{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T15:14:34Z","timestamp":1783782874222,"version":"3.55.0"},"reference-count":100,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"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":["Int J Comput Vis"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1007\/s11263-026-02929-6","type":"journal-article","created":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T14:55:57Z","timestamp":1783781757000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Stable Source-Free Domain Adaptive Semantic Segmentation"],"prefix":"10.1007","volume":"134","author":[{"given":"Dong","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Zang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Pu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinlong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicu","family":"Sebe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8202-0544","authenticated-orcid":false,"given":"Zhun","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,11]]},"reference":[{"key":"2929_CR1","doi-asserted-by":"crossref","unstructured":"Araslanov, N., & Roth., S. (2021). Self-supervised augmentation consistency for adapting semantic segmentation In: CVPR, pp. 15384\u201315394.","DOI":"10.1109\/CVPR46437.2021.01513"},{"key":"2929_CR2","doi-asserted-by":"crossref","unstructured":"Chen, D., Wang, D., Darrell, T., & Ebrahimi, S. (2022). Contrastive test-time adaptation In: CVPR, pp. 295\u2013305.","DOI":"10.1109\/CVPR52688.2022.00039"},{"issue":"4","key":"2929_CR3","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. (2018). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. TPAMI, 40(4), 834\u2013848. https:\/\/doi.org\/10.1109\/TPAMI.2017.2699184","journal-title":"TPAMI"},{"key":"2929_CR4","doi-asserted-by":"crossref","unstructured":"Colomer, M. B., Dovesi, P. L., Panagiotakopoulos, T., Carvalho, J. F., H\u00e4renstam-Nielsen, L., Azizpour, H., Kjellstr\u00f6m, H., Cremers, D., & Poggi, M. (2023). To adapt or not to adapt? real-time adaptation for semantic segmentation In: ICCV, pp. 16548\u201316559.","DOI":"10.1109\/ICCV51070.2023.01517"},{"key":"2929_CR5","doi-asserted-by":"crossref","unstructured":"Ding, N., Xu, Y., Tang, Y., Xu, C., Wang, Y., & Tao, D. (2022). Source-free domain adaptation via distribution estimation. In: CVPR, pp 7212\u20137222","DOI":"10.1109\/CVPR52688.2022.00707"},{"key":"2929_CR6","doi-asserted-by":"publisher","DOI":"10.4135\/9781412985475","volume-title":"Principal components analysis,","author":"GH Dunteman","year":"1989","unstructured":"Dunteman, G. H. (1989). Principal components analysis, (Vol. 69). Sage."},{"key":"2929_CR7","unstructured":"Eastwood, C., Mason, I., Williams, C. K., & Sch\u00f6lkopf, B. (2022). Source-free adaptation to measurement shift via bottom-up feature restoration. In: ICLR."},{"key":"2929_CR8","unstructured":"Fang, H., et al. (2023). Eva-clip: Improving vision-language models with masked modeling arXiv preprint arXiv:2303.13495 ."},{"key":"2929_CR9","unstructured":"Fleuret, F., etal.(2021). Uncertainty reduction for model adaptation in semantic segmentation. In: CVPR, pp. 9613\u20139623."