{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T13:33:05Z","timestamp":1790775185120,"version":"4.1.0"},"publisher-location":"Cham","reference-count":85,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585167","type":"print"},{"value":"9783030585174","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\/978-3-030-58517-4_24","type":"book-chapter","created":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T19:03:11Z","timestamp":1602270191000},"page":"404-421","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":87,"title":["An Ensemble of Epoch-Wise Empirical Bayes for Few-Shot Learning"],"prefix":"10.1007","author":[{"given":"Yaoyao","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bernt","family":"Schiele","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianru","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,10,10]]},"reference":[{"key":"24_CR1","unstructured":"Antoniou, A., Edwards, H., Storkey, A.: How to train your MAML. In: ICLR (2019)"},{"key":"24_CR2","doi-asserted-by":"crossref","unstructured":"Bart, E., Ullman, S.: Cross-generalization: learning novel classes from a single example by feature replacement. In: CVPR, pp. 672\u2013679 (2005)","DOI":"10.1109\/CVPR.2005.117"},{"issue":"8","key":"24_CR3","doi-asserted-by":"publisher","first-page":"1889","DOI":"10.1162\/089976600300015187","volume":"12","author":"Y Bengio","year":"2000","unstructured":"Bengio, Y.: Gradient-based optimization of hyperparameters. Neural Comput. 12(8), 1889\u20131900 (2000)","journal-title":"Neural Comput."},{"key":"24_CR4","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281\u2013305 (2012)","journal-title":"J. Mach. Learn. Res."},{"issue":"518","key":"24_CR5","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1080\/01621459.2017.1285773","volume":"112","author":"DM Blei","year":"2017","unstructured":"Blei, D.M., Kucukelbir, A., McAuliffe, J.D.: Variational inference: a review for statisticians. J. Am. Stat. Assoc. 112(518), 859\u2013877 (2017)","journal-title":"J. Am. Stat. Assoc."},{"issue":"1","key":"24_CR6","first-page":"49","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L.: Stacked regressions. Mach. Learn. 24(1), 49\u201364 (1996)","journal-title":"Mach. Learn."},{"key":"24_CR7","unstructured":"Caruana, R.: Learning many related tasks at the same time with backpropagation. In: NIPS, pp. 657\u2013664 (1995)"},{"key":"24_CR8","unstructured":"Chen, W.Y., Liu, Y.C., Kira, Z., Wang, Y.C., Huang, J.B.: A closer look at few-shot classification. In: ICLR (2019)"},{"issue":"9","key":"24_CR9","doi-asserted-by":"publisher","first-page":"4594","DOI":"10.1109\/TIP.2019.2910052","volume":"28","author":"Z Chen","year":"2019","unstructured":"Chen, Z., Fu, Y., Zhang, Y., Jiang, Y., Xue, X., Sigal, L.: Multi-level semantic feature augmentation for one-shot learning. IEEE Trans. Image Process. 28(9), 4594\u20134605 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"24_CR10","unstructured":"Domke, J.: Generic methods for optimization-based modeling. In: AISTATS, pp. 318\u2013326 (2012)"},{"key":"24_CR11","unstructured":"Dvornik, N., Schmid, C., Julien, M.: f-VAEGAN-D2: A feature generating framework for any-shot learning. In: ICCV, pp. 10275\u201310284 (2019)"},{"key":"24_CR12","doi-asserted-by":"crossref","unstructured":"Dvornik, N., Schmid, C., Mairal, J.: Diversity with cooperation: Ensemble methods for few-shot classification. In: ICCV, pp. 3722\u20133730 (2019)","DOI":"10.1109\/ICCV.2019.00382"},{"key":"24_CR13","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: ICML, pp. 1126\u20131135 (2017)"},{"key":"24_CR14","unstructured":"Finn, C., Xu, K., Levine, S.: Probabilistic model-agnostic meta-learning. In: NeurIPS, pp. 9537\u20139548 (2018)"},{"key":"24_CR15","unstructured":"Franceschi, L., Frasconi, P., Salzo, S., Grazzi, R., Pontil, M.: Bilevel programming for hyperparameter optimization and meta-learning. In: ICML, pp. 1563\u20131572 (2018)"},{"issue":"1","key":"24_CR16","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119\u2013139 (1997)","journal-title":"J. Comput. Syst. Sci."