{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T16:49:39Z","timestamp":1774802979791,"version":"3.50.1"},"reference-count":87,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T00:00:00Z","timestamp":1697587200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T00:00:00Z","timestamp":1697587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001381","name":"National Research Foundation Singapore","doi-asserted-by":"publisher","award":["AISG Award No: AISG2-RP-2020-016"],"award-info":[{"award-number":["AISG Award No: AISG2-RP-2020-016"]}],"id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001459","name":"Ministry of Education - Singapore","doi-asserted-by":"publisher","award":["MOE-T2EP20120-0011"],"award-info":[{"award-number":["MOE-T2EP20120-0011"]}],"id":[{"id":"10.13039\/501100001459","id-type":"DOI","asserted-by":"publisher"}]},{"name":"EU H2020","award":["951911"],"award-info":[{"award-number":["951911"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s11263-023-01911-w","type":"journal-article","created":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T08:02:39Z","timestamp":1697616159000},"page":"837-853","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Style-Hallucinated Dual Consistency Learning: A Unified Framework for Visual Domain Generalization"],"prefix":"10.1007","volume":"132","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4754-0325","authenticated-orcid":false,"given":"Yuyang","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Zhun","family":"Zhong","sequence":"additional","affiliation":[]},{"given":"Na","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Nicu","family":"Sebe","sequence":"additional","affiliation":[]},{"given":"Gim Hee","family":"Lee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,18]]},"reference":[{"key":"1911_CR1","unstructured":"Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein generative adversarial networks. In ICML"},{"key":"1911_CR2","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020). End-to-end object detection with transformers. In ECCV","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"1911_CR3","doi-asserted-by":"crossref","unstructured":"Carlucci, F. M., D\u2019Innocente, A., Bucci, S., Caputo, B., & Tommasi, T. (2019). Domain generalization by solving jigsaw puzzles. In CVPR","DOI":"10.1109\/CVPR.2019.00233"},{"key":"1911_CR4","doi-asserted-by":"crossref","unstructured":"Chen, H., Zhao, L., Zhang, H., Wang, Z., Zuo, Z., Li, A., Xing, W., & Lu, D. (2021a). Diverse image style transfer via invertible cross-space mapping. In ICCV","DOI":"10.1109\/ICCV48922.2021.01461"},{"key":"1911_CR5","doi-asserted-by":"crossref","unstructured":"Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. In ECCV","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"1911_CR6","doi-asserted-by":"crossref","unstructured":"Chen, M., Zheng, Z., Yang, Y., & Chua, T. S. (2022). PiPa: Pixel-and patch-wise self-supervised learning for domain adaptative semantic segmentation. arXiv preprint arXiv:2211.07609","DOI":"10.1145\/3581783.3611708"},{"key":"1911_CR7","unstructured":"Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In ICML"},{"key":"1911_CR8","doi-asserted-by":"publisher","first-page":"2223","DOI":"10.1007\/s11263-021-01447-x","volume":"129","author":"Y Chen","year":"2021","unstructured":"Chen, Y., Wang, H., Li, W., Sakaridis, C., Dai, D., & Van Gool, L. (2021). Scale-aware domain adaptive faster R-CNN. IJCV, 129, 2223\u20132243.","journal-title":"IJCV"},{"key":"1911_CR9","doi-asserted-by":"crossref","unstructured":"Choi, S., Jung, S., Yun, H., Kim, J. T., Kim, S., & Choo, J. (2021). RobustNet: Improving domain generalization in urban-scene segmentation via instance selective whitening. In CVPR","DOI":"10.1109\/CVPR46437.2021.01141"},{"key":"1911_CR10","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016). The cityscapes dataset for semantic urban scene understanding. In CVPR","DOI":"10.1109\/CVPR.2016.350"},{"key":"1911_CR11","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. In CVPR","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1911_CR12","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., & Uszkoreit, J. (2021). An image is worth 16 x 16 