{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:16:33Z","timestamp":1778080593548,"version":"3.51.4"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031198175","type":"print"},{"value":"9783031198182","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-19818-2_13","type":"book-chapter","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T16:21:10Z","timestamp":1666369270000},"page":"217-234","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Dense Gaussian Processes for\u00a0Few-Shot Segmentation"],"prefix":"10.1007","author":[{"given":"Joakim","family":"Johnander","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Johan","family":"Edstedt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Felsberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fahad Shahbaz","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Danelljan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,22]]},"reference":[{"key":"13_CR1","unstructured":"Allen, K., Shelhamer, E., Shin, H., Tenenbaum, J.: Infinite mixture prototypes for few-shot learning. In: International Conference on Machine Learning, pp. 232\u2013241. PMLR (2019)"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Azad, R., Fayjie, A.R., Kauffmann, C., Ben Ayed, I., Pedersoli, M., Dolz, J.: On the texture bias for few-shot CNN segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2674\u20132683 (2021)","DOI":"10.1109\/WACV48630.2021.00272"},{"key":"13_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1007\/978-3-030-01216-8_30","volume-title":"Computer Vision \u2013 ECCV 2018","author":"G Bhat","year":"2018","unstructured":"Bhat, G., Johnander, J., Danelljan, M., Khan, F.S., Felsberg, M.: Unveiling the power of deep tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 493\u2013509. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01216-8_30"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Boudiaf, M., Kervadec, H., Masud, Z.I., Piantanida, P., Ben Ayed, I., Dolz, J.: Few-shot segmentation without meta-learning: a good transductive inference is all you need? In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13979\u201313988 (2021)","DOI":"10.1109\/CVPR46437.2021.01376"},{"key":"13_CR5","doi-asserted-by":"publisher","unstructured":"Calandra, R., Peters, J., Rasmussen, C.E., Deisenroth, M.P.: Manifold Gaussian Processes for regression. In: Proceedings of the International Joint Conference on Neural Networks (2016). https:\/\/doi.org\/10.1109\/IJCNN.2016.7727626","DOI":"10.1109\/IJCNN.2016.7727626"},{"key":"13_CR6","unstructured":"Dong, N., Xing, E.P.: Few-shot semantic segmentation with prototype learning. In: British Machine Vision Conference 2018, BMVC 2018 (2019)"},{"key":"13_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vision (2010). https:\/\/doi.org\/10.1007\/s11263-009-0275-4","journal-title":"Int. J. Comput. Vision"},{"key":"13_CR8","doi-asserted-by":"publisher","unstructured":"Hariharan, B., Arbel\u00e1ez, P., Bourdev, L., Maji, S., Malik, J.: Semantic contours from inverse detectors. In: Proceedings of the IEEE International Conference on Computer Vision (2011). https:\/\/doi.org\/10.1109\/ICCV.2011.6126343","DOI":"10.1109\/ICCV.2011.6126343"},{"key":"13_CR9","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":"13_CR10","doi-asserted-by":"crossref","unstructured":"Hu, T., Yang, P., Zhang, C., Yu, G., Mu, Y., Snoek, C.G.: Attention-based multi-context guiding for few-shot semantic segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8441\u20138448 (2019)","DOI":"10.1609\/aaai.v33i01.33018441"},{"key":"13_CR11","unstructured":"Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings (2015)"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Li, G., Jampani, V., Sevilla-Lara, L., Sun, D., Kim, J., Kim, J.: Adaptive prototype learning and allocation for few-shot segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8334\u20138343 (2021)","DOI":"10.1109\/CVPR46437.2021.00823"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"13_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Liu, L., Cao, J., Liu, M., Guo, Y., Chen, Q., Tan, M.: Dynamic extension nets for few-shot semantic segmentation. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 1441\u20131449 (2020)","DOI":"10.1145\/3394171.3413915"},{"key":"13_CR16","doi-asserted-by":"publisher","unstructured":"Liu, W., Zhang, C., Lin, G., Liu, F.: CRNet: cross-reference networks for few-shot segmentation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 4164\u20134172 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00422","DOI":"10.1109\/CVPR42600.2020.00422"},{"key":"13_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1007\/978-3-030-58545-7_9","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Zhang, X., Zhang, S., He, X.: Part-aware prototype network for few-shot semantic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 142\u2013158. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58545-7_9"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"13_CR19","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=Bkg6RiCqY7"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Lu, Z., He, S., Zhu, X., Zhang, L., Song, Y.Z., Xiang, T.: Simpler is better: few-shot semantic segmentation with classifier weight transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8741\u20138750 (2021)","DOI":"10.1109\/ICCV48922.2021.00862"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Min, J., Kang, D., Cho, M.: Hypercorrelation squeeze for few-shot segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6941\u20136952 (2021)","DOI":"10.1109\/ICCV48922.2021.00686"},{"key":"13_CR22","doi-asserted-by":"publisher","unstructured":"Nguyen, K., Todorovic, S.: Feature weighting and boosting for few-shot segmentation. In: Proceedings of the IEEE International Conference on Computer Vision. vol. 2019-Octob, pp. 622\u2013631 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00071","DOI":"10.1109\/ICCV.2019.00071"},{"key":"13_CR23","unstructured":"Patacchiola, M., Turner, J., Crowley, E.J., Storkey, A.: Bayesian meta-learning for the few-shot setting via deep kernels. In: Advances in Neural Information Processing Systems (2020)"},{"key":"13_CR24","unstructured":"Rakelly, K., Shelhamer, E., Darrell, T., Efros, A., Levine, S.: Conditional networks for few-shot semantic segmentation. In: 6th International Conference on Learning Representations, ICLR 2018 - Workshop Track Proceedings (2018)"},{"key":"13_CR25","doi-asserted-by":"publisher","unstructured":"Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning (2006). https:\/\/doi.org\/10.7551\/mitpress\/3206.001.0001","DOI":"10.7551\/mitpress\/3206.001.0001"},{"issue":"3","key":"13_CR26","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int. J. Comput. Vision"},{"key":"13_CR27","unstructured":"Salakhutdinov, R., Hinton, G.: Using deep belief nets to learn covariance kernels for Gaussian processes. In: Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference (2009)"},{"key":"13_CR28","doi-asserted-by":"publisher","unstructured":"Shaban, A., Bansal, S., Liu, Z., Essa, I., Boots, B.: One-shot learning for semantic segmentation. In: British Machine Vision Conference 2017, BMVC 2017 (2017). https:\/\/doi.org\/10.5244\/c.31.167","DOI":"10.5244\/c.31.167"},{"key":"13_CR29","doi-asserted-by":"publisher","unstructured":"Siam, M., Oreshkin, B., Jagersand, M.: AMP: adaptive masked proxies for few-shot segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2019-Octob, pp. 5248\u20135257 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00535","DOI":"10.1109\/ICCV.2019.00535"},{"key":"13_CR30","unstructured":"Snell, J., Zemel, R.: Bayesian few-shot classification with one-vs-each p\u00f3lya-gamma augmented gaussian processes. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=lgNx56yZh8a"},{"key":"13_CR31","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2020.3013717","author":"Z Tian","year":"2020","unstructured":"Tian, Z., Zhao, H., Shu, M., Yang, Z., Li, R., Jia, J.: Prior Guided Feature Enrichment Network for few-shot segmentation. IEEE Trans. Pattern Anal. Mach. Intell. (2020). https:\/\/doi.org\/10.1109\/tpami.2020.3013717","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"13_CR32","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"13_CR33","doi-asserted-by":"crossref","unstructured":"Wang, H., Yang, Y., Cao, X., Zhen, X., Snoek, C., Shao, L.: Variational prototype inference for few-shot semantic segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 525\u2013534 (2021)","DOI":"10.1109\/WACV48630.2021.00057"},{"key":"13_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1007\/978-3-030-58601-0_43","volume-title":"Computer Vision \u2013 ECCV 2020","author":"H Wang","year":"2020","unstructured":"Wang, H., Zhang, X., Hu, Y., Yang, Y., Cao, X., Zhen, X.: Few-shot semantic segmentation with democratic attention networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12358, pp. 730\u2013746. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58601-0_43"},{"key":"13_CR35","doi-asserted-by":"publisher","unstructured":"Wang, K., Liew, J.H., Zou, Y., Zhou, D., Feng, J.: PANet: few-shot image semantic segmentation with prototype alignment. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2019-Octob, pp. 9196\u20139205 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00929","DOI":"10.1109\/ICCV.2019.00929"},{"key":"13_CR36","unstructured":"Wilson, A.G., Hu, Z., Salakhutdinov, R., Xing, E.P.: Deep kernel learning. In: Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016 (2016)"},{"key":"13_CR37","doi-asserted-by":"crossref","unstructured":"Wu, Z., Shi, X., Lin, G., Cai, J.: Learning meta-class memory for few-shot semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 517\u2013526 (2021)","DOI":"10.1109\/ICCV48922.2021.00056"},{"key":"13_CR38","doi-asserted-by":"crossref","unstructured":"Xie, G.S., Liu, J., Xiong, H., Shao, L.: Scale-aware graph neural network for few-shot semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5475\u20135484 (2021)","DOI":"10.1109\/CVPR46437.2021.00543"},{"key":"13_CR39","doi-asserted-by":"crossref","unstructured":"Xie, G.S., Xiong, H., Liu, J., Yao, Y., Shao, L.: Few-shot semantic segmentation with cyclic memory network. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7293\u20137302 (2021)","DOI":"10.1109\/ICCV48922.2021.00720"},{"key":"13_CR40","doi-asserted-by":"publisher","unstructured":"Yang, B., Liu, C., Li, B., Jiao, J., Ye, Q.: Prototype mixture models for few-shot semantic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 763\u2013778. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58598-3_45","DOI":"10.1007\/978-3-030-58598-3_45"},{"key":"13_CR41","doi-asserted-by":"crossref","unstructured":"Yang, L., Zhuo, W., Qi, L., Shi, Y., Gao, Y.: Mining latent classes for few-shot segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8721\u20138730 (2021)","DOI":"10.1109\/ICCV48922.2021.00860"},{"key":"13_CR42","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1007\/978-3-030-37734-2_7","volume-title":"MultiMedia Modeling","author":"Y Yang","year":"2020","unstructured":"Yang, Y., Meng, F., Li, H., Wu, Q., Xu, X., Chen, S.: A new local transformation module for few-shot segmentation. In: Ro, Y.M., Cheng, W.-H., Kim, J., Chu, W.-T., Cui, P., Choi, J.-W., Hu, M.-C., De Neve, W. (eds.) MMM 2020. LNCS, vol. 11962, pp. 76\u201387. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-37734-2_7"},{"key":"13_CR43","doi-asserted-by":"publisher","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Learning a Discriminative Feature Network for Semantic Segmentation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00199","DOI":"10.1109\/CVPR.2018.00199"},{"key":"13_CR44","doi-asserted-by":"crossref","unstructured":"Zhang, B., Xiao, J., Qin, T.: Self-guided and cross-guided learning for few-shot segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8312\u20138321 (2021)","DOI":"10.1109\/CVPR46437.2021.00821"},{"key":"13_CR45","doi-asserted-by":"publisher","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: Proceedings of the IEEE International Conference on Computer Vision, vol. 2019-Octob, pp. 9586\u20139594 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00968","DOI":"10.1109\/ICCV.2019.00968"},{"key":"13_CR46","doi-asserted-by":"publisher","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: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00536","DOI":"10.1109\/CVPR.2019.00536"},{"key":"13_CR47","unstructured":"Zhang, G., Kang, G., Yang, Y., Wei, Y.: Few-shot segmentation via cycle-consistent transformer. In: Advances in Neural Information Processing Systems 34 (2021)"},{"issue":"9","key":"13_CR48","doi-asserted-by":"publisher","first-page":"3855","DOI":"10.1109\/TCYB.2020.2992433","volume":"50","author":"X Zhang","year":"2020","unstructured":"Zhang, X., Wei, Y., Yang, Y., Huang, T.S.: Sg-one: similarity guidance network for one-shot semantic segmentation. IEEE Trans. Cybern. 50(9), 3855\u20133865 (2020)","journal-title":"IEEE Trans. Cybern."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19818-2_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T14:21:05Z","timestamp":1710339665000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19818-2_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198175","9783031198182"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19818-2_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"22 October 2022","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)"}}]}}