{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:03:02Z","timestamp":1776884582752,"version":"3.51.2"},"publisher-location":"Cham","reference-count":50,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031733895","type":"print"},{"value":"9783031733901","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73390-1_27","type":"book-chapter","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T16:24:01Z","timestamp":1730305441000},"page":"467-483","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["MemBN: Robust Test-Time Adaptation via\u00a0Batch Norm with\u00a0Statistics Memory"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3902-0188","authenticated-orcid":false,"given":"Juwon","family":"Kang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1233-028X","authenticated-orcid":false,"given":"Nayeong","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4742-2473","authenticated-orcid":false,"given":"Jungseul","family":"Ok","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4567-9091","authenticated-orcid":false,"given":"Suha","family":"Kwak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,31]]},"reference":[{"key":"27_CR1","unstructured":"Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)"},{"key":"27_CR2","doi-asserted-by":"crossref","unstructured":"Boudiaf, M., Mueller, R., Ben\u00a0Ayed, I., Bertinetto, L.: Parameter-free online test-time adaptation. In: Proceedings. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8344\u20138353 (2022)","DOI":"10.1109\/CVPR52688.2022.00816"},{"key":"27_CR3","doi-asserted-by":"crossref","unstructured":"Chen, D., Wang, D., Darrell, T., Ebrahimi, S.: Contrastive test-time adaptation. In: Proceedings. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 295\u2013305 (2022)","DOI":"10.1109\/CVPR52688.2022.00039"},{"key":"27_CR4","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with Atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"27_CR6","unstructured":"Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: Proceedings. International Conference on Machine Learning (ICML), pp. 1180\u20131189. PMLR (2015)"},{"key":"27_CR7","unstructured":"Gong, T., Jeong, J., Kim, T., Kim, Y., Shin, J., Lee, S.J.: NOTE: robust continual test-time adaptation against temporal correlation. In: Proceedings Neural Information Processing Systems (NeurIPS). vol.\u00a035, pp. 27253\u201327266 (2022)"},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"27_CR9","unstructured":"Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. In: Proceedings. International Conference on Learning Representations (ICLR) (2019)"},{"key":"27_CR10","unstructured":"Hu, X., et al.: MixNorm: test-Time Adaptation Through Online Normalization Estimation arXiv:2110.11478 (2021)"},{"key":"27_CR11","unstructured":"Ioffe, S.: Batch ReNormalization: towards reducing minibatch dependence in batch-normalized models. In: Proceedings. Neural Information Processing Systems (NeurIPS), vol.\u00a030 (2017)"},{"key":"27_CR12","unstructured":"Ioffe, S., Szegedy, C.: Batch Normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings International Conference on Machine Learning (ICML) (2015)"},{"key":"27_CR13","unstructured":"Iwasawa, Y., Matsuo, Y.: Test-time classifier adjustment module for model-agnostic domain generalization. In: Proceedings Neural Information Processing Systems (NeurIPS), vol.\u00a034 (2021)"},{"key":"27_CR14","unstructured":"Jang, M., Chung, S.Y., Chung, H.W.: Test-time adaptation via self-training with nearest neighbor information. In: Proceedings International Conference on Learning Representations (ICLR) (2023)"},{"key":"27_CR15","unstructured":"Kang, J., Kim, N., Kwon, D., Ok, J., Kwak, S.: Leveraging proxy of training data for test-time adaptation. In: Proceedings International Conference on Machine Learning (ICML) (2023)"},{"key":"27_CR16","unstructured":"Khurana, A., Paul, S., Rai, P., Biswas, S., Aggarwal, G.: SITA: single image test-time adaptation. arXiv preprint arXiv:2112.02355 (2021)"},{"key":"27_CR17","doi-asserted-by":"crossref","unstructured":"Kirkpatrick, J., et\u00a0al.: Overcoming catastrophic forgetting in neural networks. vol.