{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T07:07:15Z","timestamp":1788937635444,"version":"build-2803163510"},"publisher-location":"Cham","reference-count":75,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032372512","type":"print"},{"value":"9783032372529","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-37252-9_23","type":"book-chapter","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T06:46:01Z","timestamp":1788936361000},"page":"409-427","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["To Adapt or\u00a0Not to\u00a0Adapt? Selective Adaptation for\u00a0Vision-Language Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2210-2116","authenticated-orcid":false,"given":"Siru","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuwei","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3890-1894","authenticated-orcid":false,"given":"Jian","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3807-991X","authenticated-orcid":false,"given":"Ran","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4054-5649","authenticated-orcid":false,"given":"Tieniu","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"23_CR1","unstructured":"Ahamed, S.A., Thanthrige, U.S.K.P.M., Rodrigo, R., Khan, M.H.: A-TPT: angular diversity calibration properties for test-time prompt tuning of vision-language models. In: ICLR (2026)"},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Alayrac, J.B., et al.: Flamingo: a visual language model for few-shot learning. In: NeurIPS, pp. 23716\u201323736 (2022)","DOI":"10.52202\/068431-1723"},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Bossard, L., Guillaumin, M., Van\u00a0Gool, L.: Food-101\u2013mining discriminative components with random forests. In: ECCV, pp. 446\u2013461 (2014)","DOI":"10.1007\/978-3-319-10599-4_29"},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., Vedaldi, A.: Describing textures in the wild. In: CVPR, pp. 3606\u20133613 (2014)","DOI":"10.1109\/CVPR.2014.461"},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Cortes, C., DeSalvo, G., Mohri, M.: Learning with rejection. In: ALT, pp. 67\u201382 (2016)","DOI":"10.1007\/978-3-319-46379-7_5"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"Dafnis, K.M., Metaxas, D.N.: Test-time spectrum-aware latent steering for zero-shot generalization in vision-language models. In: NeurIPS. pp. 151169\u2013151194 (2025)","DOI":"10.52202\/085713-5056"},{"key":"23_CR7","unstructured":"Dastmalchi, H., An, A.: Etta: efficient test-time adaptation for vision-language models through dynamic embedding updates. In: BMVC (2025)"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: ICML, pp. 233\u2013240 (2006)","DOI":"10.1145\/1143844.1143874"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: CVPR, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Farina, M., Franchi, G., Iacca, G., Mancini, M., Ricci, E.: Frustratingly easy test-time adaptation of vision-language models. In: NeurIPS, pp. 129062\u2013129093 (2024)","DOI":"10.52202\/079017-4099"},{"issue":"8","key":"23_CR11","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861\u2013874 (2006)","journal-title":"Pattern Recogn. Lett."