words: Transformers for image recognition at scale. In ICLR"},{"key":"1911_CR13","doi-asserted-by":"publisher","first-page":"2842","DOI":"10.1007\/s11263-022-01674-w","volume":"130","author":"D Du","year":"2022","unstructured":"Du, D., Chen, J., Li, Y., Ma, K., Wu, G., Zheng, Y., & Wang, L. (2022). Cross-domain gated learning for domain generalization. IJCV, 130, 2842\u20132857.","journal-title":"IJCV"},{"key":"1911_CR14","unstructured":"Dumoulin, V., Shlens, J., & Kudlur, M. (2017). A learned representation for artistic style. In ICLR"},{"key":"1911_CR15","doi-asserted-by":"crossref","unstructured":"Fini, E., Sangineto, E., Lathuili\u00e8re, S., Zhong, Z., Nabi, M., & Ricci, E. (2021). A unified objective for novel class discovery. In ICCV","DOI":"10.1109\/ICCV48922.2021.00915"},{"key":"1911_CR16","unstructured":"French, G., Laine, S., Aila, T., Mackiewicz, M., & Finlayson, G. (2020). Semi-supervised semantic segmentation needs strong, varied perturbations. In BMVC"},{"key":"1911_CR17","doi-asserted-by":"publisher","first-page":"2865","DOI":"10.1007\/s11263-021-01496-2","volume":"129","author":"R Gong","year":"2021","unstructured":"Gong, R., Li, W., Chen, Y., Dai, D., & Van Gool, L. (2021). DLOW: Domain flow and applications. IJCV, 129, 2865\u20132888.","journal-title":"IJCV"},{"key":"1911_CR18","unstructured":"Gretton, A., Borgwardt, K. M., Rasch, M. J., Sch\u00f6lkopf, B., & Smola, A. (2012). A kernel two-sample test. JMLR."},{"key":"1911_CR19","doi-asserted-by":"crossref","unstructured":"Halmos, P. R. (1987). Finite-dimensional vector spaces. Springer.","DOI":"10.1007\/978-1-4612-6387-6_1"},{"key":"1911_CR20","doi-asserted-by":"publisher","first-page":"4230","DOI":"10.1109\/TITS.2020.3014013","volume":"22","author":"M Hassaballah","year":"2020","unstructured":"Hassaballah, M., Kenk, M. A., Muhammad, K., & Minaee, S. (2020). Vehicle detection and tracking in adverse weather using a deep learning framework. IEEE Transactions on Intelligent Transportation Systems, 22, 4230\u20134242.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"1911_CR21","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":"1911_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., & Girshick, R. (2017). Mask R-CNN. In ICCV","DOI":"10.1109\/ICCV.2017.322"},{"key":"1911_CR23","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020). Momentum contrast for unsupervised visual representation learning. In CVPR","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"1911_CR24","unstructured":"Hendrycks, D., Mu, N., Cubuk, E. D., Zoph, B., Gilmer, J., & Lakshminarayanan, B. (2020). AugMix: A simple data processing method to improve robustness and uncertainty. In ICLR"},{"key":"1911_CR25","unstructured":"Hoffman, J., Wang, D., Yu, F., & Darrell, T. (2016). FCNs in the wild: Pixel-level adversarial and constraint-based adaptation. arXiv preprint arXiv:1612.02649"},{"key":"1911_CR26","doi-asserted-by":"crossref","unstructured":"Hoyer, L., Dai, D., & Van\u00a0Gool, L. (2022). DAFormer: Improving network architectures and training strategies for domain-adaptive semantic segmentation. In CVPR","DOI":"10.1109\/CVPR52688.2022.00969"},{"key":"1911_CR27","doi-asserted-by":"crossref","unstructured":"Huang, J., Guan, D., Xiao, A., & Lu, S. (2021). FSDR: Frequency space domain randomization for domain generalization. In CVPR","DOI":"10.1109\/CVPR46437.2021.00682"},{"key":"1911_CR28","doi-asserted-by":"crossref","unstructured":"Huang, L., Zhou, Y., Zhu, F., Liu, L., & Shao, L. (2019). Iterative normalization: Beyond standardization towards efficient whitening. In CVPR","DOI":"10.1109\/CVPR.2019.00501"},{"key":"1911_CR29","doi-asserted-by":"crossref","unstructured":"Huang, X., & Belongie, S. (2017). Arbitrary style transfer in real-time with adaptive instance normalization. In ICCV","DOI":"10.1109\/ICCV.2017.167"},{"key":"1911_CR30","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wang, H., Xing, E. P., & Huang, D. (2020). Self-challenging improves cross-domain generalization. In ECCV","DOI":"10.1007\/978-3-030-58536-5_8"},{"key":"1911_CR31","unstructured":"Kannan, H., Kurakin, A., & Goodfellow, I. (2018). Adversarial logit pairing. In ICML"},{"key":"1911_CR32","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J., Park, J., Min, D., & Sohn, K. (2022). Pin