\u00a0114, pp. 3521\u20133526. National ACAD Sciences (2017)","DOI":"10.1073\/pnas.1611835114"},{"key":"27_CR18","unstructured":"Lee, Y., et al.: Surgical fine-tuning improves adaptation to distribution shifts. In: Proceedings International Conference on Learning Representations (ICLR) (2023)"},{"key":"27_CR19","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, Y., Song, Y.Z., Hospedales, T.M.: Deeper, broader and artier domain generalization. In: Proceedings IEEE International Conference on Computer Vision (ICCV), pp. 5542\u20135550 (2017)","DOI":"10.1109\/ICCV.2017.591"},{"key":"27_CR20","unstructured":"Lim, H., Kim, B., Choo, J., Choi, S.: TTN: a domain-shift aware batch normalization in test-time adaptation. In: Proceedings International Conference on Learning Representations (ICLR) (2023)"},{"key":"27_CR21","unstructured":"Liu, Y., Kothari, P., van Delft, B., Bellot-Gurlet, B., Mordan, T., Alahi, A.: TTT++: when does self-supervised test-time training fail or thrive? In: Advances in Neural Information Processing Systems (NeurIPS), vol.\u00a034 (2021)"},{"key":"27_CR22","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11976\u201311986 (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"27_CR23","unstructured":"Nado, Z., Padhy, S., Sculley, D., D\u2019Amour, A., Lakshminarayanan, B., Snoek, J.: Evaluating prediction-time batch normalization for robustness under covariate shift arXiv:2006.10963 (2021)"},{"key":"27_CR24","doi-asserted-by":"crossref","unstructured":"Neuhold, G., Ollmann, T., Rota\u00a0Bulo, S., Kontschieder, P.: The mapillary vistas dataset for semantic understanding of street scenes. In: Proceedings IEEE International Conference on Computer Vision (ICCV), pp. 4990\u20134999 (2017)","DOI":"10.1109\/ICCV.2017.534"},{"key":"27_CR25","doi-asserted-by":"crossref","unstructured":"Nguyen, A.T., Nguyen-Tang, T., Lim, S.N., Torr, P.H.: TIPI: test time adaptation with transformation invariance. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 24162\u201324171 (2023)","DOI":"10.1109\/CVPR52729.2023.02314"},{"key":"27_CR26","unstructured":"Niu, S., et al.: Efficient test-time model adaptation without forgetting. In: Proceedings. International Conference on Machine Learning (ICML), pp. 16888\u201316905. PMLR (2022)"},{"key":"27_CR27","unstructured":"Niu, S., et al.: Towards stable test-time adaptation in dynamic wild world. In: Proceedings International Conference on Learning Representations (ICLR) (2023)"},{"key":"27_CR28","doi-asserted-by":"crossref","unstructured":"Poynton, C.: Digital video and HD: algorithms and Interfaces. Elsevier (2012)","DOI":"10.1016\/B978-0-12-391926-7.50063-1"},{"key":"27_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1007\/978-3-319-46475-6_7","volume-title":"Computer Vision \u2013 ECCV 2016","author":"SR Richter","year":"2016","unstructured":"Richter, S.R., Vineet, V., Roth, S., Koltun, V.: Playing for data: ground truth from computer games. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 102\u2013118. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_7"},{"key":"27_CR30","doi-asserted-by":"crossref","unstructured":"Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.M.: The SYNTHIA Dataset: a large collection of synthetic images for semantic segmentation of urban scenes. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3234\u20133243 (2016)","DOI":"10.1109\/CVPR.2016.352"},{"key":"27_CR31","unstructured":"Schneider, S., Rusak, E., Eck, L., Bringmann, O., Brendel, W., Bethge, M.: Improving robustness against common corruptions by covariate shift adaptation. In: Proceedings of Neural Information Processing Systems (NeurIPS), vol.\u00a033, pp. 11539\u201311551 (2020)"},{"key":"27_CR32","doi-asserted-by":"crossref","unstructured":"Song, J., Lee, J., Kweon, I.S., Choi, S.: EcoTTA: memory-efficient continual test-time adaptation via self-distilled regularization. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11920\u201311929 (2023)","DOI":"10.1109\/CVPR52729.2023.01147"},{"key":"27_CR33","unstructured":"Su, Y., Xu, X., Jia, K.: Revisiting realistic test-time training: sequential inference and adaptation by anchored clustering. In: Proceedings of Neural Information Processing Systems (NeurIPS) (2022)"},{"key":"27_CR34","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/978-3-319-49409-8_35","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III","author":"B Sun","year":"2016","unstructured":"Sun, B., Saenko, K.: Deep CORAL: correlation alignment for deep domain adaptation. In: Hua, G., J\u00e9gou, H. (eds.) Computer Vision \u2013 ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III, pp. 