{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T12:50:43Z","timestamp":1754398243507,"version":"3.40.3"},"publisher-location":"Cham","reference-count":63,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030695316"},{"type":"electronic","value":"9783030695323"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-69532-3_29","type":"book-chapter","created":{"date-parts":[[2021,2,26]],"date-time":"2021-02-26T08:04:33Z","timestamp":1614326673000},"page":"470-487","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Semantic Synthesis of Pedestrian Locomotion"],"prefix":"10.1007","author":[{"given":"Maria","family":"Priisalu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ciprian","family":"Paduraru","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aleksis","family":"Pirinen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cristian","family":"Sminchisescu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,27]]},"reference":[{"key":"29_CR1","doi-asserted-by":"crossref","unstructured":"Chang, M.F., et al.: Argoverse: 3d tracking and forecasting with rich maps. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00895"},{"key":"29_CR2","doi-asserted-by":"crossref","unstructured":"Sun, P., et al.: Scalability in perception for autonomous driving: Waymo open dataset (2019)","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"29_CR4","doi-asserted-by":"crossref","unstructured":"Behley, J., et al.: SemanticKITTI: a dataset for semantic scene understanding of lidar sequences. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00939"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Huang, X., et al.: The apolloscape dataset for autonomous driving. In: CVPR Workshops (2018)","DOI":"10.1109\/CVPRW.2018.00141"},{"key":"29_CR6","unstructured":"Kesten, R., et al.: Lyft level 5 AV dataset 2019, vol. 2, p. 5 (2019). https.level5.lyft.com\/dataset"},{"key":"29_CR7","doi-asserted-by":"crossref","unstructured":"Mangalam, K., Adeli, E., Lee, K.H., Gaidon, A., Niebles, J.C.: Disentangling human dynamics for pedestrian locomotion forecasting with noisy supervision. In: The IEEE Winter Conference on Applications of Computer Vision, pp. 2784\u20132793 (2020)","DOI":"10.1109\/WACV45572.2020.9093350"},{"key":"29_CR8","doi-asserted-by":"publisher","first-page":"1803","DOI":"10.1109\/TITS.2018.2836305","volume":"20","author":"RQ M\u00ednguez","year":"2018","unstructured":"M\u00ednguez, R.Q., Alonso, I.P., Fern\u00e1ndez-Llorca, D., Sotelo, M.\u00c1.: Pedestrian path, pose, and intention prediction through Gaussian process dynamical models and pedestrian activity recognition. IEEE Trans. Intell. Transp. Syst. 20, 1803\u20131814 (2018)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"29_CR9","unstructured":"Rasouli, A., Kotseruba, I., Tsotsos, J.K.: Pedestrian action anticipation using contextual feature fusion in stacked RNNs. arXiv preprint arXiv:2005.06582 (2020)"},{"key":"29_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1007\/978-3-319-46454-1_34","volume-title":"Computer Vision \u2013 ECCV 2016","author":"F Bogo","year":"2016","unstructured":"Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., Black, M.J.: Keep it SMPL: automatic estimation of 3D human pose and shape from a single image. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 561\u2013578. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46454-1_34"},{"key":"29_CR11","doi-asserted-by":"crossref","unstructured":"Zanfir, M., Oneata, E., Popa, A.I., Zanfir, A., Sminchisescu, C.: Human synthesis and scene compositing. In: AAAI, pp. 12749\u201312756 (2020)","DOI":"10.1609\/aaai.v34i07.6969"},{"key":"29_CR12","doi-asserted-by":"crossref","unstructured":"Wang, M., et al.: Example-guided style-consistent image synthesis from semantic labeling. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00159"},{"key":"29_CR13","unstructured":"Cheng, S., et al.: Improving 3d object detection through progressive population based augmentation. arXiv preprint arXiv:2004.00831 (2020)"},{"key":"29_CR14","unstructured":"Ho, J., Ermon, S.: Generative adversarial imitation learning. In: NIPS (2016)"},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Rhinehart, N., Kitani, K.M., Vernaza, P.: R2p2: a reparameterized pushforward policy for diverse, precise generative path forecasting. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01261-8_47"},{"key":"29_CR16","doi-asserted-by":"crossref","unstructured":"Li, Y.: Which way are you going? Imitative decision learning for path forecasting in dynamic scenes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00038"},{"key":"29_CR17","unstructured":"van der Heiden, T., Nagaraja, N.S., Weiss, C., Gavves, E.: SafeCritic: collision-aware trajectory prediction. In: British Machine Vision Conference Workshop (2019)"},{"key":"29_CR18","doi-asserted-by":"crossref","unstructured":"Zou, H., Su, H., Song, S., Zhu, J.: Understanding human behaviors in crowds by imitating the decision-making process. ArXiv abs\/1801.08391 (2018)","DOI":"10.1609\/aaai.v32i1.12316"},{"key":"29_CR19","doi-asserted-by":"crossref","unstructured":"Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., Alahi, A.: Social GAN: socially acceptable trajectories with generative adversarial networks. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00240"},{"key":"29_CR20","unstructured":"Kosaraju, V., Sadeghian, A., Mart\u00edn-Mart\u00edn, R., Reid, I., Rezatofighi, H., Savarese, S.: Social-BiGAT: multimodal trajectory forecasting using bicycle-GAN and graph attention networks. In: NeurIPS (2019)"},{"key":"29_CR21","unstructured":"Zhang, L., She, Q., Guo, P.: Stochastic trajectory prediction with social graph network. CoRR abs\/1907.10233 (2019)"},{"key":"29_CR22","doi-asserted-by":"crossref","unstructured":"Huang, Y., Bi, H., Li, Z., Mao, T., Wang, Z.: STGAT: modeling spatial-temporal interactions for human trajectory prediction. In: The IEEE International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00637"},{"key":"29_CR23","doi-asserted-by":"crossref","unstructured":"Mohamed, A., Qian, K., Elhoseiny, M., Claudel, C.: Social-STGCNN: a social spatio-temporal graph convolutional neural network for human trajectory prediction. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.01443"},{"key":"29_CR24","doi-asserted-by":"crossref","unstructured":"Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Li, F., Savarese, S.: Social LSTM: human trajectory prediction in crowded spaces. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.110"},{"key":"29_CR25","doi-asserted-by":"crossref","unstructured":"Lee, N., Choi, W., Vernaza, P., Choy, C.B., Torr, P.H., Chandraker, M.: Desire: distant future prediction in dynamic scenes with interacting agents. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.233"},{"key":"29_CR26","doi-asserted-by":"crossref","unstructured":"Luo, W., Yang, B., Urtasun, R.: Fast and furious: real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00376"},{"key":"29_CR27","doi-asserted-by":"crossref","unstructured":"Zhao, T., et al.: Multi-agent tensor fusion for contextual trajectory prediction. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01240"},{"key":"29_CR28","doi-asserted-by":"crossref","unstructured":"Sadeghian, A., Kosaraju, V., Sadeghian, A., Hirose, N., Rezatofighi, H., Savarese, S.: SoPhie: an attentive GAN for predicting paths compliant to social and physical constraints. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00144"},{"key":"29_CR29","doi-asserted-by":"crossref","unstructured":"Malla, S., Dariush, B., