{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:32:17Z","timestamp":1778347937288,"version":"3.51.4"},"publisher-location":"Cham","reference-count":59,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585259","type":"print"},{"value":"9783030585266","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\/978-3-030-58526-6_31","type":"book-chapter","created":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T21:03:07Z","timestamp":1602018187000},"page":"522-539","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["Stable Low-Rank Tensor Decomposition for Compression of Convolutional Neural Network"],"prefix":"10.1007","author":[{"given":"Anh-Huy","family":"Phan","sequence":"first","affiliation":[]},{"given":"Konstantin","family":"Sobolev","sequence":"additional","affiliation":[]},{"given":"Konstantin","family":"Sozykin","sequence":"additional","affiliation":[]},{"given":"Dmitry","family":"Ermilov","sequence":"additional","affiliation":[]},{"given":"Julia","family":"Gusak","sequence":"additional","affiliation":[]},{"given":"Petr","family":"Tichavsk\u00fd","sequence":"additional","affiliation":[]},{"given":"Valeriy","family":"Glukhov","sequence":"additional","affiliation":[]},{"given":"Ivan","family":"Oseledets","sequence":"additional","affiliation":[]},{"given":"Andrzej","family":"Cichocki","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,7]]},"reference":[{"key":"31_CR1","doi-asserted-by":"publisher","unstructured":"Astrid, M., Lee, S.: CP-decomposition with tensor power method for convolutional neural networks compression. In: 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017, Jeju Island, South Korea, 13\u201316 February 2017, pp. 115\u2013118. IEEE (2017). https:\/\/doi.org\/10.1109\/BIGCOMP.2017.7881725","DOI":"10.1109\/BIGCOMP.2017.7881725"},{"key":"31_CR2","unstructured":"Bulat, A., Kossaifi, J., Tzimiropoulos, G., Pantic, M.: Matrix and tensor decompositions for training binary neural networks. arXiv preprint arXiv:1904.07852 (2019)"},{"key":"31_CR3","doi-asserted-by":"crossref","unstructured":"Bulat, A., Kossaifi, J., Tzimiropoulos, G., Pantic, M.: Incremental multi-domain learning with network latent tensor factorization. In: AAAI (2020)","DOI":"10.1609\/aaai.v34i07.6617"},{"key":"31_CR4","unstructured":"Chen, T., Lin, J., Lin, T., Han, S., Wang, C., Zhou, D.: Adaptive mixture of low-rank factorizations for compact neural modeling. In: CDNNRIA Workshop, NIPS (2018)"},{"key":"31_CR5","doi-asserted-by":"crossref","unstructured":"Cichocki, A., Lee, N., Oseledets, I., Phan, A.H., Zhao, Q., Mandic, D.P.: Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions. Found. Trends$$\\textregistered $$ Mach. Learn. 9(4\u20135), 249\u2013429 (2016)","DOI":"10.1561\/2200000059"},{"key":"31_CR6","doi-asserted-by":"crossref","unstructured":"De Lathauwer, L.: Decompositions of a higher-order tensor in block terms \u2013 Part I and II. SIAM J. Matrix Anal. Appl. 30(3), 1022\u20131066 (2008). http:\/\/publi-etis.ensea.fr\/2008\/De08e. special Issue on Tensor Decompositions and Applications","DOI":"10.1137\/070690729"},{"key":"31_CR7","doi-asserted-by":"crossref","unstructured":"De Lathauwer, L., De Moor, B., Vandewalle, J.: On the best rank-1 and rank-(R1, R2,., RN) approximation of higher-order tensors. SIAM J. Matrix Anal. Appl. 21, 1324\u20131342 (2000)","DOI":"10.1137\/S0895479898346995"},{"key":"31_CR8","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"31_CR9","unstructured":"Denil, M., Shakibi, B., Dinh, L., Ranzato, M., de Freitas, N.: Predicting parameters in deep learning. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2, NIPS 2013, pp. 2148\u20132156. Curran Associates Inc. (2013)"},{"key":"31_CR10","unstructured":"Denton, E.L., Zaremba, W., Bruna, J., LeCun, Y., Fergus, R.: Exploiting linear structure within convolutional networks for efficient evaluation. In: Advances in Neural Information Processing Systems, vol. 27, pp. 1269\u20131277. Curran Associates, Inc. (2014)"},{"issue":"6","key":"31_CR11","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1007\/s00791-012-0183-y","volume":"14","author":"M Espig","year":"2011","unstructured":"Espig, M., Hackbusch, W., Handschuh, S., Schneider, R.: Optimization problems in contracted tensor networks. Comput. Vis. Sci. 14(6), 271\u2013285 (2011)","journal-title":"Comput. Vis. Sci."},{"key":"31_CR12","unstructured":"Figurnov, M., Ibraimova, A., Vetrov, D.P., Kohli, P.: PerforatedCNNs: acceleration through elimination of redundant convolutions. In: Advances in Neural Information Processing Systems, pp. 947\u2013955 (2016)"},{"key":"31_CR13","unstructured":"Gao, X., Zhao, Y., Dudziak, \u0141., Mullins, R., Xu, C.Z.: Dynamic channel pruning: feature boosting and suppression. In: International Conference on Learning Representations (2019)"},{"key":"31_CR14","doi-asserted-by":"crossref","unstructured":"Gusak, J., et al.: Automated multi-stage compression of neural networks. In: 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 2501\u20132508 (2019)","DOI":"10.1109\/ICCVW.2019.00306"},{"key":"31_CR15","unstructured":"Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 28, pp. 1135\u20131143 (2015)"},{"key":"31_CR16","unstructured":"Handschuh, S.: Numerical Methods in Tensor Networks. Ph.D. thesis, Faculty of Mathematics and Informatics, University Leipzig, Germany, Leipzig, Germany (2015)"},{"key":"31_CR17","unstructured":"Harshman, R.A.: Foundations of the PARAFAC procedure: models and conditions for an \u201cexplanatory\u201d multimodal factor analysis. In: UCLA Working Papers in Phonetics, vol. 16 pp. 1\u201384 (1970)"},{"key":"31_CR18","unstructured":"Harshman, R.A.: The problem and nature of degenerate solutions or decompositions of 3-way arrays. In: Tensor Decomposition Workshop, Palo Alto, CA (2004)"},{"key":"31_CR19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"31_CR20","doi-asserted-by":"crossref","unstructured":"He, Y., Kang, G., Dong, X., Fu, Y., Yang, Y.: Soft filter pruning for accelerating deep convolutional neural networks. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, pp. 2234\u20132240 (7 2018)","DOI":"10.24963\/ijcai.2018\/309"},{"key":"31_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1007\/978-3-030-01234-2_48","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y He","year":"2018","unstructured":"He, Y., Lin, J., Liu, Z., Wang, H., Li, L.-J., Han, S.: AMC: AutoML for model compression and acceleration on mobile devices. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 815\u2013832. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_48"},{"issue":"6","key":"31_CR22","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1145\/2512329","volume":"60","author":"CJ Hillar","year":"2013","unstructured":"Hillar, C.J., Lim, L.H.: Most tensor problems are NP-hard. J. ACM (JACM) 60(6), 45 (2013)","journal-title":"J. ACM (JACM)"},{"key":"31_CR23","doi-asserted-by":"crossref","unstructured":"Howard, A., et al.: Searching for MobileNetv3. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1314\u20131324 (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"key":"31_CR24","unstructured":"Hua, W., Zhou, Y., De Sa, C.M., Zhang, Z., Suh, G.E.: Channel gating neural networks. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9 Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, pp. 1886\u20131896 (2019)"},{"issue":"2","key":"31_CR25","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/s00365-011-9131-1","volume":"34","author":"B Khoromskij","year":"2011","unstructured":"Khoromskij, B.: $$O(d \\log N) $$-quantics approximation of $$N$$-$$d$$ tensors in high-dimensional numerical modeling. Constr. Approximation 34(2), 257\u2013280 (2011)","journal-title":"Constr. Approximation"},{"key":"31_CR26","unstructured":"Kim, Y., Park, E., Yoo, S., Choi, T., Yang, L., Shin, D.: Compression of deep convolutional neural networks for fast and low power mobile applications. In: 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, 2\u20134 May 2016, Conference Track Proceedings (2016). http:\/\/arxiv.org\/abs\/1511.06530"},{"key":"31_CR27","doi-asserted-by":"crossref","unstructured":"Kossaifi, J., Bulat, A., Tzimiropoulos, G., Pantic, M.: T-net: parametrizing fully convolutional nets with a single high-order tensor. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7822\u20137831 (2019)","DOI":"10.1109\/CVPR.2019.00801"},{"key":"31_CR28","doi-asserted-by":"crossref","unstructured":"Kossaifi, J., Toisoul, A., Bulat, A., Panagakis, Y., Hospedales, T.M., Pantic, M.: Factorized higher-order CNNs with an application to spatio-temporal emotion estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6060\u20136069 (2020)","DOI":"10.1109\/CVPR42600.2020.00610"},{"key":"31_CR29","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1007\/s11336-008-9056-1","volume":"73","author":"W Krijnen","year":"2008","unstructured":"Krijnen, W., Dijkstra, T., Stegeman, A.: On the non-existence of optimal solutions and the occurrence of \u201cdegeneracy\u201d in the CANDECOMP\/PARAFAC model. Psychometrika 73, 431\u2013439 (2008)","journal-title":"Psychometrika"},{"key":"31_CR30","unstructured":"Krizhevsky, A.: Learning multiple layers of features from tiny images. Technical Report TR-2009, University of Toronto, Toronto (2009)"},{"key":"31_CR31","volume-title":"Tensors: Geometry and Applications","author":"JM Landsberg","year":"2012","unstructured":"Landsberg, J.M.: Tensors: Geometry and Applications, vol. 128. American Mathematical Society, Providence (2012)"},{"key":"31_CR32","doi-asserted-by":"crossref","unstructured":"Lebedev, V.: Algorithms for speeding up convolutional neural networks. Ph.D. thesis, Skoltech, Russia (2018). https:\/\/www.skoltech.ru\/app\/data\/uploads\/2018\/10\/Thesis-Final.pdf","DOI":"10.24425\/bpas.2018.125927"},{"key":"31_CR33","unstructured":"Lebedev, V., Ganin, Y., Rakhuba, M., Oseledets, I., Lempitsky, V.: Speeding-up convolutional neural networks using fine-tuned CP-decomposition. In: International Conference on Learning Representations (2015)"},{"issue":"7\u20138","key":"31_CR34","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1002\/cem.1244","volume":"23","author":"LH Lim","year":"2009","unstructured":"Lim, L.H., Comon, P.: Nonnegative approximations of nonnegative tensors. J. Chemom. 23(7\u20138), 432\u2013441 (2009)","journal-title":"J. Chemom."},{"key":"31_CR35","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1002\/cem.1180080207","volume":"8","author":"BC Mitchell","year":"1994","unstructured":"Mitchell, B.C., Burdick, D.S.: Slowly converging PARAFAC sequences: Swamps and two-factor degeneracies. J. Chemom. 8, 155\u2013168 (1994)","journal-title":"J. Chemom."},{"key":"31_CR36","unstructured":"Molchanov, D., Ashukha, A., Vetrov, D.: Variational dropout sparsifies deep neural networks. In: Proceedings of the 34th International Conference on Machine Learning - Volume 70, pp. 2498\u20132507 (2017). JMLR.org"},{"key":"31_CR37","unstructured":"Novikov, A., Podoprikhin, D., Osokin, A., Vetrov, D.: Tensorizing neural networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, NIPS 2015, pp. 442\u2013450. MIT Press, Cambridge (2015)"},{"issue":"5","key":"31_CR38","doi-asserted-by":"publisher","first-page":"3744","DOI":"10.1137\/090748330","volume":"31","author":"I Oseledets","year":"2009","unstructured":"Oseledets, I., Tyrtyshnikov, E.: Breaking the curse of dimensionality, or how to use SVD in many dimensions. SIAM J. Sci. Comput. 