},{"key":"2929_CR10","doi-asserted-by":"crossref","unstructured":"Gao, J., Zhang, J., Liu, X., Darrell, T., Shelhamer, E., & Wang, D. (2023). Back to the source: Diffusion-driven adaptation to test-time corruption In: CVPR, pp. 11786\u201311796.","DOI":"10.1109\/CVPR52729.2023.01134"},{"key":"2929_CR11","first-page":"6204","volume":"35","author":"S Goyal","year":"2022","unstructured":"Goyal, S., Sun, M., Raghunathan, A., & Kolter, J. Z. (2022). Test time adaptation via conjugate pseudo-labels. NeurIPS, 35, 6204\u20136218.","journal-title":"NeurIPS"},{"key":"2929_CR12","first-page":"281","volume":"367","author":"Y Grandvalet","year":"2005","unstructured":"Grandvalet, Y., Bengio, Y., et al. (2005). Semi-supervised learning by entropy minimization. CAP, 367, 281\u2013296.","journal-title":"Semi-supervised learning by entropy minimization. CAP"},{"key":"2929_CR13","doi-asserted-by":"crossref","unstructured":"Guo, X., Yang, C., Li, B., & Yuan, Y. (2021). Metacorrection: Domain-aware meta loss correction for unsupervised domain adaptation in semantic segmentation In: CVPR, pp. 3927\u20133936.","DOI":"10.1109\/CVPR46437.2021.00392"},{"issue":"8","key":"2929_CR14","doi-asserted-by":"publisher","first-page":"9846","DOI":"10.1109\/TPAMI.2023.3246392","volume":"45","author":"X Guo","year":"2023","unstructured":"Guo, X., Liu, J., Liu, T., & Yuan, Y. (2023). Handling open-set noise and novel target recognition in domain adaptive semantic segmentation. TPAMI, 45(8), 9846\u20139861. https:\/\/doi.org\/10.1109\/TPAMI.2023.3246392","journal-title":"TPAMI"},{"key":"2929_CR15","doi-asserted-by":"crossref","unstructured":"Guo, X., Liu, J., Liu, T., & Yuan, Y. (2023b). Handling open-set noise and novel target recognition in domain adaptive semantic segmentation. TPAMI","DOI":"10.1109\/TPAMI.2023.3246392"},{"key":"2929_CR16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In: CVPR.","DOI":"10.1109\/CVPR.2016.90"},{"key":"2929_CR17","doi-asserted-by":"crossref","unstructured":"Hoyer, L., Dai, D., & Van\u00a0Gool, L. (2022a) .Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation. In: CVPR, pp 9924\u20139935","DOI":"10.1109\/CVPR52688.2022.00969"},{"key":"2929_CR18","doi-asserted-by":"crossref","unstructured":"Hoyer, L., Dai, D., & Van\u00a0Gool, L. (2022b). Hrda: Context-aware high-resolution domain-adaptive semantic segmentation. In: ECCV, Springer, pp 372\u2013391","DOI":"10.1007\/978-3-031-20056-4_22"},{"key":"2929_CR19","doi-asserted-by":"crossref","unstructured":"Hoyer, L., Dai, D., Wang, H., & Van Gool, L. (2023). Mic: Masked image consistency for context-enhanced domain adaptationIn: CVPR, pp. 11721\u201311732.","DOI":"10.1109\/CVPR52729.2023.01128"},{"key":"2929_CR20","first-page":"3635","volume":"34","author":"J Huang","year":"2021","unstructured":"Huang, J., Guan, D., Xiao, A., & Lu, S. (2021). Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data. NeurIPS, 34, 3635\u20133649.","journal-title":"NeurIPS"},{"key":"2929_CR21","unstructured":"II\/4 IWG (2018) Isprs 2d semantic labeling challenge. International Society for Photogrammetry and Remote Sensing (ISPRS) http:\/\/www2.isprs.org\/commissions\/comm2\/wg4\/"},{"key":"2929_CR22","unstructured":"Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., & Wilson, A. G. (2018). Averaging weights leads to wider optima and better generalization arXiv preprint arXiv:1803.05407."},{"key":"2929_CR23","doi-asserted-by":"crossref","unstructured":"Jia, M., Tang, L., Chen, B.C., Cardie, C., Belongie, S., Hariharan, B., & Lim, S.N. (2022). Visual prompt tuning. In: ECCV, Springer, pp 709\u2013727","DOI":"10.1007\/978-3-031-19827-4_41"},{"key":"2929_CR24","doi-asserted-by":"crossref","unstructured":"Karim, N., Mithun, N. C., Rajvanshi, A., Hp, C., Samarasekera, S., & Rahnavard, N. (2023). C-sfda: A curriculum learning aided self-training framework for efficient source free domain adaptation In: CVPR, pp. 24120\u201324131.","DOI":"10.1109\/CVPR52729.2023.02310"},{"key":"2929_CR25","doi-asserted-by":"crossref","unstructured":"Kirillov, A. (2023). Segment anything. arXiv preprint