the memory: Learning to generalize semantic segmentation. In CVPR","DOI":"10.1109\/CVPR52688.2022.00431"},{"key":"1911_CR33","doi-asserted-by":"crossref","unstructured":"Lee, S., Seong, H., Lee, S., & Kim, E. (2022). WildNet: Learning domain generalized semantic segmentation from the wild. In CVPR","DOI":"10.1109\/CVPR52688.2022.00970"},{"key":"1911_CR34","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, Y., Song, Y. Z., & Hospedales, T. M. (2017). Deeper, broader and artier domain generalization. In ICCV","DOI":"10.1109\/ICCV.2017.591"},{"key":"1911_CR35","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, Y., Song, Y. Z., & Hospedales, T. (2018a). Learning to generalize: Meta-learning for domain generalization. In AAAI","DOI":"10.1609\/aaai.v32i1.11596"},{"key":"1911_CR36","doi-asserted-by":"crossref","unstructured":"Li, Y., Tian, X., Gong, M., Liu, Y., Liu, T., Zhang, K., & Tao, D. (2018b). Deep domain generalization via conditional invariant adversarial networks. In ECCV","DOI":"10.1609\/aaai.v32i1.11682"},{"key":"1911_CR37","doi-asserted-by":"crossref","unstructured":"Lin, C., Yuan, Z., Zhao, S., Sun, P., Wang, C., & Cai, J. (2021). Domain-invariant disentangled network for generalizable object detection. In ICCV","DOI":"10.1109\/ICCV48922.2021.00865"},{"key":"1911_CR38","unstructured":"Liu, W., Rabinovich, A., & Berg, A. C. (2015). ParseNet: Looking wider to see better. arXiv preprint arXiv:1506.04579"},{"key":"1911_CR39","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1911_CR40","unstructured":"Loshchilov, I., & Hutter, F. (2019). Decoupled weight decay regularization. In ICLR"},{"key":"1911_CR41","unstructured":"MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability"},{"key":"1911_CR42","doi-asserted-by":"crossref","unstructured":"Neuhold, G., Ollmann, T., Rota\u00a0Bulo, S., & Kontschieder, P. (2017). The mapillary vistas dataset for semantic understanding of street scenes. In ICCV","DOI":"10.1109\/ICCV.2017.534"},{"key":"1911_CR43","doi-asserted-by":"crossref","unstructured":"Nuriel, O., Benaim, S., & Wolf, L. (2021). Permuted AdaIN: Reducing the bias towards global statistics in image classification. In CVPR","DOI":"10.1109\/CVPR46437.2021.00936"},{"key":"1911_CR44","doi-asserted-by":"crossref","unstructured":"Pan, X., Luo, P., Shi, J., & Tang, X. (2018). Two at once: Enhancing learning and generalization capacities via IBN-Net. In ECCV","DOI":"10.1007\/978-3-030-01225-0_29"},{"key":"1911_CR45","doi-asserted-by":"crossref","unstructured":"Pan, X., Zhan, X., Shi, J., Tang, X., & Luo, P. (2019). Switchable whitening for deep representation learning. In ICCV","DOI":"10.1109\/ICCV.2019.00195"},{"key":"1911_CR46","first-page":"6594","volume":"30","author":"D Peng","year":"2021","unstructured":"Peng, D., Lei, Y., Liu, L., Zhang, P., & Liu, J. (2021). Global and local texture randomization for synthetic-to-real semantic segmentation. IEEE TIP, 30, 6594\u20136608.","journal-title":"IEEE TIP"},{"key":"1911_CR47","doi-asserted-by":"crossref","unstructured":"Peng, D., Lei, Y., Hayat, M., Guo, Y., & Li, W. (2022). Semantic-aware domain generalized segmentation. In CVPR","DOI":"10.1109\/CVPR52688.2022.00262"},{"key":"1911_CR48","unstructured":"Qi, C. R., Yi, L., Su, H., & Guibas, L. J. (2017). PointNet++: Deep hierarchical feature learning on point sets in a metric space. In NeurIPS"},{"key":"1911_CR49","doi-asserted-by":"crossref","unstructured":"Qiao, F., Zhao, L., & Peng, X. (2020). Learning to learn single domain generalization. In CVPR","DOI":"10.1109\/CVPR42600.2020.01257"},{"key":"1911_CR50","unstructured":"Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In NeurIPS"},{"key":"1911_CR51","doi-asserted-by":"crossref","unstructured":"Richter, S. R., Vineet, V., Roth, S., & Koltun, V. (2016). Playing for data: Ground truth from computer games. In ECCV","DOI":"10.1007\/978-3-319-46475-6_7"},{"key":"1911_CR52","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":"1911_CR53","doi-asserted-by":"crossref","unstructured":"Roy, S., Liu, M., Zhong, Z., Sebe, N., & Ricci, E. (2022). Class-incremental novel class discovery. In