{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T20:04:58Z","timestamp":1780776298316,"version":"3.54.1"},"publisher-location":"Cham","reference-count":54,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200649","type":"print"},{"value":"9783031200656","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\/978-3-031-20065-6_36","type":"book-chapter","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T20:24:03Z","timestamp":1667420643000},"page":"625-642","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":75,"title":["SmoothNet: A Plug-and-Play Network for\u00a0Refining Human Poses in\u00a0Videos"],"prefix":"10.1007","author":[{"given":"Ailing","family":"Zeng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Ju","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiefeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianyi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"key":"36_CR1","doi-asserted-by":"crossref","unstructured":"Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 2D human pose estimation: new benchmark and state of the art analysis. In: Proceedings of the IEEE Conference on computer Vision and Pattern Recognition, pp. 3686\u20133693 (2014)","DOI":"10.1109\/CVPR.2014.471"},{"key":"36_CR2","unstructured":"Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv arXiv:abs\/1803.01271 (2018)"},{"issue":"8","key":"36_CR3","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1145\/358198.358222","volume":"27","author":"DR Brownrigg","year":"1984","unstructured":"Brownrigg, D.R.: The weighted median filter. Commun. ACM 27(8), 807\u2013818 (1984)","journal-title":"Commun. ACM"},{"key":"36_CR4","doi-asserted-by":"crossref","unstructured":"Casiez, G., Roussel, N., Vogel, D.: 1\u20ac filter: a simple speed-based low-pass filter for noisy input in interactive systems. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2527\u20132530 (2012)","DOI":"10.1145\/2207676.2208639"},{"key":"36_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., Sun, J.: Cascaded pyramid network for multi-person pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7103\u20137112 (2018)","DOI":"10.1109\/CVPR.2018.00742"},{"key":"36_CR6","doi-asserted-by":"crossref","unstructured":"Choi, H., Moon, G., Chang, J.Y., Lee, K.M.: Beyond static features for temporally consistent 3D human pose and shape from a video. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1964\u20131973 (2021)","DOI":"10.1109\/CVPR46437.2021.00200"},{"key":"36_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/978-3-030-58607-2_2","volume-title":"Computer Vision \u2013 ECCV 2020","author":"V Choutas","year":"2020","unstructured":"Choutas, V., Pavlakos, G., Bolkart, T., Tzionas, D., Black, M.J.: Monocular expressive body regression through body-driven attention. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12355, pp. 20\u201340. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58607-2_2"},{"key":"36_CR8","doi-asserted-by":"crossref","unstructured":"Coskun, H., Achilles, F., DiPietro, R.S., Navab, N., Tombari, F.: Long short-term memory kalman filters: recurrent neural estimators for pose regularization. 2017 IEEE International Conference on Computer Vision (ICCV), pp. 5525\u20135533 (2017)","DOI":"10.1109\/ICCV.2017.589"},{"key":"36_CR9","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"issue":"4","key":"36_CR10","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1080\/00222895.1984.10735329","volume":"16","author":"MG Fischman","year":"1984","unstructured":"Fischman, M.G.: Programming time as a function of number of movement parts and changes in movement direction. J. Mot. Behav. 16(4), 405\u2013423 (1984)","journal-title":"J. Mot. Behav."},{"issue":"6","key":"36_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00814-2","volume":"2","author":"JF Gauss","year":"2021","unstructured":"Gauss, J.F., Brandin, C., Heberle, A., L\u00f6we, W.: Smoothing skeleton avatar visualizations using signal processing technology. SN Comput. Sci. 2(6), 1\u201317 (2021)","journal-title":"SN Comput. Sci."