{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T04:34:10Z","timestamp":1773635650003,"version":"3.50.1"},"reference-count":130,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T00:00:00Z","timestamp":1750809600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T00:00:00Z","timestamp":1750809600000},"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":[[2025,10]]},"DOI":"10.1007\/s11263-025-02465-9","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T06:17:03Z","timestamp":1750832223000},"page":"6768-6793","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Robust Object Detection with Domain-Invariant Training and Continual Test-Time Adaptation"],"prefix":"10.1007","volume":"133","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2644-4457","authenticated-orcid":false,"given":"Qi","family":"Fan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mattia","family":"Segu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernt","family":"Schiele","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dengxin","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Wing","family":"Tai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi-Keung","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,25]]},"reference":[{"key":"2465_CR1","doi-asserted-by":"crossref","unstructured":"An, J., Huang, S., Song, Y., Dou, D., Liu, W. & Luo, J. (2021). Artflow: Unbiased image style transfer via reversible neural flows. In: CVPR.","DOI":"10.1109\/CVPR46437.2021.00092"},{"key":"2465_CR2","unstructured":"Balaji, Y., Sankaranarayanan, S. & Chellappa, R. (2018). Metareg: Towards domain generalization using meta-regularization. In: NeurIPS."},{"key":"2465_CR3","unstructured":"Bartler, A., B\u00fchler, A., Wiewel, F., D\u00f6bler, M., & Yang, B. (2022). Mt3: Meta test-time training for self-supervised test-time adaption. In: ICAIS."},{"key":"2465_CR4","unstructured":"Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y.M. (2020) Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934."},{"key":"2465_CR5","doi-asserted-by":"crossref","unstructured":"Borgwardt, K.M., Gretton, A., Rasch, M.J., Kriegel, H.-P., Sch\u00f6lkopf, B., & Smola, A.J. (2006). Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics.","DOI":"10.1093\/bioinformatics\/btl242"},{"key":"2465_CR6","unstructured":"Borlino, F.C., Polizzotto, S., Caputo, B., & Tommasi, T. (2022). Self-supervision & meta-learning for one-shot unsupervised cross-domain detection. CVIU."},{"key":"2465_CR7","unstructured":"Bui, M.-H., Tran, T., Tran, A., & Phung, D. (2021). Exploiting domain-specific features to enhance domain generalization. NeurIPS."},{"key":"2465_CR8","doi-asserted-by":"crossref","unstructured":"Cai, Q., Pan, Y., Ngo, C.-W., Tian, X., Duan, L. & Yao, T. (2019). Exploring object relation in mean teacher for cross-domain detection. In: CVPR.","DOI":"10.1109\/CVPR.2019.01172"},{"key":"2465_CR9","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":"2465_CR10","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":"2465_CR11","doi-asserted-by":"crossref","unstructured":"Carlucci, F.M., Russo, P., Tommasi, T., & Caputo, B. (2019). Hallucinating agnostic images to generalize across domains. In: ICCV Workshop.","DOI":"10.1109\/ICCVW.2019.00403"},{"key":"2465_CR12","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., Sakaridis, C., Dai, D., & Van\u00a0Gool, L. (2018). Domain adaptive faster r-cnn for object detection in the wild. In: CVPR.","DOI":"10.1109\/CVPR.2018.00352"},{"key":"2465_CR13","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":"2465_CR14","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":"2465_CR15","doi-asserted-by":"crossref","unstructured":"Choi, J., Kim, T., & Kim, C. (2019). Self-ensembling with gan-based data augmentation for domain adaptation in semantic segmentation. In: ICCV.","DOI":"10.1109\/ICCV.2019.00693"},{"key":"2465_CR16","doi-asserted-by":"crossref","unstructured":"Choi, S., Kim, T., Jeong, M., Park, H., & Kim, C. (2021). Meta batch-instance normalization for generalizable person re-identification. In: CVPR.","DOI":"10.1109\/CVPR46437.2021.00343"},{"key":"2465_CR17","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":"2465_CR18","unstructured":"Dayal, A., KB, V., Cenkeramaddi, L.R., Mohan, C., Kumar, A., & N\u00a0Balasubramanian, V. (2024). Madg: margin-based adversarial learning for domain generalization. NeurIPS."