{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:43:03Z","timestamp":1778082183897,"version":"3.51.4"},"publisher-location":"Cham","reference-count":62,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585570","type":"print"},{"value":"9783030585587","type":"electronic"}],"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\/978-3-030-58558-7_4","type":"book-chapter","created":{"date-parts":[[2020,10,28]],"date-time":"2020-10-28T09:03:08Z","timestamp":1603875788000},"page":"53-70","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["View-Invariant Probabilistic Embedding for Human Pose"],"prefix":"10.1007","author":[{"given":"Jennifer J.","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaping","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang-Chieh","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Florian","family":"Schroff","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hartwig","family":"Adam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,29]]},"reference":[{"key":"4_CR1","doi-asserted-by":"crossref","unstructured":"Akhter, I., Black, M.J.: Pose-conditioned joint angle limits for 3D human pose reconstruction. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298751"},{"key":"4_CR2","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: CVPR (2014)","DOI":"10.1109\/CVPR.2014.471"},{"key":"4_CR3","unstructured":"Bojchevski, A., G\u00fcnnemann, S.: Deep Gaussian embedding of graphs: Unsupervised inductive learning via ranking. In: ICLR (2018)"},{"key":"4_CR4","doi-asserted-by":"crossref","unstructured":"Bromley, J., Guyon, I., LeCun, Y., S\u00e4ckinger, E., Shah, R.: Signature verification using a \u201csiamese\u201d time delay neural network. In: NeurIPS (1994)","DOI":"10.1142\/9789812797926_0003"},{"issue":"3","key":"4_CR5","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1109\/TCYB.2017.2756840","volume":"48","author":"C Cao","year":"2017","unstructured":"Cao, C., Zhang, Y., Zhang, C., Lu, H.: Body joint guided 3-D deep convolutional descriptors for action recognition. IEEE Trans. Cybern. 48(3), 1095\u20131108 (2017)","journal-title":"IEEE Trans. Cybern."},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Chen, C.H., Ramanan, D.: 3D human pose estimation = 2D pose estimation + matching. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.610"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Chen, C.H., Tyagi, A., Agrawal, A., Drover, D., Stojanov, S., Rehg, J.M.: Unsupervised 3D pose estimation with geometric self-supervision. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00586"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"Chu, R., Sun, Y., Li, Y., Liu, Z., Zhang, C., Wei, Y.: Vehicle re-identification with viewpoint-aware metric learning. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00837"},{"key":"4_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/978-3-030-11018-5_7","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"D Drover","year":"2019","unstructured":"Drover, D., M. V, R., Chen, C.-H., Agrawal, A., Tyagi, A., Huynh, C.P.: Can 3D pose be learned from 2D projections alone? In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11132, pp. 78\u201394. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11018-5_7"},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"Du, W., Wang, Y., Qiao, Y.: RPAN: an end-to-end recurrent pose-attention network for action recognition in videos. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.402"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Dwibedi, D., Aytar, Y., Tompson, J., Sermanet, P., Zisserman, A.: Temporal cycle-consistency learning. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00190"},{"key":"4_CR12","unstructured":"Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: CVPR (2006)"},{"key":"4_CR13","unstructured":"Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv:1703.07737 (2017)"},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Ho, C.H., Morgado, P., Persekian, A., Vasconcelos, N.: PIEs: pose invariant embeddings. In: CVPR, pp. 12377\u201312386 (2019)","DOI":"10.1109\/CVPR.2019.01266"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Hu, W., Zhu, S.C.: Learning a probabilistic model mixing 3D and 2D primitives for view invariant object recognition. In: CVPR (2010)","DOI":"10.1109\/CVPR.2010.5539910"},{"key":"4_CR16","unstructured":"Huang, C., Loy, C.C., Tang, X.: Local similarity-aware deep feature embedding. In: NeurIPS (2016)"},{"key":"4_CR17","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1109\/TPAMI.2013.248","volume":"36","author":"C Ionescu","year":"2013","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 TPAMI 36, 1325\u20131339 (2013)","journal-title":"IEEE TPAMI"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Iqbal, U., Garbade, M., Gall, J.: Pose for action-action for pose. In: FG (2017)","DOI":"10.1109\/FG.2017.61"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Iscen, A., Tolias, G., Avrithis, Y., Chum, O.: Mining on manifolds: metric learning without labels. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00797"},{"key":"4_CR20","doi-asserted-by":"crossref","unstructured":"Iskakov, K., Burkov, E., Lempitsky, V., Malkov, Y.: Learnable triangulation of human pose. