{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T18:33:16Z","timestamp":1757701996594,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":56,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819626403","type":"print"},{"value":"9789819626410","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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\/978-981-96-2641-0_2","type":"book-chapter","created":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T00:57:11Z","timestamp":1743382631000},"page":"17-35","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Physically Interpretable Probabilistic Domain Characterization"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3743-2969","authenticated-orcid":false,"given":"Ana\u00efs","family":"Halin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8076-1157","authenticated-orcid":false,"given":"S\u00e9bastien","family":"Pi\u00e9rard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1752-1195","authenticated-orcid":false,"given":"Renaud","family":"Vandeghen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3249-0656","authenticated-orcid":false,"given":"Beno\u00eet","family":"G\u00e9rin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4030-3704","authenticated-orcid":false,"given":"Maxime","family":"Zanella","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5852-573X","authenticated-orcid":false,"given":"Martin","family":"Colot","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7907-2895","authenticated-orcid":false,"given":"Jan","family":"Held","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5314-9015","authenticated-orcid":false,"given":"Anthony","family":"Cioppa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1544-7080","authenticated-orcid":false,"given":"Emmanuel","family":"Jean","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8621-316X","authenticated-orcid":false,"given":"Gianluca","family":"Bontempi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sa\u00efd","family":"Mahmoudi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7243-4778","authenticated-orcid":false,"given":"Beno\u00eet","family":"Macq","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6260-6487","authenticated-orcid":false,"given":"Marc Van","family":"Droogenbroeck","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,29]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","unstructured":"Agrawal, A., Verschueren, R., Diamond, S., Boyd, S.: A rewriting system for convex optimization problems. J. Control Decis. 5(1), 42\u201360 (2018). https:\/\/doi.org\/10.1080\/23307706.2017.1397554","DOI":"10.1080\/23307706.2017.1397554"},{"key":"2_CR2","doi-asserted-by":"publisher","unstructured":"Boudiaf, M., Mueller, R., Ayed, I.B., Bertinetto, L.: Parameter-free online test-time adaptation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8334\u20138343. Institute of Electrical and Electronics Engineers (IEEE), New Orleans, Louisiana, USA (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.00816","DOI":"10.1109\/CVPR52688.2022.00816"},{"key":"#cr-split#-2_CR3.1","doi-asserted-by":"crossref","unstructured":"Colwell, I., Phan, B., Saleem, S., Salay, R., Czarnecki, K.: An automated vehicle safety concept based on runtime restriction of the operational design domain. In: 2018 IEEE Intelligent Vehicles Symposium","DOI":"10.1109\/IVS.2018.8500530"},{"key":"#cr-split#-2_CR3.2","doi-asserted-by":"crossref","unstructured":"(IV) (2018). https:\/\/doi.org\/10.1109\/IVS.2018.8500530","DOI":"10.1109\/IVS.2018.8500530"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Cranmer, K., Brehmer, J., Louppe, G.: The frontier of simulation-based inference. Proc. Nat. Acad. Sci. (PNAS) 117(48), 30055\u201330062 (2020). https:\/\/doi.org\/10.1073\/pnas.1912789117","DOI":"10.1073\/pnas.1912789117"},{"issue":"83","key":"2_CR5","first-page":"1","volume":"17","author":"S Diamond","year":"2016","unstructured":"Diamond, S., Boyd, S.: CVXPY: a Python-embedded modeling language for convex optimization. J. Mach. Learn. Res. 17(83), 1\u20135 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"2_CR6","unstructured":"Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: an open urban driving simulator. In: Annual Conference on Robot Learning. Proceedings of Machine Learning Research, vol.