},{"key":"2465_CR19","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":"2465_CR20","unstructured":"DeVries, T. & Taylor, G.W. (2017). Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552."},{"key":"2465_CR21","doi-asserted-by":"crossref","unstructured":"D\u2019Innocente, A., Borlino, F.C., Bucci, S., Caputo, B. & Tommasi, T. (2020). One-shot unsupervised cross-domain detection. In: ECCV.","DOI":"10.1007\/978-3-030-58517-4_43"},{"key":"2465_CR22","doi-asserted-by":"crossref","unstructured":"Fahes, M., Vu, T.-H., Bursuc, A., P\u00e9rez, P. & De\u00a0Charette, R. (2023). Poda: Prompt-driven zero-shot domain adaptation. In: ICCV.","DOI":"10.1109\/ICCV51070.2023.01707"},{"key":"2465_CR23","unstructured":"Fan, Q., Segu, M., Tai, Y.-W., Yu, F., Tang, C.-K., Schiele, B., & Dai, D. (2022). Normalization perturbation: A simple domain generalization method for real-world domain shifts. arXiv preprint arXiv:2211.04393."},{"key":"2465_CR24","doi-asserted-by":"crossref","unstructured":"Fan, X., Wang, Q., Ke, J., Yang, F., Gong, B., & Zhou, M. (2021). Adversarially adaptive normalization for single domain generalization. In: CVPR.","DOI":"10.1109\/CVPR46437.2021.00811"},{"key":"2465_CR25","doi-asserted-by":"crossref","unstructured":"Fang, H.-S., Sun, J., Wang, R., Gou, M., Li, Y.-L. & Lu, C. (2019). Instaboost: Boosting instance segmentation via probability map guided copy-pasting. In: ICCV.","DOI":"10.1109\/ICCV.2019.00077"},{"key":"2465_CR26","unstructured":"Ganin, Y., & Lempitsky, V. (2015). Unsupervised domain adaptation by backpropagation. In: ICML."},{"key":"2465_CR27","unstructured":"Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A. & Brendel, W. (2018). Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In: ICLR."},{"key":"2465_CR28","unstructured":"Ghiasi, G., Lin, T.-Y. & Le, Q.V. (2018). Dropblock: A regularization method for convolutional networks. NeurIPS."},{"key":"2465_CR29","doi-asserted-by":"crossref","unstructured":"Gholami, B., Sahu, P., Rudovic, O., Bousmalis, K., & Pavlovic, V. (2020). Unsupervised multi-target domain adaptation: An information theoretic approach. IEEE TIP.","DOI":"10.1109\/TIP.2019.2963389"},{"key":"2465_CR30","doi-asserted-by":"crossref","unstructured":"Gong, R., Li, W., Chen, Y., & Gool, L.V. (2019). Dlow: Domain flow for adaptation and generalization. In: CVPR.","DOI":"10.1109\/CVPR.2019.00258"},{"key":"2465_CR31","doi-asserted-by":"crossref","unstructured":"Grigorescu, S., Trasnea, B., Cocias, T., & Macesanu, G. (2020). A survey of deep learning techniques for autonomous driving. Journal of Field Robotics.","DOI":"10.1002\/rob.21918"},{"key":"2465_CR32","doi-asserted-by":"crossref","unstructured":"He, M., Wang, Y., Wu, J., Wang, Y., Li, H., Li, B., Gan, W., Wu, W. & Qiao, Y. (2022). Cross domain object detection by target-perceived dual branch distillation. In: CVPR.","DOI":"10.1109\/CVPR52688.2022.00935"},{"key":"2465_CR33","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":"2465_CR34","doi-asserted-by":"crossref","unstructured":"Hsu, C.-C., Tsai, Y.-H., Lin, Y.-Y. & Yang, M.-H. (2020). Every pixel matters: Center-aware feature alignment for domain adaptive object detector. In: ECCV.","DOI":"10.1007\/978-3-030-58545-7_42"},{"key":"2465_CR35","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":"2465_CR36","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":"2465_CR37","unstructured":"Ioffe, S. & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: ICML."