{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:17:22Z","timestamp":1774628242231,"version":"3.50.1"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031250620","type":"print"},{"value":"9783031250637","type":"electronic"}],"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\/978-3-031-25063-7_22","type":"book-chapter","created":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T20:10:15Z","timestamp":1676491815000},"page":"361-377","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["DSR: Towards Drone Image Super-Resolution"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3240-3683","authenticated-orcid":false,"given":"Xiaoyu","family":"Lin","sequence":"first","affiliation":[]},{"given":"Baran","family":"Ozaydin","sequence":"additional","affiliation":[]},{"given":"Vidit","family":"Vidit","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7469-2404","authenticated-orcid":false,"given":"Majed","family":"El Helou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0441-6068","authenticated-orcid":false,"given":"Sabine","family":"S\u00fcsstrunk","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,16]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Timofte, R.: NTIRE 2017 challenge on single image super-resolution: dataset and study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 126\u2013135 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"22_CR2","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1016\/j.eswa.2017.09.033","volume":"92","author":"A Al-Kaff","year":"2018","unstructured":"Al-Kaff, A., Martin, D., Garcia, F., de la Escalera, A., Armingol, J.M.: Survey of computer vision algorithms and applications for unmanned aerial vehicles. Expert Syst. Appl. 92, 447\u2013463 (2018)","journal-title":"Expert Syst. Appl."},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.L.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding (2012)","DOI":"10.5244\/C.26.135"},{"key":"22_CR4","unstructured":"Bhardwaj, K., et al.: Collapsible linear blocks for super-efficient super resolution. In: Proceedings of Machine Learning and Systems, vol. 4, pp. 529\u2013547 (2022)"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Bhat, G., Danelljan, M., Van Gool, L., Timofte, R.: Deep burst super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9209\u20139218 (2021)","DOI":"10.1109\/CVPR46437.2021.00909"},{"key":"22_CR6","unstructured":"Cai, J., Gu, S., Timofte, R., Zhang, L.: NTIRE 2019 challenge on real image super-resolution: methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Cai, J., Zeng, H., Yong, H., Cao, Z., Zhang, L.: Toward real-world single image super-resolution: a new benchmark and a new model. In: Proceedings of the IEEE International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00318"},{"issue":"5","key":"22_CR8","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1016\/j.patrec.2008.11.008","volume":"30","author":"TM Chan","year":"2009","unstructured":"Chan, T.M., Zhang, J., Pu, J., Huang, H.: Neighbor embedding based super-resolution algorithm through edge detection and feature selection. Pattern Recogn. Lett. 30(5), 494\u2013502 (2009)","journal-title":"Pattern Recogn. Lett."},{"key":"22_CR9","unstructured":"DJI: DJI Mavic 3 - user manual v1.6 (2022). https:\/\/dl.djicdn.com\/downloads\/DJI_Mavic_3\/20220531\/DJI_Mavic_3_User_Manual_v1.6_en.pdf"},{"key":"22_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1007\/978-3-030-01249-6_23","volume-title":"Computer Vision \u2013 ECCV 2018","author":"D Du","year":"2018","unstructured":"Du, D., et al.: The unmanned aerial vehicle benchmark: object detection and tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 375\u2013391. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_23"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Dudhane, A., Zamir, S.W., Khan, S., Khan, F., Yang, M.H.: Burst image restoration and enhancement. arXiv preprint arXiv:2110.03680 (2021)","DOI":"10.1109\/CVPR52688.2022.00567"},{"key":"22_CR12","doi-asserted-by":"publisher","first-page":"4885","DOI":"10.1109\/TIP.2020.2976814","volume":"29","author":"M El Helou","year":"2020","unstructured":"El Helou, M., S\u00fcsstrunk, S.: Blind universal Bayesian image denoising with Gaussian noise level learning. IEEE Trans. Image Process. 29, 4885\u20134897 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"22_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1007\/978-3-030-58517-4_44","volume-title":"Computer Vision \u2013 ECCV 2020","author":"M El Helou","year":"2020","unstructured":"El Helou, M., Zhou, R., S\u00fcsstrunk, S.: Stochastic frequency masking to improve super-resolution and denoising networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12361, pp. 749\u2013766. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58517-4_44"},{"issue":"6","key":"22_CR14","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1145\/358669.358692","volume":"24","author":"MA Fischler","year":"1981","unstructured":"Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381\u2013395 (1981)","journal-title":"Commun. ACM"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Hsieh, M.R., Lin, Y.L., Hsu, W.H.: Drone-based object counting by spatially regularized regional proposal network. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4145\u20134153 (2017)","DOI":"10.1109\/ICCV.2017.446"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5197\u20135206 (2015)","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"22_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-319-46475-6_43","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Johnson","year":"2016","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694\u2013711. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43"},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401\u20134410 (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"22_CR19","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Lecouat, B., Ponce, J., Mairal, J.: Lucas-kanade reloaded: end-to-end super-resolution from raw image bursts. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2370\u20132379 (2021)","DOI":"10.1109\/ICCV48922.2021.00237"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: SwinIR: image restoration using swin transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1833\u20131844 (2021)","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136\u2013144 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"22_CR24","doi-asserted-by":"publisher","first-page":"1719","DOI":"10.1109\/LSP.2021.3104769","volume":"28","author":"X Lin","year":"2021","unstructured":"Lin, X., Bhattacharjee, D., El Helou, M., S\u00fcsstrunk, S.: Fidelity estimation improves noisy-image classification with pretrained networks. IEEE Sig. Process. Lett. 28, 1719\u20131723 (2021)","journal-title":"IEEE Sig. Process. Lett."},{"issue":"9","key":"22_CR25","doi-asserted-by":"publisher","first-page":"1938","DOI":"10.1109\/LGRS.2015.2439517","volume":"12","author":"K Liu","year":"2015","unstructured":"Liu, K., Mattyus, G.: Fast multiclass vehicle detection on aerial images. IEEE Geosci. Remote Sens. Lett. 12(9), 1938\u20131942 (2015)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"22_CR26","doi-asserted-by":"crossref","unstructured":"Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of IEEE International Conference on Computer Vision, vol. 2, pp. 1150\u20131157. IEEE (1999)","DOI":"10.1109\/ICCV.1999.790410"},{"issue":"2","key":"22_CR27","doi-asserted-by":"publisher","first-page":"1004","DOI":"10.1109\/TIP.2016.2631888","volume":"26","author":"K Ma","year":"2016","unstructured":"Ma, K., et al.: Waterloo exploration database: new challenges for image quality assessment models. IEEE Trans. Image Process. 26(2), 1004\u20131016 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"22_CR28","doi-asserted-by":"crossref","unstructured":"Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2, pp. 416\u2013423. IEEE (2001)","DOI":"10.1109\/ICCV.2001.937655"},{"key":"22_CR29","doi-asserted-by":"crossref","unstructured":"Mei, Y., Fan, Y., Zhou, Y.: Image super-resolution with non-local sparse attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3517\u20133526 (6 2021)","DOI":"10.1109\/CVPR46437.2021.00352"},{"key":"22_CR30","doi-asserted-by":"crossref","unstructured":"Oh, S., et al.: A large-scale benchmark dataset for event recognition in surveillance video. In: CVPR 2011, pp. 3153\u20133160. IEEE (2011)","DOI":"10.1109\/CVPR.2011.5995586"},{"key":"22_CR31","doi-asserted-by":"crossref","unstructured":"Shocher, A., Cohen, N., Irani, M.: \u201czero-shot\u201d super-resolution using deep internal learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3118\u20133126 (2018)","DOI":"10.1109\/CVPR.2018.00329"},{"key":"22_CR32","unstructured":"Sun, J., Xu, Z., Shum, H.Y.: Image super-resolution using gradient profile prior. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2008)"},{"key":"22_CR33","doi-asserted-by":"crossref","unstructured":"Timofte, R., Agustsson, E., Van Gool, L., Yang, M.H., Zhang, L.: NTIRE 2017 challenge on single image super-resolution: methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 114\u2013125 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"22_CR34","doi-asserted-by":"crossref","unstructured":"Wang, L., et al.: Unsupervised degradation representation learning for blind super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10581\u201310590 (2021)","DOI":"10.1109\/CVPR46437.2021.01044"},{"key":"22_CR35","doi-asserted-by":"crossref","unstructured":"Wang, X., Dong, C., Shan, Y.: RepSR: training efficient VGG-style super-resolution networks with structural re-parameterization and batch normalization. arXiv preprint arXiv:2205.05671 (2022)","DOI":"10.1145\/3503161.3547915"},{"key":"22_CR36","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-030-11021-5_5","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"X Wang","year":"2019","unstructured":"Wang, X., et al.: ESRGAN: enhanced super-resolution generative adversarial networks. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 63\u201379. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11021-5_5"},{"issue":"4","key":"22_CR37","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"22_CR38","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1109\/TPAMI.2019.2932429","volume":"43","author":"X Xu","year":"2019","unstructured":"Xu, X., et al.: DAC-SDC low power object detection challenge for UAV applications. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 392\u2013403 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"22_CR39","doi-asserted-by":"publisher","first-page":"204","DOI":"10.3390\/rs8030204","volume":"8","author":"Y Xu","year":"2016","unstructured":"Xu, Y., Ou, J., He, H., Zhang, X., Mills, J.: Mosaicking of unmanned aerial vehicle imagery in the absence of camera poses. Remote Sens. 8(3), 204 (2016)","journal-title":"Remote Sens."},{"key":"22_CR40","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/978-3-642-27413-8_47","volume-title":"Curves and Surfaces","author":"R Zeyde","year":"2012","unstructured":"Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Boissonnat, J.-D., et al. (eds.) Curves and Surfaces 2010. LNCS, vol. 6920, pp. 711\u2013730. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-27413-8_47"},{"key":"22_CR41","doi-asserted-by":"crossref","unstructured":"Zhang, K., Liang, J., Van Gool, L., Timofte, R.: Designing a practical degradation model for deep blind image super-resolution. In: IEEE International Conference on Computer Vision, pp. 4791\u20134800 (2021)","DOI":"10.1109\/ICCV48922.2021.00475"},{"key":"22_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Zhang, L.: Learning a single convolutional super-resolution network for multiple degradations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00344"},{"key":"22_CR43","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zeng, H., Zhang, L.: Edge-oriented convolution block for real-time super resolution on mobile devices. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 4034\u20134043 (2021)","DOI":"10.1145\/3474085.3475291"},{"key":"22_CR44","doi-asserted-by":"crossref","unstructured":"Zhang, X., Chen, Q., Ng, R., Koltun, V.: Zoom to learn, learn to zoom. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3762\u20133770 (2019)","DOI":"10.1109\/CVPR.2019.00388"},{"key":"22_CR45","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1007\/978-3-030-01234-2_18","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 294\u2013310. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_18"},{"key":"22_CR46","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2472\u20132481 (2018)","DOI":"10.1109\/CVPR.2018.00262"},{"key":"22_CR47","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1007\/978-3-030-66415-2_31","volume-title":"Computer Vision \u2013 ECCV 2020 Workshops","author":"R Zhou","year":"2020","unstructured":"Zhou, R., El Helou, M., Sage, D., Laroche, T., Seitz, A., S\u00fcsstrunk, S.: W2S: microscopy data with joint denoising and super-resolution for widefield to SIM mapping. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12535, pp. 474\u2013491. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-66415-2_31"},{"key":"22_CR48","doi-asserted-by":"crossref","unstructured":"Zhou, R., Lahoud, F., El Helou, M., S\u00fcsstrunk, S.: A comparative study on wavelets and residuals in deep super resolution. In: Electronic Imaging (2019)","DOI":"10.2352\/ISSN.2470-1173.2019.13.COIMG-135"}],"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\/978-3-031-25063-7_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T09:53:35Z","timestamp":1728899615000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25063-7_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250620","9783031250637"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25063-7_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"16 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)"}}]}}