},{"key":"23_CR12","unstructured":"Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: CVPRW, pp. 178\u2013178 (2004)"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Feng, C.M., Yu, K., Liu, Y., Khan, S., Zuo, W.: Diverse data augmentation with diffusions for effective test-time prompt tuning. In: ICCV, pp. 2704\u20132714 (2023)","DOI":"10.1109\/ICCV51070.2023.00255"},{"key":"23_CR14","unstructured":"Fisch, A., Jaakkola, T.S., Barzilay, R.: Calibrated selective classification. TMLR (2022)"},{"key":"23_CR15","unstructured":"Geifman, Y., El-Yaniv, R.: Selective classification for deep neural networks. In: NeurIPS, pp. 4885\u20134894 (2017)"},{"key":"23_CR16","unstructured":"Geifman, Y., El-Yaniv, R.: Selectivenet: a deep neural network with an integrated reject option. In: ICML, pp. 2151\u20132159 (2019)"},{"key":"23_CR17","unstructured":"Granese, F., Romanelli, M., Gorla, D., Palamidessi, C., Piantanida, P.: Doctor: a simple method for detecting misclassification errors. In: NeurIPS, pp, 5669\u20135681 (2021)"},{"key":"23_CR18","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: ICML, pp. 1321\u20131330 (2017)"},{"key":"23_CR19","doi-asserted-by":"crossref","unstructured":"Helber, P., Bischke, B., Dengel, A., Borth, D.: Introducing eurosat: a novel dataset and deep learning benchmark for land use and land cover classification. In: IGARSS, pp, 204\u2013207 (2018)","DOI":"10.1109\/IGARSS.2018.8519248"},{"key":"23_CR20","unstructured":"Hendrycks, D., et al.: Scaling out-of-distribution detection for real-world settings. In: ICML, pp. 8759\u20138773 (2022)"},{"key":"23_CR21","doi-asserted-by":"crossref","unstructured":"Hendrycks, D., et\u00a0al.: The many faces of robustness: a critical analysis of out-of-distribution generalization. In: ICCV, pp. 8340\u20138349 (2021)","DOI":"10.1109\/ICCV48922.2021.00823"},{"key":"23_CR22","unstructured":"Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: ICLR (2017)"},{"key":"23_CR23","unstructured":"Hendrycks, D., Mu, N., Cubuk, E.D., Zoph, B., Gilmer, J., Lakshminarayanan, B.: Augmix: a simple data processing method to improve robustness and uncertainty. In: ICLR (2020)"},{"key":"23_CR24","doi-asserted-by":"crossref","unstructured":"Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., Song, D.: Natural adversarial examples. In: CVPR, pp. 15262\u201315271 (2021)","DOI":"10.1109\/CVPR46437.2021.01501"},{"key":"23_CR25","doi-asserted-by":"crossref","unstructured":"Imam, R., Gani, H., Huzaifa, M., Nandakumar, K.: Test-time low rank adaptation via confidence maximization for zero-shot generalization of vision-language models. In: WACV, pp. 5449\u20135459 (2025)","DOI":"10.1109\/WACV61041.2025.00532"},{"key":"23_CR26","unstructured":"Jia, C., et al.: Scaling up visual and vision-language representation learning with noisy text supervision. In: ICML, pp. 4904\u20134916 (2021)"},{"key":"23_CR27","doi-asserted-by":"crossref","unstructured":"Karmanov, A., Guan, D., Lu, S., El Saddik, A., Xing, E.: Efficient test-time adaptation of vision-language models. In: CVPR, pp. 14162\u201314171 (2024)","DOI":"10.1109\/CVPR52733.2024.01343"},{"key":"23_CR28","unstructured":"Kim, I., Kim, Y., Kim, S.: Learning loss for test-time augmentation. In: NeurIPS, pp. 4163\u20134174 (2020)"},{"issue":"6","key":"23_CR29","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1109\/TAI.2021.3110179","volume":"2","author":"Y Kim","year":"2021","unstructured":"Kim, Y., Cho, D., Han, K., Panda, P., Hong, S.: Domain adaptation without source data. IEEE Trans. Artif. Intell. 2(6), 508\u2013518 (2021)","journal-title":"IEEE Trans. Artif. Intell."