{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T14:10:57Z","timestamp":1765807857653,"version":"3.40.3"},"publisher-location":"Cham","reference-count":56,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030585228"},{"type":"electronic","value":"9783030585235"}],"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\/978-3-030-58523-5_32","type":"book-chapter","created":{"date-parts":[[2020,12,3]],"date-time":"2020-12-03T20:13:16Z","timestamp":1607026396000},"page":"547-563","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Towards Causal Benchmarking of Bias in Face Analysis Algorithms"],"prefix":"10.1007","author":[{"given":"Guha","family":"Balakrishnan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanjun","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pietro","family":"Perona","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,12,4]]},"reference":[{"key":"32_CR1","unstructured":"https:\/\/aws.amazon.com\/sagemaker\/groundtruth\/"},{"key":"32_CR2","doi-asserted-by":"crossref","unstructured":"Albiero, V., KS, K., Vangara, K., Zhang, K., King, M.C., Bowyer, K.W.: Analysis of gender inequality in face recognition accuracy. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision Workshops, pp. 81\u201389 (2020)","DOI":"10.1109\/WACVW50321.2020.9096947"},{"key":"32_CR3","doi-asserted-by":"crossref","unstructured":"Angrist, J.D., Imbens, G.W.: Identification and estimation of local average treatment effects. Technical report, National Bureau of Economic Research (1995)","DOI":"10.3386\/t0118"},{"issue":"1","key":"32_CR4","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/BF01420984","volume":"12","author":"JL Barron","year":"1994","unstructured":"Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. Int. J. Comput. Vis. 12(1), 43\u201377 (1994)","journal-title":"Int. J. Comput. Vis."},{"issue":"4","key":"32_CR5","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1257\/0002828042002561","volume":"94","author":"M Bertrand","year":"2004","unstructured":"Bertrand, M., Mullainathan, S.: Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. Am. Econ. Rev. 94(4), 991\u20131013 (2004)","journal-title":"Am. Econ. Rev."},{"key":"32_CR6","unstructured":"Bowyer, K., Phillips, P.J.: Empirical Evaluation Techniques in Computer Vision. IEEE Computer Society Press (1998)"},{"key":"32_CR7","unstructured":"Brandao, M.: Age and gender bias in pedestrian detection algorithms. arXiv preprint arXiv:1906.10490 (2019)"},{"key":"32_CR8","unstructured":"Buhrmester, M., Kwang, T., Gosling, S.D.: Amazon\u2019s mechanical turk: a new source of inexpensive, yet high-quality data? (2016)"},{"key":"32_CR9","unstructured":"Buolamwini, J., Gebru, T.: Gender shades: intersectional accuracy disparities in commercial gender classification. In: Conference on Fairness, Accountability and Transparency, pp. 77\u201391 (2018)"},{"issue":"3","key":"32_CR10","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995)","journal-title":"Mach. Learn."},{"key":"32_CR11","unstructured":"Denton, E., Hutchinson, B., Mitchell, M., Gebru, T.: Detecting bias with generative counterfactual face attribute augmentation. arXiv preprint arXiv:1906.06439 (2019)"},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"Drozdowski, P., Rathgeb, C., Dantcheva, A., Damer, N., Busch, C.: Demographic bias in biometrics: a survey on an emerging challenge. IEEE Trans. Technol. Soc. (2020)","DOI":"10.1109\/TTS.2020.2992344"},{"key":"32_CR13","unstructured":"Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: 2004 Conference on Computer Vision and Pattern Recognition Workshop, pp. 178\u2013178. IEEE (2004)"},{"key":"32_CR14","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511790942","volume-title":"Data Analysis Using Regression and Multilevel\/Hierarchical Models","author":"A Gelman","year":"2006","unstructured":"Gelman, A., Hill, J.: Data Analysis Using Regression and Multilevel\/Hierarchical Models. Cambridge University Press, Cambridge (2006)"},{"key":"32_CR15","doi-asserted-by":"crossref","unstructured":"Grother, P., Ngan, M., Hanaoka, K.: Ongoing face recognition vendor test (FRVT) part 1: verification. Technical report, National Institute of Standards and Technology (2018)","DOI":"10.6028\/NIST.IR.8238"},{"key":"32_CR16","doi-asserted-by":"crossref","unstructured":"Grother, P.J., Ngan, M.L., Hanaoka, K.K.: Ongoing face recognition vendor test (FRVT) part 2: identification. Technical report (2018)","DOI":"10.6028\/NIST.IR.8238"},{"key":"32_CR17","doi-asserted-by":"publisher","unstructured":"Hanaoka, P.G.N.K.: Face recognition vendor test (FRVT) part 3: demographic effects. IR 8280, NIST (2019). https:\/\/doi.org\/10.6028\/NIST.IR.8280","DOI":"10.6028\/NIST.IR.8280"},{"key":"32_CR18","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":"32_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-642-57615-7_1","volume-title":"Econometric Evaluation of Labour Market Policies","author":"JJ Heckman","year":"2001","unstructured":"Heckman, J.J., Vytlacil, E.J.: Instrumental variables, selection models, and tight bounds on the average treatment effect. In: Lechner, M., Pfeiffer, F. (eds.) Econometric Evaluation of Labour Market Policies, vol. 13, pp. 1\u201315. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/978-3-642-57615-7_1"},{"issue":"1","key":"32_CR20","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","volume":"12","author":"AE Hoerl","year":"1970","unstructured":"Hoerl, A.E., Kennard, R.W.: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1), 55\u201367 (1970)","journal-title":"Technometrics"},{"key":"32_CR21","unstructured":"K\u00e4rkk\u00e4inen, K., Joo, J.: FairFace: face attribute dataset for balanced race, gender, and age. arXiv preprint arXiv:1908.04913 (2019)"},{"key":"32_CR22","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4401\u20134410 (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"32_CR23","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of StyleGAN. arXiv preprint arXiv:1912.04958 (2019)","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"32_CR24","unstructured":"Kearns, M., Neel, S., Roth, A., Wu, Z.S.: Preventing fairness gerrymandering: auditing and learning for subgroup fairness. arXiv preprint arXiv:1711.05144 (2017)"},{"key":"32_CR25","volume-title":"The Ethical Algorithm: The Science of Socially Aware Algorithm Design","author":"M Kearns","year":"2019","unstructured":"Kearns, M., Roth, A.: The Ethical Algorithm: The Science of Socially Aware Algorithm Design. Oxford University Press, Oxford (2019)"},{"issue":"6","key":"32_CR26","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1109\/TIFS.2012.2214212","volume":"7","author":"BF Klare","year":"2012","unstructured":"Klare, B.F., Burge, M.J., Klontz, J.C., Bruegge, R.W.V., Jain, A.K.: Face recognition performance: role of demographic information. IEEE Trans. Inf. Forensics Secur. 7(6), 1789\u20131801 (2012)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"32_CR27","doi-asserted-by":"crossref","unstructured":"Kleinberg, J., Ludwig, J., Mullainathany, S., Sunstein, C.R.: Discrimination in the age of algorithms. Published by Oxford University Press on behalf of The John M. Olin Center for Law, Economics and Business at Harvard Law School (2019). https:\/\/academic.oup.com\/jla\/article-abstract\/doi\/10.1093\/jla\/laz001\/5476086","DOI":"10.3386\/w25548"},{"key":"32_CR28","doi-asserted-by":"crossref","unstructured":"Kortylewski, A., Egger, B., Schneider, A., Gerig, T., Morel-Forster, A., Vetter, T.: Empirically analyzing the effect of dataset biases on deep face recognition systems. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2093\u20132102 (2018)","DOI":"10.1109\/CVPRW.2018.00283"},{"key":"32_CR29","doi-asserted-by":"crossref","unstructured":"Kortylewski, A., Egger, B., Schneider, A., Gerig, T., Morel-Forster, A., Vetter, T.: Analyzing and reducing the damage of dataset bias to face recognition with synthetic data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)","DOI":"10.1109\/CVPRW.2019.00279"},{"key":"32_CR30","unstructured":"Krishnapriya, K.S., Vangara, K., King, M., Albiero, V., Bowyer, K.: Characterizing the variability in face recognition accuracy relative to race. ArXiv 1904.07325, April 2019"},{"key":"32_CR31","doi-asserted-by":"crossref","unstructured":"Li, Y., Vasconcelos, N.: REPAIR: removing representation bias by dataset resampling. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9572\u20139581 (2019)","DOI":"10.1109\/CVPR.2019.00980"},{"key":"32_CR32","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.425"},{"key":"32_CR33","unstructured":"Lohr, S.: Facial recognition is accurate, if you\u2019re a white guy. New York Times, 9 February 2018. https:\/\/nyti.ms\/2BNurVq"},{"issue":"1","key":"32_CR34","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/TBIOM.2018.2890577","volume":"1","author":"B Lu","year":"2019","unstructured":"Lu, B., Chen, J.C., Castillo, C.D., Chellappa, R.: An experimental evaluation of covariates effects on unconstrained face verification. IEEE Trans. Biometr. Behav. Identity Sci. 1(1), 42\u201355 (2019)","journal-title":"IEEE Trans. Biometr. Behav. Identity Sci."},{"issue":"4","key":"32_CR35","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1056\/NEJM199307223290429","volume":"329","author":"RB Merkatz","year":"1993","unstructured":"Merkatz, R.B., Temple, R., Sobel, S., Feiden, K., Kessler, D.A.: Working group on women in clinical trials: women in clinical trials of new drugs-a change in food and drug administration policy. New Engl. J. Med. 329(4), 292\u2013296 (1993)","journal-title":"New Engl. J. Med."},{"key":"32_CR36","unstructured":"Merler, M., Ratha, N., Feris, R.S., Smith, J.R.: Diversity in faces. arXiv preprint arXiv:1901.10436 (2019)"},{"key":"32_CR37","unstructured":"Muthukumar, V., et al.: Understanding unequal gender classification accuracy from face images. arXiv preprint arXiv:1812.00099 (2018)"},{"issue":"1","key":"32_CR38","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1257\/000282806776157641","volume":"96","author":"P Oreopoulos","year":"2006","unstructured":"Oreopoulos, P.: Estimating average and local average treatment effects of education when compulsory schooling laws really matter. Am. Econ. Rev. 96(1), 152\u2013175 (2006)","journal-title":"Am. Econ. Rev."},{"key":"32_CR39","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511803161","volume-title":"Causality","author":"J Pearl","year":"2009","unstructured":"Pearl, J.: Causality. Cambridge University Press, Cambridge (2009)"},{"key":"32_CR40","doi-asserted-by":"crossref","unstructured":"Phillips, P.J., Grother, P., Micheals, R., Blackburn, D.M., Tabassi, E., Bone, M.: Face recognition vendor test 2002. In: Proceedings of the 2003 IEEE International SOI Conference (Cat. No. 03CH37443), p. 44. IEEE (2003)","DOI":"10.1109\/AMFG.2003.1240822"},{"issue":"5","key":"32_CR41","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/S0262-8856(97)00070-X","volume":"16","author":"PJ Phillips","year":"1998","unstructured":"Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.J.: The feret database and evaluation procedure for face-recognition algorithms. Image Vis. Comput. 16(5), 295\u2013306 (1998)","journal-title":"Image Vis. Comput."},{"issue":"24","key":"32_CR42","doi-asserted-by":"publisher","first-page":"6171","DOI":"10.1073\/pnas.1721355115","volume":"115","author":"PJ Phillips","year":"2018","unstructured":"Phillips, P.J., et al.: Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms. Proc. Natl. Acad. Sci. 115(24), 6171\u20136176 (2018)","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"19","key":"32_CR43","doi-asserted-by":"publisher","first-page":"2917","DOI":"10.1002\/sim.1296","volume":"21","author":"SJ Pocock","year":"2002","unstructured":"Pocock, S.J., Assmann, S.E., Enos, L.E., Kasten, L.E.: Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practiceand problems. Stat. Med. 21(19), 2917\u20132930 (2002)","journal-title":"Stat. Med."