{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T14:58:03Z","timestamp":1773413883084,"version":"3.50.1"},"publisher-location":"Cham","reference-count":55,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585419","type":"print"},{"value":"9783030585426","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\/978-3-030-58542-6_15","type":"book-chapter","created":{"date-parts":[[2020,11,16]],"date-time":"2020-11-16T17:06:34Z","timestamp":1605546394000},"page":"238-254","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Fully Trainable and Interpretable Non-local Sparse Models for Image Restoration"],"prefix":"10.1007","author":[{"given":"Bruno","family":"Lecouat","sequence":"first","affiliation":[]},{"given":"Jean","family":"Ponce","sequence":"additional","affiliation":[]},{"given":"Julien","family":"Mairal","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,17]]},"reference":[{"issue":"11","key":"15_CR1","doi-asserted-by":"publisher","first-page":"4311","DOI":"10.1109\/TSP.2006.881199","volume":"54","author":"M Aharon","year":"2006","unstructured":"Aharon, M., Elad, M., Bruckstein, A.: K-svd: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311\u20134322 (2006)","journal-title":"IEEE Trans. Signal Process."},{"issue":"1","key":"15_CR2","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1137\/080716542","volume":"2","author":"A Beck","year":"2009","unstructured":"Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183\u2013202 (2009)","journal-title":"SIAM J. Imaging Sci."},{"issue":"3","key":"15_CR3","doi-asserted-by":"publisher","first-page":"034005","DOI":"10.1088\/1361-6420\/ab460a","volume":"36","author":"C Bertocchi","year":"2019","unstructured":"Bertocchi, C., Chouzenoux, E., Corbineau, M.C., Pesquet, J.C., Prato, M.: Deep unfolding of a proximal interior point method for image restoration. Inverse Prob. 36(3), 034005 (2019)","journal-title":"Inverse Prob."},{"key":"15_CR4","unstructured":"Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: Proceeding of Conference on Computer Vision and Pattern Recognition (CVPR) (2005)"},{"key":"15_CR5","unstructured":"Chen, X., Liu, J., Wang, Z., Yin, W.: Theoretical linear convergence of unfolded ISTA and its practical weights and thresholds. In: Proceeding of Advances in Neural Information Processing Systems (NeurIPS) (2018)"},{"issue":"6","key":"15_CR6","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.1109\/TPAMI.2016.2596743","volume":"39","author":"Y Chen","year":"2016","unstructured":"Chen, Y., Pock, T.: Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1256\u20131272 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"8","key":"15_CR7","doi-asserted-by":"publisher","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","volume":"16","author":"K Dabov","year":"2007","unstructured":"Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080\u20132095 (2007)","journal-title":"IEEE Trans. Image Process."},{"key":"15_CR8","unstructured":"Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: BM3D image denoising with shape-adaptive principal component analysis (2009)"},{"issue":"2","key":"15_CR9","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2016","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 38(2), 295\u2013307 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (PAMI)"},{"issue":"4","key":"15_CR10","doi-asserted-by":"publisher","first-page":"1620","DOI":"10.1109\/TIP.2012.2235847","volume":"22","author":"W Dong","year":"2012","unstructured":"Dong, W., Zhang, L., Shi, G., Li, X.: Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Process. 22(4), 1620\u20131630 (2012)","journal-title":"IEEE Trans. Image Process."},{"issue":"12","key":"15_CR11","doi-asserted-by":"publisher","first-page":"3736","DOI":"10.1109\/TIP.2006.881969","volume":"15","author":"M Elad","year":"2006","unstructured":"Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736\u20133745 (2006)","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"15_CR12","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1109\/TIP.2003.814255","volume":"12","author":"MAT Figueiredo","year":"2003","unstructured":"Figueiredo, M.A.T., Nowak, R.D.: An EM algorithm for wavelet-based image restoration. IEEE Trans. Image Process. 12(8), 906\u2013916 (2003)","journal-title":"IEEE Trans. Image Process."},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Fletcher, A.K., Pandit, P., Rangan, S., Sarkar, S., Schniter, P.: Plug-in estimation in high-dimensional linear inverse problems: a rigorous analysis. In: Proceeding of Advances in Neural Information Processing Systems (NeurIPS) (2018)","DOI":"10.1088\/1742-5468\/ab321a"},{"issue":"5","key":"15_CR14","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1109\/TIP.2007.891788","volume":"16","author":"A Foi","year":"2007","unstructured":"Foi, A., Katkovnik, V., Egiazarian, K.: Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images. IEEE Trans. on Image Process. 16(5), 1395\u20131411 (2007)","journal-title":"IEEE Trans. on Image Process."