{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T15:57:47Z","timestamp":1776441467537,"version":"3.51.2"},"publisher-location":"Cham","reference-count":59,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031736605","type":"print"},{"value":"9783031736612","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,10]],"date-time":"2024-11-10T00:00:00Z","timestamp":1731196800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,10]],"date-time":"2024-11-10T00:00:00Z","timestamp":1731196800000},"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\/978-3-031-73661-2_11","type":"book-chapter","created":{"date-parts":[[2024,11,9]],"date-time":"2024-11-09T11:08:28Z","timestamp":1731150508000},"page":"189-206","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["CAT-SAM: Conditional Tuning for\u00a0Few-Shot Adaptation of\u00a0Segment Anything Model"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2956-0613","authenticated-orcid":false,"given":"Aoran","family":"Xiao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4271-9035","authenticated-orcid":false,"given":"Weihao","family":"Xuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9512-7140","authenticated-orcid":false,"given":"Heli","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9839-0120","authenticated-orcid":false,"given":"Yun","family":"Xing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3444-4150","authenticated-orcid":false,"given":"Ruijie","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0958-7285","authenticated-orcid":false,"given":"Xiaoqin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8264-6117","authenticated-orcid":false,"given":"Ling","family":"Shao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6766-2506","authenticated-orcid":false,"given":"Shijian","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,10]]},"reference":[{"key":"11_CR1","unstructured":"Bommasani, R., et\u00a0al.: On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021)"},{"issue":"21","key":"11_CR2","doi-asserted-by":"publisher","first-page":"4441","DOI":"10.3390\/rs13214441","volume":"13","author":"K Chen","year":"2021","unstructured":"Chen, K., Zou, Z., Shi, Z.: Building extraction from remote sensing images with sparse token transformers. Remote Sens. 13(21), 4441 (2021)","journal-title":"Remote Sens."},{"key":"11_CR3","unstructured":"Chen, S., et al.: AdaptFormer: adapting vision transformers for scalable visual recognition. In: Advances in Neural Information Processing Systems, vol. 35, pp. 16664\u201316678 (2022)"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Chen, T., et al.: SAM-adapter: adapting segment anything in underperformed scenes. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops, pp. 3367\u20133375 (2023)","DOI":"10.1109\/ICCVW60793.2023.00361"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Chen, T., et al.: SAM fails to segment anything?\u2013SAM-adapter: adapting SAM in underperformed scenes: camouflage, shadow, and more. arXiv preprint arXiv:2304.09148 (2023)","DOI":"10.1109\/ICCVW60793.2023.00361"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhu, L., Wan, L., Wang, S., Feng, W., Heng, P.A.: A multi-task mean teacher for semi-supervised shadow detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5611\u20135620 (2020)","DOI":"10.1109\/CVPR42600.2020.00565"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Cheng, B., Girshick, R., Doll\u00e1r, P., Berg, A.C., Kirillov, A.: Boundary IoU: improving object-centric image segmentation evaluation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15334\u201315342 (2021)","DOI":"10.1109\/CVPR46437.2021.01508"},{"key":"11_CR8","unstructured":"Cheng, J., et\u00a0al.: SAM-med2d. arXiv preprint arXiv:2308.16184 (2023)"},{"key":"11_CR9","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth $$16 \\times 16$$ words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"issue":"1","key":"11_CR10","doi-asserted-by":"publisher","first-page":"9803","DOI":"10.1038\/s41598-023-36940-5","volume":"13","author":"RG Dumitru","year":"2023","unstructured":"Dumitru, R.G., Peteleaza, D., Craciun, C.: Using duck-net for polyp image segmentation. Sci. Rep. 13(1), 9803 (2023)","journal-title":"Sci. Rep."