{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T19:41:49Z","timestamp":1742931709037,"version":"3.40.3"},"publisher-location":"Cham","reference-count":61,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030586003"},{"type":"electronic","value":"9783030586010"}],"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\/978-3-030-58601-0_28","type":"book-chapter","created":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T19:02:52Z","timestamp":1606503772000},"page":"463-480","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Efficient Transfer Learning via Joint Adaptation of Network Architecture and Weight"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5948-2708","authenticated-orcid":false,"given":"Ming","family":"Sun","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0237-6402","authenticated-orcid":false,"given":"Haoxuan","family":"Dou","sequence":"additional","affiliation":[]},{"given":"Junjie","family":"Yan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,28]]},"reference":[{"key":"28_CR1","unstructured":"Baker, B., Gupta, O., Naik, N., Raskar, R.: Designing neural network architectures using reinforcement learning. CoRR abs\/1611.02167 (2016). http:\/\/arxiv.org\/abs\/1611.02167"},{"key":"28_CR2","unstructured":"Bender, G., Kindermans, P.J., Zoph, B., Vasudevan, V., Le, Q.: Understanding and simplifying one-shot architecture search. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 550\u2013559. PMLR, Stockholmsm\u00fcssan, Stockholm Sweden, 10\u201315 July 2018. http:\/\/proceedings.mlr.press\/v80\/bender18a.html"},{"key":"28_CR3","unstructured":"Cai, H., Zhu, L., Han, S.: ProxylessNAS: direct neural architecture search on target task and hardware. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=HylVB3AqYm"},{"key":"28_CR4","unstructured":"Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. CoRR abs\/1712.00726 (2017). http:\/\/arxiv.org\/abs\/1712.00726"},{"key":"28_CR5","doi-asserted-by":"crossref","unstructured":"Cui, Y., Song, Y., Sun, C., Howard, A., Belongie, S.: Large scale fine-grained categorization and domain-specific transfer learning. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018","DOI":"10.1109\/CVPR.2018.00432"},{"key":"28_CR6","doi-asserted-by":"publisher","unstructured":"Dai, W., Yang, Q., Xue, G.R., Yu, Y.: Boosting for transfer learning. In: Proceedings of the 24th International Conference on Machine Learning, ICML 2007, pp. 193\u2013200. ACM, New York (2007). https:\/\/doi.org\/10.1145\/1273496.1273521. http:\/\/doi.acm.org\/10.1145\/1273496.1273521","DOI":"10.1145\/1273496.1273521"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR 2009 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"28_CR8","unstructured":"Erhan, D., Bengio, Y., Courville, A., Manzagol, P.A., Vincent, P., Bengio, S.: Why does unsupervised pre-training help deep learning? J. Mach. Learn. Res. 11, 625\u2013660 (2010). http:\/\/dl.acm.org\/citation.cfm?id=1756006.1756025"},{"key":"28_CR9","unstructured":"Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning, ICML 2015, vol. 37, pp. 1180\u20131189. JMLR.org (2015). http:\/\/dl.acm.org\/citation.cfm?id=3045118.3045244"},{"key":"28_CR10","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2096\u20132030 (2016). http:\/\/dl.acm.org\/citation.cfm?id=2946645.2946704"},{"key":"28_CR11","unstructured":"Ghiasi, G., Lin, T.Y., Le, Q.V.: Dropblock: a regularization method for convolutional networks. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS 2018, pp. 10750\u201310760. Curran Associates Inc., USA (2018). http:\/\/dl.acm.org\/citation.cfm?id=3327546.3327732"},{"key":"28_CR12","unstructured":"Guo, Z., et al.: Single path one-shot neural architecture search with uniform sampling. CoRR abs\/1904.00420 (2019). http:\/\/arxiv.org\/abs\/1904.00420"},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.B.: Mask R-CNN. CoRR abs\/1703.06870 (2017). http:\/\/arxiv.org\/abs\/1703.06870","DOI":"10.1109\/ICCV.2017.322"},{"key":"28_CR15","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs\/1512.03385 (2015). http:\/\/arxiv.org\/abs\/1512.03385"},{"key":"28_CR16","doi-asserted-by":"crossref","unstructured":"He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., Li, M.: Bag of tricks for image classification with convolutional neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019","DOI":"10.1109\/CVPR.2019.00065"},{"key":"28_CR17","unstructured":"Hinton, G.E., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv abs\/1503.02531 (2015)"},{"key":"28_CR18","doi-asserted-by":"publisher","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141, June 2018. https:\/\/doi.org\/10.1109\/CVPR.2018.00745","DOI":"10.1109\/CVPR.2018.00745"},{"key":"28_CR19","unstructured":"Huang, G., Sun, Y., Liu, Z., Sedra, D., Weinberger, K.Q.: Deep networks with stochastic depth. CoRR abs\/1603.09382 (2016). http:\/\/arxiv.org\/abs\/1603.09382"},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Huang, J., et al.: Speed\/accuracy trade-offs for modern convolutional object detectors. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.351"},{"key":"28_CR21","unstructured":"Jang, Y., Lee, H., Hwang, S.J., Shin, J.: Learning what and where to transfer. In: Proceedings of the 36th International Conference on Machine Learning, ICML 2019, Long Beach, California, USA, 9\u201315 June 2019, pp. 3030\u20133039 (2019). http:\/\/proceedings.mlr.press\/v97\/jang19b.html"},{"key":"28_CR22","doi-asserted-by":"crossref","unstructured":"Kornblith, S., Shlens, J., Le, Q.V.: Do better imagenet models transfer better? In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019","DOI":"10.1109\/CVPR.2019.00277"},{"key":"28_CR23","doi-asserted-by":"crossref","unstructured":"Li, Q., Jin, S., Yan, J.: Mimicking very efficient network for object detection. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.776"},{"key":"28_CR24","unstructured":"Liang, F., et al.: Computation reallocation for object detection. In: International Conference on Learning Representations (2020). https:\/\/openreview.net\/forum?id=SkxLFaNKwB"},{"key":"28_CR25","doi-asserted-by":"publisher","unstructured":"Lin, T., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 936\u2013944, July 2017. https:\/\/doi.org\/10.1109\/CVPR.2017.106","DOI":"10.1109\/CVPR.2017.106"},{"key":"28_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"28_CR27","unstructured":"Liu, H., Simonyan, K., Yang, Y.: DARTS: differentiable architecture search. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=S1eYHoC5FX"},{"key":"28_CR28","doi-asserted-by":"publisher","first-page":"2389","DOI":"10.1109\/ACCESS.2017.2782884","volume":"6","author":"X Liu","year":"2018","unstructured":"Liu, X., Liu, Z., Wang, G., Cai, Z., Zhang, H.: Ensemble transfer learning algorithm. IEEE Access 6, 2389\u20132396 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2017.2782884","journal-title":"IEEE Access"},{"key":"28_CR29","unstructured":"Long, M., Wang, J.: Learning transferable features with deep adaptation networks. CoRR abs\/1502.02791 (2015). http:\/\/arxiv.org\/abs\/1502.02791"},{"key":"28_CR30","doi-asserted-by":"crossref","unstructured":"Lu, X., Li, B., Yue, Y., Li, Q., Yan, J.: Grid R-CNN. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019","DOI":"10.1109\/CVPR.2019.00754"},{"key":"28_CR31","unstructured":"Luo, W., Li, Y., Urtasun, R., Zemel, R.: Understanding the effective receptive field in deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 4898\u20134906 (2016)"},{"key":"28_CR32","unstructured":"Mahajan, D., et al.: Exploring the limits of weakly supervised pretraining. CoRR abs\/1805.00932 (2018). http:\/\/arxiv.org\/abs\/1805.00932"},{"key":"28_CR33","unstructured":"Miikkulainen, R., et al.: Evolving deep neural networks. CoRR abs\/1703.00548 (2017). http:\/\/arxiv.org\/abs\/1703.00548"},{"issue":"10","key":"28_CR34","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2010). https:\/\/doi.org\/10.1109\/TKDE.2009.191","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"28_CR35","unstructured":"Pardoe, D., Stone, P.: Boosting for regression transfer. In: Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML 2010, Omnipress, USA, pp. 863\u2013870 (2010). http:\/\/dl.acm.org\/citation.cfm?id=3104322.3104432"},{"key":"28_CR36","unstructured":"Paszke, A., et al.: Automatic differentiation in pytorch (2017)"},{"key":"28_CR37","unstructured":"Pham, H., Guan, M.Y., Zoph, B., Le, Q.V., Dean, J.: Efficient neural architecture search via parameter sharing. CoRR abs\/1802.03268 (2018). http:\/\/arxiv.org\/abs\/1802.03268"},{"key":"28_CR38","unstructured":"Real, E., Aggarwal, A., Huang, Y., Le, Q.V.: Regularized evolution for image classifier architecture search (2018). https:\/\/arxiv.org\/pdf\/1802.01548.pdf"},{"key":"28_CR39","unstructured":"Real, E., et al.: Large-scale evolution of image classifiers. In: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, vol. 70, pp. 2902\u20132911. JMLR.org (2017). http:\/\/dl.acm.org\/citation.cfm?id=3305890.3305981"},{"key":"28_CR40","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91\u201399 (2015)"},{"key":"28_CR41","unstructured":"Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. CoRR abs\/1506.01497 (2015). http:\/\/arxiv.org\/abs\/1506.01497"},{"key":"28_CR42","unstructured":"Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., Chen, L.: Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation. CoRR abs\/1801.04381 (2018). http:\/\/arxiv.org\/abs\/1801.04381"},{"key":"28_CR43","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C.: A survey on deep transfer learning. CoRR abs\/1808.01974 (2018). http:\/\/arxiv.org\/abs\/1808.01974"},{"key":"28_CR44","doi-asserted-by":"crossref","unstructured":"Tan, M., et al.: Mnasnet: platform-aware neural architecture search for mobile. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019","DOI":"10.1109\/CVPR.2019.00293"},{"key":"28_CR45","unstructured":"Tan, M., Le, Q.V.: Efficientnet: rethinking model scaling for convolutional neural networks. CoRR abs\/1905.11946 (2019). http:\/\/arxiv.org\/abs\/1905.11946"},{"key":"28_CR46","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: Simultaneous deep transfer across domains and tasks. In: The IEEE International Conference on Computer Vision (ICCV), December 2015","DOI":"10.1109\/ICCV.2015.463"},{"key":"28_CR47","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.316"},{"key":"28_CR48","unstructured":"Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., Darrell, T.: Deep domain confusion: maximizing for domain invariance. CoRR abs\/1412.3474 (2014). http:\/\/arxiv.org\/abs\/1412.3474"},{"key":"28_CR49","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The Caltech-UCSD Birds-200-2011 Dataset. Technical report (2011)"},{"key":"28_CR50","doi-asserted-by":"publisher","unstructured":"Wang, C., Wu, Y., Liu, Z.: Hierarchical boosting for transfer learning with multi-source. In: Proceedings of the International Conference on Artificial Intelligence and Robotics and the International Conference on Automation, Control and Robotics Engineering, ICAIR-CACRE 2016, pp. 15:1\u201315:5. ACM, New York (2016). https:\/\/doi.org\/10.1145\/2952744.2952756. http:\/\/doi.acm.org\/10.1145\/2952744.2952756","DOI":"10.1145\/2952744.2952756"},{"key":"28_CR51","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018","DOI":"10.1109\/CVPR.2018.00813"},{"key":"28_CR52","unstructured":"Welinder, P., et al.: Caltech-UCSD Birds 200. Technical report, CNS-TR-2010-001, California Institute of Technology (2010)"},{"key":"28_CR53","doi-asserted-by":"crossref","unstructured":"Wu, B., et al.: FBNet: hardware-aware efficient convnet design via differentiable neural architecture search. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019","DOI":"10.1109\/CVPR.2019.01099"},{"key":"28_CR54","doi-asserted-by":"crossref","unstructured":"Xie, L., Yuille, A.: Genetic CNN. In: The IEEE International Conference on Computer Vision (ICCV), October 2017","DOI":"10.1109\/ICCV.2017.154"},{"key":"28_CR55","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Dollar, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.634"},{"key":"28_CR56","unstructured":"Xie, S., Zheng, H., Liu, C., Lin, L.: SNAS: stochastic neural architecture search. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=rylqooRqK7"},{"issue":"6","key":"28_CR57","doi-asserted-by":"publisher","first-page":"1158","DOI":"10.1109\/TKDE.2017.2669193","volume":"29","author":"Y Xu","year":"2017","unstructured":"Xu, Y., et al.: A unified framework for metric transfer learning. IEEE Trans. Knowl. Data Eng. 29(6), 1158\u20131171 (2017). https:\/\/doi.org\/10.1109\/TKDE.2017.2669193","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"28_CR58","unstructured":"Yue, K., Sun, M., Yuan, Y., Zhou, F., Ding, E., Xu, F.: Compact generalized non-local network. In: Advances in Neural Information Processing Systems, pp. 6510\u20136519 (2018)"},{"key":"28_CR59","doi-asserted-by":"crossref","unstructured":"Zhang, J., Li, W., Ogunbona, P.: Joint geometrical and statistical alignment for visual domain adaptation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.547"},{"key":"28_CR60","unstructured":"Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. CoRR abs\/1611.01578 (2016). http:\/\/arxiv.org\/abs\/1611.01578"},{"key":"28_CR61","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. CoRR abs\/1707.07012 (2017). http:\/\/arxiv.org\/abs\/1707.07012"}],"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\/978-3-030-58601-0_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:14:03Z","timestamp":1732666443000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58601-0_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030586003","9783030586010"],"references-count":61,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58601-0_28","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":"28 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. 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)"}}]}}