{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T21:13:44Z","timestamp":1743110024723,"version":"3.40.3"},"publisher-location":"Cham","reference-count":63,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030585259"},{"type":"electronic","value":"9783030585266"}],"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_26","type":"book-chapter","created":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T21:03:07Z","timestamp":1602018187000},"page":"434-452","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Learning to Learn Words from Visual Scenes"],"prefix":"10.1007","author":[{"given":"D\u00eddac","family":"Sur\u00eds","sequence":"first","affiliation":[]},{"given":"Dave","family":"Epstein","sequence":"additional","affiliation":[]},{"given":"Heng","family":"Ji","sequence":"additional","affiliation":[]},{"given":"Shih-Fu","family":"Chang","sequence":"additional","affiliation":[]},{"given":"Carl","family":"Vondrick","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,7]]},"reference":[{"key":"26_CR1","doi-asserted-by":"crossref","unstructured":"Adams, O., Makarucha, A., Neubig, G., Bird, S., Cohn, T.: Cross-lingual word embeddings for low-resource language modeling. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, Valencia, Spain, pp. 937\u2013947. Association for Computational Linguistics, April 2017. https:\/\/www.aclweb.org\/anthology\/E17-1088","DOI":"10.18653\/v1\/E17-1088"},{"key":"26_CR2","unstructured":"Alberti, C., Ling, J., Collins, M., Reitter, D.: Fusion of detected objects in text for visual question answering (B2T2), August 2019. http:\/\/arxiv.org\/abs\/1908.05054"},{"key":"26_CR3","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":"26_CR4","doi-asserted-by":"crossref","unstructured":"Anne Hendricks, L., Venugopalan, S., Rohrbach, M., Mooney, R., Saenko, K., Darrell, T.: Deep compositional captioning: describing novel object categories without paired training data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u201310 (2016)","DOI":"10.1109\/CVPR.2016.8"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Artetxe, M., Schwenk, H.: Massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond. Tech. rep. (2019)","DOI":"10.1162\/tacl_a_00288"},{"key":"26_CR6","unstructured":"Bengio, S., Bengio, Y., Cloutier, J., Gecsei, J.: On the optimization of a synaptic learning rule (2002)"},{"key":"26_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Y.C., et al.: UNITER: Learning UNiversal Image-TExt Representations. Tech. rep. (2019)","DOI":"10.1007\/978-3-030-58577-8_7"},{"key":"26_CR8","unstructured":"Damen, D., et al.: Scaling egocentric vision: the EPIC-KITCHENS Dataset. In: The European Conference on Computer Vision (ECCV) (2018). http:\/\/youtu.be\/Dj6Y3H0ubDw"},{"key":"26_CR9","unstructured":"Dasgupta, I., Guo, D., Stuhlm\u00fcller, A., Gershman, S.J., Goodman, N.D.: Evaluating compositionality in sentence embeddings. arXiv preprint arXiv:1802.04302 (2018)"},{"key":"26_CR10","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. Tech. rep. https:\/\/github.com\/tensorflow\/tensor2tensor"},{"key":"26_CR11","unstructured":"Duan, Y., Schulman, J., Chen, X., Bartlett, P.L., Sutskever, I., Abbeel, P.: Rl2: fast reinforcement learning via slow reinforcement learning. arXiv preprint arXiv:1611.02779 (2016)"},{"key":"26_CR12","unstructured":"Ettinger, A., Elgohary, A., Phillips, C., Resnik, P.: Assessing composition in sentence vector representations. arXiv preprint arXiv:1809.03992 (2018)"},{"key":"26_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/978-3-642-15561-1_2","volume-title":"Computer Vision \u2013 ECCV 2010","author":"A Farhadi","year":"2010","unstructured":"Farhadi, A., et al.: Every picture tells a story: generating sentences from images. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 15\u201329. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15561-1_2"},{"key":"26_CR14","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1126\u20131135. JMLR. org (2017)"},{"key":"26_CR15","unstructured":"Frans, K., Ho, J., Chen, X., Abbeel, P., Schulman, J.: Meta learning shared hierarchies. arXiv preprint arXiv:1710.09767 (2017)"},{"key":"26_CR16","unstructured":"Gandhi, K., Lake, B.M.: Mutual exclusivity as a challenge for neural networks. arXiv preprint arXiv:1906.10197 (2019)"},{"key":"26_CR17","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":"26_CR18","doi-asserted-by":"crossref","unstructured":"Herbelot, A., Baroni, M.: High-risk learning: acquiring new word vectors from tiny data. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 304\u2013309 (2017)","DOI":"10.18653\/v1\/D17-1030"},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"Hu, Z., Chen, T., Chang, K.W., Sun, Y.: Few-shot representation learning for out-of-vocabulary words. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4102\u20134112 (2019)","DOI":"10.18653\/v1\/P19-1402"},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Johnson, J., Fei-Fei, L., Hariharan, B., Zitnick, C.L., Van Der Maaten, L., Girshick, R.: CLEVR: a diagnostic dataset for compositional language and elementary visual reasoning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017). https:\/\/arxiv.org\/pdf\/1612.06890.pdf","DOI":"10.1109\/CVPR.2017.215"},{"key":"26_CR21","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1162\/tacl_a_00065","volume":"5","author":"M Johnson","year":"2017","unstructured":"Johnson, M., et al.: Google\u2019s multilingual neural machine translation system: enabling zero-shot translation. Trans. Assoc. Comput. Linguist. 5, 339\u2013351 (2017)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"26_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1007\/978-3-030-01264-9_15","volume-title":"Computer Vision \u2013 ECCV 2018","author":"K Kato","year":"2018","unstructured":"Kato, K., Li, Y., Gupta, A.: Compositional Learning for Human Object Interaction. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018. LNCS, vol. 11218, pp. 247\u2013264. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01264-9_15"},{"key":"26_CR23","doi-asserted-by":"crossref","unstructured":"Khodak, M., Saunshi, N., Liang, Y., Ma, T., Stewart, B.M., Arora, S.: A la carte embedding: cheap but effective induction of semantic feature vectors. In: 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, pp. 12\u201322. Association for Computational Linguistics (ACL) (2018)","DOI":"10.18653\/v1\/P18-1002"},{"key":"26_CR24","unstructured":"Lake, B.M.: Compositional generalization through meta sequence-to-sequence learning. In: NeurIPS (2019)"},{"key":"26_CR25","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1111\/cogs.12481","volume":"41","author":"A Lazaridou","year":"2017","unstructured":"Lazaridou, A., Marelli, M., Baroni, M.: Multimodal word meaning induction from minimal exposure to natural text. Cogn. Sci. 41, 677\u2013705 (2017)","journal-title":"Cogn. Sci."},{"key":"26_CR26","unstructured":"Li, G., Duan, N., Fang, Y., Jiang, D., Zhou, M.: Unicoder-VL: a universal encoder for vision and language by cross-modal pre-training. ArXiv abs\/1908.06066 (2019)"},{"key":"26_CR27","unstructured":"Li, L.H., Yatskar, M., Yin, D., Hsieh, C.J., Chang, K.W.: VisualBERT: a simple and performant baseline for vision and language. Tech. rep. (2019). http:\/\/arxiv.org\/abs\/1908.03557"},{"key":"26_CR28","doi-asserted-by":"crossref","unstructured":"Li, Y., Yao, T., Pan, Y., Chao, H., Mei, T.: Pointing novel objects in image captioning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12497\u201312506 (2019)","DOI":"10.1109\/CVPR.2019.01278"},{"key":"26_CR29","unstructured":"Li, Z., Zhou, F., Chen, F., Li, H.: Meta-SGD: learning to learn quickly for few-shot learning. arXiv preprint arXiv:1707.09835 (2017)"},{"key":"26_CR30","unstructured":"Lu, J., Batra, D., Parikh, D., Lee, S.: ViLBERT: pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In: Neural Information Processing Systems (NeurIPS) (2019). http:\/\/arxiv.org\/abs\/1908.02265"},{"key":"26_CR31","doi-asserted-by":"crossref","unstructured":"Lu, J., Yang, J., Batra, D., Parikh, D.: Neural baby talk. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7219\u20137228 (2018)","DOI":"10.1109\/CVPR.2018.00754"},{"key":"26_CR32","unstructured":"Mao, J., Gan, C., Kohli, P., Tenenbaum, J.B., Wu, J.: The neuro-symbolic concept learner: interpreting scenes, words, and sentences from natural supervision. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=rJgMlhRctm"},{"key":"26_CR33","unstructured":"Mishra, N., Rohaninejad, M., Chen, X., Abbeel, P.: A simple neural attentive meta-learner. arXiv preprint arXiv:1707.03141 (2017)"},{"key":"26_CR34","doi-asserted-by":"crossref","unstructured":"Misra, I., Gupta, A., Hebert, M.: From red wine to red tomato: composition with context. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1792\u20131801 (2017)","DOI":"10.1109\/CVPR.2017.129"},{"key":"26_CR35","doi-asserted-by":"crossref","unstructured":"Nagarajan, T., Grauman, K.: Attributes as operators: factorizing unseen attribute-object compositions. In: European Conference on Computer Vision (ECCV) (2018). https:\/\/arxiv.org\/pdf\/1803.09851.pdf","DOI":"10.1007\/978-3-030-01246-5_11"},{"key":"26_CR36","doi-asserted-by":"crossref","unstructured":"Nangia, N., Bowman, S.R.: Human vs. muppet: a conservative estimate of human performance on the glue benchmark. arXiv preprint arXiv:1905.10425 (2019)","DOI":"10.18653\/v1\/P19-1449"},{"key":"26_CR37","doi-asserted-by":"crossref","unstructured":"Nikolaus, M., Abdou, M., Lamm, M., Aralikatte, R., Elliott, D.: Compositional generalization in image captioning. In: CoNLL (2018)","DOI":"10.18653\/v1\/K19-1009"},{"key":"26_CR38","unstructured":"Peters, M.E., et al.: Deep contextualized word representations. arXiv preprint arXiv:1802.05365 (2018)"},{"key":"26_CR39","unstructured":"Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training (2018)"},{"key":"26_CR40","unstructured":"Rahman, W., Hasan, M.K., Zadeh, A., Morency, L.P., Hoque, M.E.: M-BERT: injecting multimodal information in the BERT structure. Tech. rep. (2019). http:\/\/arxiv.org\/abs\/1908.05787"},{"key":"26_CR41","unstructured":"Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning (2016)"},{"issue":"3","key":"26_CR42","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":"26_CR43","doi-asserted-by":"crossref","unstructured":"Schick, T., Sch\u00fctze, H.: Attentive mimicking: Better word embeddings by attending to informative contexts. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 489\u2013494 (2019)","DOI":"10.18653\/v1\/N19-1048"},{"key":"26_CR44","doi-asserted-by":"crossref","unstructured":"Schick, T., Sch\u00fctze, H.: Learning semantic representations for novel words: leveraging both form and context. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6965\u20136973 (2019)","DOI":"10.1609\/aaai.v33i01.33016965"},{"key":"26_CR45","doi-asserted-by":"crossref","unstructured":"Schick, T., Sch\u00fctze, H.: Rare words: A major problem for contextualized embeddings and how to fix it by attentive mimicking. arXiv preprint arXiv:1904.06707 (2019)","DOI":"10.1609\/aaai.v34i05.6403"},{"key":"26_CR46","unstructured":"Schmidhuber, J.: Evolutionary Principles in Self-Referential Learning. On Learning now to Learn: The Meta-Meta-Meta...-Hook. Diploma thesis, Technische Universitat Munchen, Germany, 14 May 1987. http:\/\/www.idsia.ch\/~juergen\/diploma.html"},{"key":"26_CR47","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 4077\u20134087. Curran Associates, Inc. (2017). http:\/\/papers.nips.cc\/paper\/6996-prototypical-networks-for-few-shot-learning.pdf"},{"key":"26_CR48","unstructured":"Su, W., et al.: VL-BERT: pre-training of generic visual-linguistic representations. Tech. rep. (2019). http:\/\/arxiv.org\/abs\/1908.08530"},{"key":"26_CR49","unstructured":"Sun, C., Baradel, F., Murphy, K., Schmid, C.: Contrastive bidirectional transformer for temporal representation learning. Tech. rep. (2019)"},{"key":"26_CR50","doi-asserted-by":"crossref","unstructured":"Sun, C., Myers, A., Vondrick, C., Murphy, K., Schmid, C.: VideoBERT: a joint model for video and language representation learning, April 2019. http:\/\/arxiv.org\/abs\/1904.01766","DOI":"10.1109\/ICCV.2019.00756"},{"key":"26_CR51","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018","DOI":"10.1109\/CVPR.2018.00131"},{"key":"26_CR52","doi-asserted-by":"crossref","unstructured":"Tan, H., Bansal, M.: LXMERT: learning cross-modality encoder representations from transformers. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, August 2019. http:\/\/arxiv.org\/abs\/1908.07490","DOI":"10.18653\/v1\/D19-1514"},{"issue":"4","key":"26_CR53","first-page":"415","volume":"30","author":"WL Taylor","year":"1953","unstructured":"Taylor, W.L.: \u201ccloze procedure\u201d: a new tool for measuring readability. J. Bull. 30(4), 415\u2013433 (1953)","journal-title":"J. Bull."},{"issue":"2","key":"26_CR54","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1111\/1467-9280.00127","volume":"10","author":"R Tincoff","year":"1999","unstructured":"Tincoff, R., Jusczyk, P.W.: Some beginnings of word comprehension in 6-month-olds. Psychol. Sci. 10(2), 172\u2013175 (1999)","journal-title":"Psychol. Sci."},{"key":"26_CR55","unstructured":"Vaswani, A., et al.: Attention Is All You Need (2017)"},{"key":"26_CR56","unstructured":"Vinyals, O., Fortunato, M., Jaitly, N.: Pointer networks. In: Advances in Neural Information Processing Systems, pp. 2692\u20132700 (2015)"},{"issue":"Feb","key":"26_CR57","first-page":"207","volume":"10","author":"KQ Weinberger","year":"2009","unstructured":"Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10(Feb), 207\u2013244 (2009)","journal-title":"J. Mach. Learn. Res."},{"key":"26_CR58","doi-asserted-by":"crossref","unstructured":"Wray, M., Larlus, D., Csurka, G., Damen, D.: Fine-grained action retrieval through multiple parts-of-speech embeddings. In: IEEE\/CVF International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00054"},{"key":"26_CR59","doi-asserted-by":"crossref","unstructured":"Wu, Y., Zhu, L., Jiang, L., Yang, Y.: Decoupled novel object captioner. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 1029\u20131037 (2018)","DOI":"10.1145\/3240508.3240640"},{"key":"26_CR60","doi-asserted-by":"crossref","unstructured":"Xian, Y., Schiele, B., Akata, Z.: Zero-shot learning - the good, the bad and the ugly. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.328"},{"key":"26_CR61","doi-asserted-by":"crossref","unstructured":"Yao, T., Pan, Y., Li, Y., Mei, T.: Incorporating copying mechanism in image captioning for learning novel objects. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6580\u20136588 (2017)","DOI":"10.1109\/CVPR.2017.559"},{"key":"26_CR62","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1162\/tacl_a_00166","volume":"2","author":"P Young","year":"2014","unstructured":"Young, P., Lai, A., Hodosh, M., Hockenmaier, J.: From image descriptions to visual denotations: new similarity metrics for semantic inference over event descriptions. Trans. Assoc. Comput. Linguist. 2, 67\u201378 (2014)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"26_CR63","doi-asserted-by":"crossref","unstructured":"Zhou, L., Palangi, H., Zhang, L., Hu, H., Corso, J.J., Gao, J.: Unified vision-language pre-training for image captioning and VQA. Tech. rep. (2019). https:\/\/github.com\/LuoweiZhou\/VLP","DOI":"10.1609\/aaai.v34i07.7005"}],"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_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T00:18:52Z","timestamp":1728173932000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58526-6_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585259","9783030585266"],"references-count":63,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58526-6_26","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":"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"}]}}