{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T09:16:38Z","timestamp":1754558198094,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031198298"},{"type":"electronic","value":"9783031198304"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-19830-4_14","type":"book-chapter","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T16:21:10Z","timestamp":1666369270000},"page":"236-251","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["ML-BPM: Multi-teacher Learning with\u00a0Bidirectional Photometric Mixing for\u00a0Open Compound Domain Adaptation in\u00a0Semantic Segmentation"],"prefix":"10.1007","author":[{"given":"Fei","family":"Pan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sungsu","family":"Hur","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seokju","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junsik","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"In So","family":"Kweon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,22]]},"reference":[{"issue":"9","key":"14_CR1","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1007\/s11263-018-1070-x","volume":"126","author":"H Abu Alhaija","year":"2018","unstructured":"Abu Alhaija, H., Mustikovela, S.K., Mescheder, L., Geiger, A., Rother, C.: Augmented reality meets computer vision: efficient data generation for urban driving scenes. IJCV 126(9), 961\u2013972 (2018)","journal-title":"IJCV"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Araslanov, N., Roth, S.: Self-supervised augmentation consistency for adapting semantic segmentation. In: CVPR, pp. 15384\u201315394 (2021)","DOI":"10.1109\/CVPR46437.2021.01513"},{"issue":"4","key":"14_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. PAMI 40(4), 834\u2013848 (2017)","journal-title":"PAMI"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Choi, S., Jung, S., Yun, H., Kim, J.T., Kim, S., Choo, J.: Robustnet: improving domain generalization in urban-scene segmentation via instance selective whitening. In: CVPR, pp. 11580\u201311590 (2021)","DOI":"10.1109\/CVPR46437.2021.01141"},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: CVPR, pp. 3213\u20133223 (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"14_CR6","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: CVPR, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"14_CR7","unstructured":"French, G., Oliver, A., Salimans, T.: Milking cowmask for semi-supervised image classification. arXiv preprint arXiv:2003.12022 (2020)"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Gong, R., et al.: Cluster, split, fuse, and update: meta-learning for open compound domain adaptive semantic segmentation. In: CVPR, pp. 8344\u20138354 (2021)","DOI":"10.1109\/CVPR46437.2021.00824"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"14_CR10","unstructured":"Hoffman, J., et al.: Cycada: cycle-consistent adversarial domain adaptation. In: ICML, pp. 1989\u20131998. PMLR (2018)"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Huang, J., Guan, D., Xiao, A., Lu, S.: Fsdr: frequency space domain randomization for domain generalization. In: CVPR, pp. 6891\u20136902 (2021)","DOI":"10.1109\/CVPR46437.2021.00682"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., Liu, W.: CCNet: criss-cross attention for semantic segmentation. In: CVPR, pp. 603\u2013612 (2019)","DOI":"10.1109\/ICCV.2019.00069"},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Open compound domain adaptation. In: CVPR, pp. 12406\u201312415 (2020)","DOI":"10.1109\/CVPR42600.2020.01242"},{"key":"14_CR14","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: CVPR, pp. 4051\u20134060 (2021)","DOI":"10.1109\/CVPR46437.2021.00404"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Olsson, V., Tranheden, W., Pinto, J., Svensson, L.: Classmix: segmentation-based data augmentation for semi-supervised learning. In: WACV, pp. 1369\u20131378 (2021)","DOI":"10.1109\/WACV48630.2021.00141"},{"key":"14_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1007\/978-3-030-58526-6_45","volume-title":"Computer Vision","author":"C Ouyang","year":"2020","unstructured":"Ouyang, C., Biffi, C., Chen, C., Kart, T., Qiu, H., Rueckert, D.: Self-supervision with superpixels: training few-shot medical image segmentation without annotation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 762\u2013780. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58526-6_45"},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Pan, F., Shin, I., Rameau, F., Lee, S., Kweon, I.S.: Unsupervised intra-domain adaptation for semantic segmentation through self-supervision. In: CVPR, pp. 3764\u20133773 (2020)","DOI":"10.1109\/CVPR42600.2020.00382"},{"key":"14_CR18","doi-asserted-by":"crossref","unstructured":"Pan, X., Luo, P., Shi, J., Tang, X.: Two at once: enhancing learning and generalization capacities via ibn-net. In: ECCV, pp. 464\u2013479 (2018)","DOI":"10.1007\/978-3-030-01225-0_29"},{"key":"14_CR19","unstructured":"Park, K., Woo, S., Shin, I., Kweon, I.S.: Discover, hallucinate, and adapt: open compound domain adaptation for semantic segmentation. In: NeurIPS (2020)"},{"key":"14_CR20","unstructured":"Rafael, C.G., Richard, E.W., Steven, L.E., Woods, R., Eddins, S.: Digital Image Processing Using MATLAB. Tata McGraw-Hill, New York (2010)"},{"key":"14_CR21","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","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":"14_CR22","doi-asserted-by":"crossref","unstructured":"Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.: The SYNTHIA dataset: a large collection of synthetic images for semantic segmentation of urban scenes. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.352"},{"key":"14_CR23","first-page":"53","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. JCAM 20, 53\u201365 (1987)","journal-title":"JCAM"},{"key":"14_CR24","doi-asserted-by":"crossref","unstructured":"Sakaridis, C., Dai, D., Van Gool, L.: ACDC: the adverse conditions dataset with correspondences for semantic driving scene understanding. In: ICCV, pp. 10765\u201310775 (2021)","DOI":"10.1109\/ICCV48922.2021.01059"},{"key":"14_CR25","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":"14_CR26","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: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00780"},{"key":"14_CR27","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: CVPR, pp. 7472\u20137481 (2018)","DOI":"10.1109\/CVPR.2018.00780"},{"key":"14_CR28","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: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00262"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: Differential treatment for stuff and things: a simple unsupervised domain adaptation method for semantic segmentation. In: CVPR, pp. 12635\u201312644 (2020)","DOI":"10.1109\/CVPR42600.2020.01265"},{"key":"14_CR30","doi-asserted-by":"crossref","unstructured":"Yue, X., Zhang, Y., Zhao, S., Sangiovanni-Vincentelli, A., Keutzer, K., Gong, B.: Domain randomization and pyramid consistency: simulation-to-real generalization without accessing target domain data. In: ICCV, pp. 2100\u20132110 (2019)","DOI":"10.1109\/ICCV.2019.00219"},{"key":"14_CR31","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: CVPR, pp. 6023\u20136032 (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"key":"14_CR32","doi-asserted-by":"crossref","unstructured":"Zendel, O., Honauer, K., Murschitz, M., Steininger, D., Dominguez, G.F.: Wilddash-creating hazard-aware benchmarks. In: ECCV, pp. 402\u2013416 (2018)","DOI":"10.1007\/978-3-030-01231-1_25"},{"key":"14_CR33","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: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01223"},{"key":"14_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: CVPR, pp. 6848\u20136856 (2018)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"14_CR35","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"14_CR36","doi-asserted-by":"crossref","unstructured":"Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: CVPR, pp. 6881\u20136890 (2021)","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"14_CR37","doi-asserted-by":"crossref","unstructured":"Zou, Y., Yu, Z., Kumar, B., Wang, J.: Unsupervised domain adaptation for semantic segmentation via class-balanced self-training. In: ECCV, pp. 289\u2013305 (2018)","DOI":"10.1007\/978-3-030-01219-9_18"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19830-4_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T23:54:15Z","timestamp":1666396455000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19830-4_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198298","9783031198304"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19830-4_14","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":"22 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","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":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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)"}}]}}