{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:14:00Z","timestamp":1742919240791,"version":"3.40.3"},"publisher-location":"Cham","reference-count":63,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031250712"},{"type":"electronic","value":"9783031250729"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-25072-9_34","type":"book-chapter","created":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T08:40:04Z","timestamp":1676623204000},"page":"492-509","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Probing Contextual Diversity for\u00a0Dense Out-of-Distribution Detection"],"prefix":"10.1007","author":[{"given":"Silvio","family":"Galesso","sequence":"first","affiliation":[]},{"given":"Maria Alejandra","family":"Bravo","sequence":"additional","affiliation":[]},{"given":"Mehdi","family":"Naouar","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Brox","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,18]]},"reference":[{"key":"34_CR1","unstructured":"Abdar, M., et al.: A review of uncertainty quantification in deep learning: Techniques, applications and challenges. arXiv preprint arXiv:2011.06225 (2020)"},{"key":"34_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/978-3-030-11723-8_16","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"C Baur","year":"2019","unstructured":"Baur, C., Wiestler, B., Albarqouni, S., Navab, N.: Deep autoencoding models for unsupervised anomaly segmentation in brain MR images. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11383, pp. 161\u2013169. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11723-8_16"},{"key":"34_CR3","unstructured":"Bergman, L., Hoshen, Y.: Classification-based anomaly detection for general data. In: International Conference on Learning Representations (2019)"},{"key":"34_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/978-3-030-33676-9_3","volume-title":"Pattern Recognition","author":"P Bevandi\u0107","year":"2019","unstructured":"Bevandi\u0107, P., Kre\u0161o, I., Or\u0161i\u0107, M., \u0160egvi\u0107, S.: Simultaneous semantic segmentation and outlier detection in presence of domain shift. In: Fink, G.A., Frintrop, S., Jiang, X. (eds.) DAGM GCPR 2019. LNCS, vol. 11824, pp. 33\u201347. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33676-9_3"},{"key":"34_CR5","unstructured":"Blum, H., Sarlin, P.E., Nieto, J., Siegwart, R., Cadena, C.: The fishyscapes benchmark: Measuring blind spots in semantic segmentation. arXiv preprint arXiv:1904.03215 (2019)"},{"key":"34_CR6","unstructured":"Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural network. In: International Conference on Machine Learning, pp. 1613\u20131622. PMLR (2015)"},{"key":"34_CR7","doi-asserted-by":"crossref","unstructured":"Cen, J., Yun, P., Cai, J., Wang, M.Y., Liu, M.: Deep metric learning for open world semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15333\u201315342 (2021)","DOI":"10.1109\/ICCV48922.2021.01505"},{"key":"34_CR8","unstructured":"Chan, R., et al.: SegMentMeifYouCan: A benchmark for anomaly segmentation (2021)"},{"key":"34_CR9","unstructured":"Chan, R., Rottmann, M., Gottschalk, H.: Entropy maximization and meta classification for out-of-distribution detection in semantic segmentation. CoRR abs\/2012.06575 (2020). https:\/\/arxiv.org\/abs\/2012.06575"},{"issue":"4","key":"34_CR10","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"34_CR11","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)"},{"key":"34_CR12","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: European Conference on Computer Vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"34_CR13","doi-asserted-by":"crossref","unstructured":"Cheng, B., et al.: Panoptic-DeepLab: a simple, strong, and fast baseline for bottom-up panoptic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12475\u201312485 (2020)","DOI":"10.1109\/CVPR42600.2020.01249"},{"key":"34_CR14","unstructured":"Cohen, N., Hoshen, Y.: Sub-image anomaly detection with deep pyramid correspondences. arXiv preprint arXiv:2005.02357 (2020)"},{"key":"34_CR15","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"34_CR16","doi-asserted-by":"crossref","unstructured":"Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233\u2013240 (2006)","DOI":"10.1145\/1143844.1143874"},{"key":"34_CR17","doi-asserted-by":"crossref","unstructured":"Di Biase, G., Blum, H., Siegwart, R., Cadena, C.: Pixel-wise anomaly detection in complex driving scenes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16918\u201316927 (2021)","DOI":"10.1109\/CVPR46437.2021.01664"},{"key":"34_CR18","unstructured":"Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: an open urban driving simulator. In: Conference on Robot Learning, pp. 1\u201316. PMLR (2017)"},{"key":"34_CR19","unstructured":"Franchi, G., Bursuc, A., Aldea, E., Dubuisson, S., Bloch, I.: One versus all for deep neural network incertitude (OVNNI) quantification. CoRR abs\/2006.00954 (2020). https:\/\/arxiv.org\/abs\/2006.00954"},{"key":"34_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/978-3-030-58520-4_7","volume-title":"Computer Vision \u2013 ECCV 2020","author":"G Franchi","year":"2020","unstructured":"Franchi, G., Bursuc, A., Aldea, E., Dubuisson, S., Bloch, I.: TRADI: tracking deep neural network weight distributions. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12362, pp. 105\u2013121. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58520-4_7"},{"key":"34_CR21","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: representing model uncertainty in deep learning. In: International Conference on Machine Learning, pp. 1050\u20131059. PMLR (2016)"},{"key":"34_CR22","unstructured":"Golan, I., El-Yaniv, R.: Deep anomaly detection using geometric transformations. In: Neural Information Processing Systems (NeurIPS) (2018)"},{"key":"34_CR23","doi-asserted-by":"crossref","unstructured":"Grci\u0107, M., Bevandi\u0107, P., \u0160egvi\u0107, S.: Dense open-set recognition with synthetic outliers generated by real NVP. arXiv preprint arXiv:2011.11094 (2020)","DOI":"10.5220\/0010260701330143"},{"key":"34_CR24","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: International Conference on Machine Learning, pp. 1321\u20131330. PMLR (2017)"},{"key":"34_CR25","doi-asserted-by":"crossref","unstructured":"Haselmann, M., Gruber, D.P., Tabatabai, P.: Anomaly detection using deep learning based image completion. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1237\u20131242. IEEE (2018)","DOI":"10.1109\/ICMLA.2018.00201"},{"key":"34_CR26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"34_CR27","doi-asserted-by":"crossref","unstructured":"Hein, M., Andriushchenko, M., Bitterwolf, J.: Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 41\u201350 (2019)","DOI":"10.1109\/CVPR.2019.00013"},{"key":"34_CR28","unstructured":"Hendrycks, D., Basart, S., Mazeika, M., Mostajabi, M., Steinhardt, J., Song, D.: Scaling out-of-distribution detection for real-world settings. arXiv preprint arXiv:1911.11132 (2019)"},{"key":"34_CR29","unstructured":"Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: Proceedings of International Conference on Learning Representations (2017)"},{"key":"34_CR30","unstructured":"Hendrycks, D., Mazeika, M., Dietterich, T.: Deep anomaly detection with outlier exposure. In: International Conference on Learning Representations (2018)"},{"key":"34_CR31","unstructured":"Hendrycks, D., Mazeika, M., Kadavath, S., Song, D.: Using self-supervised learning can improve model robustness and uncertainty. In: Advances in Neural Information Processing Systems (NeurIPS) (2019)"},{"key":"34_CR32","doi-asserted-by":"crossref","unstructured":"Ilg, E., et al.: Uncertainty estimates and multi-hypotheses networks for optical flow. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 652\u2013667 (2018)","DOI":"10.1007\/978-3-030-01234-2_40"},{"key":"34_CR33","doi-asserted-by":"crossref","unstructured":"Jung, S., Lee, J., Gwak, D., Choi, S., Choo, J.: Standardized max logits: a simple yet effective approach for identifying unexpected road obstacles in urban-scene segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 15425\u201315434 (October 2021)","DOI":"10.1109\/ICCV48922.2021.01514"},{"key":"34_CR34","doi-asserted-by":"crossref","unstructured":"Kendall, A., Badrinarayanan, V., Cipolla, R.: Bayesian SegNet: model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. In: British Machine Vision Conference 2017. BMVC 2017 (2017)","DOI":"10.5244\/C.31.57"},{"key":"34_CR35","unstructured":"Kirichenko, P., Izmailov, P., Wilson, A.G.: Why normalizing flows fail to detect out-of-distribution data. arXiv preprint arXiv:2006.08545 (2020)"},{"key":"34_CR36","doi-asserted-by":"crossref","unstructured":"Kong, S., Ramanan, D.: OpenGAN: open-set recognition via open data generation. arXiv preprint arXiv:2104.02939 (2021)","DOI":"10.1109\/ICCV48922.2021.00085"},{"key":"34_CR37","unstructured":"Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Neural Information Processing Systems (NeurIPS) (2017)"},{"key":"34_CR38","unstructured":"Lee, K., Lee, K., Lee, H., Shin, J.: A simple unified framework for detecting out-of-distribution samples and adversarial attacks. In: Advances in Neural Information Processing Systems. 31 (2018)"},{"key":"34_CR39","unstructured":"Lee, S., Purushwalkam, S., Cogswell, M., Crandall, D., Batra, D.: Why m heads are better than one: training a diverse ensemble of deep networks. arXiv preprint arXiv:1511.06314 (2015)"},{"key":"34_CR40","unstructured":"Li, H., Ng, J.Y.H., Natsev, P.: EnsembleNet: end-to-end optimization of multi-headed models. arXiv preprint arXiv:1905.09979 (2019)"},{"key":"34_CR41","unstructured":"Liang, S., Li, Y., Srikant, R.: Enhancing the reliability of out-of-distribution image detection in neural networks. In: International Conference on Learning Representations (2018)"},{"key":"34_CR42","doi-asserted-by":"publisher","unstructured":"Lis, K.M., Nakka, K.K., Fua, P., Salzmann, M.: Detecting the unexpected via image resynthesis. In: International Conference On Computer Vision (ICCV), pp. 2152\u20132161 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00224, http:\/\/infoscience.epfl.ch\/record\/269093","DOI":"10.1109\/ICCV.2019.00224"},{"key":"34_CR43","doi-asserted-by":"crossref","unstructured":"Liu, L., et al.: Deep neural network ensembles against deception: Ensemble diversity, accuracy and robustness. In: 2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), pp. 274\u2013282. IEEE (2019)","DOI":"10.1109\/MASS.2019.00040"},{"key":"34_CR44","unstructured":"Malinin, A., Mlodozeniec, B., Gales, M.: Ensemble distribution distillation. arXiv preprint arXiv:1905.00076 (2019)"},{"key":"34_CR45","unstructured":"Nalisnick, E., Matsukawa, A., Teh, Y.W., Gorur, D., Lakshminarayanan, B.: Do deep generative models know what they don\u2019t know? arXiv preprint arXiv:1810.09136 (2018)"},{"key":"34_CR46","unstructured":"Narayanan, A.R., Zela, A., Saikia, T., Brox, T., Hutter, F.: Multi-headed neural ensemble search. In: Workshop on Uncertainty and Robustness in Deep Learning (UDL@ICML2021) (2021)"},{"key":"34_CR47","unstructured":"Nguyen, D.T., Lou, Z., Klar, M., Brox, T.: Anomaly detection with multiple-hypotheses predictions. In: International Conference on Machine Learning, pp. 4800\u20134809. PMLR (2019)"},{"key":"34_CR48","unstructured":"Paszke, A., et al.: Automatic differentiation in pytorch. In: NIPS 2017 Workshop on Autodiff (NIPS-W) (2017)"},{"key":"34_CR49","doi-asserted-by":"crossref","unstructured":"Pinggera, P., Ramos, S., Gehrig, S., Franke, U., Rother, C., Mester, R.: Lost and found: detecting small road hazards for self-driving vehicles. In: 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1099\u20131106. IEEE (2016)","DOI":"10.1109\/IROS.2016.7759186"},{"key":"34_CR50","doi-asserted-by":"crossref","unstructured":"Rudolph, M., Wandt, B., Rosenhahn, B.: Same Same but DifferNet: semi-supervised defect detection with normalizing flows. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1907\u20131916 (2021)","DOI":"10.1109\/WACV48630.2021.00195"},{"key":"34_CR51","doi-asserted-by":"crossref","unstructured":"Schirrmeister, R., Zhou, Y., Ball, T., Zhang, D.: Understanding anomaly detection with deep invertible networks through hierarchies of distributions and features. In: Advances in Neural Information Processing Systems. 33 (2020)","DOI":"10.1007\/s00521-020-05091-3"},{"key":"34_CR52","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1007\/978-3-319-59050-9_12","volume-title":"Information Processing in Medical Imaging","author":"T Schlegl","year":"2017","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Niethammer, M. (ed.) IPMI 2017. LNCS, vol. 10265, pp. 146\u2013157. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_12"},{"key":"34_CR53","unstructured":"Smith, L., Gal, Y.: Understanding measures of uncertainty for adversarial example detection. arXiv preprint arXiv:1803.08533 (2018)"},{"key":"34_CR54","doi-asserted-by":"crossref","unstructured":"Vojir, T., Sipka, T., Aljundi, R., Chumerin, N., Reino, D.O., Matas, J.: Road anomaly detection by partial image reconstruction with segmentation coupling. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15651\u201315660 (2021)","DOI":"10.1109\/ICCV48922.2021.01536"},{"key":"34_CR55","doi-asserted-by":"crossref","unstructured":"Vyas, A., Jammalamadaka, N., Zhu, X., Das, D., Kaul, B., Willke, T.L.: Out-of-distribution detection using an ensemble of self supervised leave-out classifiers. In: Proceedings of the European Conference on Computer Vision (ECCV) (September 2018)","DOI":"10.1007\/978-3-030-01237-3_34"},{"key":"34_CR56","unstructured":"Winkens, J., et al.: Contrastive training for improved out-of-distribution detection. arXiv preprint arXiv:2007.05566 (2020)"},{"key":"34_CR57","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/978-3-030-58452-8_9","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Xia","year":"2020","unstructured":"Xia, Y., Zhang, Y., Liu, F., Shen, W., Yuille, A.L.: Synthesize then compare: detecting failures and anomalies for semantic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 145\u2013161. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_9"},{"key":"34_CR58","unstructured":"Yan, H., Zhang, C., Wu, M.: Lawin transformer: Improving semantic segmentation transformer with multi-scale representations via large window attention. CoRR abs\/2201.01615 (2022), https:\/\/arxiv.org\/abs\/2201.01615"},{"key":"34_CR59","doi-asserted-by":"crossref","unstructured":"Yoo, D., Park, S., Lee, J.Y., So Kweon, I.: Multi-scale pyramid pooling for deep convolutional representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 71\u201380 (2015)","DOI":"10.1109\/CVPRW.2015.7301274"},{"key":"34_CR60","unstructured":"Yu, F., Xian, W., Chen, Y., Liu, F., Liao, M., Madhavan, V., Darrell, T.: BDD100K: a diverse driving video database with scalable annotation tooling. arXiv preprint arXiv:1805.04687 2(5), 6 (2018)"},{"key":"34_CR61","unstructured":"Zaidi, S., Zela, A., Elsken, T., Holmes, C., Hutter, F., Teh, Y.W.: Neural ensemble search for performant and calibrated predictions. In: Workshop on Uncertainty and Robustness in Deep Learning (UDL@ICML2020) (2020)"},{"key":"34_CR62","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1007\/978-3-030-58580-8_7","volume-title":"Computer Vision \u2013 ECCV 2020","author":"H Zhang","year":"2020","unstructured":"Zhang, H., Li, A., Guo, J., Guo, Y.: Hybrid models for open set recognition. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 102\u2013117. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58580-8_7"},{"key":"34_CR63","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25072-9_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T13:25:34Z","timestamp":1728912334000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25072-9_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250712","9783031250729"],"references-count":63,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25072-9_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"18 February 2023","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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":"From the workshops, 367 reviewed full papers have been selected for publication","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)"}}]}}