{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T14:28:25Z","timestamp":1748615305399,"version":"3.40.3"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030585280"},{"type":"electronic","value":"9783030585297"}],"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\/978-3-030-58529-7_23","type":"book-chapter","created":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T09:06:09Z","timestamp":1605171969000},"page":"386-403","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Gait Recognition from a Single Image Using a Phase-Aware Gait Cycle Reconstruction Network"],"prefix":"10.1007","author":[{"given":"Chi","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasushi","family":"Makihara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasushi","family":"Yagi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianfeng","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,13]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"Akae, N., Makihara, Y., Yagi, Y.: Gait recognition using periodic temporal super resolution for low frame-rate videos. In: Proceedings of the International Joint Conference on Biometrics (IJCB2011), Washington D.C., USA, pp. 1\u20137, October 2011","DOI":"10.1109\/IJCB.2011.6117530"},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Akae, N., Mansur, A., Makihara, Y., Yagi, Y.: Video from nearly still: an application to low frame-rate gait recognition. In: Proceedings of the 25th IEEE Conference on Computer Vision and Pattern Recognition (CVPR2012), Providence, RI, USA, pp. 1537\u20131543, June 2012","DOI":"10.1109\/CVPR.2012.6247844"},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Al-Huseiny, M.S., Mahmoodi, S., Nixon, M.S.: Gait learning-based regenerative model: a level set approach. In: The 20th International Conference on Pattern Recognition, Istanbul, Turkey, pp. 2644\u20132647, August 2010","DOI":"10.1109\/ICPR.2010.648"},{"key":"23_CR4","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.neucom.2019.01.091","volume":"338","author":"M Babaee","year":"2019","unstructured":"Babaee, M., Li, L., Rigoll, G.: Person identification from partial gait cycle using fully convolutional neural networks. Neurocomputing 338, 116\u2013125 (2019)","journal-title":"Neurocomputing"},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Bashir, K., Xiang, T., Gong, S.: Cross view gait recognition using correlation strength. In: BMVC (2010)","DOI":"10.5244\/C.24.109"},{"issue":"4","key":"23_CR6","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1111\/j.1556-4029.2011.01793.x","volume":"56","author":"I Bouchrika","year":"2011","unstructured":"Bouchrika, I., Goffredo, M., Carter, J., Nixon, M.: On using gait in forensic biometrics. J. Forensic Sci. 56(4), 882\u2013889 (2011)","journal-title":"J. Forensic Sci."},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Chao, H., He, Y., Zhang, J., Feng, J.: GaitSet: regarding gait as a set for cross-view gait recognition. In: Proceedings of the 33th AAAI Conference on Artificial Intelligence (AAAI 2019) (2019)","DOI":"10.1609\/aaai.v33i01.33018126"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"El-Alfy, H., Xu, C., Makihara, Y., Muramatsu, D., Yagi, Y.: A geometric view transformation model using free-form deformation for cross-view gait recognition. In: Proceedings of the 4th Asian Conference on Pattern Recognition (ACPR 2017). IEEE, November 2017","DOI":"10.1109\/ACPR.2017.153"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Gao, R., Xiong, B., Grauman, K.: Im2Flow: motion hallucination from static images for action recognition. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00622"},{"issue":"7","key":"23_CR10","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.1109\/TPAMI.2014.2366766","volume":"37","author":"Y Guan","year":"2015","unstructured":"Guan, Y., Li, C., Roli, F.: On reducing the effect of covariate factors in gait recognition: a classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 37(7), 1521\u20131528 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"23_CR11","doi-asserted-by":"crossref","unstructured":"Guan, Y., Li, C.T.: A robust speed-invariant gait recognition system for walker and runner identification. In: Proceedings of the 6th IAPR International Conference on Biometrics, pp. 1\u20138 (2013)","DOI":"10.1109\/ICB.2013.6612965"},{"key":"23_CR12","doi-asserted-by":"publisher","unstructured":"Guan, Y., Li, C.T., Choudhury, S.: Robust gait recognition from extremely low frame-rate videos. In: 2013 International Workshop on Biometrics and Forensics (IWBF), pp. 1\u20134, April 2013. https:\/\/doi.org\/10.1109\/IWBF.2013.6547319","DOI":"10.1109\/IWBF.2013.6547319"},{"issue":"2","key":"23_CR13","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1109\/TPAMI.2006.38","volume":"28","author":"J Han","year":"2006","unstructured":"Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 316\u2013322 (2006)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"23_CR14","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1109\/TIFS.2018.2844819","volume":"14","author":"Y He","year":"2019","unstructured":"He, Y., Zhang, J., Shan, H., Wang, L.: Multi-task GANs for view-specific feature learning in gait recognition. IEEE Trans. Inf. Forensics Secur. 14(1), 102\u2013113 (2019). https:\/\/doi.org\/10.1109\/TIFS.2018.2844819","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"23_CR15","unstructured":"Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. CoRR abs\/1703.07737 (2017). http:\/\/arxiv.org\/abs\/1703.07737"},{"key":"23_CR16","doi-asserted-by":"publisher","unstructured":"Horst, F., Lapuschkin, S., Samek, W., M\u00fcller, K., Sch\u00f6llhorn, W.: Explaining the unique nature of individual gait patterns with deep learning. Sci. Rep. 9, 2391 (2019). https:\/\/doi.org\/10.1038\/s41598-019-38748-8","DOI":"10.1038\/s41598-019-38748-8"},{"issue":"6","key":"23_CR17","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1016\/j.patcog.2009.12.020","volume":"43","author":"MA Hossain","year":"2010","unstructured":"Hossain, M.A., Makihara, Y., Wang, J., Yagi, Y.: Clothing-invariant gait identification using part-based clothing categorization and adaptive weight control. Pattern Recogn. 43(6), 2281\u20132291 (2010)","journal-title":"Pattern Recogn."},{"key":"23_CR18","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. CoRR abs\/1502.03167 (2015). http:\/\/arxiv.org\/abs\/1502.03167"},{"key":"23_CR19","doi-asserted-by":"publisher","first-page":"163","DOI":"10.2197\/ipsjtcva.5.163","volume":"5","author":"H Iwama","year":"2013","unstructured":"Iwama, H., Muramatsu, D., Makihara, Y., Yagi, Y.: Gait verification system for criminal investigation. IPSJ Trans. Comput. Vis. Appl. 5, 163\u2013175 (2013)","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"23_CR20","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv: 1412.6980 (2014)"},{"key":"23_CR21","doi-asserted-by":"publisher","unstructured":"Kourtzi, Z., Kanwisher, N.: Activation in human MT\/MST by static images with implied motion. J. Cogn. Neurosci. 12, 48\u201355 (2000). https:\/\/doi.org\/10.1162\/08989290051137594","DOI":"10.1162\/08989290051137594"},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Kusakunniran, W., Wu, Q., Zhang, J., Li, H.: Support vector regression for multi-view gait recognition based on local motion feature selection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2010, San Francisco, CA, USA, pp. 1\u20138, June 2010","DOI":"10.1109\/CVPR.2010.5540113"},{"issue":"12","key":"23_CR23","doi-asserted-by":"publisher","first-page":"3102","DOI":"10.1109\/TIFS.2019.2912577","volume":"14","author":"X Li","year":"2019","unstructured":"Li, X., Makihara, Y., Xu, C., Yagi, Y., Ren, M.: Joint intensity transformer network for gait recognition robust against clothing and carrying status. IEEE Trans. Inf. Forensics Secur. 