{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T19:53:12Z","timestamp":1781639592969,"version":"3.54.5"},"publisher-location":"Cham","reference-count":56,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585914","type":"print"},{"value":"9783030585921","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\/978-3-030-58592-1_28","type":"book-chapter","created":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T00:34:03Z","timestamp":1604363643000},"page":"466-483","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Towards Recognizing Unseen Categories in Unseen Domains"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8595-9955","authenticated-orcid":false,"given":"Massimiliano","family":"Mancini","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1432-7747","authenticated-orcid":false,"given":"Zeynep","family":"Akata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0228-1147","authenticated-orcid":false,"given":"Elisa","family":"Ricci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7169-0158","authenticated-orcid":false,"given":"Barbara","family":"Caputo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,11,3]]},"reference":[{"key":"28_CR1","doi-asserted-by":"crossref","unstructured":"Akata, Z., Perronnin, F., Harchaoui, Z., Schmid, C.: Label-embedding for attribute-based classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 819\u2013826 (2013)","DOI":"10.1109\/CVPR.2013.111"},{"key":"28_CR2","doi-asserted-by":"crossref","unstructured":"Akata, Z., Reed, S., Walter, D., Lee, H., Schiele, B.: Evaluation of output embeddings for fine-grained image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2927\u20132936 (2015)","DOI":"10.1109\/CVPR.2015.7298911"},{"key":"28_CR3","unstructured":"Balaji, Y., Sankaranarayanan, S., Chellappa, R.: MetaReg: towards domain generalization using meta-regularization. In: Advances in Neural Information Processing Systems, pp. 998\u20131008 (2018)"},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Bendale, A., Boult, T.: Towards open world recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1893\u20131902 (2015)","DOI":"10.1109\/CVPR.2015.7298799"},{"key":"28_CR5","doi-asserted-by":"crossref","unstructured":"Carlucci, F.M., D\u2019Innocente, A., Bucci, S., Caputo, B., Tommasi, T.: Domain generalization by solving jigsaw puzzles. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2229\u20132238 (2019)","DOI":"10.1109\/CVPR.2019.00233"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Changpinyo, S., Chao, W.L., Gong, B., Sha, F.: Synthesized classifiers for zero-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5327\u20135336 (2016)","DOI":"10.1109\/CVPR.2016.575"},{"key":"28_CR7","series-title":"Advances in Computer Vision and Pattern Recognition","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-58347-1_1","volume-title":"Domain Adaptation in Computer Vision Applications","author":"G Csurka","year":"2017","unstructured":"Csurka, G.: A comprehensive survey on domain adaptation for visual applications. In: Csurka, G. (ed.) Domain Adaptation in Computer Vision Applications. ACVPR, pp. 1\u201335. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-58347-1_1"},{"key":"28_CR8","doi-asserted-by":"crossref","unstructured":"Dutta, A., Akata, Z.: Semantically tied paired cycle consistency for zero-shot sketch-based image retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5089\u20135098 (2019)","DOI":"10.1109\/CVPR.2019.00523"},{"issue":"11","key":"28_CR9","doi-asserted-by":"publisher","first-page":"2332","DOI":"10.1109\/TPAMI.2015.2408354","volume":"37","author":"Y Fu","year":"2015","unstructured":"Fu, Y., Hospedales, T.M., Xiang, T., Gong, S.: Transductive multi-view zero-shot learning. IEEE Trans. Pattern Anal. Mach. Intell. 37(11), 2332\u20132345 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"28_CR10","doi-asserted-by":"crossref","unstructured":"Gan, C., Yang, T., Gong, B.: Learning attributes equals multi-source domain generalization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 87\u201397 (2016)","DOI":"10.1109\/CVPR.2016.17"},{"key":"28_CR11","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2096\u20132030 (2016)"},{"key":"28_CR12","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"28_CR13","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":"28_CR14","doi-asserted-by":"crossref","unstructured":"Hoffman, J., Darrell, T., Saenko, K.: Continuous manifold based adaptation for evolving visual domains. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 867\u2013874 (2014)","DOI":"10.1109\/CVPR.2014.116"},{"key":"28_CR15","doi-asserted-by":"crossref","unstructured":"Khosla, A., Zhou, T., Malisiewicz, T., Efros, A.A., Torralba, A.: Undoing the damage of dataset bias. In: European Conference on Computer Vision, pp. 158\u2013171 (2012)","DOI":"10.1007\/978-3-642-33718-5_12"},{"key":"28_CR16","doi-asserted-by":"crossref","unstructured":"Kodirov, E., Xiang, T., Fu, Z., Gong, S.: Unsupervised domain adaptation for zero-shot learning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2452\u20132460 (2015)","DOI":"10.1109\/ICCV.2015.282"},{"issue":"3","key":"28_CR17","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1109\/TPAMI.2013.140","volume":"36","author":"CH Lampert","year":"2013","unstructured":"Lampert, C.H., Nickisch, H., Harmeling, S.: Attribute-based classification for zero-shot visual object categorization. IEEE Trans. Pattern Anal. Mach. Intell. 