},{"issue":"6","key":"15_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2980179.2982399","volume":"35","author":"M Gharbi","year":"2016","unstructured":"Gharbi, M., Chaurasia, G., Paris, S., Durand, F.: Deep joint demosaicking and denoising. ACM Trans. Graph. (TOG) 35(6), 1\u201312 (2016)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"15_CR16","unstructured":"Gregor, K., LeCun, Y.: Learning fast approximations of sparse coding. In: Proceeding of International Conference on Machine Learning (ICML) (2010)"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Gu, S., Zhang, L., Zuo, W., Feng, X.: Weighted nuclear norm minimization with application to image denoising. In: Proceeding of Conference on Computer Vision and Pattern Recognition (CVPR) (2014)","DOI":"10.1109\/CVPR.2014.366"},{"issue":"9","key":"15_CR18","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1109\/TIP.2002.801121","volume":"11","author":"B Gunturk","year":"2002","unstructured":"Gunturk, B., Altunbasak, Y., Mersereau, R.: Color plane interpolation using alternating projections. IEEE Trans. Image Process. 11(9), 997\u20131013 (2002)","journal-title":"IEEE Trans. Image Process."},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceeding of Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"15_CR20","first-page":"2777","volume":"12","author":"R Jenatton","year":"2011","unstructured":"Jenatton, R., Audibert, J.Y., Bach, F.: Structured variable selection with sparsity-inducing norms. J. Mach. Learn. Res. (JMLR) 12, 2777\u20132824 (2011)","journal-title":"J. Mach. Learn. Res. (JMLR)"},{"key":"15_CR21","doi-asserted-by":"crossref","unstructured":"Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: Proceeding of Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"15_CR22","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2013)"},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"Kokkinos, F., Lefkimmiatis, S.: Deep image demosaicking using a cascade of convolutional residual denoising networks. In: Proceeding of European Conference on Computer Vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01264-9_19"},{"issue":"8","key":"15_CR24","doi-asserted-by":"publisher","first-page":"4177","DOI":"10.1109\/TIP.2019.2905991","volume":"28","author":"F Kokkinos","year":"2019","unstructured":"Kokkinos, F., Lefkimmiatis, S.: Iterative joint image demosaicking and denoising using a residual denoising network. IEEE Trans. Image Process. 28(8), 4177\u20134188 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Lefkimmiatis, S.: Non-local color image denoising with convolutional neural networks. In: Proceeding of Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.623"},{"key":"15_CR26","doi-asserted-by":"crossref","unstructured":"Lefkimmiatis, S.: Universal denoising networks: a novel CNN architecture for image denoising. In: Proceeding of Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00338"},{"key":"15_CR27","unstructured":"Liu, D., Wen, B., Fan, Y., Loy, C.C., Huang, T.S.: Non-local recurrent network for image restoration. In: Proceeding of Advances in Neural Information Processing Systems (NeurIPS) (2018)"},{"key":"15_CR28","unstructured":"Liu, J., Chen, X., Wang, Z., Yin, W.: Alista: analytic weights are as good as learned weights in LISTA. In: Proceeding of International Conference on Learning Representations (ICLR) (2019)"},{"issue":"12","key":"15_CR29","doi-asserted-by":"publisher","first-page":"5226","DOI":"10.1109\/TIP.2013.2283400","volume":"22","author":"X Liu","year":"2013","unstructured":"Liu, X., Tanaka, M., Okutomi, M.: Single-image noise level estimation for blind denoising. IEEE Trans. Image Process. 22(12), 5226\u20135237 (2013)","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"15_CR30","doi-asserted-by":"publisher","first-page":"1004","DOI":"10.1109\/TIP.2016.2631888","volume":"26","author":"K Ma","year":"2016","unstructured":"Ma, K., et al.: Waterloo exploration database: new challenges for image quality assessment models. IEEE Trans. Image Process. 26(2), 1004\u20131016 (2016)","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"15_CR31","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1109\/TPAMI.2011.156","volume":"34","author":"J Mairal","year":"2011","unstructured":"Mairal, J., Bach, F., Ponce, J.: Task-driven dictionary learning. IEEE Trans. Pattern Anal. Mach. Intell. 34(4), 791\u2013804 (2011)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"Jan","key":"15_CR32","first-page":"19","volume":"11","author":"J Mairal","year":"2010","unstructured":"Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res. (JMLR) 11(Jan), 19\u201360 (2010)","journal-title":"J. Mach. Learn. Res. (JMLR)"},{"issue":"2\u20133","key":"15_CR33","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1561\/0600000058","volume":"8","author":"J Mairal","year":"2014","unstructured":"Mairal, J., Bach, F., Ponce, J., et al.: Sparse modeling for image and vision processing. Found. Trends Comput. Graph. Vis. 8(2\u20133), 85\u2013283 (2014)","journal-title":"Found. Trends Comput. Graph. Vis."},{"key":"15_CR34","doi-asserted-by":"crossref","unstructured":"Mairal, J., Bach, F.R., Ponce, J., Sapiro, G., Zisserman, A.: Non-local sparse models for image restoration. In: Proceeding of International Conference on Computer Vision (ICCV) (2009)","DOI":"10.1109\/ICCV.2009.5459452"},{"key":"15_CR35","volume-title":"A Wavelet Tour of Signal Processing","author":"S Mallat","year":"1999","unstructured":"Mallat, S.: A Wavelet Tour of Signal Processing, 2nd edn. Academic Press, New York (1999)","edition":"2"},{"key":"15_CR36","unstructured":"Martin, D., et al.