},{"key":"32_CR44","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/11957959_2","volume-title":"Toward Category-Level Object Recognition","author":"J Ponce","year":"2006","unstructured":"Ponce, J., et al.: Dataset issues in object recognition. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds.) Toward Category-Level Object Recognition. LNCS, vol. 4170, pp. 29\u201348. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11957959_2"},{"key":"32_CR45","doi-asserted-by":"crossref","unstructured":"Robinson, L.D., Jewell, N.P.: Some surprising results about covariate adjustment in logistic regression models. Int. Stat. Rev.\/Revue Inte. Stat. 227\u2013240 (1991)","DOI":"10.2307\/1403444"},{"key":"32_CR46","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511810725","volume-title":"Matched Sampling for Causal Effects","author":"DB Rubin","year":"2006","unstructured":"Rubin, D.B.: Matched Sampling for Causal Effects. Cambridge University Press, Cambridge (2006)"},{"key":"32_CR47","doi-asserted-by":"crossref","unstructured":"Shen, Y., Gu, J., Tang, X., Zhou, B.: Interpreting the latent space of GANs for semantic face editing. arXiv preprint arXiv:1907.10786 (2019)","DOI":"10.1109\/CVPR42600.2020.00926"},{"key":"32_CR48","doi-asserted-by":"crossref","unstructured":"Simon, V.: Wanted: women in clinical trials (2005)","DOI":"10.1002\/0471667196.ess0268"},{"key":"32_CR49","unstructured":"Singla, S., Pollack, B., Chen, J., Batmanghelich, K.: Explanation by progressive exaggeration. arXiv preprint arXiv:1911.00483 (2019)"},{"key":"32_CR50","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)"},{"key":"32_CR51","doi-asserted-by":"crossref","unstructured":"Torralba, A., Efros, A.A., et al.: Unbiased look at dataset bias. In: CVPR, vol. 1, p. 7 (2011)","DOI":"10.1109\/CVPR.2011.5995347"},{"issue":"1","key":"32_CR52","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1214\/12-AOS1058","volume":"41","author":"TJ VanderWeele","year":"2013","unstructured":"VanderWeele, T.J., Shpitser, I.: On the definition of a confounder. Ann. Stat. 41(1), 196 (2013)","journal-title":"Ann. Stat."},{"issue":"5","key":"32_CR53","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1002\/hec.843","volume":"13","author":"AR Willan","year":"2004","unstructured":"Willan, A.R., Briggs, A.H., Hoch, J.S.: Regression methods for covariate adjustment and subgroup analysis for non-censored cost-effectiveness data. Health Econ. 13(5), 461\u2013475 (2004)","journal-title":"Health Econ."},{"issue":"158","key":"32_CR54","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1080\/01621459.1927.10502953","volume":"22","author":"EB Wilson","year":"1927","unstructured":"Wilson, E.B.: Probable inference, the law of succession, and statistical inference. J. Am. Stat. Assoc. 22(158), 209\u2013212 (1927)","journal-title":"J. Am. Stat. Assoc."},{"key":"32_CR55","doi-asserted-by":"crossref","unstructured":"Xiao, T., Hong, J., Ma, J.: ELEGANT: exchanging latent encodings with GAN for transferring multiple face attributes. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 168\u2013184 (2018)","DOI":"10.1007\/978-3-030-01249-6_11"},{"key":"32_CR56","doi-asserted-by":"publisher","first-page":"2131","DOI":"10.1109\/TPAMI.2018.2858759","volume":"41","author":"B Zhou","year":"2018","unstructured":"Zhou, B., Bau, D., Oliva, A., Torralba, A.: Interpreting deep visual representations via network dissection. IEEE Trans. Pattern Anal. Mach. Intell. 41, 2131\u20132145 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"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\/978-3-030-58523-5_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:10:56Z","timestamp":1733184656000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58523-5_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585228","9783030585235"],"references-count":56,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58523-5_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"4 December 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)"}}]}}