36(3), 453\u2013465 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"28_CR18","unstructured":"Lange, M.D., et al.: Continual learning: a comparative study on how to defy forgetting in classification tasks. arXiv:1909.08383 (2019)"},{"key":"28_CR19","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, Y., Song, Y.Z., Hospedales, T.M.: Deeper, broader and artier domain generalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5542\u20135550 (2017)","DOI":"10.1109\/ICCV.2017.591"},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, Y., Song, Y.Z., Hospedales, T.M.: Learning to generalize: meta-learning for domain generalization. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.11596"},{"key":"28_CR21","doi-asserted-by":"crossref","unstructured":"Li, D., Zhang, J., Yang, Y., Liu, C., Song, Y.Z., Hospedales, T.M.: Episodic training for domain generalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1446\u20131455 (2019)","DOI":"10.1109\/ICCV.2019.00153"},{"key":"28_CR22","doi-asserted-by":"crossref","unstructured":"Li, H., Jialin Pan, S., Wang, S., Kot, A.C.: Domain generalization with adversarial feature learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5400\u20135409 (2018)","DOI":"10.1109\/CVPR.2018.00566"},{"key":"28_CR23","doi-asserted-by":"crossref","unstructured":"Mancini, M., Bulo, S.R., Caputo, B., Ricci, E.: AdaGraph: unifying predictive and continuous domain adaptation through graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6568\u20136577 (2019)","DOI":"10.1109\/CVPR.2019.00673"},{"key":"28_CR24","doi-asserted-by":"crossref","unstructured":"Mancini, M., Karaoguz, H., Ricci, E., Jensfelt, P., Caputo, B.: Kitting in the wild through online domain adaptation. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1103\u20131109 (2018)","DOI":"10.1109\/IROS.2018.8593862"},{"key":"28_CR25","unstructured":"Mancini, M., Porzi, L., Bulo, S.R., Caputo, B., Ricci, E.: Inferring latent domains for unsupervised deep domain adaptation. IEEE Trans. Pattern Anal. Mach. Intell. (2019)"},{"key":"28_CR26","doi-asserted-by":"crossref","unstructured":"Mancini, M., Porzi, L., Rota Bul\u00f2, S., Caputo, B., Ricci, E.: Boosting domain adaptation by discovering latent domains. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3771\u20133780 (2018)","DOI":"10.1109\/CVPR.2018.00397"},{"key":"28_CR27","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: 1st International Conference on Learning Representations Workshop Track Proceedings (2013)"},{"key":"28_CR28","unstructured":"Muandet, K., Balduzzi, D., Sch\u00f6lkopf, B.: Domain generalization via invariant feature representation. In: International Conference on Machine Learning, pp. 10\u201318 (2013)"},{"key":"28_CR29","doi-asserted-by":"crossref","unstructured":"Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, pp. 722\u2013729. IEEE (2008)","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"28_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/978-3-319-46466-4_5","volume-title":"Computer Vision \u2013 ECCV 2016","author":"M Noroozi","year":"2016","unstructured":"Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 69\u201384. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46466-4_5"},{"key":"28_CR31","doi-asserted-by":"crossref","unstructured":"Patterson, G., Hays, J.: Sun attribute database: discovering, annotating, and recognizing scene attributes. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2751\u20132758. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6247998"},{"key":"28_CR32","doi-asserted-by":"crossref","unstructured":"Peng, K.C., Wu, Z., Ernst, J.: Zero-shot deep domain adaptation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 764\u2013781 (2018)","DOI":"10.1007\/978-3-030-01252-6_47"},{"key":"28_CR33","doi-asserted-by":"crossref","unstructured":"Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., Wang, B.: Moment matching for multi-source domain adaptation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1406\u20131415 (2019)","DOI":"10.1109\/ICCV.2019.00149"},{"key":"28_CR34","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"28_CR35","doi-asserted-by":"crossref","unstructured":"Reed, S., Akata, Z., Lee, H., Schiele, B.: Learning deep representations of fine-grained visual descriptions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 49\u201358 (2016)","DOI":"10.1109\/CVPR.2016.13"},{"key":"28_CR36","doi-asserted-by":"crossref","unstructured":"Roy, S., Siarohin, A., Sangineto, E., Bulo, S.R., Sebe, N., Ricci, E.: Unsupervised domain adaptation using feature-whitening and consensus loss. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9471\u20139480 (2019)","DOI":"10.1109\/CVPR.2019.00970"},{"issue":"3","key":"28_CR37","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"28_CR38","doi-asserted-by":"crossref","unstructured":"Schonfeld, E., Ebrahimi, S., Sinha, S., Darrell, T., Akata, Z.: Generalized zero-and few-shot learning via aligned variational autoencoders. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8247\u20138255 (2019)","DOI":"10.1109\/CVPR.2019.00844"},{"key":"28_CR39","unstructured":"Shankar, S., Piratla, V., Chakrabarti, S., Chaudhuri, S., Jyothi, P., Sarawagi, S.: Generalizing across domains via cross-gradient training. In: International Conference on Learning Representations (2018)"},{"key":"28_CR40","doi-asserted-by":"crossref","unstructured":"Thong, W., Mettes, P., Snoek, C.G.: Open cross-domain visual search. arXiv preprint arXiv:1911.08621 (2019)","DOI":"10.1016\/j.cviu.2020.103045"},{"key":"28_CR41","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1007\/978-3-319-71246-8_48","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"VK Verma","year":"2017","unstructured":"Verma, V.K., Rai, P.: A simple exponential family framework for\u00a0zero-shot learning. In: Ceci, M., Hollm\u00e9n, J., Todorovski, L., Vens, C., D\u017eeroski, S. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10535, pp. 792\u2013808. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-71246-8_48"},{"key":"28_CR42","doi-asserted-by":"crossref","unstructured":"Volpi, R., Murino, V.: Addressing model vulnerability to distributional shifts over image transformation sets. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 7980\u20137989 (2019)","DOI":"10.1109\/ICCV.2019.00807"},{"key":"28_CR43","unstructured":"Volpi, R., Namkoong, H., Sener, O., Duchi, J.C., Murino, V., Savarese, S.: Generalizing to unseen domains via adversarial data augmentation. In: Advances in Neural Information Processing Systems, pp. 5334\u20135344 (2018)"},{"key":"28_CR44","unstructured":"Welinder, P., et al.: Caltech-UCSD birds 200 (2010)"},{"key":"28_CR45","doi-asserted-by":"crossref","unstructured":"Xian, Y., Akata, Z., Sharma, G., Nguyen, Q., Hein, M., Schiele, B.: Latent embeddings for zero-shot classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 69\u201377 (2016)","DOI":"10.1109\/CVPR.2016.15"},{"key":"28_CR46","doi-asserted-by":"crossref","unstructured":"Xian, Y., Choudhury, S., He, Y., Schiele, B., Akata, Z.: Semantic projection network for zero-and few-label semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8256\u20138265 (2019)","DOI":"10.1109\/CVPR.2019.00845"},{"issue":"9","key":"28_CR47","doi-asserted-by":"publisher","first-page":"2251","DOI":"10.1109\/TPAMI.2018.2857768","volume":"41","author":"Y Xian","year":"2018","unstructured":"Xian, Y., Lampert, C.H., Schiele, B., Akata, Z.: Zero-shot learning-a comprehensive evaluation of the good, the bad and the ugly. IEEE Trans. Pattern Anal. Mach. Intell. 41(9), 2251\u20132265 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"28_CR48","doi-asserted-by":"crossref","unstructured":"Xian, Y., Lorenz, T., Schiele, B., Akata, Z.: Feature generating networks for zero-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5542\u20135551 (2018)","DOI":"10.1109\/CVPR.2018.00581"},{"key":"28_CR49","doi-asserted-by":"crossref","unstructured":"Xian, Y., Schiele, B., Akata, Z.: Zero-shot learning-the good, the bad and the ugly. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4582\u20134591 (2017)","DOI":"10.1109\/CVPR.2017.328"},{"key":"28_CR50","doi-asserted-by":"crossref","unstructured":"Xian, Y., Sharma, S., Schiele, B., Akata, Z.: f-VAEGAN-D2: a feature generating framework for any-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10275\u201310284 (2019)","DOI":"10.1109\/CVPR.2019.01052"},{"key":"28_CR51","doi-asserted-by":"crossref","unstructured":"Xu, M., Zhang, J., Ni, B., Li, T., Wang, C., Tian, Q., Zhang, W.: Adversarial domain adaptation with domain mixup. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, pp. 6502\u20136509. AAAI Press (2020)","DOI":"10.1609\/aaai.v34i04.6123"},{"key":"28_CR52","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1007\/978-3-030-01225-0_19","volume-title":"Computer Vision \u2013 ECCV 2018","author":"SK Yelamarthi","year":"2018","unstructured":"Yelamarthi, S.K., Reddy, S.K., Mishra, A., Mittal, A.: A zero-shot framework for sketch based image retrieval. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 316\u2013333. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01225-0_19"},{"key":"28_CR53","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. In: International Conference on Learning Representations (2018)"},{"key":"28_CR54","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Saligrama, V.: Zero-shot learning via semantic similarity embedding. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4166\u20134174 (2015)","DOI":"10.1109\/ICCV.2015.474"},{"key":"28_CR55","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Saligrama, V.: Zero-shot learning via joint latent similarity embedding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6034\u20136042 (2016)","DOI":"10.1109\/CVPR.2016.649"},{"key":"28_CR56","doi-asserted-by":"crossref","unstructured":"Zhuo, J., Wang, S., Cui, S., Huang, Q.: Unsupervised open domain recognition by semantic discrepancy minimization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 750\u2013759 (2019)","DOI":"10.1109\/CVPR.2019.00084"}],"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\/978-3-030-58592-1_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:29:14Z","timestamp":1730593754000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58592-1_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585914","9783030585921"],"references-count":56,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58592-1_28","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":"3 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.","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)"}}]}}