ECCV","DOI":"10.1007\/978-3-031-19827-4_19"},{"key":"1911_CR54","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. (2018). Semantic foggy scene understanding with synthetic data. IJCV, 126, 973\u2013992.","journal-title":"IJCV"},{"key":"1911_CR55","doi-asserted-by":"crossref","unstructured":"Sakaridis, C., Dai, D., & Gool, L. V. (2019). Guided curriculum model adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation. In ICCV","DOI":"10.1109\/ICCV.2019.00747"},{"key":"1911_CR56","doi-asserted-by":"crossref","unstructured":"Sakaridis, C., Dai, D., & Van\u00a0Gool, L. (2021). ACDC: The adverse conditions dataset with correspondences for semantic driving scene understanding. In ICCV","DOI":"10.1109\/ICCV48922.2021.01059"},{"key":"1911_CR57","unstructured":"Shankar, S., Piratla, V., Chakrabarti, S., Chaudhuri, S., Jyothi, P., & Sarawagi, S. (2018). Generalizing across domains via cross-gradient training. In ICLR"},{"key":"1911_CR58","unstructured":"Shui, C., Li, Z., Li, J., Gagn\u00e9, C., Ling, C. X., & Wang, B. (2021). Aggregating from multiple target-shifted sources. In ICML"},{"key":"1911_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109808","volume":"257","author":"C Shui","year":"2022","unstructured":"Shui, C., Chen, Q., Wen, J., Zhou, F., Gagn\u00e9, C., & Wang, B. (2022). A novel domain adaptation theory with Jensen\u2013Shannon divergence. Knowledge-Based Systems, 257, 109808.","journal-title":"Knowledge-Based Systems"},{"key":"1911_CR60","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1007\/s10994-021-06080-w","volume":"111","author":"C Shui","year":"2022","unstructured":"Shui, C., Wang, B., & Gagn\u00e9, C. (2022). On the benefits of representation regularization in invariance based domain generalization. Machine Learning, 111, 895\u2013915.","journal-title":"Machine Learning"},{"key":"1911_CR61","unstructured":"Shui, C., Xu, G., Chen, Q., Li, J., Ling, C. X., Arbel, T., Wang, B., & Gagn\u00e9, C. (2022c). On learning fairness and accuracy on multiple subgroups. In NeurIPS"},{"key":"1911_CR62","unstructured":"Tang, Z., Gao, Y., Zhu, Y., Zhang, Z., Li, M., & Metaxas, D. (2021). SelfNorm and CrossNorm for out-of-distribution robustness. In ICCV"},{"key":"1911_CR63","unstructured":"Tarvainen, A., & Valpola, H. (2017). Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In NeurIPS"},{"key":"1911_CR64","unstructured":"Vapnik, V. (2013). The nature of statistical learning theory. Springer science & business media."},{"key":"1911_CR65","unstructured":"Wang, H., Xiao, C., Kossaifi, J., Yu, Z., Anandkumar, A., & Wang, Z. (2021a). AugMax: Adversarial composition of random augmentations for robust training. In NeurIPS"},{"key":"1911_CR66","doi-asserted-by":"crossref","unstructured":"Wang, P., Li, Y., & Vasconcelos, N. (2021b). Rethinking and improving the robustness of image style transfer. In CVPR","DOI":"10.1109\/CVPR46437.2021.00019"},{"key":"1911_CR67","doi-asserted-by":"crossref","unstructured":"Wang, Z., Luo, Y., Qiu, R., Huang, Z., & Baktashmotlagh, M. (2021c). Learning to diversify for single domain generalization. In ICCV","DOI":"10.1109\/ICCV48922.2021.00087"},{"key":"1911_CR68","doi-asserted-by":"crossref","unstructured":"Wu, A., & Deng, C. (2022). Single-domain generalized object detection in urban scene via cyclic-disentangled self-distillation. In CVPR","DOI":"10.1109\/CVPR52688.2022.00092"},{"key":"1911_CR69","doi-asserted-by":"crossref","unstructured":"Wu, A., Liu, R., Han, Y., Zhu, L., & Yang, Y. (2021). Vector-decomposed disentanglement for domain-invariant object detection. In ICCV","DOI":"10.1109\/ICCV48922.2021.00921"},{"key":"1911_CR70","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":"1911_CR71","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","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"1911_CR72","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1007\/s11263-022-01712-7","volume":"131","author":"J Yuan","year":"2022","unstructured":"Yuan, J., Ma, X., Chen, D., Kuang, K., Wu, F., & Lin, L. (2022). Domain-specific bias filtering for single labeled domain generalization. IJCV, 131, 552\u2013571.","journal-title":"IJCV"},{"key":"1911_CR73","doi-asserted-by":"crossref","unstructured":"Yue, X., Zhang, Y., Zhao, S., Sangiovanni-Vincentelli, A., Keutzer, K., & Gong, B. (2019). Domain