{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:08:56Z","timestamp":1778166536377,"version":"3.51.4"},"publisher-location":"Cham","reference-count":62,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585822","type":"print"},{"value":"9783030585839","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-58583-9_24","type":"book-chapter","created":{"date-parts":[[2020,11,18]],"date-time":"2020-11-18T10:08:18Z","timestamp":1605694098000},"page":"392-409","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":140,"title":["Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-learner"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9967-1186","authenticated-orcid":false,"given":"Eugene","family":"Lee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Evan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen-Yi","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,19]]},"reference":[{"key":"24_CR1","doi-asserted-by":"crossref","unstructured":"Balakrishnan, G., Durand, F., Guttag, J.: Detecting pulse from head motions in video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3430\u20133437 (2013)","DOI":"10.1109\/CVPR.2013.440"},{"key":"24_CR2","unstructured":"Bengio, Y., et al.: A meta-transfer objective for learning to disentangle causal mechanisms. arXiv preprint arXiv:1901.10912 (2019)"},{"key":"24_CR3","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.patrec.2017.10.017","volume":"124","author":"S Bobbia","year":"2019","unstructured":"Bobbia, S., Macwan, R., Benezeth, Y., Mansouri, A., Dubois, J.: Unsupervised skin tissue segmentation for remote photoplethysmography. Pattern Recogn. Lett. 124, 82\u201390 (2019)","journal-title":"Pattern Recogn. Lett."},{"issue":"20","key":"24_CR4","doi-asserted-by":"publisher","first-page":"4364","DOI":"10.3390\/app9204364","volume":"9","author":"F Bousefsaf","year":"2019","unstructured":"Bousefsaf, F., Pruski, A., Maaoui, C.: 3D convolutional neural networks for remote pulse rate measurement and mapping from facial video. Appl. Sci. 9(20), 4364 (2019)","journal-title":"Appl. Sci."},{"key":"24_CR5","unstructured":"Bradski, G.: The OpenCV library. Dr. Dobb\u2019s J. Softw. Tools (2000)"},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Cao, W., Mirjalili, V., Raschka, S.: Rank-consistent ordinal regression for neural networks. arXiv preprint arXiv:1901.07884 (2019)","DOI":"10.1016\/j.patrec.2020.11.008"},{"issue":"5","key":"24_CR7","doi-asserted-by":"publisher","first-page":"4867","DOI":"10.1364\/OE.18.004867","volume":"18","author":"G Cennini","year":"2010","unstructured":"Cennini, G., Arguel, J., Ak\u015fit, K., van Leest, A.: Heart rate monitoring via remote photoplethysmography with motion artifacts reduction. Opt. Express 18(5), 4867\u20134875 (2010)","journal-title":"Opt. Express"},{"key":"24_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1007\/978-3-030-01216-8_22","volume-title":"Computer Vision \u2013 ECCV 2018","author":"W Chen","year":"2018","unstructured":"Chen, W., McDuff, D.: DeepPhys: video-based physiological measurement using convolutional attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 356\u2013373. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01216-8_22"},{"key":"24_CR9","doi-asserted-by":"crossref","unstructured":"Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886\u2013893. IEEE (2005)","DOI":"10.1109\/CVPR.2005.177"},{"issue":"10","key":"24_CR10","doi-asserted-by":"publisher","first-page":"2878","DOI":"10.1109\/TBME.2013.2266196","volume":"60","author":"G De Haan","year":"2013","unstructured":"De Haan, G., Jeanne, V.: Robust pulse rate from chrominance-based rPPG. IEEE Trans. Biomed. Eng. 60(10), 2878\u20132886 (2013)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"11","key":"24_CR11","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1007\/s10439-014-1037-1","volume":"42","author":"P Digiglio","year":"2014","unstructured":"Digiglio, P., Li, R., Wang, W., Pan, T.: Microflotronic arterial tonometry for continuous wearable non-invasive hemodynamic monitoring. Ann. Biomed. Eng. 42(11), 2278\u20132288 (2014)","journal-title":"Ann. Biomed. Eng."