{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T23:47:41Z","timestamp":1776037661741,"version":"3.50.1"},"publisher-location":"Cham","reference-count":57,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585228","type":"print"},{"value":"9783030585235","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\/978-3-030-58523-5_7","type":"book-chapter","created":{"date-parts":[[2020,12,3]],"date-time":"2020-12-03T20:13:16Z","timestamp":1607026396000},"page":"103-120","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Two Stream Active Query Suggestion for Active Learning in Connectomics"],"prefix":"10.1007","author":[{"given":"Zudi","family":"Lin","sequence":"first","affiliation":[]},{"given":"Donglai","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Won-Dong","family":"Jang","sequence":"additional","affiliation":[]},{"given":"Siyan","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Xupeng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xueying","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Richard","family":"Schalek","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Berger","sequence":"additional","affiliation":[]},{"given":"Brian","family":"Matejek","sequence":"additional","affiliation":[]},{"given":"Lee","family":"Kamentsky","sequence":"additional","affiliation":[]},{"given":"Adi","family":"Peleg","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Haehn","sequence":"additional","affiliation":[]},{"given":"Thouis","family":"Jones","sequence":"additional","affiliation":[]},{"given":"Toufiq","family":"Parag","sequence":"additional","affiliation":[]},{"given":"Jeff","family":"Lichtman","sequence":"additional","affiliation":[]},{"given":"Hanspeter","family":"Pfister","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,12,4]]},"reference":[{"key":"7_CR1","unstructured":"Abramson, Y., Freund, Y.: Active learning for visual object detection (2006)"},{"key":"7_CR2","first-page":"1864","volume":"32","author":"C Becker","year":"2013","unstructured":"Becker, C., Ali, K., Knott, G., Fua, P.: Learning context cues for synapse segmentation. IEEE TMI 32, 1864\u20131877 (2013)","journal-title":"IEEE TMI"},{"key":"7_CR3","unstructured":"Belkin, M., Niyogi, P.: Using manifold stucture for partially labeled classification. In: NIPS (2003)"},{"key":"7_CR4","unstructured":"Bietti, A.: Active learning for object detection on satellite images. Technical report, Caltech (2012)"},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Buhmann, J., et al.: Synaptic partner prediction from point annotations in insect brains. arXiv preprint arXiv:1806.08205 (2018)","DOI":"10.1007\/978-3-030-00934-2_35"},{"key":"7_CR6","doi-asserted-by":"crossref","unstructured":"Cheng, H.C., Varshney, A.: Volume segmentation using convolutional neural networks with limited training data. In: ICIP (2017)","DOI":"10.1109\/ICIP.2017.8296349"},{"issue":"4","key":"7_CR7","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1038\/nmeth.4206","volume":"14","author":"S Dorkenwald","year":"2017","unstructured":"Dorkenwald, S., et al.: Automated synaptic connectivity inference for volume electron microscopy. Nat. Methods 14(4), 435 (2017)","journal-title":"Nat. Methods"},{"key":"7_CR8","unstructured":"Ducoffe, M., Precioso, F.: Adversarial active learning for deep networks: a margin based approach. In: ICML (2018)"},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Dutt Jain, S., Grauman, K.: Active image segmentation propagation. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.313"},{"key":"7_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1007\/978-3-319-10593-2_37","volume-title":"Computer Vision \u2013 ECCV 2014","author":"A Freytag","year":"2014","unstructured":"Freytag, A., Rodner, E., Denzler, J.: Selecting influential examples: active learning with expected model output changes. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 562\u2013577. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10593-2_37"},{"key":"7_CR11","unstructured":"Funke, J., Saalfeld, S., Bock, D., Turaga, S., Perlman, E.: Circuit reconstruction from electron microscopy images (2016). https:\/\/cremi.org"},{"key":"7_CR12","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":"7_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"7_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1007\/978-3-030-00934-2_36","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"L Heinrich","year":"2018","unstructured":"Heinrich, L., Funke, J., Pape, C., Nunez-Iglesias, J., Saalfeld, S.: Synaptic cleft segmentation in non-isotropic volume electron microscopy of the complete Drosophila brain. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 317\u2013325. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_36"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Hoffer, E., Ailon, N.: Deep metric learning using triplet network. In: International Workshop on Similarity-Based Pattern Recognition (2015)","DOI":"10.1007\/978-3-319-24261-3_7"},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"7_CR17","unstructured":"Huang, G.B., Plaza, S.: Identifying synapses using deep and wide multiscale recursive networks. arXiv preprint arXiv:1409.1789 (2014)"},{"key":"7_CR18","unstructured":"Huang, G.B., Scheffer, L.K., Plaza, S.M.: Fully-automatic synapse prediction and validation on a large data set. arXiv preprint arXiv:1604.03075 (2016)"},{"key":"7_CR19","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.patrec.2013.06.001","volume":"43","author":"V Jagadeesh","year":"2014","unstructured":"Jagadeesh, V., Anderson, J., Jones, B., Marc, R., Fisher, S., Manjunath, B.: Synapse classification and localization in electron micrographs. Pattern Recogn. Lett. 43, 17\u201324 (2014)","journal-title":"Pattern Recogn. Lett."},{"key":"7_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1007\/978-3-030-20876-9_32","volume-title":"Computer Vision \u2013 ACCV 2018","author":"C-C Kao","year":"2019","unstructured":"Kao, C.-C., Lee, T.-Y., Sen, P., Liu, M.-Y.: Localization-aware active learning for object detection. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11366, pp. 506\u2013522. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20876-9_32"},{"issue":"3","key":"7_CR21","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1016\/j.cell.2015.06.054","volume":"162","author":"N Kasthuri","year":"2015","unstructured":"Kasthuri, N., et al.: Saturated reconstruction of a volume of neocortex. Cell 162(3), 648\u2013661 (2015)","journal-title":"Cell"},{"key":"7_CR22","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. ICLR (2013)"},{"key":"7_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1007\/978-3-319-24553-9_81","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"A Kreshuk","year":"2015","unstructured":"Kreshuk, A., Funke, J., Cardona, A., Hamprecht, F.A.: Who is talking to whom: synaptic partner detection in anisotropic volumes of insect brain. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 661\u2013668. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24553-9_81"},{"issue":"2","key":"7_CR24","doi-asserted-by":"publisher","first-page":"e87351","DOI":"10.1371\/journal.pone.0087351","volume":"9","author":"A Kreshuk","year":"2014","unstructured":"Kreshuk, A., Koethe, U., Pax, E., Bock, D.D., Hamprecht, F.A.: Automated detection of synapses in serial section transmission electron microscopy image stacks. PLoS One 9(2), e87351 (2014)","journal-title":"PLoS One"},{"issue":"10","key":"7_CR25","doi-asserted-by":"publisher","first-page":"e24899","DOI":"10.1371\/journal.pone.0024899","volume":"6","author":"A Kreshuk","year":"2011","unstructured":"Kreshuk, A., et al.: Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images. PLoS One 6(10), e24899 (2011)","journal-title":"PLoS One"},{"issue":"3","key":"7_CR26","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.conb.2008.08.010","volume":"18","author":"JW Lichtman","year":"2008","unstructured":"Lichtman, J.W., Sanes, J.R.: Ome sweet ome: what can the genome tell us about the connectome? Curr. Opin. Neurobiol. 18(3), 346\u2013353 (2008)","journal-title":"Curr. Opin. Neurobiol."