},{"issue":"4","key":"36_CR12","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1080\/00224065.1986.11979014","volume":"18","author":"JS Hunter","year":"1986","unstructured":"Hunter, J.S.: The exponentially weighted moving average. J. Qual. Technol. 18(4), 203\u2013210 (1986)","journal-title":"J. Qual. Technol."},{"key":"36_CR13","doi-asserted-by":"crossref","unstructured":"Hyndman, R.J.: Moving averages (2011)","DOI":"10.1007\/978-3-642-04898-2_380"},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3.6m: large scale datasets and predictive methods for 3D human sensing in natural environments. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1325\u20131339 (2013)","DOI":"10.1109\/TPAMI.2013.248"},{"key":"36_CR15","doi-asserted-by":"crossref","unstructured":"Jiang, T., Camgoz, N.C., Bowden, R.: Skeletor: skeletal transformers for robust body-pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3394\u20133402 (2021)","DOI":"10.1109\/CVPRW53098.2021.00378"},{"key":"36_CR16","doi-asserted-by":"crossref","unstructured":"Joo, H., Neverova, N., Vedaldi, A.: Exemplar fine-tuning for 3d human model fitting towards in-the-wild 3D human pose estimation. In: 2021 International Conference on 3D Vision (3DV), pp. 42\u201352. IEEE (2021)","DOI":"10.1109\/3DV53792.2021.00015"},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Kalman, R.E.: A new approach to linear filtering and prediction problems (1960)","DOI":"10.1115\/1.3662552"},{"key":"36_CR18","doi-asserted-by":"crossref","unstructured":"Kanazawa, A., Black, M.J., Jacobs, D.W., Malik, J.: End-to-end recovery of human shape and pose. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7122\u20137131 (2018)","DOI":"10.1109\/CVPR.2018.00744"},{"key":"36_CR19","doi-asserted-by":"crossref","unstructured":"Kanazawa, A., Zhang, J.Y., Felsen, P., Malik, J.: Learning 3D human dynamics from video. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5614\u20135623 (2019)","DOI":"10.1109\/CVPR.2019.00576"},{"issue":"13","key":"36_CR20","doi-asserted-by":"publisher","first-page":"4572","DOI":"10.3390\/s21134572","volume":"21","author":"DY Kim","year":"2021","unstructured":"Kim, D.Y., Chang, J.Y.: Attention-based 3D human pose sequence refinement network. Sensors 21(13), 4572 (2021)","journal-title":"Sensors"},{"key":"36_CR21","doi-asserted-by":"crossref","unstructured":"Kocabas, M., Athanasiou, N., Black, M.J.: Vibe: video inference for human body pose and shape estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5253\u20135263 (2020)","DOI":"10.1109\/CVPR42600.2020.00530"},{"key":"36_CR22","doi-asserted-by":"crossref","unstructured":"Kolotouros, N., Pavlakos, G., Black, M.J., Daniilidis, K.: Learning to reconstruct 3D human pose and shape via model-fitting in the loop. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2252\u20132261 (2019)","DOI":"10.1109\/ICCV.2019.00234"},{"key":"36_CR23","doi-asserted-by":"crossref","unstructured":"Lee, C.H., Lin, C.R., Chen, M.S.: Sliding-window filtering: an efficient algorithm for incremental mining. In: Proceedings of the Tenth International Conference on Information and Knowledge Management, pp. 263\u2013270 (2001)","DOI":"10.1145\/502585.502630"},{"key":"36_CR24","doi-asserted-by":"crossref","unstructured":"Li, J., Bian, S., Zeng, A., Wang, C., Pang, B., Liu, W., Lu, C.: Human pose regression with residual log-likelihood estimation. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01084"},{"key":"36_CR25","doi-asserted-by":"crossref","unstructured":"Li, R., Yang, S., Ross, D.A., Kanazawa, A.: AI choreographer: music conditioned 3D dance generation with AIST++. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 13401\u201313412, October 2021","DOI":"10.1109\/ICCV48922.2021.01315"},{"key":"36_CR26","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., et al.: 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":"36_CR27","doi-asserted-by":"crossref","unstructured":"Luo, Z., Golestaneh, S.A., Kitani, K.M.: 3D human motion estimation via motion compression and refinement. In: Proceedings of the Asian Conference on Computer Vision (2020)","DOI":"10.1007\/978-3-030-69541-5_20"},{"key":"36_CR28","doi-asserted-by":"crossref","unstructured":"von Marcard, T., Henschel, R., Black, M.J., Rosenhahn, B., Pons-Moll, G.: Recovering accurate 3D human pose in the wild using IMUs and a moving camera. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 601\u2013617 (2018)","DOI":"10.1007\/978-3-030-01249-6_37"},{"key":"36_CR29","doi-asserted-by":"crossref","unstructured":"Martinez, J., Hossain, R., Romero, J., Little, J.J.: A simple yet effective baseline for 3D human pose estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2640\u20132649 (2017)","DOI":"10.1109\/ICCV.2017.288"},{"key":"36_CR30","doi-asserted-by":"crossref","unstructured":"Mehta, D., et al.: Monocular 3D human pose estimation in the wild using improved CNN supervision. In: 2017 International Conference on 3D Vision (3DV), pp. 506\u2013516. IEEE (2017)","DOI":"10.1109\/3DV.2017.00064"},{"key":"36_CR31","doi-asserted-by":"crossref","unstructured":"Mehta, D., et al.: XNect: real-time multi-person 3D motion capture with a single RGB camera. ACM Trans. Graph. (TOG) 39(4), 82-1 (2020)","DOI":"10.1145\/3386569.3392410"},{"key":"36_CR32","doi-asserted-by":"crossref","unstructured":"Mehta, D., et al.: Single-shot multi-person 3D pose estimation from monocular RGB. In: 2018 International Conference on 3D Vision (3DV), pp. 120\u2013130 (2018)","DOI":"10.1109\/3DV.2018.00024"},{"issue":"4","key":"36_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3072959.3073596","volume":"36","author":"D Mehta","year":"2017","unstructured":"Mehta, D., et al.: VNect: real-time 3D human pose estimation with a single RGB camera. ACM Trans. Graph. (TOG) 36(4), 1\u201314 (2017)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"36_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/978-3-319-46484-8_29","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Newell","year":"2016","unstructured":"Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483\u2013499. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_29"},{"key":"36_CR35","doi-asserted-by":"crossref","unstructured":"Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3D human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7753\u20137762 (2019)","DOI":"10.1109\/CVPR.2019.00794"},{"issue":"6","key":"36_CR36","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1063\/1.4822961","volume":"4","author":"WH Press","year":"1990","unstructured":"Press, W.H., Teukolsky, S.A.: Savitzky-Golay smoothing filters. Comput. Phys. 4(6), 669\u2013672 (1990)","journal-title":"Comput. Phys."},{"key":"36_CR37","unstructured":"So, D., Le, Q., Liang, C.: The evolved transformer. In: International Conference on Machine Learning, pp. 5877\u20135886. PMLR (2019)"},{"key":"36_CR38","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5693\u20135703 (2019)","DOI":"10.1109\/CVPR.2019.00584"},{"key":"36_CR39","doi-asserted-by":"crossref","unstructured":"Tripathi, S., Ranade, S., Tyagi, A., Agrawal, A.: Posenet3d: learning temporally consistent 3D human pose via knowledge distillation. In: 2020 International Conference on 3D Vision (3DV), pp. 311\u2013321. IEEE (2020)","DOI":"10.1109\/3DV50981.2020.00041"},{"key":"36_CR40","unstructured":"Tsuchida, S., Fukayama, S., Hamasaki, M., Goto, M.: AIST dance video database: multi-genre, multi-dancer, and multi-camera database for dance information processing. In: ISMIR, pp. 501\u2013510 (2019)"},{"key":"36_CR41","doi-asserted-by":"crossref","unstructured":"Van Loan, C.: Computational frameworks for the fast Fourier transform. SIAM (1992)","DOI":"10.1137\/1.9781611970999"},{"key":"36_CR42","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"36_CR43","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1007\/978-3-030-63830-6_47","volume-title":"Neural Information Processing","author":"M V\u00e9ges","year":"2020","unstructured":"V\u00e9ges, M., L\u0151rincz, A.: Temporal smoothing for 3D human pose estimation and localization for occluded people. In: Yang, H., Pasupa, K., Leung, A.C.-S., Kwok, J.T., Chan, J.H., King, I. (eds.) ICONIP 2020. LNCS, vol. 12532, pp. 557\u2013568. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-63830-6_47"},{"key":"36_CR44","doi-asserted-by":"crossref","unstructured":"Wan, Z., Li, Z., Tian, M., Liu, J., Yi, S., Li, H.: Encoder-decoder with multi-level attention for 3D human shape and pose estimation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 13033\u201313042 (2021)","DOI":"10.1109\/ICCV48922.2021.01279"},{"key":"36_CR45","doi-asserted-by":"crossref","unstructured":"Wang, J., Yan, S., Xiong, Y., Lin, D.: Motion guided 3D pose estimation from videos. arXiv abs\/2004.13985 (2020)","DOI":"10.1007\/978-3-030-58601-0_45"},{"issue":"2","key":"36_CR46","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/0165-1684(95)00020-E","volume":"44","author":"IT Young","year":"1995","unstructured":"Young, I.T., Van Vliet, L.J.: Recursive implementation of the gaussian filter. Signal Process. 44(2), 139\u2013151 (1995)","journal-title":"Signal Process."},{"key":"36_CR47","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/978-3-030-58568-6_30","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Zeng","year":"2020","unstructured":"Zeng, A., Sun, X., Huang, F., Liu, M., Xu, Q., Lin, S.: SRNet: improving generalization in 3D human pose estimation with a split-and-recombine approach. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 507\u2013523. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58568-6_30"},{"key":"36_CR48","doi-asserted-by":"crossref","unstructured":"Zeng, A., Sun, X., Yang, L., Zhao, N., Liu, M., Xu, Q.: Learning skeletal graph neural networks for hard 3D pose estimation. In: Proceedings of the IEEE International Conference on Computer Vision (2021)","DOI":"10.1109\/ICCV48922.2021.01124"},{"key":"36_CR49","doi-asserted-by":"crossref","unstructured":"Zhang, S., Zhang, Y., Bogo, F., Pollefeys, M., Tang, S.: Learning motion priors for 4D human body capture in 3D scenes. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11343\u201311353 (2021)","DOI":"10.1109\/ICCV48922.2021.01115"},{"key":"36_CR50","doi-asserted-by":"crossref","unstructured":"Zhao, L., Peng, X., Tian, Y., Kapadia, M., Metaxas, D.N.: Semantic graph convolutional networks for 3D human pose regression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3425\u20133435 (2019)","DOI":"10.1109\/CVPR.2019.00354"},{"key":"36_CR51","doi-asserted-by":"crossref","unstructured":"Zheng, C., Zhu, S., Mendieta, M., Yang, T., Chen, C., Ding, Z.: 3D human pose estimation with spatial and temporal transformers. arXiv preprint arXiv:2103.10455 (2021)","DOI":"10.1109\/ICCV48922.2021.01145"},{"key":"36_CR52","doi-asserted-by":"crossref","unstructured":"Zhou, H., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of AAAI (2021)","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"36_CR53","doi-asserted-by":"crossref","unstructured":"Zhou, K., Bhatnagar, B.L., Lenssen, J.E., Pons-Moll, G.: TOCH: spatio-temporal object correspondence to hand for motion refinement. arXiv, May 2022","DOI":"10.1007\/978-3-031-20062-5_1"},{"key":"36_CR54","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Barnes, C., Lu, J., Yang, J., Li, H.: On the continuity of rotation representations in neural networks. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5738\u20135746 (2019)","DOI":"10.1109\/CVPR.2019.00589"}],"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\/978-3-031-20065-6_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T03:38:54Z","timestamp":1728272334000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20065-6_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200649","9783031200656"],"references-count":54,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20065-6_36","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":"3 November 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)"}}]}}