},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Gao, P., et al.: Clip-adapter: better vision-language models with feature adapters. Int. J. Comput. Vis. 1\u201315 (2023)","DOI":"10.1007\/s11263-023-01891-x"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Gong, Z., Li, F., Deng, Y., Bhattacharjee, D., Zhu, X., Ji, Z.: Coda: instructive chain-of-domain adaptation with severity-aware visual prompt tuning (2024)","DOI":"10.1007\/978-3-031-72980-5_8"},{"key":"11_CR13","unstructured":"Gu, J., et al.: A systematic survey of prompt engineering on vision-language foundation models. arXiv preprint arXiv:2307.12980 (2023)"},{"key":"11_CR14","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.isprsjprs.2021.11.005","volume":"183","author":"H Guo","year":"2022","unstructured":"Guo, H., Du, B., Zhang, L., Su, X.: A coarse-to-fine boundary refinement network for building footprint extraction from remote sensing imagery. ISPRS J. Photogramm. Remote. Sens. 183, 240\u2013252 (2022)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"11_CR15","unstructured":"Houlsby, N., et al.: Parameter-efficient transfer learning for NLP. In: International Conference on Machine Learning, pp. 2790\u20132799. PMLR (2019)"},{"key":"11_CR16","unstructured":"Hu, E.J., et\u00a0al.: Lora: low-rank adaptation of large language models. In: International Conference on Learning Representations (2021)"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Hu, X., Zhu, L., Fu, C.W., Qin, J., Heng, P.A.: Direction-aware spatial context features for shadow detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7454\u20137462 (2018)","DOI":"10.1109\/CVPR.2018.00778"},{"issue":"1","key":"11_CR18","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1109\/TGRS.2018.2858817","volume":"57","author":"S Ji","year":"2018","unstructured":"Ji, S., Wei, S., Lu, M.: Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set. IEEE Trans. Geosci. Remote Sens. 57(1), 574\u2013586 (2018)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Ji, W., Li, J., Bi, Q., Li, W., Cheng, L.: Segment anything is not always perfect: an investigation of SAM on different real-world applications. arXiv preprint arXiv:2304.05750 (2023)","DOI":"10.1007\/s11633-024-1526-0"},{"key":"11_CR20","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1007\/978-3-031-19827-4_41","volume-title":"ECCV 2022","author":"M Jia","year":"2022","unstructured":"Jia, M., et al.: Visual prompt tuning. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13693, pp. 709\u2013727. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19827-4_41"},{"key":"11_CR21","unstructured":"Ke, L., et al.: Segment anything in high quality. arXiv preprint arXiv:2306.01567 (2023)"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Khattak, M.U., Rasheed, H., Maaz, M., Khan, S., Khan, F.S.: Maple: multi-modal prompt learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19113\u201319122 (2023)","DOI":"10.1109\/CVPR52729.2023.01832"},{"key":"11_CR23","unstructured":"Kirillov, A., et\u00a0al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)"},{"key":"11_CR24","doi-asserted-by":"publisher","unstructured":"Lan, X., et al.: FoodSAM: any food segmentation. IEEE Trans. Multimed. 1\u201314 (2023). https:\/\/doi.org\/10.1109\/TMM.2023.3330047","DOI":"10.1109\/TMM.2023.3330047"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Lester, B., Al-Rfou, R., Constant, N.: The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"11_CR26","unstructured":"Li, F., et al.: Semantic-SAM: segment and recognize anything at any granularity. arXiv preprint arXiv:2307.04767 (2023)"},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Li, X.L., Liang, P.: Prefix-tuning: optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190 (2021)","DOI":"10.18653\/v1\/2021.acl-long.353"},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"Liew, J.H., Cohen, S., Price, B., Mai, L., Feng, J.: Deep interactive thin object selection. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 305\u2013314 (2021)","DOI":"10.1109\/WACV48630.2021.00035"},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Liu, W., Shen, X., Pun, C.M., Cun, X.: Explicit visual prompting for low-level structure segmentations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19434\u201319445 (2023)","DOI":"10.1109\/CVPR52729.2023.01862"},{"issue":"1","key":"11_CR30","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":"11_CR31","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"11_CR32","unstructured":"Mnih, V.: Machine learning for aerial image labeling. Ph.D. thesis, University of Toronto (2013)"},{"key":"11_CR33","doi-asserted-by":"crossref","unstructured":"Nagendra, S., Kifer, D.: PatchRefineNet: improving binary segmentation by incorporating signals from optimal patch-wise binarization. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1361\u20131372 (2024)","DOI":"10.1109\/WACV57701.2024.00139"},{"key":"11_CR34","doi-asserted-by":"crossref","unstructured":"Pogorelov, K., et\u00a0al.: KVASIR: a multi-class image dataset for computer aided gastrointestinal disease detection. In: Proceedings of the 8th ACM on Multimedia Systems Conference, pp. 164\u2013169 (2017)","DOI":"10.1145\/3083187.3083212"},{"key":"11_CR35","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1007\/978-3-031-19797-0_3","volume-title":"ECCV 2022","author":"X Qin","year":"2022","unstructured":"Qin, X., Dai, H., Hu, X., Fan, D.P., Shao, L., Van Gool, L.: Highly accurate dichotomous image segmentation. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13678, pp. 38\u201356. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19797-0_3"},{"key":"11_CR36","doi-asserted-by":"crossref","unstructured":"Rahman, M.M., Marculescu, R.: Medical image segmentation via cascaded attention decoding. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 6222\u20136231 (2023)","DOI":"10.1109\/WACV56688.2023.00616"},{"key":"11_CR37","unstructured":"Rebuffi, S.A., Bilen, H., Vedaldi, A.: Learning multiple visual domains with residual adapters. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"11_CR38","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.A., Bilen, H., Vedaldi, A.: Efficient parametrization of multi-domain deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8119\u20138127 (2018)","DOI":"10.1109\/CVPR.2018.00847"},{"issue":"1","key":"11_CR39","doi-asserted-by":"publisher","first-page":"71","DOI":"10.2214\/ajr.174.1.1740071","volume":"174","author":"J Shiraishi","year":"2000","unstructured":"Shiraishi, J., et al.: Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists\u2019 detection of pulmonary nodules. Am. J. Roentgenol. 174(1), 71\u201374 (2000)","journal-title":"Am. J. Roentgenol."},{"key":"11_CR40","doi-asserted-by":"crossref","unstructured":"Singh, D., Valdenegro-Toro, M.: The marine debris dataset for forward-looking sonar semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3741\u20133749 (2021)","DOI":"10.1109\/ICCVW54120.2021.00417"},{"key":"11_CR41","unstructured":"Song, Y., Zhou, Q., Lu, X., Shao, Z., Ma, L.: Simada: a simple unified framework for adapting segment anything model in underperformed scenes. arXiv preprint arXiv:2401.17803 (2024)"},{"key":"11_CR42","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1007\/978-3-319-46466-4_49","volume-title":"Computer Vision \u2013 ECCV 2016","author":"TFY Vicente","year":"2016","unstructured":"Vicente, T.F.Y., Hou, L., Yu, C.-P., Hoai, M., Samaras, D.: Large-scale training of shadow detectors with noisily-annotated shadow examples. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 816\u2013832. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46466-4_49"},{"key":"11_CR43","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: SAM-clip: merging vision foundation models towards semantic and spatial understanding. arXiv preprint arXiv:2310.15308 (2023)","DOI":"10.1109\/CVPRW63382.2024.00367"},{"key":"11_CR44","first-page":"1","volume":"60","author":"L Wang","year":"2022","unstructured":"Wang, L., Fang, S., Meng, X., Li, R.: Building extraction with vision transformer. IEEE Trans. Geosci. Remote Sens. 60, 1\u201311 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"11_CR45","doi-asserted-by":"publisher","first-page":"120234","DOI":"10.1109\/ACCESS.2020.3005861","volume":"8","author":"S Wei","year":"2020","unstructured":"Wei, S., Zeng, X., Qu, Q., Wang, M., Su, H., Shi, J.: HRSID: a high-resolution SAR images dataset for ship detection and instance segmentation. IEEE Access 8, 120234\u2013120254 (2020)","journal-title":"IEEE Access"},{"key":"11_CR46","unstructured":"Wu, J., et al.: Medical SAM adapter: adapting segment anything model for medical image segmentation. arXiv preprint arXiv:2304.12620 (2023)"},{"key":"11_CR47","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1007\/978-3-031-45857-6_16","volume-title":"DART 2023","author":"Q Wu","year":"2024","unstructured":"Wu, Q., Zhang, Y., Elbatel, M.: Self-prompting large vision models for few-shot medical image segmentation. In: Koch, L., et al. (eds.) DART 2023. LNCS, vol. 14293, pp. 156\u2013167. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-45857-6_16"},{"key":"11_CR48","doi-asserted-by":"crossref","unstructured":"Xiong, Y., et\u00a0al.: EfficientSAM: leveraged masked image pretraining for efficient segment anything. arXiv preprint arXiv:2312.00863 (2023)","DOI":"10.1109\/CVPR52733.2024.01525"},{"key":"11_CR49","doi-asserted-by":"crossref","unstructured":"Xu, M., Zhang, Z., Wei, F., Hu, H., Bai, X.: Side adapter network for open-vocabulary semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2945\u20132954 (2023)","DOI":"10.1109\/CVPR52729.2023.00288"},{"key":"11_CR50","unstructured":"Yang, H., Ma, C., Wen, B., Jiang, Y., Yuan, Z., Zhu, X.: Recognize any regions. arXiv preprint arXiv:2311.01373 (2023)"},{"key":"11_CR51","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Zhang, P., Zhang, J., Lin, Z., Lu, H.: Towards high-resolution salient object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7234\u20137243 (2019)","DOI":"10.1109\/ICCV.2019.00733"},{"key":"11_CR52","unstructured":"Zhang, C., et al.: Faster segment anything: towards lightweight SAM for mobile applications. arXiv preprint arXiv:2306.14289 (2023)"},{"key":"11_CR53","doi-asserted-by":"crossref","unstructured":"Zhang, K., Liu, D.: Customized segment anything model for medical image segmentation. arXiv preprint arXiv:2304.13785 (2023)","DOI":"10.2139\/ssrn.4495221"},{"key":"11_CR54","unstructured":"Zhang, R., et al.: Personalize segment anything model with one shot. arXiv preprint arXiv:2305.03048 (2023)"},{"key":"11_CR55","unstructured":"Zhao, X., et al.: Fast segment anything. arXiv preprint arXiv:2306.12156 (2023)"},{"key":"11_CR56","doi-asserted-by":"crossref","unstructured":"Zheng, Q., Qiao, X., Cao, Y., Lau, R.W.: Distraction-aware shadow detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5167\u20135176 (2019)","DOI":"10.1109\/CVPR.2019.00531"},{"key":"11_CR57","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Conditional prompt learning for vision-language models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16816\u201316825 (2022)","DOI":"10.1109\/CVPR52688.2022.01631"},{"issue":"9","key":"11_CR58","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. Int. J. Comput. Vis. 130(9), 2337\u20132348 (2022)","journal-title":"Int. J. Comput. Vis."},{"key":"11_CR59","doi-asserted-by":"crossref","unstructured":"Zhu, L., Deng, Z., Hu, X., Fu, C.W., Xu, X., Qin, J., Heng, P.A.: Bidirectional feature pyramid network with recurrent attention residual modules for shadow detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 121\u2013136 (2018)","DOI":"10.1007\/978-3-030-01231-1_8"}],"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\/978-3-031-73661-2_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,9]],"date-time":"2024-11-09T12:04:48Z","timestamp":1731153888000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73661-2_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,10]]},"ISBN":["9783031736605","9783031736612"],"references-count":59,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73661-2_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,10]]},"assertion":[{"value":"10 November 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"}}]}}