{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T12:57:06Z","timestamp":1769259426680,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":37,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819557639","type":"print"},{"value":"9789819557646","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-5764-6_36","type":"book-chapter","created":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T06:08:34Z","timestamp":1769148514000},"page":"530-545","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DAWA: Dynamic Ambiguity-Wise Adaptation for\u00a0Real-Time Domain Adaptive Semantic Segmentation"],"prefix":"10.1007","author":[{"given":"Taorong","family":"Liu","sequence":"first","affiliation":[]},{"given":"Zhen","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Liang","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Chia-Wen","family":"Lin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,24]]},"reference":[{"key":"36_CR1","doi-asserted-by":"crossref","unstructured":"Br\u00fcggemann, D., Sakaridis, C., Truong, P., Van\u00a0Gool, L.: Refign: align and refine for adaptation of semantic segmentation to adverse conditions. In: WACV, pp. 3174\u20133184 (2023)","DOI":"10.1109\/WACV56688.2023.00319"},{"issue":"4","key":"36_CR2","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 Anal. Mach. Intell. 40(4), 834\u2013848 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"36_CR3","doi-asserted-by":"crossref","unstructured":"Colomer, M.B., et al.: To adapt or not to adapt? Real-time adaptation for semantic segmentation. In: ICCV, pp. 16548\u201316559 (2023)","DOI":"10.1109\/ICCV51070.2023.01517"},{"key":"36_CR4","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"},{"issue":"7","key":"36_CR5","doi-asserted-by":"publisher","first-page":"2360","DOI":"10.1109\/TPAMI.2020.2969421","volume":"43","author":"A Dundar","year":"2020","unstructured":"Dundar, A., Liu, M.Y., Yu, Z., Wang, T.C., Zedlewski, J., Kautz, J.: Domain stylization: a fast covariance matching framework towards domain adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 43(7), 2360\u20132372 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"59","key":"36_CR6","first-page":"1","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(59), 1\u201335 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"36_CR7","unstructured":"Guo, Z., Jin, T.: Smoothing the shift: towards stable test-time adaptation under complex multimodal noises. In: ICLR\u200c (2025)"},{"key":"36_CR8","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: CVPR, pp. 9924\u20139935 (2022)","DOI":"10.1109\/CVPR52688.2022.00969"},{"key":"36_CR9","doi-asserted-by":"crossref","unstructured":"Hoyer, L., Dai, D., Van\u00a0Gool, L.: HRDA: context-aware high-resolution domain-adaptive semantic segmentation. In: ECCV, pp. 372\u2013391 (2022)","DOI":"10.1007\/978-3-031-20056-4_22"},{"key":"36_CR10","doi-asserted-by":"crossref","unstructured":"Hoyer, L., Dai, D., Wang, H., Van\u00a0Gool, L.: MIC: masked image consistency for context-enhanced domain adaptation. In: CVPR, pp. 11721\u201311732 (2023)","DOI":"10.1109\/CVPR52729.2023.01128"},{"key":"36_CR11","unstructured":"Hurst, A., et\u00a0al.: GPT-4o system card. arXiv preprint arXiv:2410.21276 (2024)"},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Jain, J., Li, J., Chiu, M.T., Hassani, A., Orlov, N., Shi, H.: OneFormer: one transformer to rule universal image segmentation. In: CVPR, pp. 2989\u20132998 (2023)","DOI":"10.1109\/CVPR52729.2023.00292"},{"key":"36_CR13","unstructured":"Lee, S., Kim, N., Kang, J., Oh, S.J., Kwak, S.: DiCoTTA: domain-invariant learning for continual test-time adaptation. arXiv preprint arXiv:2504.04981 (2025)"},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Li, M., Xie, B., Li, S., Liu, C.H., Cheng, X.: VBLC: visibility boosting and logit-constraint learning for domain adaptive semantic segmentation under adverse conditions. In: AAAI, vol.\u00a037, pp. 8605\u20138613 (2023)","DOI":"10.1609\/aaai.v37i7.26036"},{"key":"36_CR15","unstructured":"Liang, J., Hu, D., Feng, J.: Do we really need to access the source data? Source hypothesis transfer for unsupervised domain adaptation. In: ICML (2020)"},{"key":"36_CR16","doi-asserted-by":"publisher","first-page":"3525","DOI":"10.1109\/TIP.2022.3172208","volume":"31","author":"L Liao","year":"2022","unstructured":"Liao, L., Chen, W., Xiao, J., Wang, Z., Lin, C.W., Satoh, S.: Unsupervised foggy scene understanding via self spatial-temporal label diffusion. IEEE Trans. Image Process. 31, 3525\u20133540 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Liao, L., et al.: Only a few classes confusing: Pixel-wise candidate labels disambiguation for foggy scene understanding. In: AAAI, vol.