},{"key":"2465_CR38","doi-asserted-by":"crossref","unstructured":"Isobe, T., Jia, X., Chen, S., He, J., Shi, Y., Liu, J., Lu, H., & Wang, S. (2021). Multi-target domain adaptation with collaborative consistency learning. In: CVPR.","DOI":"10.1109\/CVPR46437.2021.00809"},{"key":"2465_CR39","unstructured":"Iwasawa, Y., & Matsuo, Y. (2021). Test-time classifier adjustment module for model-agnostic domain generalization. In: NeurIPS."},{"key":"2465_CR40","unstructured":"Jackson, P.T., Abarghouei, A.A., Bonner, S., Breckon, T.P., & Obara, B. (2019). Style augmentation: data augmentation via style randomization. In: CVPR Workshops."},{"key":"2465_CR41","doi-asserted-by":"crossref","unstructured":"Jin, X., Lan, C., Zeng, W., & Chen, Z. (2021). Style normalization and restitution for domain generalization and adaptation. TMM.","DOI":"10.1109\/TMM.2021.3104379"},{"key":"2465_CR42","doi-asserted-by":"crossref","unstructured":"Johnson-Roberson, M., Barto, C., Mehta, R., Sridhar, S.N., Rosaen, K. & Vasudevan, R. (2017). Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks? In: ICRA.","DOI":"10.1109\/ICRA.2017.7989092"},{"key":"2465_CR43","unstructured":"Kaggle: Painter by Numbers. https:\/\/www.kaggle.com\/c\/painter-by-numbers\/"},{"key":"2465_CR44","doi-asserted-by":"crossref","unstructured":"Kang, G., Zheng, L., Yan, Y., & Yang, Y. (2018). Deep adversarial attention alignment for unsupervised domain adaptation: the benefit of target expectation maximization. In: ECCV.","DOI":"10.1007\/978-3-030-01252-6_25"},{"key":"2465_CR45","doi-asserted-by":"crossref","unstructured":"Kim, T., Jeong, M., Kim, S., Choi, S., & Kim, C. (2019). Diversify and match: A domain adaptive representation learning paradigm for object detection. In: CVPR.","DOI":"10.1109\/CVPR.2019.01274"},{"key":"2465_CR46","doi-asserted-by":"crossref","unstructured":"Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., & Grabska-Barwinska, A., et\u00a0al. (2017). Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences.","DOI":"10.1073\/pnas.1611835114"},{"key":"2465_CR47","doi-asserted-by":"crossref","unstructured":"Lee, W., Hong, D., Lim, H., & Myung, H. (2024). Object-aware domain generalization for object detection. In: AAAI.","DOI":"10.1609\/aaai.v38i4.28076"},{"key":"2465_CR48","unstructured":"Li, X., Dai, Y., Ge, Y., Liu, J., Shan, Y., & Duan, L.-Y. (2022). Uncertainty modeling for out-of-distribution generalization. In: ICLR."},{"key":"2465_CR49","doi-asserted-by":"crossref","unstructured":"Li, Y.-J., Dai, X., Ma, C.-Y., Liu, Y.-C., Chen, K., Wu, B., He, Z., Kitani, K. & Vajda, P. (2022). Cross-domain adaptive teacher for object detection. In: CVPR.","DOI":"10.1109\/CVPR52688.2022.00743"},{"key":"2465_CR50","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., et\u00a0al. (2022). Yolov6: a single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976."},{"key":"2465_CR51","doi-asserted-by":"crossref","unstructured":"Li, P., Li, D., Li, W., Gong, S., Fu, Y., & Hospedales, T.M. (2021). A simple feature augmentation for domain generalization. In: ICCV.","DOI":"10.1109\/ICCV48922.2021.00876"},{"key":"2465_CR52","doi-asserted-by":"crossref","unstructured":"Li, H., Pan, S.J., Wang, S. & Kot, A.C. (2018). Domain generalization with adversarial feature learning. In: CVPR.","DOI":"10.1109\/CVPR.2018.00566"},{"key":"2465_CR53","unstructured":"Li, Y., Wang, N., Shi, J., Liu, J., & Hou, X. (2016). Revisiting batch normalization for practical domain adaptation. arXiv preprint arXiv:1603.04779."