{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T04:06:23Z","timestamp":1769832383949,"version":"3.49.0"},"publisher-location":"Cham","reference-count":52,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031733369","type":"print"},{"value":"9783031733376","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-73337-6_17","type":"book-chapter","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T23:02:27Z","timestamp":1730329347000},"page":"293-311","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["CardiacNet: Learning to\u00a0Reconstruct Abnormalities for\u00a0Cardiac Disease Assessment from\u00a0Echocardiogram Videos"],"prefix":"10.1007","author":[{"given":"Jiewen","family":"Yang","sequence":"first","affiliation":[]},{"given":"Yiqun","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Pu","sequence":"additional","affiliation":[]},{"given":"Jiarong","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Xiaowei","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Xiaomeng","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,31]]},"reference":[{"key":"17_CR1","unstructured":"Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. NIPS 26 (2013)"},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis. In: CVPR, pp. 12873\u201312883 (2021)","DOI":"10.1109\/CVPR46437.2021.01268"},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Ganame, J., Mertens, L., et\u00a0al.: Regional myocardial deformation in children with hypertrophic cardiomyopathy: morphological and clinical correlations. Eur. Heart J. 28(23), 2886\u20132894 (2007)","DOI":"10.1093\/eurheartj\/ehm444"},{"issue":"4","key":"17_CR4","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1093\/ehjci\/jet234","volume":"15","author":"JB Geske","year":"2014","unstructured":"Geske, J.B., Bos, J.M., Gersh, B.J., Ommen, S.R., Eidem, B.W., Ackerman, M.J.: Deformation patterns in genotyped patients with hypertrophic cardiomyopathy. Eur. Heart J. Cardiovascu. Imaging 15(4), 456\u2013465 (2014)","journal-title":"Eur. Heart J. Cardiovascu. Imaging"},{"issue":"1","key":"17_CR5","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1038\/s41746-019-0216-8","volume":"3","author":"A Ghorbani","year":"2020","unstructured":"Ghorbani, A., et al.: Deep learning interpretation of echocardiograms. NPJ digit. med. 3(1), 10 (2020)","journal-title":"NPJ digit. med."},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Gong, D., et al.: Memorizing normality to detect anomaly: memory-augmented deep autoencoder for unsupervised anomaly detection. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00179"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Hara, K., Kataoka, H., Satoh, Y.: Can spatiotemporal 3D CNNs retrace the history of 2D cnns and imagenet? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6546\u20136555 (2018)","DOI":"10.1109\/CVPR.2018.00685"},{"key":"17_CR8","unstructured":"H\u00f6rmander, F., Totaro, N., Waldschmidt, A.V.M.: Grundlehren der mathematischen wissenschaften 332, vol. 5. Springer (2006)"},{"key":"17_CR9","doi-asserted-by":"publisher","first-page":"105534","DOI":"10.1016\/j.bspc.2023.105534","volume":"87","author":"X Huo","year":"2024","unstructured":"Huo, X., et al.: HiFuse: hierarchical multi-scale feature fusion network for medical image classification. Biomed. Signal Process. Control 87, 105534 (2024)","journal-title":"Biomed. Signal Process. Control"},{"key":"17_CR10","doi-asserted-by":"publisher","unstructured":"Kamran, S.A., Hossain, K.F., Tavakkoli, A., Zuckerbrod, S.L., Baker, S.A.: VTGAN: semi-supervised retinal image synthesis and disease prediction using vision transformers. In: 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 3228\u20133238 (2021). https:\/\/doi.org\/10.1109\/ICCVW54120.2021.00362","DOI":"10.1109\/ICCVW54120.2021.00362"},{"key":"17_CR11","unstructured":"Kascenas, A., Pugeault, N., O\u2019Neil, A.Q.: Denoising autoencoders for unsupervised anomaly detection in brain MRI. In: International Conference on Medical Imaging with Deep Learning, pp. 653\u2013664. PMLR (2022)"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Lai, W.W., Mertens, L.L., Cohen, M.S., Geva, T.: Echocardiography in Pediatric and Congenital Heart Disease: From Fetus to Adult. John Wiley & Sons (2015)","DOI":"10.1002\/9781118742440"},{"issue":"9","key":"17_CR13","doi-asserted-by":"publisher","first-page":"2198","DOI":"10.1109\/TMI.2019.2900516","volume":"38","author":"S Leclerc","year":"2019","unstructured":"Leclerc, S., et al.: Deep learning for segmentation using an open large-scale dataset in 2D echocardiography. IEEE Trans. Med. Imaging 38(9), 2198\u20132210 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"17_CR14","doi-asserted-by":"publisher","first-page":"903660","DOI":"10.3389\/fcvm.2022.903660","volume":"9","author":"X Lin","year":"2022","unstructured":"Lin, X., et al.: Echocardiography-based AI detection of regional wall motion abnormalities and quantification of cardiac function in myocardial infarction. Front. Cardiovasc. Med. 9, 903660 (2022)","journal-title":"Front. Cardiovasc. Med."