{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T10:40:27Z","timestamp":1770460827101,"version":"3.49.0"},"publisher-location":"Cham","reference-count":64,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030695347","type":"print"},{"value":"9783030695354","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-69535-4_29","type":"book-chapter","created":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T15:13:11Z","timestamp":1614179591000},"page":"474-491","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Large-Scale Cross-Domain Few-Shot Learning"],"prefix":"10.1007","author":[{"given":"Jiechao","family":"Guan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manli","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiwu","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,25]]},"reference":[{"key":"29_CR1","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1109\/TPAMI.2006.79","volume":"28","author":"F Li","year":"2006","unstructured":"Li, F., Fergus, R., Perona, P.: One-shot learning of object categories. TPAMI 28, 594\u2013611 (2006)","journal-title":"TPAMI"},{"key":"29_CR2","doi-asserted-by":"crossref","unstructured":"Gidaris, S., Komodakis, N.: Dynamic few-shot visual learning without forgetting. In: CVPR, pp. 4367\u20134375 (2018)","DOI":"10.1109\/CVPR.2018.00459"},{"key":"29_CR3","unstructured":"Oreshkin, B., L\u00f3pez, P.R., Lacoste, A.: Tadam: task dependent adaptive metric for improved few-shot learning. In: Advances in Neural Information Processing Systems, pp. 721\u2013731 (2018)"},{"key":"29_CR4","first-page":"1345","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. TKDE 22, 1345\u20131359 (2010)","journal-title":"TKDE"},{"key":"29_CR5","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","volume":"22","author":"SJ Pan","year":"2011","unstructured":"Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22, 199\u2013210 (2011)","journal-title":"IEEE Trans. Neural Netw."},{"key":"29_CR6","unstructured":"Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T.: Meta-learning with memory-augmented neural networks. In: ICML, pp. 1842\u20131850 (2016)"},{"key":"29_CR7","unstructured":"Andrychowicz, M., et al.: Learning to learn by gradient descent by gradient descent. In: Advances in Neural Information Processing Systems, pp. 3981\u20133989 (2016)"},{"key":"29_CR8","unstructured":"Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning. In: ICLR (2017)"},{"key":"29_CR9","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: ICML, pp. 1126\u20131135 (2017)"},{"key":"29_CR10","unstructured":"Munkhdalai, T., Yu, H.: Meta networks. In: ICML, pp. 2554\u20132563 (2017)"},{"key":"29_CR11","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., Wierstra, D.: Matching networks for one shot learning. In: Advances in Neural Information Processing Systems, pp. 3630\u20133638 (2016)"},{"key":"29_CR12","unstructured":"Bertinetto, L., Henriques, J.F., Valmadre, J., Torr, P.H.S., Vedaldi, A.: Learning feed-forward one-shot learners. In: Advances in Neural Information Processing Systems, pp. 523\u2013531 (2016)"},{"key":"29_CR13","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, pp. 4077\u20134087 (2017)"},{"key":"29_CR14","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H.S., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: CVPR, pp. 1199\u20131208 (2018)","DOI":"10.1109\/CVPR.2018.00131"},{"key":"29_CR15","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. IJCV 115, 211\u2013252 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"IJCV"},{"key":"29_CR16","doi-asserted-by":"crossref","unstructured":"Hariharan, B., Girshick, R.B.: Low-shot visual recognition by shrinking and hallucinating features. In: ICCV, pp. 3037\u20133046 (2017)","DOI":"10.1109\/ICCV.2017.328"},{"key":"29_CR17","doi-asserted-by":"crossref","unstructured":"Qiao, S., Liu, C., Shen, W., Yuille, A.L.: Few-shot image recognition by predicting parameters from activations. In: CVPR, pp. 7229\u20137238 (2018)","DOI":"10.1109\/CVPR.2018.00755"},{"key":"29_CR18","doi-asserted-by":"crossref","unstructured":"Li, A., Luo, T., Lu, Z., Xiang, T., Wang, L., Wen, J.: Large-scale few-shot learning: knowledge transfer with class hierarchy. In: CVPR, pp. 7212\u20137220 (2019)","DOI":"10.1109\/CVPR.2019.00738"},{"key":"29_CR19","unstructured":"Goodfellow, I.J., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"key":"29_CR20","unstructured":"Liu, M., Tuzel, O.: Coupled generative adversarial networks. In: Advances in Neural Information Processing Systems, pp. 469\u2013477 (2016)"},{"key":"29_CR21","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: CVPR, pp. 2962\u20132971 (2017)","DOI":"10.1109\/CVPR.2017.316"},{"key":"29_CR22","unstructured":"Motiian, S., Jones, Q., Iranmanesh, S.M., Doretto, G.: Few-shot adversarial domain adaptation. In: Advances in Neural Information Processing Systems, pp. 6673\u20136683 (2017)"},{"key":"29_CR23","doi-asserted-by":"crossref","unstructured":"Hong, W., Wang, Z., Yang, M., Yuan, J.: Conditional generative adversarial network for structured domain adaptation. In: CVPR, pp. 1335\u20131344 (2018)","DOI":"10.1109\/CVPR.2018.00145"},{"key":"29_CR24","unstructured":"Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: Advances in Neural Information Processing Systems, pp. 1647\u20131657 (2018)"},{"key":"29_CR25","doi-asserted-by":"crossref","unstructured":"Qi, H., Brown, M., Lowe, D.G.: Low-shot learning with imprinted weights. In: CVPR, pp. 5822\u20135830 (2018)","DOI":"10.1109\/CVPR.2018.00610"},{"key":"29_CR26","doi-asserted-by":"crossref","unstructured":"Douze, M., Szlam, A., Hariharan, B., J\u00e9gou, H.: Low-shot learning with large-scale diffusion. In: CVPR, pp. 3349\u20133358 (2018)","DOI":"10.1109\/CVPR.2018.00353"},{"key":"29_CR27","unstructured":"Gao, H., Shou, Z., Zareian, A., Zhang, H., Chang, S.: Low-shot learning via covariance-preserving adversarial augmentation networks. In: Advances in Neural Information Processing Systems, pp. 983\u2013993 (2018)"},{"key":"29_CR28","doi-asserted-by":"crossref","unstructured":"Motiian, S., Piccirilli, M., Adjeroh, D.A., Doretto, G.: Unified deep supervised domain adaptation and generalization. In: ICCV, pp. 5716\u20135726 (2017)","DOI":"10.1109\/ICCV.2017.609"},{"key":"29_CR29","unstructured":"Klys, J., Snell, J., Zemel, R.: Learning latent subspaces in variational autoencoders. In: Advances in Neural Information Processing Systems, pp. 6444\u20136454 (2018)"},{"key":"29_CR30","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111\u20133119 (2013)"},{"key":"29_CR31","unstructured":"Ranzato, M., Boureau, Y., Chopra, S., LeCun, Y.: A unified energy-based framework for unsupervised learning. In: AISTATS, pp. 371\u2013379 (2007)"},{"key":"29_CR32","doi-asserted-by":"crossref","unstructured":"Rifai, S., Vincent, P., Muller, X., Glorot, X., Bengio, Y.: Contractive auto-encoders: explicit invariance during feature extraction. In: ICML, pp. 833\u2013840 (2011)","DOI":"10.1007\/978-3-642-23783-6_41"},{"key":"29_CR33","unstructured":"Reed, S.E., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Generative adversarial text to image synthesis. In: ICML, pp. 1060\u20131069 (2016)"},{"key":"29_CR34","doi-asserted-by":"crossref","unstructured":"Kodirov, E., Xiang, T., Gong, S.: Semantic autoencoder for zero-shot learning. In: CVPR, pp. 4447\u20134456 (2017)","DOI":"10.1109\/CVPR.2017.473"},{"key":"29_CR35","doi-asserted-by":"crossref","unstructured":"Sun, Q., Liu, Y., Chua, T.S., Schiele, B.: Meta-transfer learning for few-shot learning. In: CVPR, pp. 403\u2013412 (2019)","DOI":"10.1109\/CVPR.2019.00049"},{"key":"29_CR36","doi-asserted-by":"crossref","unstructured":"Jamal, M.A., Qi, G.J.: Task agnostic meta-learning for few-shot