},{"key":"2465_CR54","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":"2465_CR55","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, D., Keuper, M., & Khoreva, A. (2024). Intra- & extra-source exemplar-based style synthesis for improved domain generalization. IJCV.","DOI":"10.1109\/WACV56688.2023.00058"},{"key":"2465_CR56","doi-asserted-by":"crossref","unstructured":"Li, D., Zhang, J., Yang, Y., Liu, C., Song, Y.-Z. & Hospedales, T.M. (2019). Episodic training for domain generalization. In: ICCV.","DOI":"10.1109\/ICCV.2019.00153"},{"key":"2465_CR57","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."},{"key":"2465_CR58","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B. & Belongie, S. (2017). Feature pyramid networks for object detection. In: CVPR.","DOI":"10.1109\/CVPR.2017.106"},{"key":"2465_CR59","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":"2465_CR60","doi-asserted-by":"crossref","unstructured":"Liu, Q., Dou, Q., Yu, L., & Heng, P.A. (2020). Ms-net: multi-site network for improving prostate segmentation with heterogeneous mri data. TMI.","DOI":"10.1109\/TMI.2020.2974574"},{"key":"2465_CR61","unstructured":"Liu, Y., Kothari, P., Delft, B., Bellot-Gurlet, B., Mordan, T., & Alahi, A. (2021). Ttt++: When does self-supervised test-time training fail or thrive? NeurIPS."},{"key":"2465_CR62","unstructured":"Long, M., Cao, Y., Wang, J., & Jordan, M. (2015). Learning transferable features with deep adaptation networks. In: ICML."},{"key":"2465_CR63","doi-asserted-by":"crossref","unstructured":"Luo, Y., Liu, P., & Yang, Y. (2024). Kill two birds with one stone: Domain generalization for semantic segmentation via network pruning. IJCV.","DOI":"10.1007\/s11263-024-02194-5"},{"key":"2465_CR64","unstructured":"Luo, P., Zhang, R., Ren, J., Peng, Z., & Li, J. (2019). Switchable normalization for learning-to-normalize deep representation. TPAMI."},{"key":"2465_CR65","unstructured":"Maaten, L., & Hinton, G. (2008). Visualizing data using t-sne. Journal of machine learning research."},{"key":"2465_CR66","doi-asserted-by":"crossref","unstructured":"Mancini, M., Akata, Z., Ricci, E., & Caputo, B. (2020). Towards recognizing unseen categories in unseen domains. In: ECCV.","DOI":"10.1007\/978-3-030-58592-1_28"},{"key":"2465_CR67","doi-asserted-by":"crossref","unstructured":"Maria\u00a0Carlucci, F., Porzi, L., Caputo, B., Ricci, E., & Rota\u00a0Bulo, S. (2017). Autodial: Automatic domain alignment layers. In: ICCV.","DOI":"10.1109\/ICCV.2017.542"},{"key":"2465_CR68","unstructured":"Michaelis, C., Mitzkus, B., Geirhos, R., Rusak, E., Bringmann, O., Ecker, A.S., Bethge, M., & Brendel, W. (2019). Benchmarking robustness in object detection: Autonomous driving when winter is coming. arXiv preprint arXiv:1907.07484."},{"key":"2465_CR69","unstructured":"MixStyle: MixStyle. https:\/\/github.com\/KaiyangZhou\/mixstyle-release"},{"key":"2465_CR70","doi-asserted-by":"crossref","unstructured":"Motiian, S., Piccirilli, M., Adjeroh, D.A. & Doretto, G. (2017). Unified deep supervised domain adaptation and generalization. In: ICCV.","DOI":"10.1109\/ICCV.2017.609"},{"key":"2465_CR71","doi-asserted-by":"crossref","unstructured":"Muhammad, U., Laaksonen, J., Romaissa\u00a0Beddiar, D., & Oussalah, M. (2024). Domain generalization via ensemble stacking for face presentation attack detection. IJCV.","DOI":"10.1007\/s11263-024-02152-1"},{"key":"2465_CR72","unstructured":"Mummadi, C.K., Hutmacher, R., Rambach, K., Levinkov, E., Brox, T. & Metzen, J.H. (2021). Test-time adaptation to distribution shift by confidence maximization and input transformation. arXiv preprint arXiv:2106.14999."