},{"key":"17_CR15","doi-asserted-by":"publisher","unstructured":"Lin, Y., Luo, Z., Zhao, W., Li, X.: Learning deep intensity field for extremely sparse-view CBCT reconstruction. In: Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023, pp. 13\u201323. Springer Nature Switzerland (2023). https:\/\/doi.org\/10.1007\/978-3-031-43999-5_2","DOI":"10.1007\/978-3-031-43999-5_2"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Lin, Y., Wang, H., Chen, J., Li, X.: Learning 3D gaussians for extremely sparse-view cone-beam CT reconstruction (2024). https:\/\/arxiv.org\/abs\/2407.01090","DOI":"10.1007\/978-3-031-72104-5_41"},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Lin, Y., Yang, J., Wang, H., Ding, X., Zhao, W., Li, X.: C\u23032rv: cross-regional and cross-view learning for sparse-view CBCT reconstruction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11205\u201311214 (June 2024)","DOI":"10.1109\/CVPR52733.2024.01065"},{"issue":"1","key":"17_CR18","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1038\/s41598-022-27211-w","volume":"13","author":"B Liu","year":"2023","unstructured":"Liu, B., et al.: A deep learning framework assisted echocardiography with diagnosis, lesion localization, phenogrouping heterogeneous disease, and anomaly detection. Sci. Rep. 13(1), 3 (2023)","journal-title":"Sci. Rep."},{"key":"17_CR19","unstructured":"Lu, Y., Li, K., Pu, B., Tan, Y., Zhu, N.: A YOLOX-based deep instance segmentation neural network for cardiac anatomical structures in fetal ultrasound images. IEEE\/ACM Trans. Comput. Biol. Bioinform. (2022)"},{"key":"17_CR20","doi-asserted-by":"publisher","unstructured":"Mallya, M., Hamarneh, G.: Deep multimodal guidance for medical image classification. In: MICCAI. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-16449-1_29","DOI":"10.1007\/978-3-031-16449-1_29"},{"issue":"36","key":"17_CR21","doi-asserted-by":"publisher","first-page":"3599","DOI":"10.1093\/eurheartj\/ehab368","volume":"42","author":"TA McDonagh","year":"2021","unstructured":"McDonagh, T.A., et al.: 2021 esc guidelines for the diagnosis and treatment of acute and chronic heart failure: Developed by the task force for the diagnosis and treatment of acute and chronic heart failure of the european society of cardiology (esc) with the special contribution of the heart failure association (hfa) of the esc. Eur. Heart J. 42(36), 3599\u20133726 (2021)","journal-title":"Eur. Heart J."},{"issue":"5\u20136","key":"17_CR22","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1016\/j.pcad.2018.11.004","volume":"61","author":"G Mcleod","year":"2018","unstructured":"Mcleod, G., et al.: Echocardiography in congenital heart disease. Prog. Cardiovasc. Dis. 61(5\u20136), 468\u2013475 (2018)","journal-title":"Prog. Cardiovasc. Dis."},{"key":"17_CR23","doi-asserted-by":"crossref","unstructured":"Meena, T., Kabiraj, A., Reddy, P.B., Roy, S.: Weakly supervised confidence aware probabilistic cam multi-thorax anomaly localization network. In: 2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI), pp. 309\u2013314. IEEE (2023)","DOI":"10.1109\/IRI58017.2023.00061"},{"issue":"2","key":"17_CR24","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1093\/eurjhf\/hfr164","volume":"14","author":"M Niemann","year":"2012","unstructured":"Niemann, M., et al.: Echocardiographic quantification of regional deformation helps to distinguish isolated left ventricular non-compaction from dilated cardiomyopathy. Eur. J. Heart Fail. 14(2), 155\u2013161 (2012)","journal-title":"Eur. J. Heart Fail."