{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:12:13Z","timestamp":1742911933146,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031204968"},{"type":"electronic","value":"9783031204975"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20497-5_13","type":"book-chapter","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T12:09:06Z","timestamp":1671192546000},"page":"154-165","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised Domain Adaptation for\u00a0Semantic Segmentation with\u00a0Global and\u00a0Local Consistency"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8197-3060","authenticated-orcid":false,"given":"Xiangxuan","family":"Shan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8769-9533","authenticated-orcid":false,"given":"Zijin","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6315-5884","authenticated-orcid":false,"given":"Jiayi","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4726-093X","authenticated-orcid":false,"given":"Kongming","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2950-2488","authenticated-orcid":false,"given":"Zhanyu","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,17]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Borse, S., Wang, Y., Zhang, Y., Porikli, F.: Inverseform: A loss function for structured boundary-aware segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5901\u20135911 (2021)","DOI":"10.1109\/CVPR46437.2021.00584"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Cardace, A., Ramirez, P.Z., Salti, S., Di Stefano, L.: Shallow features guide unsupervised domain adaptation for semantic segmentation at class boundaries. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1160\u20131170 (2022)","DOI":"10.1109\/WACV51458.2022.00207"},{"issue":"4","key":"13_CR3","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC 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 Analysis Mach. Intell. 40(4), 834\u2013848 (2017)","journal-title":"IEEE Trans. Pattern Analysis Mach. Intell."},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Chen, M., Xue, H., Cai, D.: Domain adaptation for semantic segmentation with maximum squares loss. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2090\u20132099 (2019)","DOI":"10.1109\/ICCV.2019.00218"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: 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":"13_CR6","doi-asserted-by":"publisher","first-page":"107249","DOI":"10.1016\/j.patcog.2020.107249","volume":"102","author":"Y Fang","year":"2020","unstructured":"Fang, Y., Deng, W., Du, J., Hu, J.: Identity-aware cyclegan for face photo-sketch synthesis and recognition. Pattern Recogn. 102, 107249 (2020)","journal-title":"Pattern Recogn."},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Gong, R., Li, W., Chen, Y., Gool, L.V.: Dlow: domain flow for adaptation and generalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2477\u20132486 (2019)","DOI":"10.1109\/CVPR.2019.00258"},{"key":"13_CR8","unstructured":"Hoffman, J., et al.: Cycada: cycle-consistent adversarial domain adaptation. In: International Conference on Machine Learning, pp. 1989\u20131998. PMLR (2018)"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"13_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-319-46475-6_43","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Johnson","year":"2016","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694\u2013711. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Li, Q., Du, J., Song, F., Wang, C., Liu, H., Lu, C.: Region-based multi-focus image fusion using the local spatial frequency. In: 2013 25th Chinese Control and Decision Conference (CCDC), pp. 3792\u20133796. IEEE (2013)","DOI":"10.1109\/CCDC.2013.6561609"},{"key":"13_CR12","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, pp. 8801\u20138811 (2021)","DOI":"10.1109\/ICCV48922.2021.00868"},{"key":"13_CR13","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":"13_CR14","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, pp. 2507\u20132516 (2019)","DOI":"10.1109\/CVPR.2019.00261"},{"key":"13_CR15","unstructured":"Piva, F.J., Dubbelman, G.: Exploiting image translations via ensemble self-supervised learning for unsupervised domain adaptation. arXiv preprint arXiv:2107.06235 (2021)"},{"key":"13_CR16","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)"},{"key":"13_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1007\/978-3-319-46475-6_7","volume-title":"Computer Vision \u2013 ECCV 2016","author":"SR Richter","year":"2016","unstructured":"Richter, S.R., Vineet, V., Roth, S., Koltun, V.: Playing for data: ground truth from computer games. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 102\u2013118. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_7"},{"key":"13_CR18","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"},{"issue":"3","key":"13_CR19","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","volume":"27","author":"CE Shannon","year":"1948","unstructured":"Shannon, C.E.: A mathematical theory of communication. Bell Syst. Technol. J 27(3), 379\u2013423 (1948)","journal-title":"Bell Syst. Technol. J"},{"key":"13_CR20","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":"13_CR21","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\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2517\u20132526 (2019)","DOI":"10.1109\/CVPR.2019.00262"},{"key":"13_CR22","unstructured":"Wang, C., et al.: Active boundary loss for semantic segmentation. arXiv preprint arXiv:2102.02696 (2021)"},{"key":"13_CR23","doi-asserted-by":"crossref","unstructured":"Wang, Z., Li, Y., Wang, S.: Noisy boundaries: lemon or lemonade for semi-supervised instance segmentation? arXiv preprint arXiv:2203.13427 (2022)","DOI":"10.1109\/CVPR52688.2022.01632"},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Xu, L., Du, J., Li, Q.: Image fusion based on nonsubsampled contourlet transform and saliency-motivated pulse coupled neural networks. Math. Prob. Eng. 2013 (2013)","DOI":"10.1155\/2013\/135182"},{"key":"13_CR25","doi-asserted-by":"crossref","unstructured":"Yang, Y., Lao, D., Sundaramoorthi, G., Soatto, S.: Phase consistent ecological domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9011\u20139020 (2020)","DOI":"10.1109\/CVPR42600.2020.00903"},{"key":"13_CR26","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, pp. 4085\u20134095 (2020)","DOI":"10.1109\/CVPR42600.2020.00414"},{"key":"13_CR27","doi-asserted-by":"crossref","unstructured":"Yin, Z., Liang, K., Ma, Z., Guo, J.: Duplex contextual relation network for polyp segmentation. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1\u20135. IEEE (2022)","DOI":"10.1109\/ISBI52829.2022.9761402"},{"key":"13_CR28","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, pp. 12414\u201312424 (2021)","DOI":"10.1109\/CVPR46437.2021.01223"},{"key":"13_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhang, H., Lu, J., Shao, L., Yang, J.: Target-targeted domain adaptation for unsupervised semantic segmentation. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 13560\u201313566. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9560785"},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Zhu, C., Zhang, X., Li, Y., Qiu, L., Han, K., Han, X.: Sharpcontour: a contour-based boundary refinement approach for efficient and accurate instance segmentation. arXiv preprint arXiv:2203.13312 (2022)","DOI":"10.1109\/CVPR52688.2022.00435"},{"key":"13_CR31","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223\u20132232 (2017)","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20497-5_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T12:15:32Z","timestamp":1671192932000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20497-5_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031204968","9783031204975"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20497-5_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"17 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CAAI International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cicai.caai.cn\/#\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"472","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"164","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.1","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}