443\u2013450. Springer International Publishing, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49409-8_35"},{"key":"27_CR35","unstructured":"Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A., Hardt, M.: Test-time training with self-supervision for generalization under distribution shifts. In: Proceedings International Conference on Machine Learning (ICML), pp. 9229\u20139248 (2020)"},{"key":"27_CR36","doi-asserted-by":"crossref","unstructured":"Tomar, D., Vray, G., Bozorgtabar, B., Thiran, J.P.: TeSLA: test-time self-learning with automatic adversarial augmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20341\u201320350 (2023)","DOI":"10.1109\/CVPR52729.2023.01948"},{"key":"27_CR37","unstructured":"Wang, D., Shelhamer, E., Liu, S., Olshausen, B., Darrell, T.: Tent: fully test-time adaptation by entropy minimization. In: Proceedings of International Conference on Learning Representations (ICLR) (2021)"},{"key":"27_CR38","doi-asserted-by":"crossref","unstructured":"Wang, Q., Fink, O., Van\u00a0Gool, L., Dai, D.: Continual test-time domain adaptation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7201\u20137211 (2022)","DOI":"10.1109\/CVPR52688.2022.00706"},{"key":"27_CR39","unstructured":"Wang, Z., Bai, Y., Zhou, Y., Xie, C.: Can CNNs be more robust than transformers? In: Proceedings of International Conference on Learning Representations (ICLR) (2023)"},{"key":"27_CR40","doi-asserted-by":"crossref","unstructured":"Wu, Y., He, K.: Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"27_CR41","unstructured":"You, F., Li, J., Zhao, Z.: Test-time batch statistics calibration for covariate shift. arXiv preprint arXiv:2110.04065 (2021)"},{"key":"27_CR42","doi-asserted-by":"crossref","unstructured":"Yu, F., et al.: BDD100K: a diverse driving dataset for heterogeneous multitask learning. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2636\u20132645 (2020)","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"27_CR43","doi-asserted-by":"crossref","unstructured":"Yuan, L., Xie, B., Li, S.: Robust test-time adaptation in dynamic scenarios. In: Proceedings. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15922\u201315932 (2023)","DOI":"10.1109\/CVPR52729.2023.01528"},{"key":"27_CR44","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)","DOI":"10.5244\/C.30.87"},{"key":"27_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, J., Qi, L., Shi, Y., Gao, Y.: DomainAdaptor: a novel approach to test-time adaptation. In: Proceedings IEEE International Conference on Computer Vision (ICCV), pp. 18971\u201318981 (2023)","DOI":"10.1109\/ICCV51070.2023.01739"},{"key":"27_CR46","unstructured":"Zhang, M., Levine, S., Finn, C.: MEMO: test time robustness via adaptation and augmentation. In: Proceedings Neural Information Processing Systems (NeurIPS). vol.\u00a035, pp. 38629\u201338642 (2022)"},{"key":"27_CR47","unstructured":"Zhang, Y., et al.: AdaNPC: exploring non-parametric classifier for test-time adaptation. In: Proceedings International Conference on Machine Learning (ICML), pp. 41647\u201341676. PMLR (2023)"},{"key":"27_CR48","unstructured":"Zhao, B., Chen, C., Xia, S.T.: DELTA: degradation-free fully test-time adaptation. In: Proceedings International Conference on Learning Representations (ICLR) (2023)"},{"key":"27_CR49","unstructured":"Zhao, H., Liu, Y., Alahi, A., Lin, T.: On pitfalls of test-time adaptation. In: Proceedings International Conference on Machine Learning (ICML) (2023)"},{"key":"27_CR50","doi-asserted-by":"publisher","unstructured":"Zou, Y., Zhang, Z., Li, CL., Zhang, H., Pfister, T., Huang, J.B.: Learning instance-specific adaptation for cross-domain segmentation. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision - ECCV 2022. ECCV 2022. LNCS, vol. 13693. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19827-4_27","DOI":"10.1007\/978-3-031-19827-4_27"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73390-1_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T16:37:56Z","timestamp":1730306276000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73390-1_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,31]]},"ISBN":["9783031733895","9783031733901"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73390-1_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,31]]},"assertion":[{"value":"31 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}