randomization and pyramid consistency: simulation-to-real generalization without accessing target domain data. In ICCV","DOI":"10.1109\/ICCV.2019.00219"},{"key":"1911_CR74","unstructured":"Zhao, L., Liu, T., Peng, X., & Metaxas, D. (2020). Maximum-entropy adversarial data augmentation for improved generalization and robustness. In NeurIPS"},{"key":"1911_CR75","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Zhong, Z., Yang, F., Luo, Z., Lin, Y., Li, S., & Nicu, S. (2021). Learning to generalize unseen domains via memory-based multi-source meta-learning for person re-identification. In CVPR","DOI":"10.1109\/CVPR46437.2021.00621"},{"key":"1911_CR76","first-page":"7019","volume":"32","author":"Y Zhao","year":"2022","unstructured":"Zhao, Y., Zhong, Z., Luo, Z., Lee, G. H., & Sebe, N. (2022). Source-free open compound domain adaptation in semantic segmentation. IEEE TCSVT, 32, 7019\u20137032.","journal-title":"IEEE TCSVT"},{"key":"1911_CR77","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Zhong, Z., Sebe, N., & Lee, G. H. (2022b). Novel class discovery in semantic segmentation. In CVPR","DOI":"10.1109\/CVPR52688.2022.00430"},{"key":"1911_CR78","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Zhong, Z., Zhao, N., Sebe, N., & Lee, G. H. (2022c). Style-hallucinated dual consistency learning for domain generalized semantic segmentation. In ECCV","DOI":"10.1007\/s11263-023-01911-w"},{"key":"1911_CR79","doi-asserted-by":"crossref","unstructured":"Zheng, Z., & Yang, Y. (2020). Unsupervised scene adaptation with memory regularization in vivo. In IJCAI","DOI":"10.24963\/ijcai.2020\/150"},{"key":"1911_CR80","doi-asserted-by":"crossref","unstructured":"Zheng, Z., & Yang, Y. (2021). Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation. In IJCV","DOI":"10.1007\/s11263-020-01395-y"},{"key":"1911_CR81","first-page":"5371","volume":"31","author":"Z Zheng","year":"2022","unstructured":"Zheng, Z., & Yang, Y. (2022). Adaptive boosting for domain adaptation: Toward robust predictions in scene segmentation. IEEE TIP, 31, 5371\u20135382.","journal-title":"IEEE TIP"},{"key":"1911_CR82","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zhu, L., Luo, Z., Li, S., Yang, Y., & Sebe, N. (2021). OpenMix: Reviving known knowledge for discovering novel visual categories in an open world. In CVPR","DOI":"10.1109\/CVPR46437.2021.00934"},{"key":"1911_CR83","unstructured":"Zhong, Z., Zhao, Y., Lee, G. H., & Sebe, N. (2022). Adversarial style augmentation for domain generalized urban-scene segmentation. In NeurIPS"},{"key":"1911_CR84","unstructured":"Zhou, K., Yang, Y., Qiao, Y., & Xiang, T. (2021a). Domain generalization with mixstyle. In ICLR"},{"key":"1911_CR85","unstructured":"Zhou, Q., Feng, Z., Gu, Q., Pang, J., Cheng, G., Lu, X., Shi, J., & Ma, L. (2021b). Context-aware mixup for domain adaptive semantic segmentation. arXiv preprint arXiv:2108.03557"},{"key":"1911_CR86","doi-asserted-by":"publisher","first-page":"103448","DOI":"10.1016\/j.cviu.2022.103448","volume":"221","author":"Q Zhou","year":"2022","unstructured":"Zhou, Q., Feng, Z., Gu, Q., Cheng, G., Lu, X., Shi, J., & Ma, L. (2022). Uncertainty-aware consistency regularization for cross-domain semantic segmentation. Computer Vision and Image Understanding, 221, 103448.","journal-title":"Computer Vision and Image Understanding"},{"key":"1911_CR87","doi-asserted-by":"crossref","unstructured":"Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In ICCV","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-023-01911-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-023-01911-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-023-01911-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T19:17:22Z","timestamp":1708111042000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-023-01911-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,18]]},"references-count":87,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["1911"],"URL":"https:\/\/doi.org\/10.1007\/s11263-023-01911-w","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,18]]},"assertion":[{"value":"16 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 October 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}