14(12), 3102\u20133115 (2019)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"23_CR24","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., Reid, I.: RefineNet: multi-path refinement networks for high-resolution semantic segmentation. In: CVPR, July 2017","DOI":"10.1109\/CVPR.2017.549"},{"key":"23_CR25","doi-asserted-by":"publisher","unstructured":"Lynnerup, N., Larsen, P.: Gait as evidence. IET Biometrics 3(2), 47\u201354 (2014). https:\/\/doi.org\/10.1049\/iet-bmt.2013.0090","DOI":"10.1049\/iet-bmt.2013.0090"},{"key":"23_CR26","doi-asserted-by":"publisher","first-page":"53","DOI":"10.2197\/ipsjtcva.4.53","volume":"4","author":"Y Makihara","year":"2012","unstructured":"Makihara, Y., et al.: The OU-ISIR gait database comprising the treadmill dataset. IPSJ Trans. Comput. Vis. Appl. 4, 53\u201362 (2012)","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"23_CR27","doi-asserted-by":"crossref","unstructured":"Makihara, Y., Mori, A., Yagi, Y.: Temporal super resolution from a single quasi-periodic image sequence based on phase registration. In: Proceedings of the 10th Asian Conference on Computer Vision, Queenstown, New Zealand, pp. 107\u2013120, November 2010","DOI":"10.1007\/978-3-642-19315-6_9"},{"key":"23_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/11744078_12","volume-title":"Computer Vision \u2013 ECCV 2006","author":"Y Makihara","year":"2006","unstructured":"Makihara, Y., Sagawa, R., Mukaigawa, Y., Echigo, T., Yagi, Y.: Gait recognition using a view transformation model in the frequency domain. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 151\u2013163. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11744078_12"},{"key":"23_CR29","doi-asserted-by":"publisher","unstructured":"Makihara, Y., Suzuki, A., Muramatsu, D., Li, X., Yagi, Y.: Joint intensity and spatial metric learning for robust gait recognition. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6786\u20136796, July 2017. https:\/\/doi.org\/10.1109\/CVPR.2017.718","DOI":"10.1109\/CVPR.2017.718"},{"key":"23_CR30","doi-asserted-by":"crossref","unstructured":"Makihara, Y., Yagi, Y.: Silhouette extraction based on iterative spatio-temporal local color transformation and graph-cut segmentation. In: Proceedings of the 19th International Conference on Pattern Recognition, Tampa, Florida, USA, December 2008","DOI":"10.1109\/ICPR.2008.4761121"},{"key":"23_CR31","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML 2010, Omnipress, USA, pp. 807\u2013814 (2010). http:\/\/dl.acm.org\/citation.cfm?id=3104322.3104425"},{"issue":"10","key":"23_CR32","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1109\/34.879790","volume":"22","author":"P Phillips","year":"2000","unstructured":"Phillips, P., Moon, H., Rizvi, S., Rauss, P.: The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090\u20131104 (2000)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"23_CR33","doi-asserted-by":"crossref","unstructured":"Pintea, S.L., Gemert, J.C., Smeulders, A.W.M.: D\u00e9j\u00e0vu: motion prediction in static images. In: ECCV (2014)","DOI":"10.1007\/978-3-319-10578-9_12"},{"key":"23_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1007\/3-540-44887-X_84","volume-title":"Audio- and Video-Based Biometric Person Authentication","author":"SP Prismall","year":"2003","unstructured":"Prismall, S.P., Nixon, M.S., Carter, J.N.: Novel temporal views of moving objects for gait biometrics. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 725\u2013733. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/3-540-44887-X_84"},{"issue":"2","key":"23_CR35","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TPAMI.2005.39","volume":"27","author":"S Sarkar","year":"2005","unstructured":"Sarkar, S., Phillips, P.J., Liu, Z., Vega, I.R., Grother, P., Bowyer, K.W.: The humanID gait challenge problem: data sets, performance, and analysis. IEEE Trans. Pattern Anal. Mach. Intell. 27(2), 162\u2013177 (2005). https:\/\/doi.org\/10.1109\/TPAMI.2005.39","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"23_CR36","doi-asserted-by":"publisher","unstructured":"Sederberg, T.W., Parry, S.R.: Free-form deformation of solid geometric models. SIGGRAPH Comput. Graph. 