},{"key":"2465_CR73","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":"2465_CR74","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":"2465_CR75","doi-asserted-by":"crossref","unstructured":"Ot\u00e1lora, S., Atzori, M., Andrearczyk, V., Khan, A., & M\u00fcller, H. (2019). Staining invariant features for improving generalization of deep convolutional neural networks in computational pathology. Frontiers in bioengineering and biotechnology.","DOI":"10.3389\/fbioe.2019.00198"},{"key":"2465_CR76","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":"2465_CR77","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":"2465_CR78","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":"2465_CR79","doi-asserted-by":"crossref","unstructured":"Peng, X., Li, Y., & Saenko, K. (2020). Domain2vec: Domain embedding for unsupervised domain adaptation. In: ECCV.","DOI":"10.1007\/978-3-030-58539-6_45"},{"key":"2465_CR80","doi-asserted-by":"crossref","unstructured":"Raghunandan, A., Raghav, P., & Aradhya, H.R., et al. (2018). Object detection algorithms for video surveillance applications. In: ICCSP.","DOI":"10.1109\/ICCSP.2018.8524461"},{"key":"2465_CR81","doi-asserted-by":"crossref","unstructured":"Rao, Z., Guo, J., Tang, L., Huang, Y., Ding, X. & Guo, S. (2024). Srcd: Semantic reasoning with compound domains for single-domain generalized object detection. TNNLS.","DOI":"10.1109\/TNNLS.2024.3480120"},{"key":"2465_CR82","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":"2465_CR83","doi-asserted-by":"crossref","unstructured":"Rezaeianaran, F., Shetty, R., Aljundi, R., Reino, D.O., Zhang, S., & Schiele, B. (2021). Seeking similarities over differences: Similarity-based domain alignment for adaptive object detection. In: ICCV.","DOI":"10.1109\/ICCV48922.2021.00907"},{"key":"2465_CR84","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":"2465_CR85","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":"2465_CR86","doi-asserted-by":"crossref","unstructured":"Roy, S., Krivosheev, E., Zhong, Z., Sebe, N., & Ricci, E. (2021). Curriculum graph co-teaching for multi-target domain adaptation. In: CVPR.","DOI":"10.1109\/CVPR46437.2021.00531"},{"key":"2465_CR87","doi-asserted-by":"crossref","unstructured":"Saito, K., Ushiku, Y., Harada, T., & Saenko, K. (2019). Strong-weak distribution alignment for adaptive object detection. In: CVPR.","DOI":"10.1109\/CVPR.2019.00712"},{"key":"2465_CR88","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":"2465_CR89","doi-asserted-by":"crossref","unstructured":"Sakaridis, C., Dai, D., & Van\u00a0Gool, L. (2018). Semantic foggy scene understanding with synthetic data. IJCV.","DOI":"10.1007\/s11263-018-1072-8"},{"key":"2465_CR90","unstructured":"Schneider, S., Rusak, E., Eck, L., Bringmann, O., Brendel, W., & Bethge, M. (2020). Improving robustness against common corruptions by covariate shift adaptation. In: NeurIPS."},{"key":"2465_CR91","unstructured":"Segu, M., Tonioni, A., & Tombari, F. (2020). Batch normalization embeddings for deep domain generalization. arXiv preprint arXiv:2011.12672."},{"key":"2465_CR92","unstructured":"Shankar, S., Piratla, V., Chakrabarti, S., Chaudhuri, S., Jyothi, P. & Sarawagi, S. (2018). Generalizing across domains via cross-gradient training. In: ICLR."},{"key":"2465_CR93","unstructured":"Somavarapu, N., Ma, C.-Y., & Kira, Z. (2020). Frustratingly simple domain generalization via image stylization. arXiv preprint arXiv:2006.11207."},{"key":"2465_CR94","doi-asserted-by":"crossref","unstructured":"Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., et al. (2020). Scalability in perception for autonomous driving: Waymo open dataset. In: CVPR.","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"2465_CR95","unstructured":"Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A., & Hardt, M. (2020). Test-time training with self-supervision for generalization under distribution shifts. In: ICML."},{"key":"2465_CR96","doi-asserted-by":"crossref","unstructured":"Tang, Z., Gao, Y., Zhu, Y., Zhang, Z., Li, M., & Metaxas, D.N. (2021). Crossnorm and selfnorm for generalization under distribution shifts. In: ICCV.","DOI":"10.1109\/ICCV48922.2021.00012"},{"key":"2465_CR97","unstructured":"Ulyanov, D., Vedaldi, A. & Lempitsky, V. (2016). Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022."},{"key":"2465_CR98","doi-asserted-by":"crossref","unstructured":"VS, V., Gupta, V., Oza, P., Sindagi, V.A., & Patel, V.M. (2021). Mega-cda: Memory guided attention for category-aware unsupervised domain adaptive object detection. In: CVPR.","DOI":"10.1109\/CVPR46437.2021.00449"},{"key":"2465_CR99","doi-asserted-by":"crossref","unstructured":"Venkateswara, H., Eusebio, J., Chakraborty, S., & Panchanathan, S. (2017). Deep hashing network for unsupervised domain adaptation. In: CVPR.","DOI":"10.1109\/CVPR.2017.572"},{"key":"2465_CR100","unstructured":"Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Lopez-Paz, D. & Bengio, Y. (2019). Manifold mixup: Better representations by interpolating hidden states. In: ICML."},{"key":"2465_CR101","doi-asserted-by":"crossref","unstructured":"Vidit, V., Engilberge, M., & Salzmann, M. (2023). Clip the gap: A single domain generalization approach for object detection. In: CVPR.","DOI":"10.1109\/CVPR52729.2023.00314"},{"key":"2465_CR102","doi-asserted-by":"crossref","unstructured":"Volpi, R., & Murino, V. (2019). Addressing model vulnerability to distributional shifts over image transformation sets. In: ICCV.","DOI":"10.1109\/ICCV.2019.00807"},{"key":"2465_CR103","unstructured":"Volpi, R., Namkoong, H., Sener, O., Duchi, J.C., Murino, V., & Savarese, S. (2018). Generalizing to unseen domains via adversarial data augmentation. In: NeurIPS."},{"key":"2465_CR104","doi-asserted-by":"crossref","unstructured":"Wan, Z., Li, L., Li, H., He, H., & Ni, Z. (2020). One-shot unsupervised domain adaptation for object detection. In: IJCNN.","DOI":"10.1109\/IJCNN48605.2020.9207244"},{"key":"2465_CR105","doi-asserted-by":"crossref","unstructured":"Wang, Q., Fink, O., Van\u00a0Gool, L., & Dai, D. (2022). Continual test-time domain adaptation. In: CVPR.","DOI":"10.1109\/CVPR52688.2022.00706"},{"key":"2465_CR106","doi-asserted-by":"crossref","unstructured":"Wang, X., Huang, T.E., Liu, B., Yu, F., Wang, X., Gonzalez, J.E., & Darrell, T. (2021). Robust object detection via instance-level temporal cycle confusion. In: ICCV.","DOI":"10.1109\/ICCV48922.2021.00901"},{"key":"2465_CR107","unstructured":"Wang, D., Shelhamer, E., Liu, S., Olshausen, B., & Darrell, T. (2020). Tent: Fully test-time adaptation by entropy minimization. In: ICLR."},{"key":"2465_CR108","doi-asserted-by":"crossref","unstructured":"Wang, K., Yang, C., & Betke, M. (2021). Consistency regularization with high-dimensional nonadversarial source-guided perturbation for unsupervised domain adaptation in segmentation. In: AAAI.","DOI":"10.1609\/aaai.v35i11.17216"},{"key":"2465_CR109","doi-asserted-by":"crossref","unstructured":"Wu, Y. & He, K. (2018). Group normalization. In: ECCV.","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"2465_CR110","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":"2465_CR111","unstructured":"Wu, Y., & Johnson, J. (2021). Rethinking\u201d batch\u201d in batchnorm. arXiv preprint arXiv:2105.07576."},{"key":"2465_CR112","doi-asserted-by":"crossref","unstructured":"Wu, Y., Chen, Y., Yuan, L., Liu, Z., Wang, L., Li, H. & Fu, Y. (2020). Rethinking classification and localization for object detection. In: CVPR.","DOI":"10.1109\/CVPR42600.2020.01020"},{"key":"2465_CR113","doi-asserted-by":"crossref","unstructured":"Xie, X., Chen, J., Li, Y., Shen, L., Ma, K., & Zheng, Y. (2020). Self-supervised cyclegan for object-preserving image-to-image domain adaptation. In: ECCV.","DOI":"10.1007\/978-3-030-58565-5_30"},{"key":"2465_CR114","unstructured":"Xu, Z., Liu, D., Yang, J., Raffel, C., & Niethammer, M. (2020). Robust and generalizable visual representation learning via random convolutions. In: ICLR."