{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T11:50:02Z","timestamp":1772797802851,"version":"3.50.1"},"publisher-location":"Cham","reference-count":46,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030695408","type":"print"},{"value":"9783030695415","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-69541-5_40","type":"book-chapter","created":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T11:03:47Z","timestamp":1614251027000},"page":"669-686","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Learning Multi-instance Sub-pixel Point Localization"],"prefix":"10.1007","author":[{"given":"Julien","family":"Schroeter","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tinne","family":"Tuytelaars","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kirill","family":"Sidorov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Marshall","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,26]]},"reference":[{"key":"40_CR1","doi-asserted-by":"crossref","unstructured":"Tompson, J., Goroshin, R., Jain, A., LeCun, Y., Bregler, C.: Efficient object localization using convolutional networks. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), pp. 648\u2013656 (2015)","DOI":"10.1109\/CVPR.2015.7298664"},{"key":"40_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/978-3-319-46484-8_29","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Newell","year":"2016","unstructured":"Newell, A., Yang, K., Deng, J.: Stacked Hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part VIII. LNCS, vol. 9912, pp. 483\u2013499. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_29"},{"key":"40_CR3","doi-asserted-by":"crossref","unstructured":"Wei, S.E., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4724\u20134732 (2016)","DOI":"10.1109\/CVPR.2016.511"},{"key":"40_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1007\/978-3-030-01231-1_29","volume-title":"Computer Vision \u2013 ECCV 2018","author":"B Xiao","year":"2018","unstructured":"Xiao, B., Wu, H., Wei, Y.: Simple baselines for human pose estimation and tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018, Part VI. LNCS, vol. 11210, pp. 472\u2013487. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01231-1_29"},{"key":"40_CR5","doi-asserted-by":"crossref","unstructured":"Merget, D., Rock, M., Rigoll, G.: Robust facial landmark detection via a fully-convolutional local-global context network. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), pp. 781\u2013790 (2018)","DOI":"10.1109\/CVPR.2018.00088"},{"key":"40_CR6","doi-asserted-by":"crossref","unstructured":"Papandreou, G., et al.: Towards accurate multi-person pose estimation in the wild. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4903\u20134911 (2017)","DOI":"10.1109\/CVPR.2017.395"},{"key":"40_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1007\/978-3-030-20893-6_35","volume-title":"Computer Vision \u2013 ACCV 2018","author":"L Neumann","year":"2019","unstructured":"Neumann, L., Vedaldi, A.: Tiny people pose. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018, Part III. LNCS, vol. 11363, pp. 558\u2013574. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20893-6_35"},{"key":"40_CR8","unstructured":"Nibali, A., He, Z., Morgan, S., Prendergast, L.: Numerical coordinate regression with convolutional neural networks. arXiv preprint arXiv:1801.07372 (2018)"},{"key":"40_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1007\/978-3-030-01231-1_33","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Sun","year":"2018","unstructured":"Sun, X., Xiao, B., Wei, F., Liang, S., Wei, Y.: Integral human pose regression. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018, Part VI. LNCS, vol. 11210, pp. 536\u2013553. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01231-1_33"},{"key":"40_CR10","doi-asserted-by":"crossref","unstructured":"Fieraru, M., Khoreva, A., Pishchulin, L., Schiele, B.: Learning to refine human pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 205\u2013214 (2018)","DOI":"10.1109\/CVPRW.2018.00058"},{"key":"40_CR11","doi-asserted-by":"publisher","first-page":"e47994","DOI":"10.7554\/eLife.47994","volume":"8","author":"JM Graving","year":"2019","unstructured":"Graving, J.M., et al.: DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning. eLife 8, e47994 (2019)","journal-title":"eLife"},{"key":"40_CR12","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.cag.2019.09.002","volume":"85","author":"DC Luvizon","year":"2019","unstructured":"Luvizon, D.C., Tabia, H., Picard, D.: Human pose regression by combining indirect part detection and contextual information. Comput. Graph. 85, 15\u201322 (2019)","journal-title":"Comput. Graph."},{"key":"40_CR13","doi-asserted-by":"crossref","unstructured":"Tai, Y., Liang, Y., Liu, X., Duan, L., Li, J., Wang, C., Huang, F., Chen, Y.: Towards highly accurate and stable face alignment for high-resolution videos. Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8893\u20138900 (2019)","DOI":"10.1609\/aaai.v33i01.33018893"},{"key":"40_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, F., Zhu, X., Dai, H., Ye, M., Zhu, C.: Distribution-aware coordinate representation for human pose estimation. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7093\u20137102 (2020)","DOI":"10.1109\/CVPR42600.2020.00712"},{"key":"40_CR15","doi-asserted-by":"crossref","unstructured":"Yang, J., Liu, Q., Zhang, K.: Stacked hourglass network for robust facial landmark localisation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 79\u201387 (2017)","DOI":"10.1109\/CVPRW.2017.253"},{"key":"40_CR16","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1038\/nmeth.3442","volume":"12","author":"D Sage","year":"2015","unstructured":"Sage, D., Kirshner, H., Pengo, T., Stuurman, N., Min, J., Manley, S., Unser, M.: Quantitative evaluation of software packages for single-molecule localization microscopy. Nat. Methods 12, 717\u2013724 (2015)","journal-title":"Nat. Methods"},{"key":"40_CR17","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1364\/OPTICA.5.000458","volume":"5","author":"E Nehme","year":"2018","unstructured":"Nehme, E., Weiss, L.E., Michaeli, T., Shechtman, Y.: Deep-STORM: super-resolution single-molecule microscopy by deep learning. Optica 5, 458\u2013464 (2018)","journal-title":"Optica"},{"key":"40_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"766","DOI":"10.1007\/978-3-319-10593-2_50","volume-title":"Computer Vision \u2013 ECCV 2014","author":"S Placht","year":"2014","unstructured":"Placht, S., F\u00fcrsattel, P., Mengue, E.A., Hofmann, H., Schaller, C., Balda, M., Angelopoulou, E.: ROCHADE: robust checkerboard advanced detection for camera calibration. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part IV. LNCS, vol. 8692, pp. 766\u2013779. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10593-2_50"},{"key":"40_CR19","doi-asserted-by":"crossref","unstructured":"Hu, D., DeTone, D., Malisiewicz, T.: Deep ChArUco: dark ChArUco marker pose estimation. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8436\u20138444 (2019)","DOI":"10.1109\/CVPR.2019.00863"},{"key":"40_CR20","doi-asserted-by":"publisher","first-page":"1858","DOI":"10.3390\/s16111858","volume":"16","author":"S Donn\u00e9","year":"2016","unstructured":"Donn\u00e9, S., De Vylder, J., Goossens, B., Philips, W.: MATE: machine learning for adaptive calibration template detection. Sensors 16, 1858 (2016)","journal-title":"Sensors"},{"key":"40_CR21","doi-asserted-by":"crossref","unstructured":"Toshev, A., Szegedy, C.: DeepPose: Human pose estimation via deep neural networks. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), pp.1653\u20131660 (2014)","DOI":"10.1109\/CVPR.2014.214"},{"key":"40_CR22","doi-asserted-by":"crossref","unstructured":"Carreira, J., Agrawal, P., Fragkiadaki, K., Malik, J.: Human pose estimation with iterative error feedback. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4733\u20134742 (2016)","DOI":"10.1109\/CVPR.2016.512"},{"key":"40_CR23","unstructured":"Zhou, X., Wang, D., Kr\u00e4henb\u00fchl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)"},{"key":"40_CR24","doi-asserted-by":"crossref","unstructured":"Li, J., Su, W., Wang, Z.: Simple pose: Rethinking and improving a bottom-up approach for multi-person pose estimation. In: Thirty-Fourth AAAI Conference on Artificial Intelligence (2020)","DOI":"10.1609\/aaai.v34i07.6797"},{"key":"40_CR25","unstructured":"Tompson, J.J., Jain, A., LeCun, Y., Bregler, C.: Joint training of a convolutional network and a graphical model for human pose estimation. In: Advances in Neural Information Processing Systems (NIPS), pp. 1799\u20131807 (2014)"},{"key":"40_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu, W., et al.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part I. LNCS, vol. 9905, pp. 21\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"key":"40_CR27","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"40_CR28","unstructured":"Vahdat, A.: Toward robustness against label noise in training deep discriminative neural networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 5596\u20135605 (2017)"},{"key":"40_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1007\/978-3-319-46478-7_44","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Bulat","year":"2016","unstructured":"Bulat, A., Tzimiropoulos, G.: Human pose estimation via convolutional part heatmap regression. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part VII. LNCS, vol. 9911, pp. 717\u2013732. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_44"},{"key":"40_CR30","doi-asserted-by":"crossref","unstructured":"Pfister, T., Charles, J., Zisserman, A.: Flowing convnets for human pose estimation in videos. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 1913\u20131921 (2015)","DOI":"10.1109\/ICCV.2015.222"},{"key":"40_CR31","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018)"},{"key":"40_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, C., Li, H., Wang, X., Yang, X.: Cross-scene crowd counting via deep convolutional neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2016.70"},{"key":"40_CR33","unstructured":"Trott, A., Xiong, C., Socher, R.: Interpretable counting for visual question answering. In: Proceedings of International Conference on Learning Representations (ICLR) (2018)"},{"key":"40_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1007\/978-3-030-01216-8_33","volume-title":"Computer Vision \u2013 ECCV 2018","author":"H Idrees","year":"2018","unstructured":"Idrees, H., et al.: Composition loss for counting, density map estimation and localization in dense crowds. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018, Part II. LNCS, vol. 11206, pp. 544\u2013559. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01216-8_33"},{"key":"40_CR35","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1214\/aoms\/1177729694","volume":"22","author":"S Kullback","year":"1951","unstructured":"Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22, 79\u201386 (1951)","journal-title":"Ann. Math. Stat."},{"key":"40_CR36","unstructured":"Schroeter, J., Sidorov, K., Marshall, D.: Weakly-supervised temporal localization via occurrence count learning. In: Proceedings of International Conference on Machine Learning (ICML), pp. 5649\u20135659 (2019)"},{"key":"40_CR37","unstructured":"Duda, A., Frese, U.: Accurate detection and localization of checkerboard corners for calibration. In: British Machine Vision Conference (BMVC) (2018)"},{"key":"40_CR38","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1016\/j.patcog.2007.06.032","volume":"41","author":"ED Sinzinger","year":"2008","unstructured":"Sinzinger, E.D.: A model-based approach to junction detection using radial energy. Pattern Recognit. 41, 494\u2013505 (2008)","journal-title":"Pattern Recognit."},{"key":"40_CR39","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1007\/978-3-319-97589-4_27","volume-title":"Intelligent Robotics and Applications","author":"B Chen","year":"2018","unstructured":"Chen, B., Xiong, C., Zhang, Q.: CCDN: checkerboard corner detection network for robust camera calibration. In: Chen, Z., Mendes, A., Yan, Y., Chen, S. (eds.) ICIRA 2018, Part II. LNCS (LNAI), vol. 10985, pp. 324\u2013334. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-97589-4_27"},{"key":"40_CR40","doi-asserted-by":"crossref","unstructured":"Scaramuzza, D., Martinelli, A., Siegwart, R.: A toolbox for easily calibrating omnidirectional cameras. In: 2006 IEEE\/RSJ International Conference on Intelligent Robots and Systems, pp. 5695\u20135701. IEEE (2006)","DOI":"10.1109\/IROS.2006.282372"},{"key":"40_CR41","first-page":"122","volume":"120","author":"G Bradski","year":"2000","unstructured":"Bradski, G.: The OpenCV library. Dr. Dobb\u2019s J. Soft. Tools 120, 122\u2013125 (2000)","journal-title":"Dr. Dobb\u2019s J. Soft. Tools"},{"key":"40_CR42","doi-asserted-by":"crossref","unstructured":"Geiger, A., Moosmann, F., Car, \u00d6., Schuster, B.: Automatic camera and range sensor calibration using a single shot. In: 2012 IEEE International Conference on Robotics and Automation, pp. 3936\u20133943. IEEE (2012)","DOI":"10.1109\/ICRA.2012.6224570"},{"key":"40_CR43","unstructured":"Hawthorne, C., et al.: Enabling factorized piano music modeling and generation with the MAESTRO dataset. In: Proceedings of International Conference on Learning Representations (ICLR) (2019)"},{"key":"40_CR44","doi-asserted-by":"publisher","first-page":"1457","DOI":"10.1109\/TASLP.2018.2830113","volume":"26","author":"CW Wu","year":"2018","unstructured":"Wu, C.W., et al.: A review of automatic drum transcription. IEEE\/ACM Trans. Audio Speech Lang. Process. 26, 1457\u20131483 (2018)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"40_CR45","doi-asserted-by":"crossref","unstructured":"McNally, W., Vats, K., Pinto, T., Dulhanty, C., McPhee, J., Wong, A.: GolfDB: a video database for golf swing sequencing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)","DOI":"10.1109\/CVPRW.2019.00311"},{"key":"40_CR46","doi-asserted-by":"crossref","unstructured":"Bao, W., Lai, W.S., Ma, C., Zhang, X., Gao, Z., Yang, M.H.: Depth-aware video frame interpolation. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3703\u20133712 (2019)","DOI":"10.1109\/CVPR.2019.00382"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2020"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-69541-5_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,18]],"date-time":"2022-12-18T23:49:46Z","timestamp":1671407386000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-69541-5_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030695408","9783030695415"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-69541-5_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"26 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/accv2020.kyoto\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"768","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"254","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}