learning. In: CVPR, pp. 11719\u201311727 (2019)","DOI":"10.1109\/CVPR.2019.01199"},{"key":"29_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhang, M., Lu, Z., Xiang, T., Wen, J.: AdarGCN: adaptive aggregation GCN for few-shot learning. arXiv preprint arXiv:2002.12641 (2020)","DOI":"10.1109\/WACV48630.2021.00352"},{"key":"29_CR38","first-page":"1","volume":"I","author":"J Guan","year":"2020","unstructured":"Guan, J., Lu, Z., Xiang, T., Li, A., Zhao, A., Wen, J.R.: Zero and few shot learning with semantic feature synthesis and competitive learning. TPAM I, 1\u201314 (2020)","journal-title":"TPAM"},{"key":"29_CR39","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-UCSD birds-200-2011 dataset. Technical Report CNS-TR-2011-001, California Institute of Technology (2011)"},{"key":"29_CR40","unstructured":"Chen, W., Liu, Y., Kira, Z., Wang, Y.F., Huang, J.: A closer look at few-shot classification. In: ICLR (2019)"},{"key":"29_CR41","doi-asserted-by":"crossref","unstructured":"Long, M., Ding, G., Wang, J., Sun, J., Guo, Y., Yu, P.S.: Transfer sparse coding for robust image representation. In: CVPR, pp. 407\u2013414 (2013)","DOI":"10.1109\/CVPR.2013.59"},{"key":"29_CR42","unstructured":"Muandet, K., Balduzzi, D., Sch\u00f6lkopf, B.: Domain generalization via invariant feature representation. In: ICML, pp. 10\u201318 (2013)"},{"key":"29_CR43","unstructured":"Long, M., Cao, Y., Wang, J., Jordan, M.I.: Learning transferable features with deep adaptation networks. In: ICML, pp. 97\u2013105 (2015)"},{"key":"29_CR44","first-page":"59:1","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. JMLR 17, 59:1\u201359:35 (2016)","journal-title":"JMLR"},{"key":"29_CR45","doi-asserted-by":"crossref","unstructured":"Motiian, S., Piccirilli, M., Adjeroh, D.A., Doretto, G.: Information bottleneck learning using privileged information for visual recognition. In: CVPR, pp. 1496\u20131505 (2016)","DOI":"10.1109\/CVPR.2016.166"},{"key":"29_CR46","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: Simultaneous deep transfer across domains and tasks. In: ICCV, pp. 4068\u20134076 (2015)","DOI":"10.1109\/ICCV.2015.463"},{"key":"29_CR47","unstructured":"Ganin, Y., Lempitsky, V.S.: Unsupervised domain adaptation by backpropagation. In: ICML, pp. 1180\u20131189 (2015)"},{"key":"29_CR48","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-642-15561-1_16","volume-title":"Computer Vision \u2013 ECCV 2010","author":"K Saenko","year":"2010","unstructured":"Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 213\u2013226. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15561-1_16"},{"key":"29_CR49","unstructured":"Zhao, A., Ding, M., Guan, J., Lu, Z., Xiang, T., Wen, J.R.: Domain-invariant projection learning for zero-shot recognition. In: Advances in Neural Information Processing Systems, pp. 1019\u20131030 (2018)"},{"key":"29_CR50","doi-asserted-by":"crossref","unstructured":"Huo, Y., Guan, J., Zhang, J., Zhang, M., Wen, J.R., Lu, Z.: Zero-shot learning with few seen class samples. In: ICME, pp. 1336\u20131341 (2019)","DOI":"10.1109\/ICME.2019.00232"},{"key":"29_CR51","doi-asserted-by":"crossref","unstructured":"Liu, G., Guan, J., Zhang, M., Zhang, J., Wang, Z., Lu, Z.: Joint projection and subspace learning for zero-shot recognition. In: ICME, pp. 1228\u20131233 (2019)","DOI":"10.1109\/ICME.2019.00214"},{"issue":"12","key":"29_CR52","doi-asserted-by":"publisher","first-page":"2810","DOI":"10.1007\/s11263-020-01342-x","volume":"128","author":"A Li","year":"2020","unstructured":"Li, A., Lu, Z., Guan, J., Xiang, T., Wang, L., Wen, J.-R.: Transferrable feature and projection learning with class hierarchy for zero-shot learning. Int. J. Comput. Vis. 128(12), 2810\u20132827 (2020). https:\/\/doi.org\/10.1007\/s11263-020-01342-x","journal-title":"Int. J. Comput. Vis."