},{"key":"7_CR27","doi-asserted-by":"crossref","unstructured":"Lucchi, A., Li, Y., Fua, P.: Learning for structured prediction using approximate subgradient descent with working sets. In: CVPR (2013)","DOI":"10.1109\/CVPR.2013.259"},{"key":"7_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1007\/978-3-642-33709-3_29","volume-title":"Computer Vision \u2013 ECCV 2012","author":"A Lucchi","year":"2012","unstructured":"Lucchi, A., Li, Y., Smith, K., Fua, P.: Structured image segmentation using kernelized features. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7573, pp. 400\u2013413. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33709-3_29"},{"key":"7_CR29","first-page":"1096","volume":"34","author":"A Lucchi","year":"2015","unstructured":"Lucchi, A., et al.: Learning structured models for segmentation of 2D and 3D imagery. IEEE TMI 34, 1096\u20131110 (2015)","journal-title":"IEEE TMI"},{"key":"7_CR30","first-page":"474","volume":"31","author":"A Lucchi","year":"2012","unstructured":"Lucchi, A., Smith, K., Achanta, R., Knott, G., Fua, P.: Supervoxel-based segmentation of mitochondria in EM image stacks with learned shape features. IEEE TMI 31, 474\u2013486 (2012)","journal-title":"IEEE TMI"},{"issue":"6","key":"7_CR31","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1038\/nmeth.2480","volume":"10","author":"JL Morgan","year":"2013","unstructured":"Morgan, J.L., Lichtman, J.W.: Why not connectomics? Nat. Methods 10(6), 494 (2013)","journal-title":"Nat. Methods"},{"key":"7_CR32","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1016\/j.patcog.2008.08.009","volume":"42","author":"R Narasimha","year":"2009","unstructured":"Narasimha, R., Ouyang, H., Gray, A., McLaughlin, S.W., Subramaniam, S.: Automatic joint classification and segmentation of whole cell 3D images. Pattern Recogn. 42, 1067\u20131079 (2009)","journal-title":"Pattern Recogn."},{"key":"7_CR33","doi-asserted-by":"crossref","unstructured":"Oztel, I., Yolcu, G., Ersoy, I., White, T., Bunyak, F.: Mitochondria segmentation in electron microscopy volumes using deep convolutional neural network. In: Bioinformatics and Biomedicine (2017)","DOI":"10.1109\/BIBM.2017.8217827"},{"key":"7_CR34","doi-asserted-by":"crossref","unstructured":"Parag, T., et al.: Detecting synapse location and connectivity by signed proximity estimation and pruning with deep nets. arXiv preprint arXiv:1807.02739 (2018)","DOI":"10.1007\/978-3-030-11024-6_25"},{"key":"7_CR35","doi-asserted-by":"crossref","unstructured":"Parag, T., Ciresan, D.C., Giusti, A.: Efficient classifier training to minimize false merges in electron microscopy segmentation. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.82"},{"key":"7_CR36","doi-asserted-by":"publisher","first-page":"126","DOI":"10.3389\/fnana.2014.00126","volume":"8","author":"AJ Perez","year":"2014","unstructured":"Perez, A.J., et al.: A workflow for the automatic segmentation of organelles in electron microscopy image stacks. Front. Neuroanat. 8, 126 (2014)","journal-title":"Front. Neuroanat."},{"key":"7_CR37","unstructured":"Plaza, S.M., Parag, T., Huang, G.B., Olbris, D.J., Saunders, M.A., Rivlin, P.K.: Annotating synapses in large EM datasets. arXiv preprint arXiv:1409.1801 (2014)"},{"key":"7_CR38","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":"7_CR39","unstructured":"Roncal, W.G., et al.: VESICLE: volumetric evaluation of synaptic interfaces using computer vision at large scale. arXiv preprint arXiv:1403.3724 (2014)"},{"key":"7_CR40","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":"7_CR41","unstructured":"Roy, S., Namboodiri, V.P., Biswas, A.: Active learning with version spaces for object detection. arXiv preprint arXiv:1611.07285 (2016)"},{"key":"7_CR42","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/3-540-44816-0_31","volume-title":"Advances in Intelligent Data Analysis","author":"T Scheffer","year":"2001","unstructured":"Scheffer, T., Decomain, C., Wrobel, S.: Active hidden Markov models for information extraction. In: Hoffmann, F., Hand, D.J., Adams, N., Fisher, D., Guimaraes, G. (eds.) IDA 2001. LNCS, vol. 2189, pp. 309\u2013318. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-44816-0_31"},{"key":"7_CR43","unstructured":"Sener, O., Savarese, S.: Active learning for convolutional neural networks: a core-set approach. In: ICLR (2018)"},{"key":"7_CR44","unstructured":"Settles, B.: Active learning literature survey. Technical report, University of Wisconsin-Madison Department of Computer Sciences (2009)"},{"key":"7_CR45","unstructured":"Settles, B.: Active learning literature survey. 2010. Computer Sciences Technical Report (2014)"},{"key":"7_CR46","doi-asserted-by":"crossref","unstructured":"Seyedhosseini, M., Ellisman, M.H., Tasdizen, T.: Segmentation of mitochondria in electron microscopy images using algebraic curves. In: ISBI, pp. 860\u2013863. IEEE (2013)","DOI":"10.1109\/ISBI.2013.6556611"},{"key":"7_CR47","unstructured":"Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: NIPS (2014)"},{"key":"7_CR48","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1007\/s00138-011-0388-y","volume":"25","author":"S Sivaraman","year":"2014","unstructured":"Sivaraman, S., Trivedi, M.M.: Active learning for on-road vehicle detection: a comparative study. Mach. Vis. Appl. 25, 599\u2013611 (2014)","journal-title":"Mach. Vis. Appl."},{"key":"7_CR49","doi-asserted-by":"crossref","unstructured":"Staffler, B., Berning, M., Boergens, K.M., Gour, A., van der Smagt, P., Helmstaedter, M.: SynEM, automated synapse detection for connectomics. Elife (2017)","DOI":"10.1101\/099994"},{"key":"7_CR50","doi-asserted-by":"crossref","unstructured":"Vazquez-Reina, A., Gelbart, M., Huang, D., Lichtman, J., Miller, E., Pfister, H.: Segmentation fusion for connectomics. In: ICCV (2011)","DOI":"10.1109\/ICCV.2011.6126240"},{"key":"7_CR51","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/s11263-014-0721-9","volume":"108","author":"S Vijayanarasimhan","year":"2014","unstructured":"Vijayanarasimhan, S., Grauman, K.: Large-scale live active learning: training object detectors with crawled data and crowds. IJCV 108, 97\u2013114 (2014)","journal-title":"IJCV"},{"key":"7_CR52","first-page":"2591","volume":"27","author":"K Wang","year":"2017","unstructured":"Wang, K., Zhang, D., Li, Y., Zhang, R., Lin, L.: Cost-effective active learning for deep image classification. IEEE TCSVT 27, 2591\u20132600 (2017)","journal-title":"IEEE TCSVT"},{"key":"7_CR53","doi-asserted-by":"crossref","unstructured":"Yoo, D., Kweon, I.S.: Learning loss for active learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 93\u2013102 (2019)","DOI":"10.1109\/CVPR.2019.00018"},{"key":"7_CR54","doi-asserted-by":"crossref","unstructured":"Yu, K., Bi, J., Tresp, V.: Active learning via transductive experimental design. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 1081\u20131088 (2006)","DOI":"10.1145\/1143844.1143980"},{"key":"7_CR55","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Lease, M., Wallace, B.C.: Active discriminative text representation learning. In: AAAI (2017)","DOI":"10.1609\/aaai.v31i1.10962"},{"key":"7_CR56","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1016\/j.cell.2018.06.019","volume":"174","author":"Z Zheng","year":"2018","unstructured":"Zheng, Z., et al.: A complete electron microscopy volume of the brain of adult drosophila melanogaster. Cell 174, 730\u2013743 (2018)","journal-title":"Cell"},{"key":"7_CR57","unstructured":"Zhu, X., Ghahramani, Z., Lafferty, J.D.: Semi-supervised learning using Gaussian fields and harmonic functions. In: ICML (2003)"}],"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\/978-3-030-58523-5_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:03:47Z","timestamp":1733184227000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58523-5_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585228","9783030585235"],"references-count":57,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58523-5_7","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":"4 December 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)"}}]}}