arXiv:2304.02643","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"2929_CR26","doi-asserted-by":"crossref","unstructured":"Kundu, J.N., Kulkarni, A., Singh, A., Jampani, V., & Babu, R.V. (2021a) .Generalize then adapt: Source-free domain adaptive semantic segmentation. In: ICCV, pp 7046\u20137056","DOI":"10.1109\/ICCV48922.2021.00696"},{"key":"2929_CR27","doi-asserted-by":"crossref","unstructured":"Kundu, J.N., Kulkarni, A., Singh, A., Jampani, V., & Babu, R.V. (2021b) .Generalize then adapt: Source-free domain adaptive semantic segmentation. In: ICCV, pp 7046\u20137056","DOI":"10.1109\/ICCV48922.2021.00696"},{"key":"2929_CR28","unstructured":"Kundu, J.N., Kulkarni, A.R., Bhambri, S., Mehta, D., Kulkarni, S.A., Jampani, V., & Radhakrishnan, V.B. (2022) .Balancing discriminability and transferability for source-free domain adaptation. In: ICML, PMLR, pp 11710\u201311728"},{"key":"2929_CR29","unstructured":"Lee, D., Yoon, J., & Hwang, S. J. (2024). Becotta: Input-dependent online blending of experts for continual test-time adaptation. In: ICML."},{"key":"2929_CR30","unstructured":"Lee, J., Park, J.H., Lee, G., Kim, B., Cha, M.H., Nam, H., Jeon, J., Lee, H., & Cho, S.I. (2025). Duet: Dual-perspective pseudo labeling and uncertainty-aware exploration & exploitation training for source-free domain adaptation. In: NeurIPS, Curran Associates, Inc., poster presentation at NeurIPS 2025, San Diego"},{"key":"2929_CR31","doi-asserted-by":"crossref","unstructured":"Li, G., Kang, G., Liu, W., Wei, Y., & Yang, Y. (2020). Content-consistent matching for domain adaptive semantic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., & Frahm, J.M. (eds) ECCV, pp 440\u2013456","DOI":"10.1007\/978-3-030-58568-6_26"},{"key":"2929_CR32","first-page":"16037","volume":"35","author":"J Li","year":"2022","unstructured":"Li, J., Jie, Z., Wang, X., Wei, X., & Ma, L. (2022). Expansion and shrinkage of localization for weakly-supervised semantic segmentation. NeurIPS, 35, 16037\u201316051.","journal-title":"NeurIPS"},{"key":"2929_CR33","doi-asserted-by":"crossref","unstructured":"Li, J., Jie, Z., Wang, X., Zhou, Y., Wei, X., & Ma, L. (2022b). Weakly supervised semantic segmentation via progressive patch learning. TMM","DOI":"10.1016\/j.neucom.2023.126821"},{"key":"2929_CR34","doi-asserted-by":"crossref","unstructured":"Li, J., Yu, Z., Du, Z., Zhu, L., & Shen, H. T. (2024). A comprehensive survey on source-free domain adaptation. TPAMI.","DOI":"10.1109\/TPAMI.2024.3370978"},{"key":"2929_CR35","doi-asserted-by":"crossref","unstructured":"Li, R., Li, S., He, C., Zhang, Y., Jia, X., & Zhang, L. (2022c). Class-balanced pixel-level self-labeling for domain adaptive semantic segmentation. In: CVPR, pp 11593\u201311603","DOI":"10.1109\/CVPR52688.2022.01130"},{"key":"2929_CR36","unstructured":"Li, X., Dai, Y., Ge, Y., Liu, J., Shan, Y., & DUAN, L. (2022d). Uncertainty modeling for out-of-distribution generalization. In: ICLR"},{"key":"2929_CR37","unstructured":"Liang, J., Hu, D., & Feng, J. (2020). Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation. In: ICML, PMLR, pp. 6028\u20136039."},{"key":"2929_CR38","doi-asserted-by":"crossref","unstructured":"Litrico, M., Del Bue, A., & Morerio, P. (2023). Guiding pseudo-labels with uncertainty estimation for source-free unsupervised domain adaptation In: CVPR, pp. 7640\u20137650.","DOI":"10.1109\/CVPR52729.2023.00738"},{"key":"2929_CR39","unstructured":"Loshchilov, I., & Hutter, F. (2017). Decoupled weight decay regularization arXiv preprint arXiv:1711.05101"},{"key":"2929_CR40","doi-asserted-by":"crossref","unstructured":"Lu, Z., Li, D., Song, Y. Z., Xiang, T., & Hospedales, T. M. (2023). Uncertainty-aware source-free domain adaptive