{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T17:08:34Z","timestamp":1780765714593,"version":"3.54.1"},"publisher-location":"Cham","reference-count":67,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585280","type":"print"},{"value":"9783030585297","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\/978-3-030-58529-7_18","type":"book-chapter","created":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T09:06:09Z","timestamp":1605171969000},"page":"288-313","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["UFO$$^2$$: A Unified Framework Towards Omni-supervised Object Detection"],"prefix":"10.1007","author":[{"given":"Zhongzheng","family":"Ren","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiding","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming-Yu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander G.","family":"Schwing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan","family":"Kautz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,11,13]]},"reference":[{"key":"18_CR1","unstructured":"Instagram statistics 2019. www.omnicoreagency.com\/instagram-statistics\/"},{"key":"18_CR2","unstructured":"Youtube statistics 2019. https:\/\/merchdope.com\/youtube-stats\/"},{"key":"18_CR3","doi-asserted-by":"crossref","unstructured":"Arbel\u00e1ez, P., Pont-Tuset, J., Barron, J., Marques, F., Malik, J.: Multiscale combinatorial grouping. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.49"},{"key":"18_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1007\/978-3-319-46478-7_34","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Bearman","year":"2016","unstructured":"Bearman, A., Russakovsky, O., Ferrari, V., Fei-Fei, L.: What\u2019s the point: semantic segmentation with point supervision. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 549\u2013565. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_34"},{"key":"18_CR5","unstructured":"Berthelot, D., Carlini, N., Goodfellow, I.J., Papernot, N., Oliver, A., Raffel, C.: MixMatch: a holistic approach to semi-supervised learning. In: NeurIPS (2019)"},{"key":"18_CR6","doi-asserted-by":"crossref","unstructured":"Bilen, H., Vedaldi, A.: Weakly supervised deep detection networks. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.311"},{"key":"18_CR7","first-page":"120","volume":"25","author":"G Bradski","year":"2000","unstructured":"Bradski, G.: The OpenCV Library. Dobb\u2019s J. Softw. Tools 25, 120\u2013125 (2000)","journal-title":"Dobb\u2019s J. Softw. Tools"},{"key":"18_CR8","volume-title":"Semi-Supervised Learning","year":"2006","unstructured":"Chapelle, O., Sch\u00f6lkopf, B., Zien, A. (eds.): Semi-Supervised Learning. The MIT Press, Cambridge (2006)"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., Sakaridis, C., Dai, D., Gool, L.V.: Domain adaptive faster R-CNN for object detection in the wild. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00352"},{"key":"18_CR10","unstructured":"Ch\u00e9ron, G., Alayrac, J.B., Laptev, I., Schmid, C.: A flexible model for training action localization with varying levels of supervision. In: NIPS (2018)"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.167"},{"key":"18_CR12","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL visual object classes (VOC) challenge. IJCV 88, 303\u2013338 (2010). https:\/\/doi.org\/10.1007\/s11263-009-0275-4","journal-title":"IJCV"},{"issue":"9","key":"18_CR13","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2010","unstructured":"Felzenszwalb, P.F., Girshick, R.B., McAllester, D.A., Ramanan, D.: Object detection with discriminatively trained part-based models. T-PAMI 32(9), 1627\u20131645 (2010)","journal-title":"T-PAMI"},{"key":"18_CR14","unstructured":"Gao, Y., et al.: C-MIDN: coupled multiple instance detection network with segmentation guidance for weakly supervised object detection. In: ICCV (2019)"},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Ge, W., Yang, S., Yu, Y.: Multi-evidence filtering and fusion for multi-label classification, object detection and semantic segmentation based on weakly supervised learning. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00139"},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Girshick, R.B.: Fast R-CNN. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Gupta, A., Dollar, P., Girshick, R.: LVIS: a dataset for large vocabulary instance segmentation. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00550"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: CVPR (2019)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"18_CR20","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"18_CR21","doi-asserted-by":"crossref","unstructured":"Hu, R., Doll\u00e1r, P., He, K., Darrell, T., Girshick, R.: Learning to segment every thing. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00445"},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Inoue, N., Furuta, R., Yamasaki, T., Aizawa, K.: Cross-domain weakly-supervised object detection through progressive domain adaptation. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00525"},{"key":"18_CR23","doi-asserted-by":"crossref","unstructured":"Jie, Z., Wei, Y., Jin, X., Feng, J., Liu, W.: Deep self-taught learning for weakly supervised object localization. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.457"},{"key":"18_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1007\/978-3-319-46454-1_22","volume-title":"Computer Vision \u2013 ECCV 2016","author":"V Kantorov","year":"2016","unstructured":"Kantorov, V., Oquab, M., Cho, M., Laptev, I.: ContextLocNet: context-aware deep network models for weakly supervised localization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 350\u2013365. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46454-1_22"},{"key":"18_CR25","doi-asserted-by":"crossref","unstructured":"Khodabandeh, M., Vahdat, A., Ranjbar, M., Macready, W.G.: A robust learning approach to domain adaptive object detection. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00057"},{"key":"18_CR26","doi-asserted-by":"crossref","unstructured":"Khoreva, A., Benenson, R., Hosang, J., Hein, M., Schiele, B.: Simple does it: weakly supervised instance and semantic segmentation. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.181"},{"key":"18_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1007\/978-3-030-01264-9_45","volume-title":"Computer Vision \u2013 ECCV 2018","author":"H Law","year":"2018","unstructured":"Law, H., Deng, J.: CornerNet: detecting objects as paired keypoints. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018. LNCS, vol. 11218, pp. 765\u2013781. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01264-9_45"},{"key":"18_CR28","unstructured":"Lee, D.H.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: ICML 2013 Workshop (2013)"},{"key":"18_CR29","doi-asserted-by":"crossref","unstructured":"Lin, D., Dai, J., Jia, J., He, K., Sun, J.: ScribbleSup: scribble-supervised convolutional networks for semantic segmentation. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.344"},{"key":"18_CR30","doi-asserted-by":"crossref","unstructured":"Lin, T., et al.: Microsoft COCO: common objects in context. CoRR (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"18_CR31","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"},{"issue":"8","key":"18_CR32","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1109\/TPAMI.2018.2858821","volume":"41","author":"T Miyato","year":"2019","unstructured":"Miyato, T., Maeda, S., Koyama, M., Ishii, S.: Virtual adversarial training: a regularization method for supervised and semi-supervised learning. T-PAMI 41(8), 1979\u20131993 (2019)","journal-title":"T-PAMI"},{"key":"18_CR33","unstructured":"Oliver, A., Odena, A., Raffel, C.A., Cubuk, E.D., Goodfellow, I.: Realistic evaluation of deep semi-supervised learning algorithms. In: NeurIPS (2018)"},{"key":"18_CR34","doi-asserted-by":"crossref","unstructured":"Papadopoulos, D.P., Uijlings, J.R.R., Keller, F., Ferrari, V.: Extreme clicking for efficient object annotation. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.528"},{"key":"18_CR35","doi-asserted-by":"crossref","unstructured":"Papadopoulos, D.P., Uijlings, J.R.R., Keller, F., Ferrari, V.: Training object class detectors with click supervision. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.27"},{"key":"18_CR36","doi-asserted-by":"crossref","unstructured":"Papandreou, G., Chen, L., Murphy, K.P., Yuille, A.L.: Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.203"},{"key":"18_CR37","unstructured":"Pardo, A., Xu, M., Thabet, A.K., Arbelaez, P., Ghanem, B.: BAOD: budget-aware object detection. CoRR abs\/1904.05443 (2019)"},{"key":"18_CR38","doi-asserted-by":"crossref","unstructured":"Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic image synthesis with spatially-adaptive normalization. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00244"},{"key":"18_CR39","doi-asserted-by":"crossref","unstructured":"Peng, X., Sun, B., Ali, K., Saenko, K.: Learning deep object detectors from 3D models. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.151"},{"key":"18_CR40","doi-asserted-by":"crossref","unstructured":"Radosavovic, I., Doll\u00e1r, P., Girshick, R.B., Gkioxari, G., He, K.: Data distillation: towards omni-supervised learning. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00433"},{"key":"18_CR41","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: unified, real-time object detection. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"18_CR42","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.690"},{"issue":"6","key":"18_CR43","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. TPAMI 39(6), 1137\u20131149 (2016)","journal-title":"TPAMI"},{"key":"18_CR44","doi-asserted-by":"crossref","unstructured":"Ren, Z., Lee, Y.J.: Cross-domain self-supervised multi-task feature learning using synthetic imagery. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00086"},{"key":"18_CR45","doi-asserted-by":"crossref","unstructured":"Ren, Z., et al.: Instance-aware, context-focused, and memory-efficient weakly supervised object detection. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01061"},{"key":"18_CR46","unstructured":"Ren, Z., Yeh, R.A., Schwing, A.G.: Not all unlabeled data are equal: learning to weight data in semi-supervised learning. arXiv preprint arXiv:2007.01293 (2020)"},{"key":"18_CR47","doi-asserted-by":"crossref","unstructured":"Rosenberg, C., Hebert, M., Schneiderman, H.: Semi-supervised self-training of object detection models. In: WACV\/MOTION (2005)","DOI":"10.1109\/ACVMOT.2005.107"},{"key":"18_CR48","doi-asserted-by":"crossref","unstructured":"Shen, Y., Ji, R., Zhang, S., Zuo, W., Wang, Y.: Generative adversarial learning towards fast weakly supervised detection. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00604"},{"key":"18_CR49","doi-asserted-by":"crossref","unstructured":"Singh, G., Saha, S., Sapienza, M., Torr, P., Cuzzolin, F.: Online real time multiple spatiotemporal action localisation and prediction on a single platform. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.393"},{"key":"18_CR50","doi-asserted-by":"crossref","unstructured":"Singh, K.K., Xiao, F., Lee, Y.J.: Track and transfer: watching videos to simulate strong human supervision for weakly-supervised object detection. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.386"},{"key":"18_CR51","unstructured":"Su, H., Deng, J., Fei-Fei, L.: Crowdsourcing annotations for visual object detection. In: AAAI Technical Report, 4th Human Computation Workshop (2012)"},{"issue":"1","key":"18_CR52","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1109\/TPAMI.2018.2876304","volume":"42","author":"P Tang","year":"2018","unstructured":"Tang, P., et al.: PCL: proposal cluster learning for weakly supervised object detection. T-PAMI 42(1), 176\u2013191 (2018)","journal-title":"T-PAMI"},{"key":"18_CR53","doi-asserted-by":"crossref","unstructured":"Tang, P., Wang, X., Bai, X., Liu, W.: Multiple instance detection network with online instance classifier refinement. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.326"},{"key":"18_CR54","unstructured":"Tarvainen, A., Valpola, H.: Weight-averaged consistency targets improve semi-supervised deep learning results. In: NeurIPS (2017)"},{"key":"18_CR55","doi-asserted-by":"crossref","unstructured":"Uijlings, J.R.R., Popov, S., Ferrari, V.: Revisiting knowledge transfer for training object class detectors. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00121"},{"key":"18_CR56","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","volume":"104","author":"J Uijlings","year":"2013","unstructured":"Uijlings, J., van de Sande, K., Gevers, T., Smeulders, A.: Selective search for object recognition. IJCV 104, 154\u2013171 (2013)","journal-title":"IJCV"},{"key":"18_CR57","unstructured":"Xie, Q., Dai, Z., Hovy, E., Luong, M.T., Le, Q.V.: Unsupervised data augmentation for consistency training. arXiv preprint arXiv:1904.12848 (2019)"},{"key":"18_CR58","doi-asserted-by":"crossref","unstructured":"Xu, J., Schwing, A.G., Urtasun, R.: Learning to segment under various forms of weak supervision. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7299002"},{"key":"18_CR59","doi-asserted-by":"crossref","unstructured":"Yang, Z., Mahajan, D., Ghadiyaram, D., Nevatia, R., Ramanathan, V.: Activity driven weakly supervised object detection. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00303"},{"key":"18_CR60","doi-asserted-by":"crossref","unstructured":"Zeng, Z., Liu, B., Fu, J., Chao, H., Zhang, L.: WSOD2: learning bottom-up and top-down objectness distillation for weakly-supervised object detection. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00838"},{"key":"18_CR61","doi-asserted-by":"crossref","unstructured":"Zhang, X., Feng, J., Xiong, H., Tian, Q.: Zigzag learning for weakly supervised object detection. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00448"},{"key":"18_CR62","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Bai, Y., Ding, M., Li, Y., Ghanem, B.: W2F: a weakly-supervised to fully-supervised framework for object detection. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00103"},{"key":"18_CR63","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., A., L., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.319"},{"key":"18_CR64","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zhuo, J., Kr\u00e4henb\u00fchl, P.: Bottom-up object detection by grouping extreme and center points. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00094"},{"key":"18_CR65","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/978-3-319-10602-1_26","volume-title":"Computer Vision \u2013 ECCV 2014","author":"CL Zitnick","year":"2014","unstructured":"Zitnick, C.L., Doll\u00e1r, P.: Edge boxes: locating object proposals from edges. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 391\u2013405. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_26"},{"key":"18_CR66","doi-asserted-by":"crossref","unstructured":"Zou, Y., Yu, Z., Liu, X., Kumar, B., Wang, J.: Confidence regularized self-training. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00608"},{"key":"18_CR67","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/978-3-030-01219-9_18","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Zou","year":"2018","unstructured":"Zou, Y., Yu, Z., Vijaya Kumar, B.V.K., Wang, J.: Unsupervised domain adaptation for semantic segmentation via class-balanced self-training. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 297\u2013313. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01219-9_18"}],"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\/978-3-030-58529-7_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:33:34Z","timestamp":1731371614000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58529-7_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585280","9783030585297"],"references-count":67,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58529-7_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)"}}]}}