{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T19:13:50Z","timestamp":1769022830768,"version":"3.49.0"},"publisher-location":"Cham","reference-count":54,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585679","type":"print"},{"value":"9783030585686","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\/978-3-030-58568-6_18","type":"book-chapter","created":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T14:04:57Z","timestamp":1605189897000},"page":"300-317","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Action Localization Through Continual Predictive Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1062-8929","authenticated-orcid":false,"given":"Sathyanarayanan","family":"Aakur","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7332-4207","authenticated-orcid":false,"given":"Sudeep","family":"Sarkar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,13]]},"reference":[{"key":"18_CR1","unstructured":"Aakur, S., de Souza, F.D., Sarkar, S.: Going deeper with semantics: exploiting semantic contextualization for interpretation of human activity in videos. In: IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE (2019)"},{"key":"18_CR2","doi-asserted-by":"crossref","unstructured":"Aakur, S.N., Sarkar, S.: A perceptual prediction framework for self supervised event segmentation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)","DOI":"10.1109\/CVPR.2019.00129"},{"key":"18_CR3","doi-asserted-by":"crossref","unstructured":"Aakur, S.N., de Souza, F.D., Sarkar, S.: Towards a knowledge-based approach for generating video descriptions. In: Conference on Computer and Robot Vision (CRV). Springer (2017)","DOI":"10.1109\/CRV.2017.51"},{"key":"18_CR4","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"18_CR5","doi-asserted-by":"publisher","first-page":"102886","DOI":"10.1016\/j.cviu.2019.102886","volume":"192","author":"V Escorcia","year":"2020","unstructured":"Escorcia, V., Dao, C.D., Jain, M., Ghanem, B., Snoek, C.: Guess where? Actor-supervision for spatiotemporal action localization. Comput. Vis. Image Underst. 192, 102886 (2020)","journal-title":"Comput. Vis. Image Underst."},{"key":"18_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1007\/978-3-642-33718-5_23","volume-title":"Computer Vision \u2013 ECCV 2012","author":"A Fathi","year":"2012","unstructured":"Fathi, A., Li, Y., Rehg, J.M.: Learning to recognize daily actions using gaze. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7572, pp. 314\u2013327. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33718-5_23"},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Gkioxari, G., Malik, J.: Finding action tubes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 759\u2013768 (2015)","DOI":"10.1109\/CVPR.2015.7298676"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Grundmann, M., Kwatra, V., Han, M., Essa, I.: Efficient hierarchical graph-based video segmentation. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2141\u20132148. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5539893"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Guo, Z., Gao, L., Song, J., Xu, X., Shao, J., Shen, H.T.: Attention-based LSTM with semantic consistency for videos captioning. In: ACM Conference on Multimedia (ACM MM), pp. 357\u2013361. ACM (2016)","DOI":"10.1145\/2964284.2967242"},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems, pp. 545\u2013552 (2007)","DOI":"10.7551\/mitpress\/7503.003.0073"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Hershey, J.R., Chen, Z., Le Roux, J., Watanabe, S.: Deep clustering: discriminative embeddings for segmentation and separation. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 31\u201335. IEEE (2016)","DOI":"10.1109\/ICASSP.2016.7471631"},{"issue":"8","key":"18_CR12","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"issue":"3","key":"18_CR13","doi-asserted-by":"publisher","first-page":"743","DOI":"10.3758\/s13423-014-0723-1","volume":"22","author":"G Horstmann","year":"2015","unstructured":"Horstmann, G., Herwig, A.: Surprise attracts the eyes and binds the gaze. Psychon. Bull. Rev. 22(3), 743\u2013749 (2015)","journal-title":"Psychon. Bull. Rev."},{"issue":"1","key":"18_CR14","doi-asserted-by":"publisher","first-page":"69","DOI":"10.3758\/s13414-015-0995-1","volume":"78","author":"G Horstmann","year":"2016","unstructured":"Horstmann, G., Herwig, A.: Novelty biases attention and gaze in a surprise trial. Atten. Percept. Psychophys. 78(1), 69\u201377 (2016)","journal-title":"Atten. Percept. Psychophys."},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Hossein Khatoonabadi, S., Vasconcelos, N., Bajic, I.V., Shan, Y.: How many bits does it take for a stimulus to be salient? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5501\u20135510 (2015)","DOI":"10.1109\/CVPR.2015.7299189"},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Hou, R., Chen, C., Shah, M.: Tube convolutional neural network (T-CNN) for action detection in videos. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 5822\u20135831 (2017)","DOI":"10.1109\/ICCV.2017.620"},{"issue":"10\u201312","key":"18_CR17","doi-asserted-by":"publisher","first-page":"1489","DOI":"10.1016\/S0042-6989(99)00163-7","volume":"40","author":"L Itti","year":"2000","unstructured":"Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vis. Res. 40(10\u201312), 1489\u20131506 (2000)","journal-title":"Vis. Res."},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Jain, M., Van Gemert, J., J\u00e9gou, H., Bouthemy, P., Snoek, C.G.: Action localization with tubelets from motion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 740\u2013747 (2014)","DOI":"10.1109\/CVPR.2014.100"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Jhuang, H., Gall, J., Zuffi, S., Schmid, C., Black, M.J.: Towards understanding action recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3192\u20133199 (2013)","DOI":"10.1109\/ICCV.2013.396"},{"key":"18_CR20","doi-asserted-by":"crossref","unstructured":"Ji, X., Henriques, J.F., Vedaldi, A.: Invariant information clustering for unsupervised image classification and segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9865\u20139874 (2019)","DOI":"10.1109\/ICCV.2019.00996"},{"key":"18_CR21","unstructured":"Jia, X., De Brabandere, B., Tuytelaars, T., Gool, L.V.: Dynamic filter networks. In: Neural Information Processing Systems, pp. 667\u2013675 (2016)"},{"key":"18_CR22","unstructured":"Jiang, Y.G., et al.: THUMOS challenge: action recognition with a large number of classes (2014)"},{"key":"18_CR23","doi-asserted-by":"crossref","unstructured":"Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1725\u20131732 (2014)","DOI":"10.1109\/CVPR.2014.223"},{"issue":"6","key":"18_CR24","first-page":"90","volume":"1","author":"TM Kodinariya","year":"2013","unstructured":"Kodinariya, T.M., Makwana, P.R.: Review on determining number of cluster in k-means clustering. Int. J. 1(6), 90\u201395 (2013)","journal-title":"Int. J."},{"key":"18_CR25","doi-asserted-by":"crossref","unstructured":"Kuehne, H., Arslan, A., Serre, T.: The language of actions: recovering the syntax and semantics of goal-directed human activities. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 780\u2013787 (2014)","DOI":"10.1109\/CVPR.2014.105"},{"key":"18_CR26","doi-asserted-by":"crossref","unstructured":"Lan, T., Wang, Y., Mori, G.: Discriminative figure-centric models for joint action localization and recognition. In: 2011 International Conference on Computer Vision, pp. 2003\u20132010. IEEE (2011)","DOI":"10.1109\/ICCV.2011.6126472"},{"issue":"5","key":"18_CR27","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1109\/TPAMI.2016.2567391","volume":"39","author":"V Leboran","year":"2016","unstructured":"Leboran, V., Garcia-Diaz, A., Fdez-Vidal, X.R., Pardo, X.M.: Dynamic whitening saliency. IEEE Trans. Pattern Anal. Mach. Intell. 39(5), 893\u2013907 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"18_CR28","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.cviu.2017.10.011","volume":"166","author":"Z Li","year":"2018","unstructured":"Li, Z., Gavrilyuk, K., Gavves, E., Jain, M., Snoek, C.G.: Videolstm convolves, attends and flows for action recognition. Comput. Vis. Image Underst. 166, 41\u201350 (2018)","journal-title":"Comput. Vis. Image Underst."},{"key":"18_CR29","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. LNCS, vol. 9905, pp. 21\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"key":"18_CR30","doi-asserted-by":"crossref","unstructured":"Ma, S., Zhang, J., Ikizler-Cinbis, N., Sclaroff, S.: Action recognition and localization by hierarchical space-time segments. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2744\u20132751 (2013)","DOI":"10.1109\/ICCV.2013.341"},{"key":"18_CR31","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 the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"18_CR32","doi-asserted-by":"crossref","unstructured":"Rodriguez, M.D., Ahmed, J., Shah, M.: Action mach a spatio-temporal maximum average correlation height filter for action recognition. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20138. IEEE (2008)","DOI":"10.1109\/CVPR.2008.4587727"},{"key":"18_CR33","unstructured":"Sharma, S., Kiros, R., Salakhutdinov, R.: Action recognition using visual attention. In: Neural Information Processing Systems: Time Series Workshop (2015)"},{"key":"18_CR34","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"18_CR35","doi-asserted-by":"crossref","unstructured":"Song, J., Gao, L., Guo, Z., Liu, W., Zhang, D., Shen, H.T.: Hierarchical LSTM with adjusted temporal attention for video captioning. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 2737\u20132743. AAAI Press (2017)","DOI":"10.24963\/ijcai.2017\/381"},{"key":"18_CR36","doi-asserted-by":"crossref","unstructured":"Soomro, K., Idrees, H., Shah, M.: Action localization in videos through context walk. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3280\u20133288 (2015)","DOI":"10.1109\/ICCV.2015.375"},{"key":"18_CR37","doi-asserted-by":"crossref","unstructured":"Soomro, K., Idrees, H., Shah, M.: Predicting the where and what of actors and actions through online action localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2648\u20132657 (2016)","DOI":"10.1109\/CVPR.2016.290"},{"key":"18_CR38","doi-asserted-by":"crossref","unstructured":"Soomro, K., Shah, M.: Unsupervised action discovery and localization in videos. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 696\u2013705 (2017)","DOI":"10.1109\/ICCV.2017.82"},{"key":"18_CR39","unstructured":"Soomro, K., Zamir, A.R., Shah, M.: Ucf101: a dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 (2012)"},{"key":"18_CR40","doi-asserted-by":"crossref","unstructured":"Tian, Y., Sukthankar, R., Shah, M.: Spatiotemporal deformable part models for action detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2642\u20132649 (2013)","DOI":"10.1109\/CVPR.2013.341"},{"issue":"4","key":"18_CR41","first-page":"891","volume":"18","author":"SP Tipper","year":"1992","unstructured":"Tipper, S.P., Lortie, C., Baylis, G.C.: Selective reaching: evidence for action-centered attention. J. Exp. Psychol.: Hum. Percept. Perform. 18(4), 891 (1992)","journal-title":"J. Exp. Psychol.: Hum. Percept. Perform."},{"key":"18_CR42","doi-asserted-by":"crossref","unstructured":"Tran, D., Yuan, J.: Optimal spatio-temporal path discovery for video event detection. In: CVPR 2011, pp. 3321\u20133328. IEEE (2011)","DOI":"10.1109\/CVPR.2011.5995416"},{"key":"18_CR43","unstructured":"Tran, D., Yuan, J.: Max-margin structured output regression for spatio-temporal action localization. In: Advances in Neural Information Processing Systems, pp. 350\u2013358 (2012)"},{"issue":"2","key":"18_CR44","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","volume":"104","author":"JR Uijlings","year":"2013","unstructured":"Uijlings, J.R., Van De Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. Int. J. Comput. Vis. (IJCV) 104(2), 154\u2013171 (2013)","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"18_CR45","unstructured":"Van Gemert, J.C., Jain, M., Gati, E., Snoek, C.G., et al.: APT: action localization proposals from dense trajectories. In: BMVC, vol. 2, p. 4 (2015)"},{"key":"18_CR46","doi-asserted-by":"crossref","unstructured":"Venugopalan, S., Rohrbach, M., Donahue, J., Mooney, R., Darrell, T., Saenko, K.: Sequence to sequence-video to text. In: IEEE International Conference on Computer Vision (ICCV), pp. 4534\u20134542 (2015)","DOI":"10.1109\/ICCV.2015.515"},{"key":"18_CR47","doi-asserted-by":"crossref","unstructured":"Venugopalan, S., Xu, H., Donahue, J., Rohrbach, M., Mooney, R., Saenko, K.: Translating videos to natural language using deep recurrent neural networks. arXiv preprint arXiv:1412.4729 (2014)","DOI":"10.3115\/v1\/N15-1173"},{"key":"18_CR48","doi-asserted-by":"crossref","unstructured":"Vondrick, C., Pirsiavash, H., Torralba, A.: Anticipating visual representations from unlabeled video. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 98\u2013106 (2016)","DOI":"10.1109\/CVPR.2016.18"},{"key":"18_CR49","doi-asserted-by":"crossref","unstructured":"Vondrick, C., Torralba, A.: Generating the future with adversarial transformers. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1020\u20131028 (2017)","DOI":"10.1109\/CVPR.2017.319"},{"key":"18_CR50","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1007\/978-3-319-10602-1_37","volume-title":"Computer Vision \u2013 ECCV 2014","author":"L Wang","year":"2014","unstructured":"Wang, L., Qiao, Yu., Tang, X.: Video action detection with relational dynamic-poselets. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 565\u2013580. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_37"},{"key":"18_CR51","unstructured":"Xie, J., Girshick, R., Farhadi, A.: Unsupervised deep embedding for clustering analysis. In: International Conference on Machine Learning (ICML), pp. 478\u2013487 (2016)"},{"issue":"1","key":"18_CR52","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1037\/0096-3445.130.1.29","volume":"130","author":"JM Zacks","year":"2001","unstructured":"Zacks, J.M., Tversky, B., Iyer, G.: Perceiving, remembering, and communicating structure in events. J. Exp. Psychol.: Gen. 130(1), 29 (2001)","journal-title":"J. Exp. Psychol.: Gen."},{"key":"18_CR53","doi-asserted-by":"crossref","unstructured":"Zhang, M., Teck Ma, K., Hwee Lim, J., Zhao, Q., Feng, J.: Deep future gaze: gaze anticipation on egocentric videos using adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4372\u20134381 (2017)","DOI":"10.1109\/CVPR.2017.377"},{"key":"18_CR54","unstructured":"Zhu, G., Porikli, F., Li, H.: Tracking randomly moving objects on edge box proposals. arXiv preprint arXiv:1507.08085 (2015)"}],"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\/978-3-030-58568-6_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:21:55Z","timestamp":1731370915000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58568-6_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585679","9783030585686"],"references-count":54,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58568-6_18","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":"13 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)"}}]}}