semantic segmentation. TIP.","DOI":"10.1109\/TIP.2023.3295929"},{"issue":"1","key":"2929_CR41","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/s11263-023-01863-1","volume":"132","author":"X Luo","year":"2024","unstructured":"Luo, X., Chen, W., Liang, Z., Yang, L., Wang, S., & Li, C. (2024). Crots: Cross-domain teacher-student learning for source-free domain adaptive semantic segmentation. IJCV, 132(1), 20\u201339.","journal-title":"IJCV"},{"key":"2929_CR42","unstructured":"Ma, X., Huang, H., Wang, Y., Romano, S., Erfani, S., & Bailey, J. (2020). Normalized loss functions for deep learning with noisy labels. ICML, PMLR (pp. 6543\u20136553)"},{"key":"2929_CR43","doi-asserted-by":"crossref","unstructured":"Ma X, Wang Y, Liu H, Guo T, Wang Y (2024) When visual prompt tuning meets source-free domain adaptive semantic segmentation. NeurIPS 36","DOI":"10.52202\/075280-0293"},{"key":"2929_CR44","doi-asserted-by":"publisher","unstructured":"Ni C, Lyu F, Tan J, Hu F, Yao R, Zhou T (2025) Maintaining consistent inter-class topology in continual test-time adaptation. In: CVPR, pp 12345\u201312355, https:\/\/doi.org\/10.1109\/CVPR.2025.11094544","DOI":"10.1109\/CVPR.2025.11094544"},{"key":"2929_CR45","unstructured":"Niu, S., Wu, J., Zhang, Y., Wen, Z., Chen, Y., Zhao, P., & Tan, M. (2023). Towards stable test-time adaptation in dynamic wild world. In: ICLR."},{"key":"2929_CR46","doi-asserted-by":"crossref","unstructured":"Pan, F., Shin, I., Rameau, F., Lee, S., & Kweon, I. S. (2020). Unsupervised intra-domain adaptation for semantic segmentation through self-supervision. In: CVPR.","DOI":"10.1109\/CVPR42600.2020.00382"},{"key":"2929_CR47","doi-asserted-by":"crossref","unstructured":"Pham, H., Dai, Z., Xie, Q., & Le, Q. V. (2021). Meta pseudo labels. In: CVPR, pp. 11557\u201311568.","DOI":"10.1109\/CVPR46437.2021.01139"},{"key":"2929_CR48","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., & Clark, J., et\u00a0al. (2021). Learning transferable visual models from natural language supervision. In: ICML, PMLR, pp 8748\u20138763"},{"key":"2929_CR49","unstructured":"Ravi, N., Gabeur, V., Hu, Y.T., Hu, R., Ryali, C., Ma, T., Khedr, H., R\u00e4dle, R., Rolland, C., Gustafson, L., Mintun, E., Pan, J., Alwala, K.V., Carion, N., Wu, C.Y., Girshick, R., Doll\u00e1r, P., & Feichtenhofer, C. (2024). Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714"},{"key":"2929_CR50","first-page":"102","volume-title":"Leibe B","author":"SR Richter","year":"2016","unstructured":"Richter, S. R., Vineet, V., Roth, S., & Koltun, V. (2016). Playing for data: Ground truth from computer games. In J. Matas, N. Sebe, & M. Welling (Eds.), Leibe B (pp. 102\u2013118). Springer International Publishing: ECCV."},{"key":"2929_CR51","doi-asserted-by":"crossref","unstructured":"Ros, G., Sellart, L., Materzynska, J., Vazquez, D., & Lopez, A. M. (2016). The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes. In:CVPR.","DOI":"10.1109\/CVPR.2016.352"},{"key":"2929_CR52","doi-asserted-by":"crossref","unstructured":"Saito, K., Kim, D., Teterwak, P., Sclaroff, S., Darrell, T., & Saenko, K. (2021). Tune it the right way: Unsupervised validation of domain adaptation via soft neighborhood density In:ICCV, pp. 9184\u20139193.","DOI":"10.1109\/ICCV48922.2021.00905"},{"key":"2929_CR53","doi-asserted-by":"crossref","unstructured":"Sakaridis, C., Dai, D., & Van Gool, L. (2021). Acdc: The adverse conditions dataset with correspondences for semantic driving scene understanding. In:ICCV, pp. 10765\u201310775.","DOI":"10.1109\/ICCV48922.2021.01059"},{"key":"2929_CR54","doi-asserted-by":"crossref","unstructured":"Shen, F., Gurram, A., Liu, Z., Wang, H., & Knoll, A. (2023). Diga: Distil to generalize and then adapt for domain adaptive semantic segmentation In:CVPR, pp. 15866\u201315877.","DOI":"10.1109\/CVPR52729.2023.01523"},{"key":"2929_CR55","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s11548-013-0926-3","volume":"9","author":"J Silva","year":"2014","unstructured":"Silva, J., Histace, A., Romain, O., Dray, X., & Granado, B. (2014). Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer. International journal of computer assisted radiology and surgery, 9, 283\u2013293.","journal-title":"International journal of computer assisted radiology and surgery"},{"key":"2929_CR56","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. (2020). Fixmatch: Simplifying semi-supervised learning with consistency and confidence. NeurIPS, 33, 596\u2013608.","journal-title":"NeurIPS"},{"key":"2929_CR57","doi-asserted-by":"crossref","unstructured":"Song, J., Lee, J., Kweon, I. S., & Choi, S. (2023). Ecotta: Memory-efficient continual test-time adaptation via self-distilled regularization In:CVPR, pp. 11920\u201311929.","DOI":"10.1109\/CVPR52729.2023.01147"},{"key":"2929_CR58","first-page":"17543","volume":"35","author":"Y Su","year":"2022","unstructured":"Su, Y., Xu, X., & Jia, K. (2022). Revisiting realistic test-time training: Sequential inference and adaptation by anchored clustering. NeurIPS, 35, 17543\u201317555.","journal-title":"NeurIPS"},{"issue":"3","key":"2929_CR59","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1007\/s11263-023-01892-w","volume":"132","author":"S Tang","year":"2024","unstructured":"Tang, S., Chang, A., Zhang, F., Zhu, X., Ye, M., & Zhang, C. (2024). Source-free domain adaptation via target prediction distribution searching. IJCV, 132(3), 654\u2013672.","journal-title":"IJCV"},{"key":"2929_CR60","doi-asserted-by":"crossref","unstructured":"Tranheden, W., Olsson, V., Pinto, J., & Svensson, L. (2021). Dacs: Domain adaptation via cross-domain mixed sampling In:WACV, pp. 1379\u20131389.","DOI":"10.1109\/WACV48630.2021.00142"},{"key":"2929_CR61","first-page":"4037190","volume":"1","author":"D V\u00e1zquez","year":"2017","unstructured":"V\u00e1zquez, D., Bernal, J., S\u00e1nchez, F. J., Fern\u00e1ndez-Esparrach, G., L\u00f3pez, A. M., Romero, A., & Drozdzal, M. (2017). Courville A (2017) A benchmark for endoluminal scene segmentation of colonoscopy images. Journal of healthcare engineering, 1, 4037190.","journal-title":"Journal of healthcare engineering"},{"key":"2929_CR62","doi-asserted-by":"crossref","unstructured":"Vu, T. H., Jain, H., Bucher, M., Cord, M., & P\u00e9rez, P. (2019). Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation In:CVPR, pp. 2517\u20132526.","DOI":"10.1109\/CVPR.2019.00262"},{"key":"2929_CR63","unstructured":"Wang, D., Shelhamer, E., Liu, S., Olshausen, B., & Darrell, T. (2021a). Tent: Fully test-time adaptation by entropy minimization. ICLR"},{"key":"2929_CR64","first-page":"18866","volume":"34","author":"H Wang","year":"2021","unstructured":"Wang, H., Gurbuzbalaban, M., Zhu, L., Simsekli, U., & Erdogdu, M. A. (2021). Convergence rates of stochastic gradient descent under infinite noise variance. Advances in Neural Information Processing Systems, 34, 18866\u201318877.