{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:06:39Z","timestamp":1779383199764,"version":"3.53.1"},"publisher-location":"Cham","reference-count":65,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031916717","type":"print"},{"value":"9783031916724","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\/978-3-031-91672-4_12","type":"book-chapter","created":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T15:25:59Z","timestamp":1747754759000},"page":"185-200","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Do Vision Foundation Models Enhance Domain Generalization in\u00a0Medical Image Segmentation?"],"prefix":"10.1007","author":[{"given":"Kerem","family":"Cekmeceli","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meva","family":"Himmetoglu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guney I.","family":"Tombak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Susmelj","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ertunc","family":"Erdil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ender","family":"Konukoglu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","first-page":"43487","DOI":"10.1109\/ACCESS.2019.2908002","volume":"7","author":"AS Al-Kafri","year":"2019","unstructured":"Al-Kafri, A.S., et al.: Boundary delineation of MRI images for lumbar spinal stenosis detection through semantic segmentation using deep neural networks. IEEE Access 7, 43487\u201343501 (2019)","journal-title":"IEEE Access"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Bateson, M., Lombaert, H., Ben\u00a0Ayed, I.: Test-time adaptation with shape moments for image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 736\u2013745. Springer (2022)","DOI":"10.1007\/978-3-031-16440-8_70"},{"key":"12_CR3","doi-asserted-by":"publisher","unstructured":"Bloch, N., et al.: NCI-ISBI 2013 challenge: automated segmentation of prostate structures (2015). https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.zF0vlOPv","DOI":"10.7937\/K9\/TCIA.2015.zF0vlOPv"},{"key":"12_CR4","unstructured":"Bommasani, R., et\u00a0al.: On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021)"},{"key":"12_CR5","doi-asserted-by":"publisher","unstructured":"Brett, M., Johnsrude, I.S., Owen, A.M.: The problem of functional localization in the human brain. Nat. Rev. Neurosci. 3(3), 243\u2013249 (2002). https:\/\/doi.org\/10.1038\/nrn756, https:\/\/www.nature.com\/articles\/nrn756","DOI":"10.1038\/nrn756"},{"key":"12_CR6","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"12_CR7","doi-asserted-by":"crossref","unstructured":"Butoi, V.I., Ortiz, J.J.G., Ma, T., Sabuncu, M.R., Guttag, J., Dalca, A.V.: UniverSeg: universal medical image segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 21438\u201321451 (2023)","DOI":"10.1109\/ICCV51070.2023.01960"},{"key":"12_CR8","doi-asserted-by":"crossref","unstructured":"Cao, H., et al.: Swin-UNet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205\u2013218. Springer (2022)","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"12_CR9","doi-asserted-by":"crossref","unstructured":"Caron, M., et al.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9650\u20139660 (2021)","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"12_CR10","doi-asserted-by":"crossref","unstructured":"Chai, S., et al.: Ladder fine-tuning approach for SAM integrating complementary network. arXiv preprint arXiv:2306.12737 (2023)","DOI":"10.1016\/j.procs.2024.09.452"},{"key":"12_CR11","first-page":"12546","volume":"33","author":"K Chaitanya","year":"2020","unstructured":"Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E.: Contrastive learning of global and local features for medical image segmentation with limited annotations. Adv. Neural. Inf. Process. Syst. 33, 12546\u201312558 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"12_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102792","volume":"87","author":"K Chaitanya","year":"2023","unstructured":"Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E.: Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation. Med. Image Anal. 87, 102792 (2023)","journal-title":"Med. Image Anal."},{"key":"12_CR13","doi-asserted-by":"crossref","unstructured":"Cheng, Z., et al.: Unleashing the potential of SAM for medical adaptation via hierarchical decoding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3511\u20133522 (2024)","DOI":"10.1109\/CVPR52733.2024.00337"},{"key":"12_CR14","unstructured":"Contributors, M.: MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark. https:\/\/github.com\/open-mmlab\/mmsegmentation (2020)"},{"key":"12_CR15","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: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"12_CR16","unstructured":"Deng, R., et\u00a0al.: Segment anything model (SAM) for digital pathology: Assess zero-shot segmentation on whole slide imaging. arXiv preprint arXiv:2304.04155 (2023)"},{"key":"12_CR17","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"issue":"6","key":"12_CR18","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1038\/mp.2013.78","volume":"19","author":"A Di Martino","year":"2014","unstructured":"Di Martino, A., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19(6), 659\u2013667 (2014)","journal-title":"Mol. Psychiatry"},{"issue":"3","key":"12_CR19","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"LR Dice","year":"1945","unstructured":"Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297\u2013302 (1945)","journal-title":"Ecology"},{"key":"12_CR20","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"12_CR21","unstructured":"Dou, Q., Coelho\u00a0de Castro, D., Kamnitsas, K., Glocker, B.: Domain generalization via model-agnostic learning of semantic features. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"12_CR22","doi-asserted-by":"publisher","first-page":"99065","DOI":"10.1109\/ACCESS.2019.2929258","volume":"7","author":"Q Dou","year":"2019","unstructured":"Dou, Q., et al.: PnP-AdaNet: plug-and-play adversarial domain adaptation network at unpaired cross-modality cardiac segmentation. IEEE Access 7, 99065\u201399076 (2019)","journal-title":"IEEE Access"},{"issue":"2","key":"12_CR23","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","volume":"62","author":"B Fischl","year":"2012","unstructured":"Fischl, B.: Freesurfer. Neuroimage 62(2), 774\u2013781 (2012)","journal-title":"Neuroimage"},{"key":"12_CR24","doi-asserted-by":"crossref","unstructured":"Fu, J., et al.: Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3146\u20133154 (2019)","DOI":"10.1109\/CVPR.2019.00326"},{"key":"12_CR25","doi-asserted-by":"crossref","unstructured":"Gao, H., Guo, J., Wang, G., Zhang, Q.: Cross-domain correlation distillation for unsupervised domain adaptation in nighttime semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9913\u20139923 (2022)","DOI":"10.1109\/CVPR52688.2022.00968"},{"key":"12_CR26","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000\u201316009 (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"12_CR27","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"12_CR28","unstructured":"He, S., Bao, R., Li, J., Grant, P.E., Ou, Y.: Accuracy of segment-anything model (SAM) in medical image segmentation tasks. arXiv preprint arXiv:2304.093242 (2023)"},{"key":"12_CR29","doi-asserted-by":"crossref","unstructured":"Hoyer, L., Dai, D., Van\u00a0Gool, L.: DAFormer: improving network architectures and training strategies for domain-adaptive semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9924\u20139935 (2022)","DOI":"10.1109\/CVPR52688.2022.00969"},{"key":"12_CR30","unstructured":"Hu, C., Xia, T., Ju, S., Li, X.: When SAM meets medical images: An investigation of segment anything model (SAM) on multi-phase liver tumor segmentation. arXiv preprint arXiv:2304.08506 (2023)"},{"key":"12_CR31","unstructured":"Hu, E.J., et al.: LoRA: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021)"},{"issue":"4","key":"12_CR32","doi-asserted-by":"publisher","first-page":"1016","DOI":"10.1109\/TMI.2018.2876633","volume":"38","author":"Y Huo","year":"2018","unstructured":"Huo, Y., et al.: SynSeg-Net: synthetic segmentation without target modality ground truth. IEEE Trans. Med. Imaging 38(4), 1016\u20131025 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"12_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1007\/978-3-319-59050-9_47","volume-title":"Information Processing in Medical Imaging","author":"K Kamnitsas","year":"2017","unstructured":"Kamnitsas, K., et al.: Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 597\u2013609. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_47"},{"key":"12_CR34","doi-asserted-by":"crossref","unstructured":"Karani, N., Chaitanya, K., Baumgartner, C., Konukoglu, E.: A lifelong learning approach to brain MR segmentation across scanners and protocols. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 476\u2013484. Springer (2018)","DOI":"10.1007\/978-3-030-00928-1_54"},{"key":"12_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101907","volume":"68","author":"N Karani","year":"2021","unstructured":"Karani, N., Erdil, E., Chaitanya, K., Konukoglu, E.: Test-time adaptable neural networks for robust medical image segmentation. Med. Image Anal. 68, 101907 (2021)","journal-title":"Med. Image Anal."},{"key":"12_CR36","unstructured":"Ke, L., Ye, M., Danelljan, M., Tai, Y.W., Tang, C.K., Yu, F., et\u00a0al.: Segment anything in high quality. Adv. Neural Inf. Process. Syst. 36 (2024)"},{"key":"12_CR37","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et\u00a0al.: Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4015\u20134026 (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"12_CR38","unstructured":"LEARNING, T.S.I.M.: Dataset shift in machine learning"},{"key":"12_CR39","doi-asserted-by":"crossref","unstructured":"Lewis, M., et al.: BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019)","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"12_CR40","unstructured":"Li, J., Li, D., Xiong, C., Hoi, S.: BLIP: bootstrapping language-image pre-training for unified vision-language understanding and generation. In: International Conference on Machine Learning, pp. 12888\u201312900. PMLR (2022)"},{"key":"12_CR41","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"issue":"1","key":"12_CR42","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","volume":"15","author":"J Ma","year":"2024","unstructured":"Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nat. Commun. 15(1), 654 (2024)","journal-title":"Nat. Commun."