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00781"},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"Jammalamadaka, N., Zisserman, A., Eichner, M., Ferrari, V., Jawahar, C.: Video retrieval by mimicking poses. In: ACM ICMR (2012)","DOI":"10.1145\/2324796.2324838"},{"issue":"1","key":"4_CR22","first-page":"13","volume":"40","author":"X Ji","year":"2009","unstructured":"Ji, X., Liu, H.: Advances in view-invariant human motion analysis: a review. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 40(1), 13\u201324 (2009)","journal-title":"IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.)"},{"key":"4_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1007\/978-3-540-85563-7_93","volume-title":"Knowledge-Based Intelligent Information and Engineering Systems","author":"X Ji","year":"2008","unstructured":"Ji, X., Liu, H., Li, Y., Brown, D.: Visual-based view-invariant human motion analysis: a review. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008. LNCS (LNAI), vol. 5177, pp. 741\u2013748. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-85563-7_93"},{"key":"4_CR24","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: NeurIPS (2017)"},{"key":"4_CR25","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. In: ICLR (2014)"},{"key":"4_CR26","doi-asserted-by":"crossref","unstructured":"Kocabas, M., Karagoz, S., Akbas, E.: Self-supervised learning of 3D human pose using multi-view geometry. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00117"},{"key":"4_CR27","unstructured":"LeCun, Y., Huang, F.J., Bottou, L., et al.: Learning methods for generic object recognition with invariance to pose and lighting. In: CVPR (2004)"},{"key":"4_CR28","unstructured":"Li, J., Wong, Y., Zhao, Q., Kankanhalli, M.: Unsupervised learning of view-invariant action representations. In: NeurIPS (2018)"},{"key":"4_CR29","doi-asserted-by":"publisher","first-page":"70061","DOI":"10.1109\/ACCESS.2018.2880231","volume":"6","author":"J Liu","year":"2018","unstructured":"Liu, J., Akhtar, N., Ajmal, M.: Viewpoint invariant action recognition using RGB-D videos. IEEE Access 6, 70061\u201370071 (2018)","journal-title":"IEEE Access"},{"key":"4_CR30","doi-asserted-by":"crossref","unstructured":"Liu, M., Yuan, J.: Recognizing human actions as the evolution of pose estimation maps. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00127"},{"key":"4_CR31","doi-asserted-by":"crossref","unstructured":"Luvizon, D.C., Tabia, H., Picard, D.: Multi-task deep learning for real-time 3D human pose estimation and action recognition. arXiv:1912.08077 (2019)","DOI":"10.1109\/TPAMI.2020.2976014"},{"key":"4_CR32","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: ICCV (2017)","DOI":"10.1109\/ICCV.2017.288"},{"key":"4_CR33","doi-asserted-by":"crossref","unstructured":"Mehta, D., et al.: Monocular 3D human pose estimation in the wild using improved CNN supervision. In: 3DV (2017)","DOI":"10.1109\/3DV.2017.00064"},{"key":"4_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1007\/978-3-319-46448-0_32","volume-title":"Computer Vision \u2013 ECCV 2016","author":"I Misra","year":"2016","unstructured":"Misra, I., Zitnick, C.L., Hebert, M.: Shuffle and learn: unsupervised learning using temporal order verification. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 527\u2013544. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_32"},{"key":"4_CR35","unstructured":"Mori, G., et al.: Pose embeddings: A deep architecture for learning to match human poses. arXiv:1507.00302 (2015)"},{"key":"4_CR36","doi-asserted-by":"crossref","unstructured":"Nie, B.X., Xiong, C., Zhu, S.C.: Joint action recognition and pose estimation from video. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298734"},{"key":"4_CR37","unstructured":"Oh, S.J., Murphy, K., Pan, J., Roth, J., Schroff, F., Gallagher, A.: Modeling uncertainty with hedged instance embedding. In: ICLR (2019)"},{"key":"4_CR38","doi-asserted-by":"crossref","unstructured":"Oh Song, H., Xiang, Y., Jegelka, S., Savarese, S.: Deep metric learning via lifted structured feature embedding. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.434"},{"key":"4_CR39","first-page":"178","volume":"104","author":"EJ Ong","year":"2006","unstructured":"Ong, E.J., Micilotta, A.S., Bowden, R., Hilton, A.: Viewpoint invariant exemplar-based 3D human tracking. CVIU 104, 178\u2013189 (2006)","journal-title":"CVIU"},{"key":"4_CR40","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1007\/978-3-030-01264-9_17","volume-title":"Computer Vision \u2013 ECCV 2018","author":"G Papandreou","year":"2018","unstructured":"Papandreou, G., Zhu, T., Chen, L.-C., Gidaris, S., Tompson, J., Murphy, K.: PersonLab: person pose estimation and instance segmentation with a bottom-up, part-based, geometric embedding model. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018. LNCS, vol. 11218, pp. 282\u2013299. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01264-9_17"},{"key":"4_CR41","doi-asserted-by":"crossref","unstructured":"Papandreou, G., et al.: Towards accurate multi-person pose estimation in the wild. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.395"},{"key":"4_CR42","doi-asserted-by":"crossref","unstructured":"Parkhi, O.M., Vedaldi, A., Zisserman, A., et al.: Deep face recognition. In: BMVC (2015)","DOI":"10.5244\/C.29.41"},{"key":"4_CR43","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: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00794"},{"key":"4_CR44","doi-asserted-by":"crossref","unstructured":"Qiu, H., Wang, C., Wang, J., Wang, N., Zeng, W.: Cross View Fusion for 3D Human Pose Estimation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00444"},{"key":"4_CR45","unstructured":"Rao, C., Shah, M.: View-invariance in action recognition. In: CVPR (2001)"},{"key":"4_CR46","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/978-3-030-01249-6_5","volume-title":"Computer Vision \u2013 ECCV 2018","author":"MRI Hossain","year":"2018","unstructured":"Hossain, M.R.I., Little, J.J.: Exploiting temporal information for 3D human pose estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 69\u201386. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_5"},{"key":"4_CR47","doi-asserted-by":"crossref","unstructured":"Rhodin, H., Constantin, V., Katircioglu, I., Salzmann, M., Fua, P.: Neural scene decomposition for multi-person motion capture. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00789"},{"key":"4_CR48","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1007\/978-3-030-01249-6_46","volume-title":"Computer Vision \u2013 ECCV 2018","author":"H Rhodin","year":"2018","unstructured":"Rhodin, H., Salzmann, M., Fua, P.: Unsupervised geometry-aware representation for 3D human pose estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 765\u2013782. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_46"},{"key":"4_CR49","doi-asserted-by":"crossref","unstructured":"Rhodin, H., et al.: Learning monocular 3D human pose estimation from multi-view images. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00880"},{"key":"4_CR50","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"4_CR51","doi-asserted-by":"crossref","unstructured":"Sermanet, P., et al.: Time-contrastive networks: self-supervised learning from video. In: ICRA (2018)","DOI":"10.1109\/ICRA.2018.8462891"},{"key":"4_CR52","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1007\/978-3-030-01231-1_33","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Sun","year":"2018","unstructured":"Sun, X., Xiao, B., Wei, F., Liang, S., Wei, Y.: Integral human pose regression. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 536\u2013553. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01231-1_33"},{"key":"4_CR53","doi-asserted-by":"crossref","unstructured":"Tekin, B., M\u00e1rquez-Neila, P., Salzmann, M., Fua, P.: Learning to fuse 2D and 3D image cues for monocular body pose estimation. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.425"},{"key":"4_CR54","doi-asserted-by":"crossref","unstructured":"Tome, D., Toso, M., Agapito, L., Russell, C.: Rethinking pose in 3D: multi-stage refinement and recovery for markerless motion capture. In: 3DV (2018)","DOI":"10.1109\/3DV.2018.00061"},{"key":"4_CR55","unstructured":"Vilnis, L., McCallum, A.: Word representations via Gaussian embedding. In: ICLR (2015)"},{"key":"4_CR56","doi-asserted-by":"crossref","unstructured":"Wang, J., et al.: Learning fine-grained image similarity with deep ranking. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.180"},{"key":"4_CR57","doi-asserted-by":"crossref","unstructured":"Wohlhart, P., Lepetit, V.: Learning descriptors for object recognition and 3D pose estimation. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298930"},{"key":"4_CR58","doi-asserted-by":"crossref","unstructured":"Wu, C.Y., Manmatha, R., Smola, A.J., Krahenbuhl, P.: Sampling matters in deep embedding learning. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.309"},{"key":"4_CR59","doi-asserted-by":"crossref","unstructured":"Xia, L., Chen, C.C., Aggarwal, J.K.: View invariant human action recognition using histograms of 3D joints. In: CVPRW (2012)","DOI":"10.1109\/CVPRW.2012.6239233"},{"key":"4_CR60","doi-asserted-by":"crossref","unstructured":"Zhang, W., Zhu, M., Derpanis, K.G.: From actemes to action: a strongly-supervised representation for detailed action understanding. In: ICCV (2013)","DOI":"10.1109\/ICCV.2013.280"},{"key":"4_CR61","first-page":"4500","volume":"28","author":"L Zheng","year":"2019","unstructured":"Zheng, L., Huang, Y., Lu, H., Yang, Y.: Pose invariant embedding for deep person re-identification. IEEE TIP 28, 4500\u20134509 (2019)","journal-title":"IEEE TIP"},{"key":"4_CR62","doi-asserted-by":"crossref","unstructured":"Zhou, X., Huang, Q., Sun, X., Xue, X., Wei, Y.: Towards 3D human pose estimation in the wild: a weakly-supervised approach. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.51"}],"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\/978-3-030-58558-7_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T08:41:46Z","timestamp":1730104906000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58558-7_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585570","9783030585587"],"references-count":62,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58558-7_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 October 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.","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)"}}]}}