},{"key":"24_CR12","unstructured":"Dou, Q., de Castro, D.C., Kamnitsas, K., Glocker, B.: Domain generalization via model-agnostic learning of semantic features. In: Advances in Neural Information Processing Systems, pp. 6450\u20136461 (2019)"},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Doyle, O.M., et\u00a0al.: Predicting progression of Alzheimer\u2019s disease using ordinal regression. PloS One 9(8) (2014)","DOI":"10.1371\/journal.pone.0105542"},{"key":"24_CR14","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 1126\u20131135 (2017). JMLR.org"},{"key":"24_CR15","unstructured":"Finn, C., Rajeswaran, A., Kakade, S., Levine, S.: Online meta-learning. arXiv preprint arXiv:1902.08438 (2019)"},{"key":"24_CR16","doi-asserted-by":"crossref","unstructured":"Gidaris, S., Komodakis, N.: Dynamic few-shot visual learning without forgetting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4367\u20134375 (2018)","DOI":"10.1109\/CVPR.2018.00459"},{"issue":"8","key":"24_CR17","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"24_CR18","unstructured":"Hu, S.X., et al.: Empirical Bayes transductive meta-learning with synthetic gradients. In: International Conference on Learning Representations (ICLR) (2020). https:\/\/openreview.net\/forum?id=Hkg-xgrYvH"},{"key":"24_CR19","unstructured":"Jaderberg, M., et al.: Decoupled neural interfaces using synthetic gradients. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 1627\u20131635 (2017). JMLR.org"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Kazemi, V., Sullivan, J.: One millisecond face alignment with an ensemble of regression trees. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1867\u20131874 (2014)","DOI":"10.1109\/CVPR.2014.241"},{"key":"24_CR21","unstructured":"Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop, vol. 2. Lille (2015)"},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Lee, E., Hsu, T.J., Lee, C.Y.: Centralized state sensing using sensor array on wearable device. In: 2019 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1\u20135. IEEE (2019)","DOI":"10.1109\/ISCAS.2019.8702451"},{"key":"24_CR23","doi-asserted-by":"crossref","unstructured":"Li, X., et al.: The OBF database: a large face video database for remote physiological signal measurement and atrial fibrillation detection. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 242\u2013249. IEEE (2018)","DOI":"10.1109\/FG.2018.00043"},{"key":"24_CR24","doi-asserted-by":"crossref","unstructured":"Li, X., Chen, J., Zhao, G., Pietikainen, M.: Remote heart rate measurement from face videos under realistic situations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4264\u20134271 (2014)","DOI":"10.1109\/CVPR.2014.543"},{"key":"24_CR25","unstructured":"Liang, S., Li, Y., Srikant, R.: Enhancing the reliability of out-of-distribution image detection in neural networks. arXiv preprint arXiv:1706.02690 (2017)"},{"key":"24_CR26","unstructured":"Liu, Y., et al.: Learning to propagate labels: transductive propagation network for few-shot learning. arXiv preprint arXiv:1805.10002 (2018)"},{"issue":"5","key":"24_CR27","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1007\/s10916-010-9506-z","volume":"35","author":"Y Maeda","year":"2011","unstructured":"Maeda, Y., Sekine, M., Tamura, T.: The advantages of wearable green reflected photoplethysmography. J. Med. Syst. 35(5), 829\u2013834 (2011)","journal-title":"J. Med. Syst."},{"key":"24_CR28","doi-asserted-by":"crossref","unstructured":"Menikdiwela, M., Nguyen, C., Li, H., Shaw, M.: CNN-based small object detection and visualization with feature activation mapping. In: 2017 International Conference on Image and Vision Computing New Zealand (IVCNZ), pp. 1\u20135. IEEE (2017)","DOI":"10.1109\/IVCNZ.2017.8402455"},{"key":"24_CR29","unstructured":"Mishra, N., Rohaninejad, M., Chen, X., Abbeel, P.: A simple neural attentive meta-learner. arXiv preprint arXiv:1707.03141 (2017)"},{"issue":"11","key":"24_CR30","doi-asserted-by":"publisher","first-page":"4718","DOI":"10.1364\/BOE.7.004718","volume":"7","author":"AV Mo\u00e7o","year":"2016","unstructured":"Mo\u00e7o, A.V., Stuijk, S., de Haan, G.: Skin inhomogeneity as a source of error in remote PPG-imaging. Biomed. Opt. Express 7(11), 4718\u20134733 (2016)","journal-title":"Biomed. Opt. Express"},{"key":"24_CR31","unstructured":"Munkhdalai, T., Yu, H.: Meta networks. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 2554\u20132563 (2017). JMLR.org"},{"key":"24_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/978-3-319-46484-8_29","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Newell","year":"2016","unstructured":"Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483\u2013499. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_29"},{"key":"24_CR33","unstructured":"Nichol, A., Achiam, J., Schulman, J.: On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999 (2018)"},{"key":"24_CR34","doi-asserted-by":"crossref","unstructured":"Niu, X., Han, H., Shan, S., Chen, X.: SynRhythm: learning a deep heart rate estimator from general to specific. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 3580\u20133585. IEEE (2018)","DOI":"10.1109\/ICPR.2018.8546321"},{"key":"24_CR35","doi-asserted-by":"crossref","unstructured":"Niu, X., Shan, S., Han, H., Chen, X.: RhythmNet: end-to-end heart rate estimation from face via spatial-temporal representation. IEEE Trans. Image Process. (2019)","DOI":"10.1109\/TIP.2019.2947204"},{"key":"24_CR36","doi-asserted-by":"crossref","unstructured":"Niu, Z., Zhou, M., Wang, L., Gao, X., Hua, G.: Ordinal regression with multiple output CNN for age estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4920\u20134928 (2016)","DOI":"10.1109\/CVPR.2016.532"},{"key":"24_CR37","unstructured":"Parra, D., Karatzoglou, A., Amatriain, X., Yavuz, I.: Implicit feedback recommendation via implicit-to-explicit ordinal logistic regression mapping. In: Proceedings of the CARS-2011, vol. 5 (2011)"},{"key":"24_CR38","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019 Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024\u20138035. Curran Associates, Inc. (2019). http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf"},{"issue":"1","key":"24_CR39","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1109\/TBME.2010.2086456","volume":"58","author":"MZ Poh","year":"2010","unstructured":"Poh, M.Z., McDuff, D.J., Picard, R.W.: Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Trans. Biomed. Eng. 58(1), 7\u201311 (2010)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"10","key":"24_CR40","doi-asserted-by":"publisher","first-page":"10762","DOI":"10.1364\/OE.18.010762","volume":"18","author":"MZ Poh","year":"2010","unstructured":"Poh, M.Z., McDuff, D.J., Picard, R.W.: Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt. Express 18(10), 10762\u201310774 (2010)","journal-title":"Opt. Express"},{"key":"24_CR41","unstructured":"Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning (2016)"},{"key":"24_CR42","unstructured":"Ren, J., et al.: Likelihood ratios for out-of-distribution detection. In: Advances in Neural Information Processing Systems, pp. 14680\u201314691 (2019)"},{"issue":"4","key":"24_CR43","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1057\/palgrave.jt.5740158","volume":"13","author":"R Rettie","year":"2005","unstructured":"Rettie, R., Grandcolas, U., Deakins, B.: Text message advertising: response rates and branding effects. J. Target. Meas. Anal. Mark. 13(4), 304\u2013312 (2005)","journal-title":"J. Target. Meas. Anal. Mark."},{"key":"24_CR44","unstructured":"Rusu, A.A., et al.: Meta-learning with latent embedding optimization. arXiv preprint arXiv:1807.05960 (2018)"},{"key":"24_CR45","unstructured":"Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T.: Meta-learning with memory-augmented neural networks. In: International Conference on Machine Learning, pp. 1842\u20131850 (2016)"},{"issue":"6","key":"24_CR46","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.2215\/CJN.02190507","volume":"2","author":"MK Sigrist","year":"2007","unstructured":"Sigrist, M.K., Taal, M.W., Bungay, P., McIntyre, C.W.: Progressive vascular calcification over 2 years is associated with arterial stiffening and increased mortality in patients with stages 4 and 5 chronic kidney disease. Clin. J. Am. Soc. Nephrol. 2(6), 1241\u20131248 (2007)","journal-title":"Clin. J. Am. Soc. Nephrol."},{"key":"24_CR47","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, pp. 4077\u20134087 (2017)"},{"issue":"1","key":"24_CR48","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/T-AFFC.2011.25","volume":"3","author":"M Soleymani","year":"2011","unstructured":"Soleymani, M., Lichtenauer, J., Pun, T., Pantic, M.: A multimodal database for affect recognition and implicit tagging. IEEE Trans. Affect. Comput. 