},{"key":"23_CR30","doi-asserted-by":"crossref","unstructured":"Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3d object representations for fine-grained categorization. In: ICCVW, pp. 554\u2013561 (2013)","DOI":"10.1109\/ICCVW.2013.77"},{"key":"23_CR31","doi-asserted-by":"crossref","unstructured":"Li, X., Zhang, D., Du, Z., Zhu, L., Chen, Z., Li, J.: Pataug: augmentation of augmentation for test-time adaptation. In: ACM MM, pp. 5080\u20135089 (2025)","DOI":"10.1145\/3746027.3755740"},{"key":"23_CR32","unstructured":"Liang, H., Peng, L., Sun, J.: Selective classification under distribution shifts. TMLR (2024)"},{"issue":"1","key":"23_CR33","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/s11263-024-02181-w","volume":"133","author":"J Liang","year":"2025","unstructured":"Liang, J., He, R., Tan, T.: A comprehensive survey on test-time adaptation under distribution shifts. IJCV 133(1), 31\u201364 (2025)","journal-title":"IJCV"},{"key":"23_CR34","doi-asserted-by":"crossref","unstructured":"Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning. In: NeurIPS, pp. 34892\u201334916 (2023)","DOI":"10.52202\/075280-1516"},{"key":"23_CR35","unstructured":"Liu, W., Wang, X., Owens, J., Li, Y.: Energy-based out-of-distribution detection. In: NeurIPS, pp. 21464\u201321475 (2020)"},{"issue":"2","key":"23_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3760390","volume":"58","author":"S Lu","year":"2025","unstructured":"Lu, S., Wang, Y., Sheng, L., He, L., Zheng, A., Liang, J.: Out-of-distribution detection: a task-oriented survey of recent advances. ACM Comput. Surv. 58(2), 1\u201339 (2025)","journal-title":"ACM Comput. Surv."},{"key":"23_CR37","unstructured":"Maji, S., Rahtu, E., Kannala, J., Blaschko, M., Vedaldi, A.: Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151 (2013)"},{"key":"23_CR38","doi-asserted-by":"crossref","unstructured":"Ming, Y., Cai, Z., Gu, J., Sun, Y., Li, W., Li, Y.: Delving into out-of-distribution detection with vision-language representations. In: NeurIPS, pp. 35087\u201335102 (2022)","DOI":"10.52202\/068431-2543"},{"key":"23_CR39","doi-asserted-by":"crossref","unstructured":"Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: ICVGIP, pp, 722\u2013729 (2008)","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"23_CR40","unstructured":"Niu, S., et al.: Efficient test-time model adaptation without forgetting. In: ICML, pp. 16888\u201316905 (2022)"},{"key":"23_CR41","doi-asserted-by":"crossref","unstructured":"Parkhi, O.M., Vedaldi, A., Zisserman, A., Jawahar, C.: Cats and dogs. In: CVPR, pp. 3498\u20133505 (2012)","DOI":"10.1109\/CVPR.2012.6248092"},{"key":"23_CR42","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: ICML, pp. 8748\u20138763 (2021)"},{"key":"23_CR43","unstructured":"Recht, B., Roelofs, R., Schmidt, L., Shankar, V.: Do imagenet classifiers generalize to imagenet? In: ICML, pp. 5389\u20135400 (2019)"},{"key":"23_CR44","doi-asserted-by":"crossref","unstructured":"Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: ECCV, pp. 213\u2013226 (2010)","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"23_CR45","doi-asserted-by":"crossref","unstructured":"Shanmugam, D., Blalock, D., Balakrishnan, G., Guttag, J.: Better aggregation in test-time augmentation. In: ICCV, pp. 1214\u20131223 (2021)","DOI":"10.1109\/ICCV48922.2021.00125"},{"key":"23_CR46","doi-asserted-by":"crossref","unstructured":"Sharifdeen, A., Munir, M.A., Baliah, S., Khan, S., Khan, M.H.: O-tpt: orthogonality constraints for calibrating test-time prompt tuning in vision-language models. In: CVPR, pp. 19942\u201319951 (2025)","DOI":"10.1109\/CVPR52734.2025.01857"},{"key":"23_CR47","doi-asserted-by":"crossref","unstructured":"Sheng, L., Liang, J., He, R., Wang, Z., Tan, T.: The illusion of progress? a critical look at test-time adaptation