31(5), 3744\u20133759 (2009)","journal-title":"SIAM J. Sci. Comput."},{"issue":"3","key":"31_CR39","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1002\/1099-128X(200005\/06)14:3<285::AID-CEM584>3.0.CO;2-1","volume":"14","author":"P Paatero","year":"2000","unstructured":"Paatero, P.: Construction and analysis of degenerate PARAFAC models. J. Chemometrics 14(3), 285\u2013299 (2000)","journal-title":"J. Chemometrics"},{"key":"31_CR40","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2956926","author":"AH Phan","year":"2020","unstructured":"Phan, A.H., Cichocki, A., Uschmajew, A., Tichavsk\u00fd, P., Luta, G., Mandic, D.: Tensor networks for latent variable analysis: novel algorithms for tensor train approximation. IEEE Trans. Neural Network Learn. Syst. (2020). https:\/\/doi.org\/10.1109\/TNNLS.2019.2956926","journal-title":"IEEE Trans. Neural Network Learn. Syst."},{"key":"31_CR41","doi-asserted-by":"crossref","unstructured":"Phan, A.H., Tichavsk\u00fd, P., Cichocki, A.: Tensor deflation for CANDECOMP\/PARAFAC. Part 1: alternating subspace update algorithm. IEEE Trans. Sig. Process. 63(12), 5924\u20135938 (2015)","DOI":"10.1109\/TSP.2015.2458785"},{"issue":"5","key":"31_CR42","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1109\/TSP.2018.2887192","volume":"67","author":"AH Phan","year":"2019","unstructured":"Phan, A.H., Tichavsk\u00fd, P., Cichocki, A.: Error preserving correction: a method for CP decomposition at a target error bound. IEEE Trans. Signal Process. 67(5), 1175\u20131190 (2019)","journal-title":"IEEE Trans. Signal Process."},{"key":"31_CR43","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04191-z","author":"AH Phan","year":"2020","unstructured":"Phan, A.H., Yamagishi, M., Mandic, D., Cichocki, A.: Quadratic programming over ellipsoids with applications to constrained linear regression and tensor decomposition. Neural Comput. Appl. (2020). https:\/\/doi.org\/10.1007\/s00521-019-04191-z","journal-title":"Neural Comput. Appl."},{"key":"31_CR44","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/978-3-319-46493-0_32","volume-title":"Computer Vision \u2013 ECCV 2016","author":"M Rastegari","year":"2016","unstructured":"Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: XNOR-Net: ImageNet classification using binary convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 525\u2013542. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_32"},{"issue":"2","key":"31_CR45","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/S0169-7439(97)00033-6","volume":"38","author":"W Rayens","year":"1997","unstructured":"Rayens, W., Mitchell, B.: Two-factor degeneracies and a stabilization of PARAFAC. Chemometr. Intell. Lab. Syst. 38(2), 173\u2013181 (1997)","journal-title":"Chemometr. Intell. Lab. Syst."},{"key":"31_CR46","doi-asserted-by":"crossref","unstructured":"Rigamonti, R., Sironi, A., Lepetit, V., Fua, P.: Learning separable filters. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. pp. 2754\u20132761. CVPR \u201913, IEEE Computer Society, Washington, DC, USA (2013)","DOI":"10.1109\/CVPR.2013.355"},{"key":"31_CR47","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1137\/06066518X","volume":"30","author":"V de Silva","year":"2008","unstructured":"de Silva, V., Lim, L.H.: Tensor rank and the ill-posedness of the best low-rank approximation problem. SIAM J. Matrix Anal. Appl. 30, 1084\u20131127 (2008)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"31_CR48","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations, ICLR (2015)"},{"issue":"7","key":"31_CR49","doi-asserted-by":"publisher","first-page":"1276","DOI":"10.1016\/j.laa.2010.06.027","volume":"433","author":"A Stegeman","year":"2010","unstructured":"Stegeman, A., Comon, P.: Subtracting a best rank-1 approximation may increase tensor rank. Linear Algebra Appl. 433(7), 1276\u20131300 (2010)","journal-title":"Linear Algebra Appl."},{"key":"31_CR50","unstructured":"Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. In: ICML (2019)"},{"issue":"11","key":"31_CR51","doi-asserted-by":"publisher","first-page":"1653","DOI":"10.1109\/LSP.2019.2943060","volume":"26","author":"P Tichavsk\u00fd","year":"2019","unstructured":"Tichavsk\u00fd, P., Phan, A.H., Cichocki, A.: Sensitivity in tensor decomposition. IEEE Signal Process. Lett. 26(11), 1653\u20131657 (2019)","journal-title":"IEEE Signal Process. Lett."},{"key":"31_CR52","first-page":"122","volume":"15","author":"LR Tucker","year":"1963","unstructured":"Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change. Probl. Measuring Change 15, 122\u2013137 (1963)","journal-title":"Probl. Measuring Change"},{"key":"31_CR53","doi-asserted-by":"publisher","unstructured":"Vasilescu, M.A.O., Terzopoulos, D.: Multilinear subspace analysis of image ensembles. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2003), Madison, WI, USA, 16\u201322 June 2003, pp. 93\u201399. IEEE Computer Society (2003). https:\/\/doi.org\/10.1109\/CVPR.2003.1211457","DOI":"10.1109\/CVPR.2003.1211457"},{"key":"31_CR54","unstructured":"Vervliet, N., Debals, O., Sorber, L., Barel, M.V., Lathauwer, L.D.: Tensorlab 3.0, March 2016. http:\/\/www.tensorlab.net"},{"key":"31_CR55","unstructured":"Wang, D., Zhao, G., Li, G., Deng, L., Wu, Y.: Lossless compression for 3DCNNs based on tensor train decomposition. CoRR abs\/1912.03647 (2019). http:\/\/arxiv.org\/abs\/1912.03647"},{"key":"31_CR56","doi-asserted-by":"crossref","unstructured":"Zacharov, I., et al.: Zhores \u2013 petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in Skolkovo Institute of Science and Technology. Open Eng. 9(1) (2019)","DOI":"10.1515\/eng-2019-0059"},{"key":"31_CR57","doi-asserted-by":"publisher","unstructured":"Zhang, T., Golub, G.H.: Rank-one approximation to high order tensors. SIAM J. Matrix Anal. Appl. 23(2), 534\u2013550 (2001). https:\/\/doi.org\/10.1137\/S0895479899352045","DOI":"10.1137\/S0895479899352045"},{"issue":"10","key":"31_CR58","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.1109\/TPAMI.2015.2502579","volume":"38","author":"X Zhang","year":"2016","unstructured":"Zhang, X., Zou, J., He, K., Sun, J.: Accelerating very deep convolutional networks for classification and detection. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 1943\u20131955 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"31_CR59","unstructured":"Zhuang, Z., et al.: Discrimination-aware channel pruning for deep neural networks. In: Advances in Neural Information Processing Systems, pp. 883\u2013894 (2018)"}],"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\/978-3-030-58526-6_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T00:20:08Z","timestamp":1728174008000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58526-6_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585259","9783030585266"],"references-count":59,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58526-6_31","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":"7 October 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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}