20(4), 151\u2013160 (1986). https:\/\/doi.org\/10.1145\/15886.15903","DOI":"10.1145\/15886.15903"},{"key":"23_CR37","doi-asserted-by":"crossref","unstructured":"Shiraga, K., Makihara, Y., Muramatsu, D., Echigo, T., Yagi, Y.: GeiNet: view-invariant gait recognition using a convolutional neural network. In: 2016 International Conference on Biometrics (ICB), pp. 1\u20138 (2016)","DOI":"10.1109\/ICB.2016.7550060"},{"key":"23_CR38","doi-asserted-by":"publisher","unstructured":"Takemura, N., Makihara, Y., Muramatsu, D., Echigo, T., Yagi, Y.: On input\/output architectures for convolutional neural network-based cross-view gait recognition. IEEE Trans. Circ. Syst. Video Technol., 1 (2018). https:\/\/doi.org\/10.1109\/TCSVT.2017.2760835","DOI":"10.1109\/TCSVT.2017.2760835"},{"issue":"1","key":"23_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41074-018-0039-6","volume":"10","author":"N Takemura","year":"2018","unstructured":"Takemura, N., Makihara, Y., Muramatsu, D., Echigo, T., Yagi, Y.: Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition. IPSJ Trans. Comput. Vis. Appl. 10(1), 1\u201314 (2018). https:\/\/doi.org\/10.1186\/s41074-018-0039-6","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"23_CR40","doi-asserted-by":"crossref","unstructured":"Wolf, T., Babaee, M., Rigoll, G.: Multi-view gait recognition using 3D convolutional neural networks. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 4165\u20134169 (2016)","DOI":"10.1109\/ICIP.2016.7533144"},{"issue":"11","key":"23_CR41","doi-asserted-by":"publisher","first-page":"1960","DOI":"10.1109\/TMM.2015.2477681","volume":"17","author":"Z Wu","year":"2015","unstructured":"Wu, Z., Huang, Y., Wang, L.: Learning representative deep features for image set analysis. IEEE Trans. Multimedia 17(11), 1960\u20131968 (2015). https:\/\/doi.org\/10.1109\/TMM.2015.2477681","journal-title":"IEEE Trans. Multimedia"},{"issue":"2","key":"23_CR42","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1109\/TPAMI.2016.2545669","volume":"39","author":"Z Wu","year":"2017","unstructured":"Wu, Z., Huang, Y., Wang, L., Wang, X., Tan, T.: A comprehensive study on cross-view gait based human identification with deep CNNs. IEEE Trans. Pattern Anal. Mach. Intell. 39(2), 209\u2013226 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"23_CR43","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/978-3-319-54184-6_4","volume-title":"Computer Vision \u2013 ACCV 2016","author":"C Xu","year":"2017","unstructured":"Xu, C., Makihara, Y., Li, X., Yagi, Y., Lu, J.: Speed Invariance vs. stability: cross-speed gait recognition using single-support gait energy image. In: Lai, S.-H., Lepetit, V., Nishino, K., Sato, Y. (eds.) ACCV 2016. LNCS, vol. 10112, pp. 52\u201367. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-54184-6_4"},{"key":"23_CR44","doi-asserted-by":"publisher","unstructured":"Xu, C., Makihara, Y., Yagi, Y., Lu, J.: Gait-based age progression\/regression: a baseline and performance evaluation by age group classification and cross-age gait identification. Mach. Vis. Appl. 30(4), 629\u2013644 (2019). https:\/\/doi.org\/10.1007\/s00138-019-01015-x","DOI":"10.1007\/s00138-019-01015-x"},{"key":"23_CR45","doi-asserted-by":"publisher","unstructured":"Yu, S., Chen, H., Reyes, E.B.G., Poh, N.: GaitGAN: invariant gait feature extraction using generative adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 532\u2013539, July 2017. https:\/\/doi.org\/10.1109\/CVPRW.2017.80","DOI":"10.1109\/CVPRW.2017.80"},{"key":"23_CR46","unstructured":"Yu, S., Tan, D., Tan, T.: A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: Proceedings of the 18th International Conference on Pattern Recognition, Hong Kong, China, vol. 4, pp. 441\u2013444, August 2006"},{"key":"23_CR47","doi-asserted-by":"crossref","unstructured":"Zhang, C., Liu, W., Ma, H., Fu, H.: Siamese neural network based gait recognition for human identification. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2832\u20132836 (2016)","DOI":"10.1109\/ICASSP.2016.7472194"},{"key":"23_CR48","doi-asserted-by":"crossref","unstructured":"Zhang, K., Luo, W., Ma, L., Liu, W., Li, H.: Learning joint gait representation via quintuplet loss minimization. In: 2019 Conference on Computer Vision and Pattern Recognition (CVPR 2019) (2019)","DOI":"10.1109\/CVPR.2019.00483"}],"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\/978-3-030-58529-7_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:34:57Z","timestamp":1731371697000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58529-7_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585280","9783030585297"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58529-7_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"13 November 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)"}}]}}