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2929_CR65","doi-asserted-by":"crossref","unstructured":"Wang, Q., Fink, O., Van\u00a0Gool, L., & Dai, D. (2022a). Continual test-time domain adaptation. In: CVPR, pp 7201\u20137211","DOI":"10.1109\/CVPR52688.2022.00706"},{"key":"2929_CR66","doi-asserted-by":"publisher","first-page":"7403","DOI":"10.1109\/TIP.2022.3222634","volume":"31","author":"S Wang","year":"2022","unstructured":"Wang, S., Zhao, D., Zhang, C., Guo, Y., Zang, Q., Gu, Y., Li, Y., & Jiao, L. (2022). Cluster alignment with target knowledge mining for unsupervised domain adaptation semantic segmentation. TIP, 31, 7403\u20137418. https:\/\/doi.org\/10.1109\/TIP.2022.3222634","journal-title":"TIP"},{"key":"2929_CR67","doi-asserted-by":"publisher","unstructured":"Wang, S., Zang, Q., Zhao, D., Fang, C., Quan, D., Wan, Y., Guo, Y., & Jiao, L. (2023a). Select, purify, and exchange: A multisource unsupervised domain adaptation method for building extraction. IEEE TNNLS pp 1\u201315, https:\/\/doi.org\/10.1109\/TNNLS.2023.3291876","DOI":"10.1109\/TNNLS.2023.3291876"},{"key":"2929_CR68","doi-asserted-by":"crossref","unstructured":"Wang, W., Zhou, T., Yu, F., Dai, J., Konukoglu, E., & Van\u00a0Gool, L. (2021c). Exploring cross-image pixel contrast for semantic segmentation. In: ICCV, pp 7303\u20137313","DOI":"10.1109\/ICCV48922.2021.00721"},{"key":"2929_CR69","doi-asserted-by":"crossref","unstructured":"Wang, W., Zhong, Z., Wang, W., Chen, X., Ling, C., Wang, B., & Sebe, N. (2023b). Dynamically instance-guided adaptation: A backward-free approach for test-time domain adaptive semantic segmentation. In: CVPR, pp 24090\u201324099","DOI":"10.1109\/CVPR52729.2023.02307"},{"key":"2929_CR70","doi-asserted-by":"crossref","unstructured":"Wang, Y., Cheng, J., Chen, Y., Shao, S., Zhu, L., Wu, Z., Liu, T., & Zhu, H. (2023c). Fvp: Fourier visual prompting for source-free unsupervised domain adaptation of medical image segmentation. TMI","DOI":"10.1109\/TMI.2023.3306105"},{"key":"2929_CR71","doi-asserted-by":"crossref","unstructured":"Wang, Y., Liang, J., & Zhang, Z. (2024). A curriculum-style self-training approach for source-free semantic segmentation. TPAMI.","DOI":"10.1109\/TPAMI.2024.3432168"},{"key":"2929_CR72","doi-asserted-by":"crossref","unstructured":"Wei, Z., Chen, L., Jin, Y., Ma, X., Liu, T., Ling, P., Wang, B., Chen, H., & Zheng, J. (2024). Stronger fewer & superior: Harnessing vision foundation models for domain generalized semantic segmentation. In: CVPR, pp 28619\u201328630","DOI":"10.1109\/CVPR52733.2024.02704"},{"issue":"7","key":"2929_CR73","doi-asserted-by":"publisher","first-page":"8827","DOI":"10.1109\/TPAMI.2022.3233584","volume":"45","author":"L Wu","year":"2023","unstructured":"Wu, L., Fang, L., He, X., He, M., Ma, J., & Zhong, Z. (2023). Querying labeled for unlabeled: Cross-image semantic consistency guided semi-supervised semantic segmentation. TPAMI, 45(7), 8827\u20138844. https:\/\/doi.org\/10.1109\/TPAMI.2022.3233584","journal-title":"TPAMI"},{"key":"2929_CR74","doi-asserted-by":"crossref","unstructured":"Wu, Y., Shu, J., Xie, Q., Zhao, Q., & Meng, D. (2021). Learning to purify noisy labels via meta soft label corrector In:AAAI, pp. 10388\u201310396.","DOI":"10.1609\/aaai.v35i12.17244"},{"key":"2929_CR75","doi-asserted-by":"crossref","unstructured":"Xia, H., Zhao, H., & Ding, Z. (2021). Adaptive adversarial network for source-free domain adaptation. In:ICCV, pp. 9010\u20139019.","DOI":"10.1109\/ICCV48922.2021.00888"},{"key":"2929_CR76","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., & Luo, P. (2021). Segformer: Simple and efficient design for semantic segmentation with transformers. NeurIPS, 34, 12077\u201312090.","journal-title":"NeurIPS"},{"key":"2929_CR77","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102457","volume":"79","author":"C Yang","year":"2022","unstructured":"Yang, C., Guo, X., Chen, Z., & Yuan, Y. (2022). Source free domain adaptation for medical image segmentation with fourier style mining. Medical Image Analysis, 79, Article 102457.","journal-title":"Medical