for vision-language models. In: NeurIPS (2025)","DOI":"10.52202\/085713-4167"},{"key":"23_CR48","doi-asserted-by":"crossref","unstructured":"Sheng, L., Liang, J., Wang, Z., He, R.: R-tpt: improving adversarial robustness of vision-language models through test-time prompt tuning. In: CVPR, pp. 29958\u201329967 (2025)","DOI":"10.1109\/CVPR52734.2025.02788"},{"key":"23_CR49","doi-asserted-by":"crossref","unstructured":"Shu, M., et al.: Test-time prompt tuning for zero-shot generalization in vision-language models. In: NeurIPS, pp. 14274\u201314289 (2022)","DOI":"10.52202\/068431-1038"},{"key":"23_CR50","unstructured":"Soomro, K., Zamir, A.R., Shah, M.: Ucf101: a dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 (2012)"},{"key":"23_CR51","doi-asserted-by":"crossref","unstructured":"Sui, E., Wang, X., Yeung-Levy, S.: Just shift it: test-time prototype shifting for zero-shot generalization with vision-language models. In: WACV, pp. 825\u2013835 (2025)","DOI":"10.1109\/WACV61041.2025.00090"},{"key":"23_CR52","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: ICML, pp. 9229\u20139248 (2020)"},{"key":"23_CR53","unstructured":"Wang, D., Shelhamer, E., Liu, S., Olshausen, B., Darrell, T.: Tent: fully test-time adaptation by entropy minimization. In: ICLR (2021)"},{"key":"23_CR54","unstructured":"Wang, H., Ge, S., Lipton, Z., Xing, E.P.: Learning robust global representations by penalizing local predictive power. In: NeurIPS, pp. 10506\u201310518 (2019)"},{"key":"23_CR55","doi-asserted-by":"crossref","unstructured":"Wang, Q., Fink, O., Van Gool, L., Dai, D.: Continual test-time domain adaptation. In: CVPR, pp. 7201\u20137211 (2022)","DOI":"10.1109\/CVPR52688.2022.00706"},{"key":"23_CR56","doi-asserted-by":"crossref","unstructured":"Wang, X., Chen, K., Zhang, J., Chen, J., Ma, X.: Tapt: test-time adversarial prompt tuning for robust inference in vision-language models. In: CVPR, pp. 19910\u201319920 (2025)","DOI":"10.1109\/CVPR52734.2025.01854"},{"key":"23_CR57","doi-asserted-by":"crossref","unstructured":"Wu, W., Luo, H., Fang, B., Wang, J., Ouyang, W.: Cap4video: what can auxiliary captions do for text-video retrieval? In: CVPR, pp. 10704\u201310713 (2023)","DOI":"10.1109\/CVPR52729.2023.01031"},{"key":"23_CR58","doi-asserted-by":"crossref","unstructured":"Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: Sun database: large-scale scene recognition from abbey to zoo. In: CVPR, pp. 3485\u20133492 (2010)","DOI":"10.1109\/CVPR.2010.5539970"},{"key":"23_CR59","unstructured":"Xiao, Z., et al.: Dynaprompt: dynamic test-time prompt tuning. In: ICLR (2025)"},{"key":"23_CR60","doi-asserted-by":"crossref","unstructured":"Xing, S., Zhao, Z., Sebe, N.: Clip is strong enough to fight back: test-time counterattacks towards zero-shot adversarial robustness of clip. In: CVPR, pp. 15172\u201315182 (2025)","DOI":"10.1109\/CVPR52734.2025.01413"},{"key":"23_CR61","doi-asserted-by":"crossref","unstructured":"Yang, J., Zhou, K., Li, Y., Liu, Z.: Generalized out-of-distribution detection: a survey. IJCV 132(12), 5635\u20135662 (2024)","DOI":"10.1007\/s11263-024-02117-4"},{"key":"23_CR62","unstructured":"Yang, P., Liang, J., Cao, J., He, R.: Auto: adaptive outlier optimization for online test-time ood detection. arXiv preprint arXiv:2303.12267 (2023)"},{"key":"23_CR63","unstructured":"Yin, D., Gontijo Lopes, R., Shlens, J., Cubuk, E.D., Gilmer, J.: A Fourier perspective on model robustness in computer vision. In: NeurIPS, vol. 32 (2019)"},{"key":"23_CR64","unstructured":"Yoon, H.S., Yoon, E., Tee, J.T.J., Hasegawa-Johnson, M.A., Li, Y., Yoo, C.D.: C-TPT: calibrated test-time prompt tuning for vision-language models via text feature dispersion. In: ICLR (2024)"},{"key":"23_CR65","unstructured":"Yu, Y., Sheng, L., He, R., Liang, J.: Benchmarking test-time adaptation against distribution shifts in image classification. arXiv preprint arXiv:2307.03133 (2023)"},{"key":"23_CR66","doi-asserted-by":"crossref","unstructured":"Yu, Y., Sheng, L., He, R., Liang, J.: Stamp: outlier-aware test-time adaptation with stable memory replay. In: ECCV, pp. 375\u2013392 (2024)","DOI":"10.1007\/978-3-031-73004-7_22"},{"key":"23_CR67","doi-asserted-by":"crossref","unstructured":"Yuan, L., Xie, B., Li, S.: Robust test-time adaptation in dynamic scenarios. In: CVPR, pp. 15922\u201315932 (2023)","DOI":"10.1109\/CVPR52729.2023.01528"},{"key":"23_CR68","doi-asserted-by":"crossref","unstructured":"Zanella, M., Ben Ayed, I.: On the test-time zero-shot generalization of vision-language models: do we really need prompt learning? In: CVPR, pp. 23783\u201323793 (2024)","DOI":"10.1109\/CVPR52733.2024.02245"},{"issue":"8","key":"23_CR69","doi-asserted-by":"publisher","first-page":"5625","DOI":"10.1109\/TPAMI.2024.3369699","volume":"46","author":"J Zhang","year":"2024","unstructured":"Zhang, J., Huang, J., Jin, S., Lu, S.: Vision-language models for vision tasks: a survey. IEEE TPAMI 46(8), 5625\u20135644 (2024)","journal-title":"IEEE TPAMI"},{"key":"23_CR70","doi-asserted-by":"crossref","unstructured":"Zhang, M.M., Levine, S., Finn, C.: Memo: test time robustness via adaptation and augmentation. In: NeurIPS (2022)","DOI":"10.52202\/068431-2799"},{"key":"23_CR71","doi-asserted-by":"crossref","unstructured":"Zhang, T., Wang, J., Guo, H., Dai, T., Chen, B., Xia, S.T.: Boostadapter: improving vision-language test-time adaptation via regional bootstrapping. In: NeurIPS, pp. 67795\u201367825 (2024)","DOI":"10.52202\/079017-2165"},{"key":"23_CR72","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Conditional prompt learning for vision-language models. In: CVPR, pp. 16816\u201316825 (2022)","DOI":"10.1109\/CVPR52688.2022.01631"},{"issue":"9","key":"23_CR73","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.1007\/s11263-022-01653-1","volume":"130","author":"K Zhou","year":"2022","unstructured":"Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Learning to prompt for vision-language models. IJCV 130(9), 2337\u20132348 (2022)","journal-title":"IJCV"},{"key":"23_CR74","doi-asserted-by":"crossref","unstructured":"Zhou, L., et al.: Bayesian test-time adaptation for vision-language models. In: CVPR, pp. 29999\u201330009 (2025)","DOI":"10.1109\/CVPR52734.2025.02792"},{"key":"23_CR75","doi-asserted-by":"crossref","unstructured":"Zhou, S., Yin, M., Sun, L., Yang, S., Xie, D., Zhu, J.: Training-free test-time adaptation via shape and style guidance for vision-language models. In: NeurIPS, pp. 152968\u2013152982 (2025)","DOI":"10.52202\/085713-5115"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-37252-9_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T06:46:17Z","timestamp":1788936377000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-37252-9_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032372512","9783032372529"],"references-count":75,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-37252-9_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"10 September 2026","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":"Malm\u00f6","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}