{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T15:48:36Z","timestamp":1750780116272,"version":"3.40.3"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030654139"},{"type":"electronic","value":"9783030654146"}],"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\/978-3-030-65414-6_30","type":"book-chapter","created":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T08:03:24Z","timestamp":1609747404000},"page":"433-449","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6850-7241","authenticated-orcid":false,"given":"Guohao","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4847-7604","authenticated-orcid":false,"given":"Alina","family":"Zare","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8463-9319","authenticated-orcid":false,"given":"Weihuang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5552-9807","authenticated-orcid":false,"given":"Roser","family":"Matamala","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3535-8665","authenticated-orcid":false,"given":"Joel","family":"Reyes-Cabrera","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0825-6855","authenticated-orcid":false,"given":"Felix B.","family":"Fritschi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9550-9288","authenticated-orcid":false,"given":"Thomas E.","family":"Juenger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,5]]},"reference":[{"key":"30_CR1","doi-asserted-by":"crossref","unstructured":"Ahn, J., Cho, S., Kwak, S.: Weakly supervised learning of instance segmentation with inter-pixel relations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2209\u20132218 (2019)","DOI":"10.1109\/CVPR.2019.00231"},{"key":"30_CR2","unstructured":"Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: Advances in Neural Information Processing Systems, pp. 577\u2013584 (2003)"},{"issue":"12","key":"30_CR3","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3527","key":"30_CR4","doi-asserted-by":"publisher","first-page":"966","DOI":"10.1038\/139966b0","volume":"139","author":"G Bates","year":"1937","unstructured":"Bates, G.: A device for the observation of root growth in the soil. Nature 139(3527), 966\u2013967 (1937)","journal-title":"Nature"},{"key":"30_CR5","doi-asserted-by":"crossref","unstructured":"Chattopadhay, A., Sarkar, A., Howlader, P., Balasubramanian, V.N.: Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 839\u2013847. IEEE (2018)","DOI":"10.1109\/WACV.2018.00097"},{"issue":"4","key":"30_CR6","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"30_CR7","doi-asserted-by":"crossref","unstructured":"Durand, T., Mordan, T., Thome, N., Cord, M.: WILDCAT: weakly supervised learning of deep convnets for image classification, pointwise localization and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 642\u2013651 (2017)","DOI":"10.1109\/CVPR.2017.631"},{"key":"30_CR8","doi-asserted-by":"crossref","unstructured":"Durand, T., Thome, N., Cord, M.: WELDON: weakly supervised learning of deep convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4743\u20134752 (2016)","DOI":"10.1109\/CVPR.2016.513"},{"issue":"1","key":"30_CR9","first-page":"91","volume":"27","author":"M Heidari","year":"2014","unstructured":"Heidari, M., et al.: A new method for root detection in minirhizotron images: hypothesis testing based on entropy-based geometric level set decision. Int. J. Eng. 27(1), 91\u2013100 (2014)","journal-title":"Int. J. Eng."},{"key":"30_CR10","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wang, X., Wang, J., Liu, W., Wang, J.: Weakly-supervised semantic segmentation network with deep seeded region growing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7014\u20137023 (2018)","DOI":"10.1109\/CVPR.2018.00733"},{"issue":"3","key":"30_CR11","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/S0098-8472(01)00077-6","volume":"45","author":"M Johnson","year":"2001","unstructured":"Johnson, M., Tingey, D., Phillips, D., Storm, M.: Advancing fine root research with minirhizotrons. Environ. Exp. Bot. 45(3), 263\u2013289 (2001)","journal-title":"Environ. Exp. Bot."