},{"issue":"4","key":"24_CR17","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","volume":"38","author":"JH Friedman","year":"2002","unstructured":"Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38(4), 367\u2013378 (2002)","journal-title":"Comput. Stat. Data Anal."},{"key":"24_CR18","unstructured":"Geoffrey, H.E., David, P.C.: Using fast weights to deblur old memories. In: CogSci, pp. 177\u2013186 (1987)"},{"key":"24_CR19","unstructured":"Girija, S.S.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. 39 (2016). tensorflow.org"},{"key":"24_CR20","unstructured":"Grant, E., Finn, C., Levine, S., Darrell, T., Griffiths, T.L.: Recasting gradient-based meta-learning as hierarchical Bayes. In: ICLR (2018)"},{"key":"24_CR21","doi-asserted-by":"crossref","unstructured":"He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., Li, M.: Bag of tricks for image classification with convolutional neural networks. In: CVPR, pp. 558\u2013567 (2019)","DOI":"10.1109\/CVPR.2019.00065"},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Ho, T.K.: Random decision forests. In: ICDAR, vol. 1, pp. 278\u2013282 (1995)","DOI":"10.1109\/ICDAR.1995.598994"},{"issue":"1","key":"24_CR23","first-page":"1303","volume":"14","author":"MD Hoffman","year":"2013","unstructured":"Hoffman, M.D., Blei, D.M., Wang, C., Paisley, J.: Stochastic variational inference. J. Mach. Learn. Res. 14(1), 1303\u20131347 (2013)","journal-title":"J. Mach. Learn. Res."},{"key":"24_CR24","unstructured":"Hou, R., Chang, H., Bingpeng, M., Shan, S., Chen, X.: Cross attention network for few-shot classification. In: NeurIPS, pp. 4005\u20134016 (2019)"},{"key":"24_CR25","unstructured":"Hu, S.X., et al.: Empirical Bayes meta-learning with synthetic gradients. In: ICLR (2020)"},{"key":"24_CR26","unstructured":"Huang, G., Li, Y., Pleiss, G., Liu, Z., Hopcroft, J.E., Weinberger, K.Q.: Snapshot ensembles: Train 1, get M for free. In: ICLR (2017)"},{"key":"24_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/978-3-642-25566-3_40","volume-title":"Learning and Intelligent Optimization","author":"F Hutter","year":"2011","unstructured":"Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 507\u2013523. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-25566-3_40"},{"key":"24_CR28","unstructured":"Jaderberg, M., et al.: Population based training of neural networks. arXiv:1711.09846 (2017)"},{"issue":"15","key":"24_CR29","doi-asserted-by":"publisher","first-page":"2800","DOI":"10.1080\/02664763.2018.1441383","volume":"45","author":"C Ju","year":"2018","unstructured":"Ju, C., Bibaut, A., van der Laan, M.: The relative performance of ensemble methods with deep convolutional neural networks for image classification. J. Appl. Stat. 45(15), 2800\u20132818 (2018)","journal-title":"J. Appl. Stat."},{"key":"24_CR30","doi-asserted-by":"crossref","unstructured":"Jung, H.G., Lee, S.W.: Few-shot learning with geometric constraints. IEEE Trans. Neural Netw. Learn. Syst. (2020)","DOI":"10.1109\/TNNLS.2019.2957187"},{"key":"24_CR31","doi-asserted-by":"crossref","unstructured":"Kim, J., Kim, T., Kim, S., Yoo, C.D.: Edge-labeling graph neural network for few-shot learning. In: CVPR, pp. 11\u201320 (2019)","DOI":"10.1109\/CVPR.2019.00010"},{"key":"24_CR32","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. In: ICLR (2014)"},{"key":"24_CR33","unstructured":"Krizhevsky, A.: Learning multiple layers of features from tiny images. University of Toronto (2009)"},{"key":"24_CR34","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1097\u20131105 (2012)"},{"issue":"2","key":"24_CR35","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1023\/A:1022859003006","volume":"51","author":"LI Kuncheva","year":"2003","unstructured":"Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 51(2), 181\u2013207 (2003)","journal-title":"Mach. Learn."},{"key":"24_CR36","unstructured":"Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. In: ICLR (2017)"},{"key":"24_CR37","doi-asserted-by":"crossref","unstructured":"Lee, K., Maji, S., Ravichandran, A., Soatto, S.: Meta-learning with differentiable convex optimization. In: CVPR, pp. 10657\u201310665 (2019)","DOI":"10.1109\/CVPR.2019.01091"},{"key":"24_CR38","unstructured":"Lee, Y., Choi, S.: Gradient-based meta-learning with learned layerwise metric and subspace. In: ICML, pp. 2933\u20132942 (2018)"},{"issue":"4","key":"24_CR39","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1109\/TPAMI.2006.79","volume":"28","author":"F Li","year":"2006","unstructured":"Li, F., Fergus, R., Perona, P.: One-shot learning of object categories. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 594\u2013611 (2006)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"24_CR40","doi-asserted-by":"crossref","unstructured":"Li, H., Eigen, D., Dodge, S., Zeiler, M., Wang, X.: Finding task-relevant features for few-shot learning by category traversal. In: CVPR, pp. 1\u201310 (2019)","DOI":"10.1109\/CVPR.2019.00009"},{"key":"24_CR41","unstructured":"Li, H., Dong, W., Mei, X., Ma, C., Huang, F., Hu, B.: LGM-Net: learning to generate matching networks for few-shot learning. In: ICML, pp. 3825\u20133834 (2019)"},{"key":"24_CR42","first-page":"185:1","volume":"18","author":"L Li","year":"2017","unstructured":"Li, L., Jamieson, K.G., DeSalvo, G., Rostamizadeh, A., Talwalkar, A.: Hyperband: a novel bandit-based approach to hyperparameter optimization. J. Mach. Learn. Res. 18, 185:1\u2013185:52 (2017)","journal-title":"J. Mach. Learn. Res."},{"key":"24_CR43","unstructured":"Li, X., et al.: Learning to self-train for semi-supervised few-shot classification. In: NeurIPS, pp. 10276\u201310286 (2019)"},{"key":"24_CR44","unstructured":"Li, Z., Zhou, F., Chen, F., Li, H.: Meta-SGD: learning to learn quickly for few shot learning. arXiv:1707.09835 (2017)"},{"key":"24_CR45","unstructured":"Liu, Y., Lee, J., Park, M., Kim, S., Yang, Y.: Learning to propagate labels: transductive propagation network for few-shot learning. In: ICLR (2019)"},{"key":"24_CR46","doi-asserted-by":"crossref","unstructured":"Liu, Y., Su, Y., Liu, A.A., Schiele, B., Sun, Q.: Mnemonics training: multi-class incremental learning without forgetting. In: CVPR, pp. 12245\u201312254 (2020)","DOI":"10.1109\/CVPR42600.2020.01226"},{"key":"24_CR47","unstructured":"Luketina, J., Raiko, T., Berglund, M., Greff, K.: Scalable gradient-based tuning of continuous regularization hyperparameters. In: ICML, pp. 2952\u20132960 (2016)"},{"key":"24_CR48","unstructured":"Maclaurin, D., Duvenaud, D.K., Adams, R.P.: Gradient-based hyperparameter optimization through reversible learning. In: ICML, pp. 2113\u20132122 (2015)"},{"key":"24_CR49","unstructured":"Metz, L., Maheswaranathan, N., Cheung, B., Sohl-Dickstein, J.: Meta-learning update rules for unsupervised representation learning. In: ICLR (2019)"},{"key":"24_CR50","unstructured":"Mishra, N., Rohaninejad, M., Chen, X., Abbeel, P.: Snail: a simple neural attentive meta-learner. In: ICLR (2018)"},{"key":"24_CR51","volume-title":"Machine Learning","author":"T Mitchell","year":"1997","unstructured":"Mitchell, T.: Machine Learning. Mcgraw-Hill Higher Education, New York (1997)"},{"key":"24_CR52","unstructured":"Munkhdalai, T., Yu, H.: Meta networks. In: ICML, pp. 2554\u20132563 (2017)"},{"key":"24_CR53","unstructured":"Oreshkin, B.N., Rodr\u00edguez, P., Lacoste, A.: TADAM: task dependent adaptive metric for improved few-shot learning. In: NeurIPS, pp. 719\u2013729 (2018)"},{"key":"24_CR54","unstructured":"Ozay, M., Vural, F.T.Y.: A new fuzzy stacked generalization technique and analysis of its performance. arXiv:1204.0171 (2012)"},{"key":"24_CR55","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: NeurIPS, pp. 8024\u20138035 (2019)"},{"key":"24_CR56","doi-asserted-by":"crossref","unstructured":"Qiao, S., Liu, C., Shen, W., Yuille, A.L.: Few-shot image recognition by predicting parameters from activations. In: CVPR, pp. 7229\u20137238 (2018)","DOI":"10.1109\/CVPR.2018.00755"},{"key":"24_CR57","unstructured":"Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning. In: ICLR (2017)"},{"key":"24_CR58","unstructured":"Ren, M., et al.: Meta-learning for semi-supervised few-shot classification. In: ICLR (2018)"},{"key":"24_CR59","unstructured":"Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. In: ICML, pp. 1278\u20131286 (2014)"},{"issue":"3","key":"24_CR60","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"24_CR61","unstructured":"Rusu, A.A., et al.: Meta-learning with latent embedding optimization. In: ICLR (2019)"},{"key":"24_CR62","unstructured":"Satorras, V.G., Estrach, J.B.: Few-shot learning with graph neural networks. In: ICLR (2018)"},{"issue":"1\u20132","key":"24_CR63","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1023\/A:1007511322260","volume":"36","author":"P Smyth","year":"1999","unstructured":"Smyth, P., Wolpert, D.: Linearly combining density estimators via stacking. Mach. Learn. 36(1\u20132), 59\u201383 (1999)","journal-title":"Mach. Learn."},{"key":"24_CR64","unstructured":"Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few-shot learning. In: NIPS, pp. 4077\u20134087 (2017)"},{"key":"24_CR65","unstructured":"Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: NIPS, pp. 2951\u20132959 (2012)"},{"key":"24_CR66","unstructured":"Snoek, J., et al.: Scalable Bayesian optimization using deep neural networks. In: ICML, pp. 2171\u20132180 (2015)"},{"key":"24_CR67","unstructured":"Snoek, J., Swersky, K., Zemel, R.S., Adams, R.P.: Input warping for Bayesian optimization of non-stationary functions. In: ICML, pp. 1674\u20131682 (2014)"},{"key":"24_CR68","unstructured":"Sollich, P., Krogh, A.: Learning with ensembles: how overfitting can be useful. In: NIPS, pp. 190\u2013196 (1996)"},{"key":"24_CR69","unstructured":"Sun, Q., Liu, Y., Chen, Z., Chua, T., Schiele, B.: Meta-transfer learning through hard tasks. arXiv:1910.03648 (2019)"},{"key":"24_CR70","doi-asserted-by":"crossref","unstructured":"Sun, Q., Liu, Y., Chua, T.S., Schiele, B.: Meta-transfer learning for few-shot learning. In: CVPR, pp. 403\u2013412 (2019)","DOI":"10.1109\/CVPR.2019.00049"},{"key":"24_CR71","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H.S., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: CVPR, pp. 1199\u20131208 (2018)","DOI":"10.1109\/CVPR.2018.00131"},{"key":"24_CR72","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-1-4615-5529-2_1","volume-title":"Learning to Learn","author":"S Thrun","year":"1998","unstructured":"Thrun, S., Pratt, L.: Learning to learn: introduction and overview. In: Thrun, S., Pratt, L. (eds.) Learning to Learn, pp. 3\u201317. Springer, Boston (1998). https:\/\/doi.org\/10.1007\/978-1-4615-5529-2_1"},{"key":"24_CR73","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., Wierstra, D.: Matching networks for one shot learning. In: NIPS, pp. 3630\u20133638 (2016)"},{"key":"24_CR74","unstructured":"Wang, X., Huang, T.E., Darrell, T., Gonzalez, J.E., Yu, F.: Frustratingly simple few-shot object detection. In: ICML (2020)"},{"key":"24_CR75","doi-asserted-by":"crossref","unstructured":"Wang, Y., Girshick, R.B., Hebert, M., Hariharan, B.: Low-shot learning from imaginary data. In: CVPR, pp. 7278\u20137286 (2018)","DOI":"10.1109\/CVPR.2018.00760"},{"key":"24_CR76","unstructured":"Wang, Y.X., Hebert, M.: Learning from small sample sets by combining unsupervised meta-training with CNNs. In: NIPS, pp. 244\u2013252 (2016)"},{"key":"24_CR77","doi-asserted-by":"crossref","unstructured":"Xian, Y., Sharma, S., Schiele, B., Akata, Z.: f-VAEGAN-D2: a feature generating framework for any-shot learning. In: CVPR, pp. 10275\u201310284 (2019)","DOI":"10.1109\/CVPR.2019.01052"},{"key":"24_CR78","unstructured":"Ye, H.J., Hu, H., Zhan, D.C., Sha, F.: Learning embedding adaptation for few-shot learning. arXiv:1812.03664 (2018)"},{"key":"24_CR79","unstructured":"Yoon, J., Kim, T., Dia, O., Kim, S., Bengio, Y., Ahn, S.: Bayesian model-agnostic meta-learning. In: NeurIPS, pp. 7343\u20137353 (2018)"},{"key":"24_CR80","unstructured":"Zhang, C., Cai, Y., Lin, G., Shen, C.: DeepEMD: differentiable earth mover\u2019s distance for few-shot learning. arXiv:2003.06777 (2020)"},{"key":"24_CR81","doi-asserted-by":"crossref","unstructured":"Zhang, C., Cai, Y., Lin, G., Shen, C.: DeepEMD: few-shot image classification with differentiable earth mover\u2019s distance and structured classifiers. In: CVPR, pp. 12203\u201312213 (2020)","DOI":"10.1109\/CVPR42600.2020.01222"},{"key":"24_CR82","doi-asserted-by":"crossref","unstructured":"Zhang, C., Lin, G., Liu, F., Guo, J., Wu, Q., Yao, R.: Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation. In: ICCV, pp. 9587\u20139595 (2019)","DOI":"10.1109\/ICCV.2019.00968"},{"key":"24_CR83","doi-asserted-by":"crossref","unstructured":"Zhang, C., Lin, G., Liu, F., Yao, R., Shen, C.: CANet: class-agnostic segmentation networks with iterative refinement and attentive few-shot learning. In: CVPR, pp. 5217\u20135226 (2019)","DOI":"10.1109\/CVPR.2019.00536"},{"key":"24_CR84","unstructured":"Zhang, L., et al.: Nonlinear regression via deep negative correlation learning. IEEE Trans. Pattern Anal. Mach. Intell. (2019)"},{"key":"24_CR85","unstructured":"Zhang, R., Che, T., Grahahramani, Z., Bengio, Y., Song, Y.: MetaGAN: an adversarial approach to few-shot learning. In: NeurIPS, pp. 2371\u20132380 (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\/978-3-030-58517-4_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:13:04Z","timestamp":1728432784000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58517-4_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585167","9783030585174"],"references-count":85,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58517-4_24","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":"10 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"}]}}