\u00a078, pp. 1\u201316. Mountain View, California, USA (2017). https:\/\/proceedings.mlr.press\/v78\/dosovitskiy17a.html"},{"key":"2_CR7","unstructured":"Durkan, C., Bekasov, A., Murray, I., Papamakarios, G.: Neural spline flows. In: Advances in Neural Information Processing Systems (NeurIPS), vol.\u00a032 (2019)"},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"G\u00e9rin, B., et al.: Multi-stream cellular test-time adaptation of real-time models evolving in dynamic environments. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), vol.\u00a033, pp. 4472\u20134482. Institute of Electrical and Electronics Engineers (IEEE), Seattle, Washington, USA (2024). https:\/\/doi.org\/10.1109\/CVPRW63382.2024.00450","DOI":"10.1109\/CVPRW63382.2024.00450"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"G\u00e9rin, B., Zanella, M., Wynen, M., Mahmoudi, S., Macq, B., De\u00a0Vleeschouwer, C.: Exploring viability of test-time training: Application to 3D segmentation in multiple sclerosis. In: IEEE Conference on Artificial Intelligence (CAI), vol.\u00a034, pp. 557\u2013562. Institute of Electrical and Electronics Engineers (IEEE), Singapore, Singapore (2024). https:\/\/doi.org\/10.1109\/CAI59869.2024.00110","DOI":"10.1109\/CAI59869.2024.00110"},{"key":"2_CR10","unstructured":"Gong, T., Jeong, J., Kim, T., Kim, Y., Shin, J., Lee, S.J.: NOTE: robust continual test-time adaptation against temporal correlation. In: Advances in Neural Information Processing Systems (NeurIPS), vol.\u00a035, pp. 27253\u201327266. Curran Associates, Inc. (2022). https:\/\/openreview.net\/forum?id=E9HNxrCFZPV"},{"key":"2_CR11","unstructured":"Greenberg, D., Nonnenmacher, M., Macke, J.H.: Automatic posterior transformation for likelihood-free inference. In: International Conference on Machine Learning (ICML). Proceedings of Machine Learning Research, vol.\u00a097, pp. 2404\u20132414 (2019). https:\/\/proceedings.mlr.press\/v97\/greenberg19a.html"},{"key":"2_CR12","unstructured":"Gyllenhammar, M., et al.: Towards an operational design domain that supports the safety argumentation of an automated driving system. In: European congress on embedded real time systems (ERTS), pp. 1\u201310. Toulouse, France (2020)"},{"key":"2_CR13","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778. Las Vegas, Nevada, USA (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"2_CR14","unstructured":"Hermans, J., Delaunoy, A., Rozet, F., Wehenkel, A., Begy, V., Louppe, G.: A trust crisis in simulation-based inference? your posterior approximations can be unfaithful. arXiv abs\/2110.06581 (2021). https:\/\/doi.org\/10.48550\/arXiv.2110.06581"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Houyon, J., et al.: Online distillation with continual learning for cyclic domain shifts. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), vol. abs 2211 16234, pp. 2437\u20132446. Institute of Electrical and Electronics Engineers (IEEE), Vancouver, Canada (2023). https:\/\/doi.org\/10.1109\/CVPRW59228.2023.00242","DOI":"10.1109\/CVPRW59228.2023.00242"},{"key":"2_CR16","doi-asserted-by":"publisher","unstructured":"Hyndman, R.J.: Computing and graphing highest density regions. Am. Stat. 50(2), 120\u2013126 (1996). https:\/\/doi.org\/10.1080\/00031305.1996.10474359","DOI":"10.1080\/00031305.1996.10474359"},{"key":"2_CR17","doi-asserted-by":"publisher","unstructured":"Ibrahim, M., Haworth, J., Cheng, T.: WeatherNet: recognising weather and visual conditions from street-level images using deep residual learning. ISPRS Int. J. Geo-Inf. 8(12), 1\u201318 (2019). https:\/\/doi.org\/10.3390\/ijgi8120549","DOI":"10.3390\/ijgi8120549"},{"key":"2_CR18","doi-asserted-by":"publisher","unstructured":"Introvigne, M., Ramazzina, A., Walz, S., Scheuble, D., Bijelic, M.: Real-time environment condition classification for autonomous vehicles. In: IEEE Intelligent Vehicles Symposium (IV). Institute of Electrical and Electronics Engineers (IEEE), Jeju Island, Republic of Korea (2024). https:\/\/doi.org\/10.1109\/iv55156.2024.10588692","DOI":"10.1109\/iv55156.2024.10588692"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Jeon, M., Seo, J., Min, J.: DA-RAW: Domain adaptive object detection for real-world adverse weather conditions. In: IEEE International Conference on Robotics and Automation (ICRA), vol.