{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T20:46:05Z","timestamp":1775335565616,"version":"3.50.1"},"publisher-location":"Cham","reference-count":68,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585259","type":"print"},{"value":"9783030585266","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\/978-3-030-58526-6_20","type":"book-chapter","created":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T21:03:07Z","timestamp":1602018187000},"page":"330-347","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":82,"title":["Jointly De-Biasing Face Recognition and Demographic Attribute Estimation"],"prefix":"10.1007","author":[{"given":"Sixue","family":"Gong","sequence":"first","affiliation":[]},{"given":"Xiaoming","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Anil K.","family":"Jain","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,7]]},"reference":[{"key":"20_CR1","unstructured":"https:\/\/yanweifu.github.io\/FG_NET_data"},{"key":"20_CR2","unstructured":"http:\/\/trillionpairs.deepglint.com\/overview"},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Abadi, M., et al.: Deep learning with differential privacy. In: Proceedings of the ACM SIGSAC Conference on Computer and Communications Security, pp. 308\u2013318 (2016)","DOI":"10.1145\/2976749.2978318"},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Amini, A., Soleimany, A., Schwarting, W., Bhatia, S., Rus, D.: Uncovering and mitigating algorithmic bias through learned latent structure. In: AAAI\/ACM Conference on AI, Ethics, and Society (2019)","DOI":"10.1145\/3306618.3314243"},{"key":"20_CR5","unstructured":"Bolukbasi, T., Chang, K.W., Zou, J.Y., Saligrama, V., Kalai, A.T.: Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In: Advances in Neural Information Processing Systems, pp. 4349\u20134357 (2016)"},{"key":"20_CR6","unstructured":"Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. arXiv preprint arXiv:1906.07413 (2019)"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: Vggface2: a dataset for recognising faces across pose and age. In: IEEE International Conference on Automatic Face & Gesture Recognition. IEEE (2018)","DOI":"10.1109\/FG.2018.00020"},{"key":"20_CR8","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Chen, B.C., Chen, C.S., Hsu, W.H.: Cross-age reference coding for age-invariant face recognition and retrieval. In: ECCV (2014)","DOI":"10.1007\/978-3-319-10599-4_49"},{"issue":"3","key":"20_CR10","doi-asserted-by":"publisher","first-page":"333","DOI":"10.26599\/TST.2018.9010090","volume":"24","author":"J Cheng","year":"2019","unstructured":"Cheng, J., Li, Y., Wang, J., Yu, L., Wang, S.: Exploiting effective facial patches for robust gender recognition. Tsinghua Sci. Technol. 24(3), 333\u2013345 (2019)","journal-title":"Tsinghua Sci. Technol."},{"key":"20_CR11","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1109\/TBIOM.2019.2897801","volume":"1","author":"CM Cook","year":"2019","unstructured":"Cook, C.M., Howard, J.J., Sirotin, Y.B., Tipton, J.L., Vemury, A.R.: Demographic effects in facial recognition and their dependence on image acquisition: an evaluation of eleven commercial systems. IEEE Trans. Biometrics Behav. Identity Sci. 1, 32\u201341 (2019)","journal-title":"IEEE Trans. Biometrics Behav. Identity Sci."},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00949"},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Deb, D., Best-Rowden, L., Jain, A.K.: Face recognition performance under aging. In: CVPRW (2017)","DOI":"10.1109\/CVPRW.2017.82"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00482"},{"key":"20_CR15","unstructured":"Dietterich, T.G., Kong, E.B.: Machine learning bias, statistical bias, and statistical variance of decision tree algorithms. Tech. rep. (1995)"},{"key":"20_CR16","unstructured":"Drummond, C., Holte, R.C., et al.: C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling. In: Workshop on Learning from Imbalanced Datasets II. Citeseer (2003)"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Fredrikson, M., Jha, S., Ristenpart, T.: Model inversion attacks that exploit confidence information and basic countermeasures. In: The 22nd ACM SIGSAC (2015)","DOI":"10.1145\/2810103.2813677"},{"key":"20_CR18","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: NIPS (2014)"},{"key":"20_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/978-3-319-46487-9_6","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Y Guo","year":"2016","unstructured":"Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: MS-Celeb-1M: a dataset and benchmark for large-scale face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 87\u2013102. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_6"},{"key":"20_CR20","unstructured":"Han, H., A, K.J., Shan, S., Chen, X.: Heterogeneous face attribute estimation: a deep multi-task learning approach. IEEE Trans. Pattern Anal. Mach. Intelli. PP(99), 1\u20131 (2017)"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Hayat, M., Khan, S., Zamir, W., Shen, J., Shao, L.: Max-margin class imbalanced learning with Gaussian affinity. arXiv preprint arXiv:1901.07711 (2019)","DOI":"10.1109\/ICCV.2019.00657"},{"key":"20_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"20_CR23","doi-asserted-by":"crossref","unstructured":"Howard, J., Sirotin, Y., Vemury, A.: The effect of broad and specific demographic homogeneity on the imposter distributions and false match rates in face recognition algorithm performance. In: IEEE BTAS (2019)","DOI":"10.1109\/BTAS46853.2019.9186002"},{"key":"20_CR24","doi-asserted-by":"crossref","unstructured":"Huang, C., Li, Y., Chen, C.L., Tang, X.: Deep imbalanced learning for face recognition and attribute prediction. IEEE Trans Pattern Anal. Mach. Intell. (2019)","DOI":"10.1109\/TPAMI.2019.2914680"},{"key":"20_CR25","unstructured":"Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments (2008)"},{"key":"20_CR26","doi-asserted-by":"crossref","unstructured":"Jourabloo, A., Yin, X., Liu, X.: Attribute preserved face de-identification. In: ICB (2015)","DOI":"10.1109\/ICB.2015.7139096"},{"key":"20_CR27","doi-asserted-by":"crossref","unstructured":"Khan, S., Hayat, M., Zamir, S.W., Shen, J., Shao, L.: Striking the right balance with uncertainty. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00019"},{"key":"20_CR28","unstructured":"Kim, H., Mnih, A.: Disentangling by factorising. arXiv preprint arXiv:1802.05983 (2018)"},{"issue":"6","key":"20_CR29","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. Inform. Forensics Secur. 7(6), 1789\u20131801 (2012)","journal-title":"IEEE Trans. Inform. Forensics Secur."},{"key":"20_CR30","doi-asserted-by":"crossref","unstructured":"Klare, B.F., et al.: Pushing the frontiers of unconstrained face detection and recognition: Iarpa janus benchmark a. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298803"},{"key":"20_CR31","doi-asserted-by":"crossref","unstructured":"Liu, F., Zeng, D., Zhao, Q., Liu, X.: Disentangling features in 3D face shapes for joint face reconstruction and recognition. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00547"},{"key":"20_CR32","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, Z., Jin, H., Wassell, I.: Multi-task adversarial network for disentangled feature learning. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00394"},{"issue":"1","key":"20_CR33","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1109\/TNN.2006.883013","volume":"18","author":"YH Liu","year":"2007","unstructured":"Liu, Y.H., Chen, Y.T.: Face recognition using total margin-based adaptive fuzzy support vector machines. IEEE Trans. Neural Networks 18(1), 178\u2013192 (2007)","journal-title":"IEEE Trans. Neural Networks"},{"key":"20_CR34","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wei, F., Shao, J., Sheng, L., Yan, J., Wang, X.: Exploring disentangled feature representation beyond face identification. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00222"},{"key":"20_CR35","unstructured":"Locatello, F., Bauer, S., Lucic, M., Gelly, S., Sch\u00f6lkopf, B., Bachem, O.: Challenging common assumptions in the unsupervised learning of disentangled representations. arXiv preprint arXiv:1811.12359 (2018)"},{"key":"20_CR36","unstructured":"Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: NIPS (2018)"},{"key":"20_CR37","doi-asserted-by":"crossref","unstructured":"Maze, B., et al.: Iarpa janus benchmark-c: face dataset and protocol. In: 2018 ICB (2018)","DOI":"10.1109\/ICB2018.2018.00033"},{"key":"20_CR38","doi-asserted-by":"crossref","unstructured":"Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., Zafeiriou, S.: AgeDB: the first manually collected, in-the-wild age database. In: CVPRW (2017)","DOI":"10.1109\/CVPRW.2017.250"},{"key":"20_CR39","doi-asserted-by":"crossref","unstructured":"Mullick, S.S., Datta, S., Das, S.: Generative adversarial minority oversampling. arXiv preprint arXiv:1903.09730 (2019)","DOI":"10.1109\/ICCV.2019.00178"},{"key":"20_CR40","unstructured":"Narayanaswamy, S., et al.: Learning disentangled representations with semi-supervised deep generative models. In: NIPS (2017)"},{"key":"20_CR41","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: CVPR (2016)","DOI":"10.1109\/CVPR.2016.532"},{"key":"20_CR42","doi-asserted-by":"crossref","unstructured":"Patrick Grother, M.N., Hanaoka, K.: Face recognition vendor test (FRVT) part 3: demographic effects. Tech. rep., National Institute of Standards and Technology (2019)","DOI":"10.6028\/NIST.IR.8280"},{"issue":"2\u20134","key":"20_CR43","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1007\/s11263-016-0940-3","volume":"126","author":"R Rothe","year":"2018","unstructured":"Rothe, R., Timofte, R., Van Gool, L.: Deep expectation of real and apparent age from a single image without facial landmarks. IJCV 126(2\u20134), 144\u2013157 (2018)","journal-title":"IJCV"},{"issue":"6","key":"20_CR44","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1162\/neco.1992.4.6.863","volume":"4","author":"J Schmidhuber","year":"1992","unstructured":"Schmidhuber, J.: Learning factorial codes by predictability minimization. Neural Comput. 4(6), 863\u2013879 (1992)","journal-title":"Neural Comput."},{"key":"20_CR45","doi-asserted-by":"crossref","unstructured":"Setty, S., et al.: Indian movie face database: a benchmark for face recognition under wide variations. In: NCVPRIPG (2013)","DOI":"10.1109\/NCVPRIPG.2013.6776225"},{"key":"20_CR46","doi-asserted-by":"crossref","unstructured":"Shi, Y., Jain, A.K., Kalka, N.D.: Probabilistic face embeddings. arXiv preprint arXiv:1904.09658 (2019)","DOI":"10.1109\/ICCV.2019.00700"},{"key":"20_CR47","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Deepface: Closing the gap to human-level performance in face verification. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.220"},{"key":"20_CR48","unstructured":"Tao, C., Lv, F., Duan, L., Wu, M.: Minimax entropy network: learning category-invariant features for domain adaptation. arXiv preprint arXiv:1904.09601 (2019)"},{"key":"20_CR49","doi-asserted-by":"crossref","unstructured":"Torralba, A., Efros, A.A., et al.: Unbiased look at dataset bias. In: CVPR (2011)","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"20_CR50","doi-asserted-by":"crossref","unstructured":"Tran, L., Yin, X., Liu, X.: Disentangled representation learning GAN for pose-invariant face recognition. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.141"},{"issue":"12","key":"20_CR51","doi-asserted-by":"publisher","first-page":"3007","DOI":"10.1109\/TPAMI.2018.2868350","volume":"41","author":"L Tran","year":"2019","unstructured":"Tran, L., Yin, X., Liu, X.: Representation learning by rotating your faces. IEEE Trans. Pattern Anal. Mach. Intell. 41(12), 3007\u20133021 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"20_CR52","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: Simultaneous deep transfer across domains and tasks. In: CVPR (2015)","DOI":"10.1109\/ICCV.2015.463"},{"key":"20_CR53","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.316"},{"issue":"7","key":"20_CR54","doi-asserted-by":"publisher","first-page":"926","DOI":"10.1109\/LSP.2018.2822810","volume":"25","author":"F Wang","year":"2018","unstructured":"Wang, F., Cheng, J., Liu, W., Liu, H.: Additive margin softmax for face verification. IEEE Signal Process. Lett. 25(7), 926\u2013930 (2018)","journal-title":"IEEE Signal Process. Lett."