},{"key":"29_CR53","unstructured":"Garcia, E.M., Tiedemann, J., Espa\u00f1a-Bonet, C., M\u00e0rquez, L.: Word\u2019s vector representations meet machine translation. In: EMNLP 2014 Workshop on Syntax, Semantics and Structure in Statistical Translation, pp. 132\u2013134 (2014)"},{"key":"29_CR54","unstructured":"McCann, B., Bradbury, J., Xiong, C., Socher, R.: Learned in translation: contextualized word vectors. In: Advances in Neural Information Processing Systems, pp. 6297\u20136308 (2017)"},{"key":"29_CR55","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: ICLR 2013 Workshop (2013)"},{"key":"29_CR56","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"29_CR57","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/978-3-319-24261-3_7","volume-title":"Similarity-Based Pattern Recognition","author":"E Hoffer","year":"2015","unstructured":"Hoffer, E., Ailon, N.: Deep metric learning using triplet network. In: Feragen, A., Pelillo, M., Loog, M. (eds.) SIMBAD 2015. LNCS, vol. 9370, pp. 84\u201392. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24261-3_7"},{"key":"29_CR58","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/11752790_2","volume-title":"Subspace, Latent Structure and Feature Selection","author":"R Rosipal","year":"2006","unstructured":"Rosipal, R., Kr\u00e4mer, N.: Overview and recent advances in partial least squares. In: Saunders, C., Grobelnik, M., Gunn, S., Shawe-Taylor, J. (eds.) SLSFS 2005. LNCS, vol. 3940, pp. 34\u201351. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11752790_2"},{"key":"29_CR59","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1145\/361573.361582","volume":"15","author":"RH Bartels","year":"1972","unstructured":"Bartels, R.H., Stewart, G.W.: Solution of the matrix equation ax+xb=c [F4] (algorithm 432). Commun. ACM 15, 820\u2013826 (1972)","journal-title":"Commun. ACM"},{"key":"29_CR60","unstructured":"Zhang, H., Dana, K.: Multi-style generative network for real-time transfer. arXiv preprint arXiv:1703.06953 (2017)"},{"key":"29_CR61","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: ICCV, pp. 1406\u20131415 (2019)","DOI":"10.1109\/ICCV.2019.00149"},{"key":"29_CR62","doi-asserted-by":"crossref","unstructured":"Lee, K., Maji, S., Ravichandran, A., Soatto, S.: Meta-learning with differentiable convex optimization. In: CVPR, pp. 10657\u201310665 (2019)","DOI":"10.1109\/CVPR.2019.01091"},{"key":"29_CR63","unstructured":"Xu, R., Li, G., Yang, J., Lin, L.: Unsupervised domain adaptation: an adaptive feature norm approach. CoRR abs\/1811.07456 (2018)"},{"key":"29_CR64","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/0098-3004(93)90090-R","volume":"19","author":"A Ma\u0107kiewicz","year":"1993","unstructured":"Ma\u0107kiewicz, A., Ratajczak, W.: Principal components analysis (PCA). Comput. Geosci. 19, 303\u2013342 (1993)","journal-title":"Comput. Geosci."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2020"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-69535-4_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,18]],"date-time":"2022-12-18T21:32:57Z","timestamp":1671399177000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-69535-4_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030695347","9783030695354"],"references-count":64,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-69535-4_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"25 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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":"30 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/accv2020.kyoto\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"768","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":"254","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":"33% - 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":"3","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.","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)"}}]}}