},{"key":"12_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102918","volume":"89","author":"MA Mazurowski","year":"2023","unstructured":"Mazurowski, M.A., Dong, H., Gu, H., Yang, J., Konz, N., Zhang, Y.: Segment anything model for medical image analysis: an experimental study. Med. Image Anal. 89, 102918 (2023)","journal-title":"Med. Image Anal."},{"issue":"10","key":"12_CR44","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"12_CR45","unstructured":"Mohapatra, S., Gosai, A., Schlaug, G.: SAM vs BET: A comparative study for brain extraction and segmentation of magnetic resonance images using deep learning. arXiv preprint arXiv:2304.04738 (2023)"},{"key":"12_CR46","unstructured":"Oquab, M., et\u00a0al.: DINOv2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193 (2023)"},{"key":"12_CR47","doi-asserted-by":"crossref","unstructured":"Ouyang, C., Kamnitsas, K., Biffi, C., Duan, J., Rueckert, D.: Data efficient unsupervised domain adaptation for cross-modality image segmentation. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part II 22, pp. 669\u2013677. Springer (2019)","DOI":"10.1007\/978-3-030-32245-8_74"},{"key":"12_CR48","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"key":"12_CR49","unstructured":"Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et\u00a0al.: Improving language understanding by generative pre-training (2018)"},{"issue":"8","key":"12_CR50","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019)","journal-title":"OpenAI Blog"},{"key":"12_CR51","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"12_CR52","unstructured":"Roy, S., et al.: SAM.MD: Zero-shot medical image segmentation capabilities of the segment anything model. arXiv preprint arXiv:2304.05396 (2023)"},{"key":"12_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102166","volume":"73","author":"A Sekuboyina","year":"2021","unstructured":"Sekuboyina, A., et al.: VerSe: a vertebrae labelling and segmentation benchmark for multi-detector CT images. Med. Image Anal. 73, 102166 (2021)","journal-title":"Med. Image Anal."},{"issue":"5","key":"12_CR54","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299\u20131312 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"12_CR55","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","volume":"29","author":"NJ Tustison","year":"2010","unstructured":"Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310\u20131320 (2010)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"12_CR56","doi-asserted-by":"publisher","unstructured":"Tustison, N.J., et al.: The ANTsX ecosystem for quantitative biological and medical imaging. Sci. Rep. 11(1), 9068 (2021). https:\/\/doi.org\/10.1038\/s41598-021-87564-6, https:\/\/www.nature.com\/articles\/s41598-021-87564-6","DOI":"10.1038\/s41598-021-87564-6"},{"key":"12_CR57","unstructured":"Valanarasu, J.M.J., Guo, P., Vibashan, V., Patel, V.M.: On-the-fly test-time adaptation for medical image segmentation. In: Medical Imaging with Deep Learning, pp. 586\u2013598. PMLR (2024)"},{"key":"12_CR58","doi-asserted-by":"crossref","unstructured":"Van\u00a0Essen, D.C., et\u00a0al.: The WU-Minn human connectome project: an overview. Neuroimage 80, 62\u201379 (2013)","DOI":"10.1016\/j.neuroimage.2013.05.041"},{"issue":"5","key":"12_CR59","doi-asserted-by":"publisher","first-page":"1018","DOI":"10.1109\/TMI.2014.2366792","volume":"34","author":"A Van Opbroek","year":"2014","unstructured":"Van Opbroek, A., Ikram, M.A., Vernooij, M.W., De Bruijne, M.: Transfer learning improves supervised image segmentation across imaging protocols. IEEE Trans. Med. Imaging 34(5), 1018\u20131030 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"12_CR60","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wu, Z., Agarwal, D., Sun, J.: MedCLIP: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.256"},{"key":"12_CR61","doi-asserted-by":"crossref","unstructured":"Wei, Z., et al.: Stronger fewer & superior: harnessing vision foundation models for domain generalized semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 28619\u201328630 (2024)","DOI":"10.1109\/CVPR52733.2024.02704"},{"key":"12_CR62","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. Adv. Neural. Inf. Process. Syst. 34, 12077\u201312090 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"12_CR63","unstructured":"Zhang, J., Qi, L., Shi, Y., Gao, Y.: Generalizable semantic segmentation via model-agnostic learning and target-specific normalization. arXiv preprint arXiv:2003.122962(3), 6 (2020)"},{"key":"12_CR64","unstructured":"Zhou, T., Zhang, Y., Zhou, Y., Wu, Y., Gong, C.: Can SAM segment polyps? arXiv preprint arXiv:2304.07583 (2023)"},{"key":"12_CR65","unstructured":"Zou, X., et al.: Segment everything everywhere all at once. Adv. Neural Inf. Process. Syst. 36 (2024)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-91672-4_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T15:26:28Z","timestamp":1747754788000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-91672-4_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031916717","9783031916724"],"references-count":65,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-91672-4_12","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":"12 May 2025","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"}}]}}