},{"key":"2465_CR115","doi-asserted-by":"crossref","unstructured":"Xu, M., Wang, H., Ni, B., Tian, Q. & Zhang, W. (2020). Cross-domain detection via graph-induced prototype alignment. In: CVPR.","DOI":"10.1109\/CVPR42600.2020.01237"},{"key":"2465_CR116","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":"2465_CR117","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":"2465_CR118","doi-asserted-by":"crossref","unstructured":"Yue, X., Zheng, Z., Zhang, S., Gao, Y., Darrell, T., Keutzer, K., & Vincentelli, A.S. (2021). Prototypical cross-domain self-supervised learning for few-shot unsupervised domain adaptation. In: CVPR.","DOI":"10.1109\/CVPR46437.2021.01362"},{"key":"2465_CR119","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J. & Yoo, Y. (2019). Cutmix: Regularization strategy to train strong classifiers with localizable features. In: ICCV.","DOI":"10.1109\/ICCV.2019.00612"},{"key":"2465_CR120","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D. & Fergus, R. (2014). Visualizing and understanding convolutional networks. In: ECCV.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"2465_CR121","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N. & Lopez-Paz, D. (2018). mixup: Beyond empirical risk minimization. In: ICLR."},{"key":"2465_CR122","doi-asserted-by":"crossref","unstructured":"Zhang, Y., David, P., & Gong, B. (2017). Curriculum domain adaptation for semantic segmentation of urban scenes. In: ICCV.","DOI":"10.1109\/ICCV.2017.223"},{"key":"2465_CR123","unstructured":"Zhang, M.M., Levine, S., & Finn, C. (2021). Memo: Test time robustness via adaptation and augmentation. In: NeurIPS Workshop."},{"key":"2465_CR124","unstructured":"Zhang, X., Xu, Z., Xu, R., Liu, J., Cui, P., Wan, W., Sun, C., & Li, C. (2022). Towards domain generalization in object detection. arXiv preprint arXiv:2203.14387."},{"key":"2465_CR125","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Zhong, Z., Zhao, N., Sebe, N., & Lee, G.H. (2024). Style-hallucinated dual consistency learning: A unified framework for visual domain generalization. IJCV.","DOI":"10.1007\/s11263-023-01911-w"},{"key":"2465_CR126","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, Y., Hospedales, T. & Xiang, T. (2020). Learning to generate novel domains for domain generalization. In: ECCV.","DOI":"10.1007\/978-3-030-58517-4_33"},{"key":"2465_CR127","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, Y., Hospedales, T., & Xiang, T. (2020). Deep domain-adversarial image generation for domain generalisation. In: AAAI.","DOI":"10.1609\/aaai.v34i07.7003"},{"key":"2465_CR128","unstructured":"Zhou, K., Yang, Y., Qiao, Y., & Xiang, T. (2020). Domain generalization with mixstyle. In: ICLR."},{"key":"2465_CR129","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, Y., Qiao, Y., & Xiang, T. (2024). Mixstyle neural networks for domain generalization and adaptation. IJCV.","DOI":"10.1007\/s11263-023-01913-8"},{"key":"2465_CR130","doi-asserted-by":"crossref","unstructured":"Zhu, X., Pang, J., Yang, C., Shi, J. & Lin, D. (2019). Adapting object detectors via selective cross-domain alignment. In: CVPR.","DOI":"10.1109\/CVPR.2019.00078"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-025-02465-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-025-02465-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-025-02465-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T08:53:05Z","timestamp":1760086385000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-025-02465-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,25]]},"references-count":130,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["2465"],"URL":"https:\/\/doi.org\/10.1007\/s11263-025-02465-9","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,25]]},"assertion":[{"value":"29 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 June 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}