":"2818_CR43","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 International Conference on Computer Vision, pp. 1456\u20131465 (2019)","DOI":"10.1109\/ICCV.2019.00154"},{"key":"2818_CR44","doi-asserted-by":"crossref","unstructured":"Biasetton, M., Michieli, U., Agresti, G., Zanuttigh, P.: Unsupervised domain adaptation for semantic segmentation of Urban scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1211\u20131220 (2019)","DOI":"10.1109\/CVPRW.2019.00160"},{"key":"2818_CR45","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1109\/TIV.2020.2980671","volume":"5","author":"U Michieli","year":"2020","unstructured":"Michieli, U., Biasetton, M., Agresti, G., Zanuttigh, P.: Adversarial learning and self-teaching techniques for domain adaptation in semantic segmentation. IEEE Trans. Intell. Vehicles 5, 508\u2013518 (2020)","journal-title":"IEEE Trans. Intell. Vehicles"},{"key":"2818_CR46","unstructured":"Spadotto, T., Toldo, M., Michieli, U., Zanuttigh, P.: Unsupervised domain adaptation with multiple domain discriminators and adaptive self-training. In: Proceedings of the International Conference on Pattern Recognition (2020)"},{"key":"2818_CR47","doi-asserted-by":"crossref","unstructured":"Toldo, M., Michieli, U., Zanuttigh, P.: Unsupervised domain adaptation in semantic segmentation via orthogonal and clustered embeddings. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1358\u20131368 (2021)","DOI":"10.1109\/WACV48630.2021.00140"},{"key":"2818_CR48","doi-asserted-by":"crossref","unstructured":"Lee, S., Kim, D., Kim, N., Jeong, S.-G.: Drop to adapt: Learning discriminative features for unsupervised domain adaptation. In: Proceedings of the International Conference on Computer Vision, pp. 91\u2013100 (2019)","DOI":"10.1109\/ICCV.2019.00018"},{"key":"2818_CR49","doi-asserted-by":"crossref","unstructured":"Park, S., Park, J., Shin, S., Moon, I.: Adversarial dropout for supervised and semi-supervised learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 3917\u20133924 (2018)","DOI":"10.1609\/aaai.v32i1.11634"},{"key":"2818_CR50","unstructured":"Saito, K., Ushiku, Y., Harada, T., Saenko, K.: Adversarial dropout regularization. In: Proceedings of the International Conference on Learning Representations (2018)"},{"key":"2818_CR51","doi-asserted-by":"crossref","unstructured":"Zou, Y., Yu, Z., Vijaya\u00a0Kumar, B., Wang, J.: Unsupervised domain adaptation for semantic segmentation via class-balanced self-training. In: Proceedings of the European Conference on Computer Vision, pp. 289\u2013305 (2018)","DOI":"10.1007\/978-3-030-01219-9_18"},{"key":"2818_CR52","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 International Conference on Computer Vision, pp. 5982\u20135991 (2019)","DOI":"10.1109\/ICCV.2019.00608"},{"key":"2818_CR53","doi-asserted-by":"crossref","unstructured":"Zhang, Y., David, P., Gong, B.: Curriculum domain adaptation for semantic segmentation of urban scenes. In: Proceedings of the International Conference on Computer Vision, pp. 2020\u20132030 (2017)","DOI":"10.1109\/ICCV.2017.223"},{"issue":"8","key":"2818_CR54","doi-asserted-by":"publisher","first-page":"1823","DOI":"10.1109\/TPAMI.2019.2903401","volume":"42","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., David, P., Foroosh, H., Gong, B.: A curriculum domain adaptation approach to the semantic segmentation of urban scenes. IEEE Trans. Pattern Anal. Mach. Intell. 42(8), 1823\u201341 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2818_CR55","doi-asserted-by":"crossref","unstructured":"Khindkar, V., Arora, C., Balasubramanian, V.N., Subramanian, A., Saluja, R., Jawahar, C.V.: To miss-attend is to misalign! residual self-attentive feature alignment for adapting object detectors. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3632\u20133642 (2022)","DOI":"10.1109\/WACV51458.2022.00045"},{"key":"2818_CR56","doi-asserted-by":"crossref","unstructured":"Chen, M., Xue, H., Cai, D.: Domain adaptation for semantic segmentation with maximum squares loss. In: Proceedings of the International Conference on Computer Vision, pp. 2090\u20132099 (2019)","DOI":"10.1109\/ICCV.2019.00218"},{"key":"2818_CR57","doi-asserted-by":"crossref","unstructured":"Vu, T.