\u00a037, pp. 1558\u20131567 (2023)","DOI":"10.1609\/aaai.v37i2.25242"},{"key":"36_CR18","doi-asserted-by":"crossref","unstructured":"Liu, J., et al.: Continual-MAE: adaptive distribution masked autoencoders for continual test-time adaptation. In: CVPR, pp. 28653\u201328663 (2024)","DOI":"10.1109\/CVPR52733.2024.02707"},{"key":"36_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, W., Wang, J.: Source-free domain adaptation for semantic segmentation. In: CVPR, pp. 1215\u20131224 (2021)","DOI":"10.1109\/CVPR46437.2021.00127"},{"key":"36_CR20","doi-asserted-by":"crossref","unstructured":"Ma, P., Rietdorf, L., Kotovenko, D., Hu, V.T., Ommer, B.: Does VLM classification benefit from LLM description semantics? In: AAAI (2025)","DOI":"10.1609\/aaai.v39i6.32638"},{"key":"36_CR21","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\/WACV48630.2021.00142"},{"key":"36_CR22","doi-asserted-by":"crossref","unstructured":"Panagiotakopoulos, T., Dovesi, P.L., H\u00e4renstam-Nielsen, L., Poggi, M.: Online domain adaptation for semantic segmentation in ever-changing conditions. In: ECCV, pp. 128\u2013146 (2022)","DOI":"10.1007\/978-3-031-19830-4_8"},{"key":"36_CR23","doi-asserted-by":"crossref","unstructured":"Rizzoli, G., Caligiuri, M., Shenaj, D., Barbato, F., Zanuttigh, P.: When cars meet drones: hyperbolic federated learning for source-free domain adaptation in adverse weather. In: WACV, pp. 1587\u20131596 (2025)","DOI":"10.1109\/WACV61041.2025.00162"},{"issue":"9","key":"36_CR24","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1007\/s11263-018-1072-8","volume":"126","author":"C Sakaridis","year":"2018","unstructured":"Sakaridis, C., Dai, D., Van Gool, L.: Semantic foggy scene understanding with synthetic data. Int. J. Comput. Vision 126(9), 973\u2013992 (2018)","journal-title":"Int. J. Comput. Vision"},{"key":"36_CR25","doi-asserted-by":"crossref","unstructured":"Sakaridis, C., Dai, D., Van\u00a0Gool, L.: ACDC: the adverse conditions dataset with correspondences for semantic driving scene understanding. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01059"},{"key":"36_CR26","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In: NIPS, vol. 30 (2017)"},{"key":"36_CR27","unstructured":"Wang, D., Shelhamer, E., Liu, S., Olshausen, B., Darrell, T.: Tent: fully test-time adaptation by entropy minimization. In: ICLR (2021)"},{"key":"36_CR28","doi-asserted-by":"crossref","unstructured":"Wang, Q., Fink, O., Van\u00a0Gool, L., Dai, D.: Continual test-time domain adaptation. In: Proceedings of Conference on Computer Vision and Pattern Recognition (2022)","DOI":"10.1109\/CVPR52688.2022.00706"},{"key":"36_CR29","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: Dynamically instance-guided adaptation: a backward-free approach for test-time domain adaptive semantic segmentation. In: CVPR, pp. 24090\u201324099 (2023)","DOI":"10.1109\/CVPR52729.2023.02307"},{"key":"36_CR30","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. In: NIPS, vol. 34, pp. 12077\u201312090 (2021)"},{"key":"36_CR31","doi-asserted-by":"crossref","unstructured":"Yang, S., et al.: Exploring sparse visual prompt for domain adaptive dense prediction. In: AAAI, vol.\u00a038, pp. 16334\u201316342 (2024)","DOI":"10.1609\/aaai.v38i15.29569"},{"key":"36_CR32","doi-asserted-by":"crossref","unstructured":"Yang, Y., Soatto, S.: FDA: Fourier domain adaptation for semantic segmentation. In: CVPR, pp. 4084\u20134094. IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00414"},{"key":"36_CR33","doi-asserted-by":"crossref","unstructured":"Zhong, X., et al.: Bat: Bi-alignment based on transformation in multi-target domain adaptation for semantic segmentation. In: ICASSP, pp.\u00a01\u20135 (2023)","DOI":"10.1109\/ICASSP49357.2023.10095210"},{"issue":"2","key":"36_CR34","doi-asserted-by":"publisher","first-page":"804","DOI":"10.1109\/TCSVT.2022.3206476","volume":"33","author":"Q Zhou","year":"2022","unstructured":"Zhou, Q., et al.: Context-aware mixup for domain adaptive semantic segmentation. IEEE Trans. Circuit Syst. Video Technol. 33(2), 804\u2013817 (2022)","journal-title":"IEEE Trans. Circuit Syst. Video Technol."},{"key":"36_CR35","doi-asserted-by":"publisher","first-page":"3842","DOI":"10.1109\/TMM.2023.3316437","volume":"26","author":"H Zhu","year":"2024","unstructured":"Zhu, H., Yuan, J., Zhong, X., Liao, L., Wang, Z.: Find gold in sand: fine-grained similarity mining for domain-adaptive crowd counting. IEEE Trans. Multimedia 26, 3842\u20133855 (2024)","journal-title":"IEEE Trans. Multimedia"},{"key":"36_CR36","doi-asserted-by":"crossref","unstructured":"Zou, Y., Yu, Z., Liu, X., Kumar, B., Wang, J.: Confidence regularized self-training. In: ICCV, pp. 5982\u20135991 (2019)","DOI":"10.1109\/ICCV.2019.00608"},{"key":"36_CR37","doi-asserted-by":"crossref","unstructured":"Zou, Z., Yu, H., Huang, J., Zhao, F.: FreqMamba: viewing mamba from a frequency perspective for image deraining. In: ACM MM (2024)","DOI":"10.1145\/3664647.3680862"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5764-6_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T06:08:45Z","timestamp":1769148525000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5764-6_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819557639","9789819557646"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5764-6_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"24 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}