Image Analysis"},{"key":"2929_CR78","doi-asserted-by":"crossref","unstructured":"Yang, C., Zhou, H., An, Z., Jiang, X., Xu, Y., & Zhang, Q. (2022b). Cross-image relational knowledge distillation for semantic segmentation. In: CVPR, pp 12319\u201312328","DOI":"10.1109\/CVPR52688.2022.01200"},{"key":"2929_CR79","unstructured":"Yang, J., Peng, X., Wang, K., Zhu, Z., Feng, J., Xie, L., & You, Y. (2023a). Divide to adapt: Mitigating confirmation bias for domain adaptation of black-box predictors. ICLR"},{"key":"2929_CR80","doi-asserted-by":"crossref","unstructured":"Yang, L., Qi, L., Feng, L., Zhang, W., & Shi, Y. (2023b). Revisiting weak-to-strong consistency in semi-supervised semantic segmentation. In: CVPR, pp 7236\u20137246","DOI":"10.1109\/CVPR52729.2023.00699"},{"key":"2929_CR81","doi-asserted-by":"crossref","unstructured":"Yang, S., Wang, Y., van\u00a0de Weijer, J., Herranz, L., & Jui, S. (2021a). Generalized source-free domain adaptation. In: ICCV, pp 8978\u20138987","DOI":"10.1109\/ICCV48922.2021.00885"},{"key":"2929_CR82","first-page":"29393","volume":"34","author":"S Yang","year":"2021","unstructured":"Yang, S., van de Weijer, J., Herranz, L., Jui, S., et al. (2021). Exploiting the intrinsic neighborhood structure for source-free domain adaptation. NeurIPS, 34, 29393\u201329405.","journal-title":"NeurIPS"},{"key":"2929_CR83","first-page":"5802","volume":"35","author":"S Yang","year":"2022","unstructured":"Yang, S., Jui, S., van de Weijer, J., et al. (2022). Attracting and dispersing: A simple approach for source-free domain adaptation. NeurIPS, 35, 5802\u20135815.","journal-title":"NeurIPS"},{"key":"2929_CR84","doi-asserted-by":"crossref","unstructured":"Yang, S., Wu, J., Liu, J., Li, X., Zhang, Q., Pan, M., Gan, Y., Chen, Z., & Zhang, S. (2024). Exploring sparse visual prompt for domain adaptive dense prediction. In: AAAI","DOI":"10.1609\/aaai.v38i15.29569"},{"key":"2929_CR85","doi-asserted-by":"crossref","unstructured":"Yang, Y., & Soatto, S. (2020). Fda: Fourier domain adaptation for semantic segmentation. In:CVPR.","DOI":"10.1109\/CVPR42600.2020.00414"},{"key":"2929_CR86","unstructured":"Yi, L., Xu, G., Xu, P., Li, J., Pu, R., Ling, C., McLeod, A.I., & Wang, B. (2023). When source-free domain adaptation meets learning with noisy labels. ICLR"},{"key":"2929_CR87","doi-asserted-by":"crossref","unstructured":"Yin, H., Molchanov, P., Alvarez, J. M., Li, Z., Mallya, A., Hoiem, D., Jha, N. K., & Kautz, J. (2020). Dreaming to distill: Data-free knowledge transfer via deepinversion In:CVPR, pp. 8715\u20138724.","DOI":"10.1109\/CVPR42600.2020.00874"},{"key":"2929_CR88","doi-asserted-by":"crossref","unstructured":"Yin, Y., Hu, W., Liu, Z., Wang, G., Xiang, S., & Zimmermann, R. (2023). Crossmatch: Source-free domain adaptive semantic segmentation via cross-modal consistency training In:ICCV, pp. 21786\u201321796.","DOI":"10.1109\/ICCV51070.2023.01991"},{"key":"2929_CR89","doi-asserted-by":"crossref","unstructured":"Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., & Darrell, T. (2020). Bdd100k: A diverse driving dataset for heterogeneous multitask learning. In: CVPR, pp 2636\u20132645","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"2929_CR90","doi-asserted-by":"crossref","unstructured":"Zang, Q., Wang, S., Zhao, D., Hu, Y., Quan, D., Li, J., Sebe, N., & Zhong, Z. (2024). Generalized source-free domain-adaptive segmentation via reliable knowledge propagation. In: ACM MM, MM \u201924, p 5967\u20135976","DOI":"10.1145\/3664647.3680567"},{"key":"2929_CR91","first-page":"1","volume":"60","author":"L Zhang","year":"2021","unstructured":"Zhang, L., Lan, M., Zhang, J., & Tao, D. (2021). Stagewise unsupervised domain adaptation with adversarial self-training for road segmentation of remote-sensing images. TGARS, 60, 1\u201313.","journal-title":"TGARS"},{"key":"2929_CR92","doi-asserted-by":"crossref","unstructured":"Zhang, P., Zhang, B., Zhang, T., Chen, D., Wang, Y., & Wen, F. (2021b). Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation. In: CVPR, pp 12414\u201312424","DOI":"10.1109\/CVPR46437.2021.01223"},{"key":"2929_CR93","first-page":"5137","volume":"35","author":"Z Zhang","year":"2022","unstructured":"Zhang, Z., Chen, W., Cheng, H., Li, Z., Li, S., Lin, L., & Li, G. (2022). Divide and contrast: Source-free domain adaptation via adaptive contrastive learning. NeurIPS, 35, 5137\u20135149.","journal-title":"NeurIPS"},{"key":"2929_CR94","doi-asserted-by":"crossref","unstructured":"Zhao, D., Wang, S., Zang, Q., Quan, D., Ye, X., & Jiao, L. (2023a). Towards better stability and adaptability: Improve online self-training for model adaptation in semantic segmentation. In: CVPR, pp 11733\u201311743","DOI":"10.1109\/CVPR52729.2023.01129"},{"key":"2929_CR95","doi-asserted-by":"crossref","unstructured":"Zhao, D., Yang, R., Wang, S., Zang, Q., Hu, Y., Jiao, L., Sebe, N., & Zhong, Z. (2023b). Semantic connectivity-driven pseudo-labeling for cross-domain segmentation. arXiv:2312.06331","DOI":"10.52202\/079017-2506"},{"key":"2929_CR96","doi-asserted-by":"crossref","unstructured":"Zhao, D., Wang, S., Zang, Q., Jiao, L., Sebe, N., & Zhong, Z. (2024). Stable neighbor denoising for source-free domain adaptive segmentation In:CVPR, pp. 23416\u201323427.","DOI":"10.1109\/CVPR52733.2024.02210"},{"key":"2929_CR97","doi-asserted-by":"publisher","unstructured":"Zhao, D., Zang, Q., Pu, N., Wang, S., Sebe, N., & Zhong, Z. (2025). Secov2: Semantic connectivity-driven pseudo-labeling for robust cross-domain semantic segmentation. TPAMI pp 1\u201317, https:\/\/doi.org\/10.1109\/TPAMI.2025.3596943","DOI":"10.1109\/TPAMI.2025.3596943"},{"issue":"4","key":"2929_CR98","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. (2021). Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation. IJCV, 129(4), 1106\u20131120.","journal-title":"IJCV"},{"issue":"7","key":"2929_CR99","doi-asserted-by":"publisher","first-page":"5655","DOI":"10.1109\/TPAMI.2025.3552484","volume":"47","author":"C Zhu","year":"2025","unstructured":"Zhu, C., Liu, K., Tang, W., Mei, K., Zou, J., & Huang, T. (2025). Hard-aware instance adaptive self-training for unsupervised cross-domain semantic segmentation. TPAMI, 47(7), 5655\u20135671. https:\/\/doi.org\/10.1109\/TPAMI.2025.3552484","journal-title":"TPAMI"},{"issue":"3","key":"2929_CR100","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1109\/TPAMI.2021.3138337","volume":"46","author":"Y Zhu","year":"2021","unstructured":"Zhu, Y., Zhang, Z., Wu, C., Zhang, Z., He, T., Zhang, H., Manmatha, R., Li, M., & Smola, A. (2021). Improving semantic segmentation via efficient self-training. TPAMI, 46(3), 1589\u20131602.","journal-title":"TPAMI"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-026-02929-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-026-02929-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-026-02929-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T14:56:07Z","timestamp":1783781767000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-026-02929-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":100,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["2929"],"URL":"https:\/\/doi.org\/10.1007\/s11263-026-02929-6","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"29 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"344"}}