},{"issue":"7802","key":"17_CR25","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1038\/s41586-020-2145-8","volume":"580","author":"D Ouyang","year":"2020","unstructured":"Ouyang, D., et al.: Video-based AI for beat-to-beat assessment of cardiac function. Nature 580(7802), 252\u2013256 (2020)","journal-title":"Nature"},{"issue":"3","key":"17_CR26","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.echo.2011.11.017","volume":"25","author":"D Oxborough","year":"2012","unstructured":"Oxborough, D., et al.: The right ventricle of the endurance athlete: the relationship between morphology and deformation. J. Am. Soc. Echocardiogr. 25(3), 263\u2013271 (2012)","journal-title":"J. Am. Soc. Echocardiogr."},{"issue":"4","key":"17_CR27","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1161\/01.CIR.54.4.538","volume":"54","author":"RL Popp","year":"1976","unstructured":"Popp, R.L.: Echocardiographic assessment of cardiac disease. Circulation 54(4), 538\u2013552 (1976)","journal-title":"Circulation"},{"key":"17_CR28","doi-asserted-by":"crossref","unstructured":"Pu, B., et al.: HFSCCD: a hybrid neural network for fetal standard cardiac cycle detection in ultrasound videos. IEEE J. Biomed. Health Inform. (2024)","DOI":"10.1109\/JBHI.2024.3370507"},{"issue":"11","key":"17_CR29","doi-asserted-by":"publisher","first-page":"5540","DOI":"10.1109\/JBHI.2022.3182722","volume":"26","author":"B Pu","year":"2022","unstructured":"Pu, B., et al.: MobileUNet-FPN: a semantic segmentation model for fetal ultrasound four-chamber segmentation in edge computing environments. IEEE J. Biomed. Health Inform. 26(11), 5540\u20135550 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"17_CR30","unstructured":"Pu, B., et\u00a0al.: Unsupervised domain adaptation for anatomical structure detection in ultrasound images. In: Forty-first International Conference on Machine Learning"},{"key":"17_CR31","doi-asserted-by":"crossref","unstructured":"Pu, B., et\u00a0al.: M3-UDA: a new benchmark for unsupervised domain adaptive fetal cardiac structure detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11621\u201311630 (2024)","DOI":"10.1109\/CVPR52733.2024.01104"},{"key":"17_CR32","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1016\/j.future.2020.09.014","volume":"115","author":"B Pu","year":"2021","unstructured":"Pu, B., Zhu, N., Li, K., Li, S.: Fetal cardiac cycle detection in multi-resource echocardiograms using hybrid classification framework. Futur. Gener. Comput. Syst. 115, 825\u2013836 (2021)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"17_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.03.072","author":"NC Ristea","year":"2023","unstructured":"Ristea, N.C., et al.: CyTran: cycle-consistent transformers for non-contrast to contrast CT translation. Neurocomputing (2023). https:\/\/doi.org\/10.1016\/j.neucom.2023.03.072","journal-title":"Neurocomputing"},{"key":"17_CR34","unstructured":"Ryser, A., Manduchi, L., Laumer, F., Michel, H., Wellmann, S., Vogt, J.E.: Anomaly detection in echocardiograms with dynamic variational trajectory models. In: Machine Learning for Healthcare Conference, pp. 425\u2013458. PMLR (2022)"},{"key":"17_CR35","doi-asserted-by":"publisher","unstructured":"Sanchez, P., Kascenas, A., Liu, X., O\u2019Neil, A.Q., Tsaftaris, S.A.: What is healthy? generative counterfactual diffusion for lesion localization. In: MICCAI Workshop on Deep Generative Models, pp. 34\u201344. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-18576-2_4","DOI":"10.1007\/978-3-031-18576-2_4"},{"issue":"1","key":"17_CR36","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s10916-023-01911-w","volume":"47","author":"G Sanjeevi","year":"2023","unstructured":"Sanjeevi, G., Gopalakrishnan, U., Pathinarupothi, R.K., Madathil, T.: Automatic diagnostic tool for detection of regional wall motion abnormality from echocardiogram. J. Med. Syst. 47(1), 13 (2023)","journal-title":"J. Med. Syst."},{"issue":"1","key":"17_CR37","doi-asserted-by":"publisher","first-page":"11912","DOI":"10.1038\/s41598-023-39226-y","volume":"13","author":"M Sch\u00e4fer","year":"2023","unstructured":"Sch\u00e4fer, M., et al.: Myocardial strain-curve deformation patterns after fontan operation. Sci. Rep. 13(1), 11912 (2023)","journal-title":"Sci. Rep."},{"key":"17_CR38","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.media.2019.01.010","volume":"54","author":"T Schlegl","year":"2019","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30\u201344 (2019)","journal-title":"Med. Image Anal."},{"key":"17_CR39","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","volume":"53","author":"J Schlemper","year":"2019","unstructured":"Schlemper, J., et al.: Attention gated networks: learning to leverage salient regions in medical images. Med. Image Anal. 53, 197\u2013207 (2019)","journal-title":"Med. Image Anal."