},{"key":"30_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1007\/978-3-319-46493-0_42","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Kolesnikov","year":"2016","unstructured":"Kolesnikov, A., Lampert, C.H.: Seed, expand and constrain: three principles for weakly-supervised image segmentation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 695\u2013711. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_42"},{"key":"30_CR13","unstructured":"Kr\u00e4henb\u00fchl, P., Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. In: Advances in Neural Information Processing Systems, pp. 109\u2013117 (2011)"},{"key":"30_CR14","doi-asserted-by":"crossref","unstructured":"Lee, J., Kim, E., Lee, S., Lee, J., Yoon, S.: FickleNet: weakly and semi-supervised semantic image segmentation using stochastic inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5267\u20135276 (2019)","DOI":"10.1109\/CVPR.2019.00541"},{"key":"30_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/978-3-642-15567-3_3","volume-title":"Computer Vision \u2013 ECCV 2010","author":"C Leistner","year":"2010","unstructured":"Leistner, C., Saffari, A., Bischof, H.: MIForests: multiple-instance learning with randomized trees. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 29\u201342. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15567-3_3"},{"key":"30_CR16","doi-asserted-by":"crossref","unstructured":"Lin, G., Shen, C., Van Den Hengel, A., Reid, I.: Efficient piecewise training of deep structured models for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3194\u20133203 (2016)","DOI":"10.1109\/CVPR.2016.348"},{"key":"30_CR17","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"30_CR18","unstructured":"Omeiza, D., Speakman, S., Cintas, C., Weldermariam, K.: Smooth grad-CAM++: an enhanced inference level visualization technique for deep convolutional neural network models. arXiv preprint arXiv:1908.01224 (2019)"},{"key":"30_CR19","doi-asserted-by":"crossref","unstructured":"Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Is object localization for free?-weakly-supervised learning with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 685\u2013694 (2015)","DOI":"10.1109\/CVPR.2015.7298668"},{"issue":"1","key":"30_CR20","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"9","author":"N Otsu","year":"1979","unstructured":"Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62\u201366 (1979)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"30_CR21","doi-asserted-by":"crossref","unstructured":"Papandreou, G., Chen, L.C., Murphy, K.P., Yuille, A.L.: Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1742\u20131750 (2015)","DOI":"10.1109\/ICCV.2015.203"},{"key":"30_CR22","doi-asserted-by":"crossref","unstructured":"Pinheiro, P.O., Collobert, R.: From image-level to pixel-level labeling with convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1713\u20131721 (2015)","DOI":"10.1109\/CVPR.2015.7298780"},{"key":"30_CR23","first-page":"337","volume":"29","author":"BH Rahmanzadeh","year":"2016","unstructured":"Rahmanzadeh, B.H., Shojaedini, S.: Novel automated method for minirhizotron image analysis: Root detection using curvelet transform. Int. J. Eng. 29, 337\u2013346 (2016)","journal-title":"Int. J. Eng."},{"key":"30_CR24","unstructured":"Rewald, B., Ephrath, J.E.: Minirhizotron techniques. In: Plant Roots: The Hidden Half, pp. 1\u201315 (2013)"},{"key":"30_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"30_CR26","doi-asserted-by":"crossref","unstructured":"Roy, A., Todorovic, S.: Combining bottom-up, top-down, and smoothness cues for weakly supervised image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3529\u20133538 (2017)","DOI":"10.1109\/CVPR.2017.770"},{"key":"30_CR27","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"30_CR28","unstructured":"Smilkov, D., Thorat, N., Kim, B., Vi\u00e9gas, F., Wattenberg, M.: SmoothGrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825 (2017)"},{"issue":"1","key":"30_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13007-020-0563-0","volume":"16","author":"AG Smith","year":"2020","unstructured":"Smith, A.G., Petersen, J., Selvan, R., Rasmussen, C.R.: Segmentation of roots in soil with U-net. Plant Methods 16(1), 1\u201315 (2020)","journal-title":"Plant Methods"},{"key":"30_CR30","doi-asserted-by":"crossref","unstructured":"Tang, M., Djelouah, A., Perazzi, F., Boykov, Y., Schroers, C.: Normalized cut loss for weakly-supervised cnn segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1818\u20131827 (2018)","DOI":"10.1109\/CVPR.2018.00195"},{"issue":"10","key":"30_CR31","doi-asserted-by":"publisher","first-page":"1850","DOI":"10.1139\/b71-261","volume":"49","author":"J Waddington","year":"1971","unstructured":"Waddington, J.: Observation of plant roots in situ. Can. J. Bot. 49(10), 1850\u20131852 (1971)","journal-title":"Can. J. Bot."