3(1), 42\u201355 (2011)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"24_CR49","unstructured":"\u0160petl\u00edk, R., Franc, V., Matas, J.: Visual heart rate estimation with convolutional neural network. In: Proceedings of the British Machine Vision Conference, Newcastle, UK, pp. 3\u20136 (2018)"},{"issue":"1","key":"24_CR50","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1001\/archneur.1995.00540250025008","volume":"52","author":"JY Streifler","year":"1995","unstructured":"Streifler, J.Y., Eliasziw, M., Benavente, O.R., Hachinski, V.C., Fox, A.J., Barnett, H.: Lack of relationship between leukoaraiosis and carotid artery disease. Arch. Neurol. 52(1), 21\u201324 (1995)","journal-title":"Arch. Neurol."},{"issue":"8","key":"24_CR51","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1016\/j.medengphy.2006.09.006","volume":"29","author":"C Takano","year":"2007","unstructured":"Takano, C., Ohta, Y.: Heart rate measurement based on a time-lapse image. Med. Eng. Phys. 29(8), 853\u2013857 (2007)","journal-title":"Med. Eng. Phys."},{"key":"24_CR52","doi-asserted-by":"crossref","unstructured":"Tulyakov, S., Alameda-Pineda, X., Ricci, E., Yin, L., Cohn, J.F., Sebe, N.: Self-adaptive matrix completion for heart rate estimation from face videos under realistic conditions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2396\u20132404 (2016)","DOI":"10.1109\/CVPR.2016.263"},{"issue":"26","key":"24_CR53","doi-asserted-by":"publisher","first-page":"21434","DOI":"10.1364\/OE.16.021434","volume":"16","author":"W Verkruysse","year":"2008","unstructured":"Verkruysse, W., Svaasand, L.O., Nelson, J.S.: Remote plethysmographic imaging using ambient light. Opt. Express 16(26), 21434\u201321445 (2008)","journal-title":"Opt. Express"},{"key":"24_CR54","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. In: Advances in Neural Information Processing Systems, pp. 3630\u20133638 (2016)"},{"issue":"7","key":"24_CR55","doi-asserted-by":"publisher","first-page":"1479","DOI":"10.1109\/TBME.2016.2609282","volume":"64","author":"W Wang","year":"2016","unstructured":"Wang, W., den Brinker, A.C., Stuijk, S., de Haan, G.: Algorithmic principles of remote PPG. IEEE Trans. Biomed. Eng. 64(7), 1479\u20131491 (2016)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"6","key":"24_CR56","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1088\/1361-6579\/aa6d02","volume":"38","author":"W Wang","year":"2017","unstructured":"Wang, W., den Brinker, A.C., Stuijk, S., de Haan, G.: Robust heart rate from fitness videos. Physiol. Meas. 38(6), 1023 (2017)","journal-title":"Physiol. Meas."},{"issue":"3","key":"24_CR57","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1136\/gut.2007.144865","volume":"58","author":"RK Weersma","year":"2009","unstructured":"Weersma, R.K., et al.: Molecular prediction of disease risk and severity in a large Dutch Crohn\u2019s disease cohort. Gut 58(3), 388\u2013395 (2009)","journal-title":"Gut"},{"key":"24_CR58","unstructured":"Wu, Y., Rosca, M., Lillicrap, T.: Deep compressed sensing. arXiv preprint arXiv:1905.06723 (2019)"},{"key":"24_CR59","doi-asserted-by":"crossref","unstructured":"Yu, H., et al.: Foal: fast online adaptive learning for cardiac motion estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4313\u20134323 (2020)","DOI":"10.1109\/CVPR42600.2020.00437"},{"key":"24_CR60","unstructured":"Yu, Z., Li, X., Zhao, G.: Remote photoplethysmograph signal measurement from facial videos using spatio-temporal networks. In: Proceedings BMVC, pp. 1\u201312 (2019)"},{"key":"24_CR61","doi-asserted-by":"crossref","unstructured":"Yu, Z., Peng, W., Li, X., Hong, X., Zhao, G.: Remote heart rate measurement from highly compressed facial videos: an end-to-end deep learning solution with video enhancement. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 151\u2013160 (2019)","DOI":"10.1109\/ICCV.2019.00024"},{"key":"24_CR62","unstructured":"Zintgraf, L.M., Shiarlis, K., Kurin, V., Hofmann, K., Whiteson, S.: Fast context adaptation via meta-learning. arXiv preprint arXiv:1810.03642 (2018)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58583-9_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:08:48Z","timestamp":1731888528000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58583-9_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585822","9783030585839"],"references-count":62,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58583-9_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"19 November 2020","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":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1360","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}