},{"key":"17_CR40","doi-asserted-by":"publisher","first-page":"102526","DOI":"10.1016\/j.media.2022.102526","volume":"80","author":"J Silva-Rodr\u00edguez","year":"2022","unstructured":"Silva-Rodr\u00edguez, J., Naranjo, V., Dolz, J.: Constrained unsupervised anomaly segmentation. Med. Image Anal. 80, 102526 (2022)","journal-title":"Med. Image Anal."},{"key":"17_CR41","doi-asserted-by":"crossref","unstructured":"Sun, D., et\u00a0al.: Chamber attention network (CAN): towards interpretable diagnosis of pulmonary artery hypertension using echocardiography. J. Adv. Res (2023)","DOI":"10.1016\/j.jare.2023.10.013"},{"key":"17_CR42","doi-asserted-by":"crossref","unstructured":"Tseng, C.H., Chien, S.J., Wang, P.S., Lee, S.J., Pu, B., Zeng, X.J.: Real-time automatic m-mode echocardiography measurement with panel attention. IEEE J. Biomed. Health Inform. (2024)","DOI":"10.1109\/JBHI.2024.3413628"},{"issue":"10","key":"17_CR43","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1136\/hrt.38.10.1001","volume":"38","author":"M Upton","year":"1976","unstructured":"Upton, M., Gibson, D., Brown, D.: Echocardiographic assessment of abnormal left ventricular relaxation in man. Heart 38(10), 1001\u20131009 (1976)","journal-title":"Heart"},{"key":"17_CR44","unstructured":"Van Den\u00a0Oord, A., Vinyals, O., et\u00a0al.: Neural discrete representation learning. NIPS 30 (2017)"},{"key":"17_CR45","doi-asserted-by":"publisher","unstructured":"Wolleb, J., Bieder, F., Sandk\u00fchler, R., Cattin, P.C.: Diffusion models for medical anomaly detection. In: MICCAI, pp. 35\u201345. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-16452-1_4","DOI":"10.1007\/978-3-031-16452-1_4"},{"key":"17_CR46","doi-asserted-by":"publisher","unstructured":"Wolleb, J., Sandk\u00fchler, R., Cattin, P.C.: DescarGAN: disease-specific anomaly detection with weak supervision. In: MICCAI, pp. 14\u201324. Springer (2020). https:\/\/doi.org\/10.1007\/978-3-030-59719-1_2","DOI":"10.1007\/978-3-030-59719-1_2"},{"key":"17_CR47","doi-asserted-by":"crossref","unstructured":"Yang, J., Ding, X., Zheng, Z., Xu, X., Li, X.: GraphECHO: graph-driven unsupervised domain adaptation for echocardiogram video segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11878\u201311887 (2023)","DOI":"10.1109\/ICCV51070.2023.01091"},{"key":"17_CR48","doi-asserted-by":"publisher","unstructured":"Yu, K., Ghosh, S., Liu, Z., Deible, C., Batmanghelich, K.: Anatomy-guided weakly-supervised abnormality localization in chest x-rays. In: MICCAI, pp. 658\u2013668. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-16443-9_63","DOI":"10.1007\/978-3-031-16443-9_63"},{"key":"17_CR49","doi-asserted-by":"crossref","unstructured":"Zaman, F., et\u00a0al.: Spatio-temporal hybrid neural networks reduce erroneous human \u201cjudgement calls\u201d in the diagnosis of takotsubo syndrome. EClinicalMedicine 40 (2021)","DOI":"10.1016\/j.eclinm.2021.101115"},{"issue":"3","key":"17_CR50","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1109\/TMI.2020.3040950","volume":"40","author":"J Zhang","year":"2020","unstructured":"Zhang, J., et al.: Viral pneumonia screening on chest x-rays using confidence-aware anomaly detection. IEEE Trans. Med. Imaging 40(3), 879\u2013890 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"17_CR51","doi-asserted-by":"publisher","unstructured":"Zheng, Z., Yang, J., Ding, X., Xu, X., Li, X.: GL-fusion: Global-local fusion network for multi-view echocardiogram video segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 78\u201388. Springer (2023). https:\/\/doi.org\/10.1007\/978-3-031-43901-8_8","DOI":"10.1007\/978-3-031-43901-8_8"},{"key":"17_CR52","unstructured":"Zimmerer, D., Kohl, S.A., Petersen, J., Isensee, F., Maier-Hein, K.H.: Context-encoding variational autoencoder for unsupervised anomaly detection. arXiv preprint arXiv:1812.05941 (2018)"}],"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\/978-3-031-73337-6_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T23:06:10Z","timestamp":1730329570000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73337-6_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,31]]},"ISBN":["9783031733369","9783031733376"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73337-6_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,31]]},"assertion":[{"value":"31 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}