},{"key":"30_CR32","unstructured":"Wang, H., Du, M., Yang, F., Zhang, Z.: Score-CAM: improved visual explanations via score-weighted class activation mapping. arXiv preprint arXiv:1910.01279 (2019)"},{"key":"30_CR33","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1016\/j.compag.2019.05.017","volume":"162","author":"T Wang","year":"2019","unstructured":"Wang, T., et al.: SegRoot: a high throughput segmentation method for root image analysis. Comput. Electron. Agric. 162, 845\u2013854 (2019)","journal-title":"Comput. Electron. Agric."},{"key":"30_CR34","doi-asserted-by":"crossref","unstructured":"Wei, Y., Feng, J., Liang, X., Cheng, M.M., Zhao, Y., Yan, S.: Object region mining with adversarial erasing: a simple classification to semantic segmentation approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1568\u20131576 (2017)","DOI":"10.1109\/CVPR.2017.687"},{"key":"30_CR35","doi-asserted-by":"crossref","unstructured":"Wei, Y., Xiao, H., Shi, H., Jie, Z., Feng, J., Huang, T.S.: Revisiting dilated convolution: a simple approach for weakly-and semi-supervised semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7268\u20137277 (2018)","DOI":"10.1109\/CVPR.2018.00759"},{"key":"30_CR36","doi-asserted-by":"publisher","unstructured":"Xu, W., et al.: Overcoming small minirhizotron datasets using transfer learning. Comput. Electronics in Agriculture 175 (2020). https:\/\/doi.org\/10.1016\/j.compag.2020.105466","DOI":"10.1016\/j.compag.2020.105466"},{"issue":"11","key":"30_CR37","doi-asserted-by":"publisher","first-page":"giz123","DOI":"10.1093\/gigascience\/giz123","volume":"8","author":"R Yasrab","year":"2019","unstructured":"Yasrab, R., Atkinson, J.A., Wells, D.M., French, A.P., Pridmore, T.P., Pound, M.P.: Rootnav 2.0: deep learning for automatic navigation of complex plant root architectures. GigaScience 8(11), giz123 (2019)","journal-title":"GigaScience"},{"key":"30_CR38","doi-asserted-by":"publisher","unstructured":"Yu, G., et al.: Root identification in minirhizotron imagery with multiple instance learning. Mach. Visi. Appl. 31 (2020). https:\/\/doi.org\/10.1007\/s00138-020-01088-z","DOI":"10.1007\/s00138-020-01088-z"},{"issue":"10","key":"30_CR39","doi-asserted-by":"publisher","first-page":"2342","DOI":"10.1109\/TPAMI.2017.2756632","volume":"40","author":"A Zare","year":"2017","unstructured":"Zare, A., Jiao, C., Glenn, T.: Discriminative multiple instance hyperspectral target characterization. IEEE Trans. Pattern Anal. Mach. Intell. 40(10), 2342\u20132354 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"30_CR40","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/s00138-006-0024-4","volume":"17","author":"G Zeng","year":"2006","unstructured":"Zeng, G., Birchfield, S.T., Wells, C.E.: Detecting and measuring fine roots in minirhizotron images using matched filtering and local entropy thresholding. Mach. Vis. Appl. 17(4), 265\u2013278 (2006)","journal-title":"Mach. Vis. Appl."},{"issue":"3","key":"30_CR41","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/s00138-008-0179-2","volume":"21","author":"G Zeng","year":"2010","unstructured":"Zeng, G., Birchfield, S.T., Wells, C.E.: Rapid automated detection of roots in minirhizotron images. Mach. Vis. Appl. 21(3), 309\u2013317 (2010)","journal-title":"Mach. Vis. Appl."},{"key":"30_CR42","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-65414-6_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T00:07:24Z","timestamp":1735949244000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-65414-6_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030654139","9783030654146"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-65414-6_30","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":"5 January 2021","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)"}}]}}