},{"key":"20_CR55","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: CosFace: large margin cosine loss for deep face recognition. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00552"},{"key":"20_CR56","doi-asserted-by":"crossref","unstructured":"Wang, M., Deng, W.: Mitigating bias in face recognition using skewness-aware reinforcement learning. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00934"},{"key":"20_CR57","doi-asserted-by":"crossref","unstructured":"Wang, M., Deng, W., Hu, J., Tao, X., Huang, Y.: Racial faces in the wild: reducing racial bias by information maximization adaptation network. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00078"},{"key":"20_CR58","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.neucom.2019.04.085","volume":"363","author":"P Wang","year":"2019","unstructured":"Wang, P., Su, F., Zhao, Z., Guo, Y., Zhao, Y., Zhuang, B.: Deep class-skewed learning for face recognition. Neurocomputing 363, 35\u201345 (2019)","journal-title":"Neurocomputing"},{"key":"20_CR59","unstructured":"Xie, W., Zisserman, A.: Multicolumn networks for face recognition. arXiv preprint arXiv:1807.09192 (2018)"},{"key":"20_CR60","doi-asserted-by":"crossref","unstructured":"Yin, B., Tran, L., Li, H., Shen, X., Liu, X.: Towards interpretable face recognition. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00944"},{"issue":"2","key":"20_CR61","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1109\/TIP.2017.2765830","volume":"27","author":"X Yin","year":"2017","unstructured":"Yin, X., Liu, X.: Multi-task convolutional neural network for pose-invariant face recognition. IEEE Trans. Image Process. 27(2), 964\u2013975 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"20_CR62","doi-asserted-by":"crossref","unstructured":"Yin, X., Yu, X., Sohn, K., Liu, X., Chandraker, M.: Towards large-pose face frontalization in the wild. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.430"},{"key":"20_CR63","doi-asserted-by":"crossref","unstructured":"Yin, X., Yu, X., Sohn, K., Liu, X., Chandraker, M.: Feature transfer learning for face recognition with under-represented data. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00585"},{"issue":"10","key":"20_CR64","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499\u20131503 (2016)","journal-title":"IEEE Signal Process. Lett."},{"key":"20_CR65","doi-asserted-by":"crossref","unstructured":"Zhang, X., Fang, Z., Wen, Y., Li, Z., Qiao, Y.: Range loss for deep face recognition with long-tailed training data. In: CVPR (2017)","DOI":"10.1109\/ICCV.2017.578"},{"issue":"10","key":"20_CR66","doi-asserted-by":"publisher","first-page":"1758","DOI":"10.1109\/TPAMI.2009.195","volume":"32","author":"Y Zhang","year":"2009","unstructured":"Zhang, Y., Zhou, Z.H.: Cost-sensitive face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(10), 1758\u20131769 (2009)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"20_CR67","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Song, Y., Qi, H.: Age progression\/regression by conditional adversarial autoencoder. In: CVPR. IEEE (2017)","DOI":"10.1109\/CVPR.2017.463"},{"key":"20_CR68","doi-asserted-by":"crossref","unstructured":"Zhang, Z., et al.: Gait recognition via disentangled representation learning. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00484"}],"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\/978-3-030-58526-6_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T00:17:17Z","timestamp":1728173837000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58526-6_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585259","9783030585266"],"references-count":68,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58526-6_20","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":"7 October 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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}