\u00a017, pp. 2013\u20132020. Institute of Electrical and Electronics Engineers (IEEE), Yokohama, Japan (2024). https:\/\/doi.org\/10.1109\/ICRA57147.2024.10611219","DOI":"10.1109\/ICRA57147.2024.10611219"},{"key":"2_CR20","doi-asserted-by":"publisher","unstructured":"Kobyzev, I., Prince, S.J., Brubaker, M.A.: Normalizing flows: An introduction and review of current methods. IEEE Trans. Pattern Anal. Mach. Intell. 43(11), 3964\u20133979 (2021). https:\/\/doi.org\/10.1109\/TPAMI.2020.2992934","DOI":"10.1109\/TPAMI.2020.2992934"},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"Kolmogorov, A.N.: Grundbegriffe der wahrscheinlichkeitsrechnung. In: Grundbegriffe der Wahrscheinlichkeitsrechnung, Ergebnisse der Mathematik und Ihrer Grenzgebiete, vol.\u00a02, p. 62. Springer, Heidelberg (1933). https:\/\/doi.org\/10.1007\/978-3-642-49888-6","DOI":"10.1007\/978-3-642-49888-6"},{"key":"2_CR22","unstructured":"Kolmogorov, A.N.: Foundations of the Theory of Probability. Chelsea Publishing Company (1950). https:\/\/archive.org\/details\/foundationsofthe00kolm"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Lee, C.W., Nayeer, N., Garcia, D.E., Agrawal, A., Liu, B.: Identifying the operational design domain for an automated driving system through assessed risk. In: IEEE Intelligent Vehicles Symposium (IV). Institute of Electrical and Electronics Engineers (IEEE) (2020). https:\/\/doi.org\/10.1109\/IV47402.2020.9304552","DOI":"10.1109\/IV47402.2020.9304552"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Lee, S., Kim, D., Kim, N., Jeong, S.G.: Drop to adapt: learning discriminative features for unsupervised domain adaptation. In: IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 91\u2013100. Institute of Electrical and Electronics Engineers (IEEE) (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00018","DOI":"10.1109\/ICCV.2019.00018"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Li, J., Xu, R., Ma, J., Zou, Q., Ma, J., Yu, H.: Domain adaptive object detection for autonomous driving under foggy weather. In: IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 612\u2013622. Institute of Electrical and Electronics Engineers (IEEE), Waikoloa, Hawaii, USA (2023). https:\/\/doi.org\/10.1109\/WACV56688.2023.00068","DOI":"10.1109\/WACV56688.2023.00068"},{"key":"2_CR26","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, Z., Lu, X.: A multi-task framework for weather recognition. In: ACM International Conference on Multimedia, pp. 1318\u20131326. Mountain View, California, USA (2017). https:\/\/doi.org\/10.1145\/3123266.3123382","DOI":"10.1145\/3123266.3123382"},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Li, Z., Li, Y., Zhong, J., Chen, Y.: Multi-class weather classification based on multi-feature weighted fusion method. In: Earth and Environmental Science. Journal of Physics: Conference Series, vol.\u00a0558, pp. 1\u201313. IOP Publishing (2020). https:\/\/doi.org\/10.1088\/1755-1315\/558\/4\/042038","DOI":"10.1088\/1755-1315\/558\/4\/042038"},{"key":"2_CR28","doi-asserted-by":"publisher","unstructured":"Lu, C., Lin, D., Jia, J., Tang, C.K.: Two-class weather classification. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2510\u20132524 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2640295","DOI":"10.1109\/TPAMI.2016.2640295"},{"key":"2_CR29","unstructured":"Lueckmann, J.M., Goncalves, P.J., Bassetto, G., \u00d6cal, K., Nonnenmacher, M., Macke, J.H.: Flexible statistical inference for mechanistic models of neural dynamics. In: Advances in Neural Information Processing Systems (NeurIPS), vol.\u00a030, pp. 1\u201311. Curran Associates, Inc., Long Beach, California, USA (November 2017)"},{"key":"2_CR30","unstructured":"Mansour, Y., Mohri, M., Rostamizadeh, R.: Domain adaptation with multiple sources. In: Advances in Neural Information Processing Systems (NeurIPS), vol.\u00a021, pp. 1041\u20131048. Vancouver, Canada (December 2008), https:\/\/papers.nips.cc\/paper_files\/paper\/2008\/hash\/0e65972dce68dad4d52d063967f0a705-Abstract.html"},{"key":"2_CR31","unstructured":"Oquab, M., et\u00a0al.: DINOv2: learning robust visual features without supervision. arXiv abs\/2304.07193 (2023). https:\/\/doi.org\/10.48550\/arXiv.2304.07193"},{"key":"2_CR32","unstructured":"Papamakarios, G., Nalisnick, E., Rezende, D.J., Mohamed, S., Lakshminarayanan, B.: Normalizing flows for probabilistic modeling and inference. J. Mach. Learn. Res. 22(57), 1\u201364 (2021). http:\/\/jmlr.org\/papers\/v22\/19-1028.html"},{"key":"2_CR33","doi-asserted-by":"crossref","unstructured":"Pappalardo, G., Caponetto, R., Varrica, R., Cafiso, S.: Assessing the operational design domain of lane support system for automated vehicles in different weather and road conditions. J. Traffic Transp. Eng. 9(4), 631\u2013644 (2022). https:\/\/doi.org\/10.1016\/j.jtte.2021.12.002","DOI":"10.1016\/j.jtte.2021.12.002"},{"key":"2_CR34","doi-asserted-by":"crossref","unstructured":"Perrels, A., Votsis, A., Nurmi, V., Pilli-Sihvola, K.: Weather conditions, weather information and car crashes. ISPRS Int. J. Geo-Inf. 4(4), 1\u201323 (2015). https:\/\/doi.org\/10.3390\/ijgi4042681","DOI":"10.3390\/ijgi4042681"},{"key":"2_CR35","doi-asserted-by":"crossref","unstructured":"Pi\u00e9rard, S., et al.: Mixture domain adaptation to improve semantic segmentation in real-world surveillance. In: IEEE\/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), pp. 22\u201331. Institute of Electrical and Electronics Engineers (IEEE), Waikoloa, Hawaii, USA (2023). https:\/\/doi.org\/10.1109\/WACVW58289.2023.00007","DOI":"10.1109\/WACVW58289.2023.00007"},{"key":"2_CR36","unstructured":"Pi\u00e9rard, S., Marcos Alvarez, A., Lejeune, A., Van\u00a0Droogenbroeck, M.: On-the-fly domain adaptation of binary classifiers. In: Belgian-Dutch Conference on Machine Learning (BENELEARN), pp. 20\u201328. Brussels, Belgium (2014)"},{"key":"2_CR37","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning (ICML). Proceedings of Machine Learning Research, vol.\u00a0139, pp. 8748\u20138763. Proceedings of Machine Learning Research (2021). https:\/\/proceedings.mlr.press\/v139\/radford21a.html"},{"key":"2_CR38","doi-asserted-by":"crossref","unstructured":"Rebut, J., Bursuc, A., Perez, P.: StyleLess layer: improving robustness for real-world driving. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 8992\u20138999. Institute of Electrical and Electronics Engineers (IEEE), Prague, Czech Republic (2021). https:\/\/doi.org\/10.1109\/IROS51168.2021.9636204","DOI":"10.1109\/IROS51168.2021.9636204"},{"key":"2_CR39","doi-asserted-by":"crossref","unstructured":"Robertson, C., Long, J.A., Nathoo, F.S., Nelson, T.A., Plouffe, C.C.F.: Assessing quality of spatial models using the structural similarity index and posterior predictive checks. Geogr. Anal. 46(1), 53\u201374 (2014). https:\/\/doi.org\/10.1111\/gean.12028","DOI":"10.1111\/gean.12028"},{"key":"2_CR40","doi-asserted-by":"publisher","unstructured":"Rozet, F., Divo, F., Schnake, S.: Zuko: normalizing flows in PyTorch. Software (2022). https:\/\/doi.org\/10.5281\/ZENODO.7625672, https:\/\/pypi.org\/project\/zuko","DOI":"10.5281\/ZENODO.7625672"},{"key":"2_CR41","doi-asserted-by":"publisher","unstructured":"Rozet, F., Miller, B.K., Delaunoy, A.: LAMPE: likelihood-free amortized posterior estimation. Software (2021). https:\/\/doi.org\/10.5281\/ZENODO.8405782, https:\/\/pypi.org\/project\/lampe","DOI":"10.5281\/ZENODO.8405782"},{"key":"2_CR42","doi-asserted-by":"crossref","unstructured":"SAE International: Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. Tech. Rep. SAE Standard J3016 202104, Society of Automobile Engineers, Warrendale, PA, USA (2021). https:\/\/doi.org\/10.4271\/J3016_202104","DOI":"10.4271\/J3016_202104"},{"key":"2_CR43","doi-asserted-by":"crossref","unstructured":"Shoker, A., Yasmin, R., Esteves-Verissimo, P.: Savvy: Trustworthy autonomous vehicles architecture. In: Symposium on Vehicles Security and Privacy (VehicleSec), pp.\u00a01\u20137. San Diego, California, USA (2024). https:\/\/www.ndss-symposium.org\/ndss-paper\/auto-draft-465\/","DOI":"10.14722\/vehiclesec.2024.23058"},{"key":"2_CR44","doi-asserted-by":"crossref","unstructured":"Sun, C., Deng, Z., Chu, W., Li, S., Cao, D.: Acclimatizing the operational design domain for autonomous driving systems. IEEE Intell. Transp. Syst. Mag. 14(2), 10\u201324 (2022). https:\/\/doi.org\/10.1109\/MITS.2021.3070651","DOI":"10.1109\/MITS.2021.3070651"},{"key":"2_CR45","doi-asserted-by":"crossref","unstructured":"Tang, H., Chen, K., Jia, K.: Unsupervised domain adaptation via structurally regularized deep clustering. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8722\u20138732. Institute of Electrical and Electronics Engineers (IEEE), Seattle, WA, USA (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00875","DOI":"10.1109\/CVPR42600.2020.00875"},{"key":"2_CR46","doi-asserted-by":"crossref","unstructured":"Tonk, A., Boussif, A.: Operational design domain or operational envelope; seeking a suitable concept for autonomous railway systems. In: European Safety and Reliability Conference, pp. 2104\u20132111. Research Publishing Services (2022). https:\/\/doi.org\/10.3850\/978-981-18-5183-4_S06-08-245-cd","DOI":"10.3850\/978-981-18-5183-4_S06-08-245-cd"},{"key":"2_CR47","doi-asserted-by":"crossref","unstructured":"Vasist, M., Rozet, F., Absil, O., Molli\u00e8re, P., Nasedkin, E., Louppe, G.: Neural posterior estimation for exoplanetary atmospheric retrieval. Astron. Astrophys. 672, A147 (2023). https:\/\/doi.org\/10.1051\/0004-6361\/202245263","DOI":"10.1051\/0004-6361\/202245263"},{"key":"2_CR48","doi-asserted-by":"publisher","unstructured":"Villarreal\u00a0Guerra, J.C., Khanam, Z., Ehsan, S., Stolkin, R., McDonald-Maier, K.: Weather classification: a new multi-class dataset, data augmentation approach and comprehensive evaluations of convolutional neural networks. In: NASA\/ESA Conference on Adaptive Hardware and Systems (AHS), pp. 305\u2013310. Institute of Electrical and Electronics Engineers (IEEE), Edinburgh, UK (2018). https:\/\/doi.org\/10.1109\/AHS.2018.8541482","DOI":"10.1109\/AHS.2018.8541482"},{"key":"2_CR49","unstructured":"Wang, D., Shelhamer, E., Liu, S., Olshausen, B., Darrell, T.: Tent: Fully test-time adaptation by entropy minimization. arXiv abs\/2006.10726 (2020). https:\/\/doi.org\/10.48550\/arXiv.2006.10726"},{"key":"2_CR50","doi-asserted-by":"crossref","unstructured":"Wang, Q., Fink, O., Van\u00a0Gool, L., Dai, D.: Continual test-time domain adaptation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7191\u20137201. Institute of Electrical and Electronics Engineers (IEEE), New Orleans, Louisiana, USA (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.00706","DOI":"10.1109\/CVPR52688.2022.00706"},{"key":"2_CR51","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: Dynamically instance-guided adaptation: A backward-free approach for test-time domain adaptive semantic segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 24090\u201324099. Institute of Electrical and Electronics Engineers (IEEE), Vancouver, Canada (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.02307","DOI":"10.1109\/CVPR52729.2023.02307"},{"key":"2_CR52","doi-asserted-by":"crossref","unstructured":"Yuan, L., Xie, B., Li, S.: Robust test-time adaptation in dynamic scenarios. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15922\u201315932. Institute of Electrical and Electronics Engineers (IEEE), Vancouver, Canada (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.01528","DOI":"10.1109\/CVPR52729.2023.01528"},{"key":"2_CR53","doi-asserted-by":"crossref","unstructured":"Zanella, M., Ayed, I.B.: On the test-time zero-shot generalization of vision-language models: Do we really need prompt learning? In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), vol.\u00a033, pp. 23783\u201323793. Institute of Electrical and Electronics Engineers (IEEE), Seattle, Washington, USA (2024). https:\/\/doi.org\/10.1109\/CVPR52733.2024.02245","DOI":"10.1109\/CVPR52733.2024.02245"},{"key":"2_CR54","unstructured":"Zanella, M., G\u00e9rin, B., Ayed, I.B.: Boosting vision-language models with transduction. arXiv abs\/2406.01837 (2024). https:\/\/doi.org\/10.48550\/arXiv.2406.01837"},{"key":"2_CR55","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Sun, J., Chen, M., Wang, Q., Yuan, Y., Ma, R.: Multi-weather classification using evolutionary algorithm on EfficientNet. In: IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), pp. 546\u2013551. Institute of Electrical and Electronics Engineers (IEEE), Kassel, Germany (2021). https:\/\/doi.org\/10.1109\/PerComWorkshops51409.2021.9430939","DOI":"10.1109\/PerComWorkshops51409.2021.9430939"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-2641-0_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T00:57:56Z","timestamp":1743382676000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-2641-0_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819626403","9789819626410"],"references-count":56,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-2641-0_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"29 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","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":"8 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}