-H., Jain, H., Bucher, M., Cord, M., P\u00e9rez, P.: Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2517\u20132526 (2019)","DOI":"10.1109\/CVPR.2019.00262"},{"key":"2818_CR58","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, pp. 8548\u20138557 (2021)","DOI":"10.1109\/ICCV48922.2021.00843"},{"key":"2818_CR59","doi-asserted-by":"crossref","unstructured":"Kang, G., Jiang, L., Yang, Y., Hauptmann, A.G.: Contrastive adaptation network for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4893\u20134902 (2019)","DOI":"10.1109\/CVPR.2019.00503"},{"key":"2818_CR60","doi-asserted-by":"crossref","unstructured":"Tian, L., Tang, Y., Hu, L., Ren, Z., Zhang, W.: Domain adaptation by class centroid matching and local manifold self-learning. arXiv preprint arXiv:2003.09391 (2020)","DOI":"10.1109\/TIP.2020.3031220"},{"key":"2818_CR61","doi-asserted-by":"crossref","unstructured":"Michieli, U., Zanuttigh, P.: Continual semantic segmentation via repulsion-attraction of sparse and disentangled latent representations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.00117"},{"issue":"2","key":"2818_CR62","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1109\/JIOT.2022.3209865","volume":"10","author":"U Michieli","year":"2023","unstructured":"Michieli, U., Toldo, M., Ozay, M.: Federated learning via attentive margin of semantic feature representations. IEEE Internet Things J. 10(2), 1517\u20131535 (2023). https:\/\/doi.org\/10.1109\/JIOT.2022.3209865","journal-title":"IEEE Internet Things J."},{"key":"2818_CR63","unstructured":"Dong, N., Xing, E.P.: Few-shot semantic segmentation with prototype learning. In: Proceedings of the British Machine Vision Conference, vol. 3 (2018)"},{"key":"2818_CR64","doi-asserted-by":"crossref","unstructured":"Wang, K., Liew, J.H., Zou, Y., Zhou, D., Feng, J.: Panet: Few-shot image semantic segmentation with prototype alignment. In: Proceedings of the International Conference on Computer Vision, pp. 9197\u20139206 (2019)","DOI":"10.1109\/ICCV.2019.00929"},{"key":"2818_CR65","doi-asserted-by":"crossref","unstructured":"Liang, J., He, R., Sun, Z., Tan, T.: Distant supervised centroid shift: a simple and efficient approach to visual domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2975\u20132984 (2019)","DOI":"10.1109\/CVPR.2019.00309"},{"key":"2818_CR66","doi-asserted-by":"crossref","unstructured":"Wang, Q., Breckon, T.P.: Unsupervised domain adaptation via structured prediction based selective pseudo-labeling. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 6243\u20136250 (2020)","DOI":"10.1609\/aaai.v34i04.6091"},{"key":"2818_CR67","unstructured":"Choi, H., Som, A., Turaga, P.: Role of orthogonality constraints in improving properties of deep networks for image classification. arXiv preprint arXiv:2009.10762 (2020)"},{"key":"2818_CR68","doi-asserted-by":"crossref","unstructured":"Pinheiro, P.O.: Unsupervised domain adaptation with similarity learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8004\u20138013 (2018)","DOI":"10.1109\/CVPR.2018.00835"},{"key":"2818_CR69","doi-asserted-by":"crossref","unstructured":"Wu, S., Zhong, J., Cao, W., Li, R., Yu, Z., Wong, H.-S.: Improving domain-specific classification by collaborative learning with adaptation networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 5450\u20135457 (2019)","DOI":"10.1609\/aaai.v33i01.33015450"},{"key":"2818_CR70","doi-asserted-by":"crossref","unstructured":"Yu, F., Koltun, V., Funkhouser, T.A.: Dilated residual networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 636\u2013644 (2017)","DOI":"10.1109\/CVPR.2017.75"},{"key":"2818_CR71","doi-asserted-by":"publisher","unstructured":"Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision 2, 1150\u201311572 (1999). https:\/\/doi.org\/10.1109\/ICCV.1999.jspa790410","DOI":"10.1109\/ICCV.1999.jspa790410"},{"key":"2818_CR72","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, pp. 1379\u20131389 (2021)","DOI":"10.1109\/WACV48630.2021.00142"},{"key":"2818_CR73","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, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2818_CR74","unstructured":"Hendrycks, D., Dietterich, T.G.: Benchmarking neural network robustness to common corruptions and surface variations (2019)"},{"key":"2818_CR75","doi-asserted-by":"crossref","unstructured":"Li, C., Du, D., Zhang, L., Wen, L., Luo, T., Wu, Y., Zhu, P.: Spatial attention pyramid network for unsupervised domain adaptation. In: Proceedings of the European Conference on Computer Vision (2020)","DOI":"10.1007\/978-3-030-58601-0_29"},{"issue":"11","key":"2818_CR76","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-02818-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-023-02818-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-02818-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T19:14:45Z","timestamp":1706037285000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-023-02818-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,15]]},"references-count":76,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["2818"],"URL":"https:\/\/doi.org\/10.1007\/s00371-023-02818-w","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"type":"print","value":"0178-2789"},{"type":"electronic","value":"1432-2315"}],"subject":[],"published":{"date-parts":[[2023,4,15]]},"assertion":[{"value":"22 February 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2023","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 no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}