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics (2001)"},{"key":"15_CR37","doi-asserted-by":"publisher","first-page":"3311","DOI":"10.1016\/S0042-6989(97)00169-7","volume":"37","author":"BA Olshausen","year":"1997","unstructured":"Olshausen, B.A., Field, D.J.: Sparse coding with an overcomplete basis set: a strategy employed by V1? Vis. Res 37, 3311\u20133325 (1997)","journal-title":"Vis. Res"},{"issue":"7","key":"15_CR38","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1109\/34.56205","volume":"12","author":"P Perona","year":"1990","unstructured":"Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 12(7), 629\u2013639 (1990)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (PAMI)"},{"key":"15_CR39","unstructured":"Pl\u00f6tz, T., Roth, S.: Neural nearest neighbors networks. In: Proceeding of Advances in Neural Information Processing Systems (NeurIPS) (2018)"},{"issue":"11","key":"15_CR40","doi-asserted-by":"publisher","first-page":"1338","DOI":"10.1109\/TIP.2003.818640","volume":"12","author":"J Portilla","year":"2003","unstructured":"Portilla, J., Strela, V., Wainwright, M., Simoncelli, E.: Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Trans. Image Process. 12(11), 1338\u20131351 (2003)","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"15_CR41","doi-asserted-by":"publisher","first-page":"1804","DOI":"10.1137\/16M1102884","volume":"10","author":"Y Romano","year":"2017","unstructured":"Romano, Y., Elad, M., Milanfar, P.: The little engine that could: regularization by denoising (red). SIAM J. Imaging Sci. 10(4), 1804\u20131844 (2017)","journal-title":"SIAM J. Imaging Sci."},{"key":"15_CR42","unstructured":"Roth, S., Black, M.J.: Fields of experts: a framework for learning image priors. In: Proceeding of Conference on Computer Vision and Pattern Recognition (CVPR) (2005)"},{"issue":"1\u20134","key":"15_CR43","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","volume":"60","author":"LI Rudin","year":"1992","unstructured":"Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D: Nonlinear phenom. 60(1\u20134), 259\u2013268 (1992)","journal-title":"Phys. D: Nonlinear phenom."},{"key":"15_CR44","unstructured":"Scetbon, M., Elad, M., Milanfar, P.: Deep k-svd denoising. arXiv preprint arXiv:1909.13164 (2019)"},{"key":"15_CR45","unstructured":"Simon, D., Elad, M.: Rethinking the CSC model for natural images. In: Advances in Neural Information Processing Systems (NeurIPS) (2019)"},{"key":"15_CR46","unstructured":"Sun, J., et al.: Deep ADMM-Net for compressive sensing MRI. In: Proceeding of Advances in Neural Information Processing Systems (NIPS) (2016)"},{"key":"15_CR47","doi-asserted-by":"crossref","unstructured":"Venkatakrishnan, S.V., Bouman, C.A., Wohlberg, B.: Plug-and-play priors for model based reconstruction. In: IEEE Global Conference on Signal and Information Processing, pp. 945\u2013948. IEEE (2013)","DOI":"10.1109\/GlobalSIP.2013.6737048"},{"key":"15_CR48","doi-asserted-by":"crossref","unstructured":"Wang, Z., Liu, D., Yang, J., Han, W., Huang, T.: Deep networks for image super-resolution with sparse prior. In: Proceeding of Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/ICCV.2015.50"},{"key":"15_CR49","unstructured":"Yu, K., Dong, C., Loy, C.C., Tang, X.: Deep convolution networks for compression artifacts reduction (2015)"},{"key":"15_CR50","doi-asserted-by":"crossref","unstructured":"Zhang, J., Ghanem, B.: ISTA-Net: interpretable optimization-inspired deep network for image compressive sensing. In: Proceeding of Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00196"},{"issue":"7","key":"15_CR51","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: residual learning of deep cnn for image denoising. IEEE Trans. Image Process. 26(7), 3142\u20133155 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"15_CR52","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Gu, S., Zhang, L.: Learning deep CNN denoiser prior for image restoration. In: Proceeding of Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.300"},{"issue":"9","key":"15_CR53","doi-asserted-by":"publisher","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","volume":"27","author":"K Zhang","year":"2018","unstructured":"Zhang, K., Zuo, W., Zhang, L.: Ffdnet: toward a fast and flexible solution for cnn-based image denoising. IEEE Trans. Image Process. 27(9), 4608\u20134622 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"15_CR54","unstructured":"Zhang, Y., Li, K., Li, K., Zhong, B., Fu, Y.: Residual non-local attention networks for image restoration. In: Proceeding of International Conference on Learning Representations (ICLR) (2019)"},{"key":"15_CR55","doi-asserted-by":"crossref","unstructured":"Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: Proceeding of Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2011)","DOI":"10.1109\/ICCV.2011.6126278"}],"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\/978-3-030-58542-6_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T00:06:45Z","timestamp":1731715605000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58542-6_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585419","9783030585426"],"references-count":55,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58542-6_15","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":"17 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)"}}]}}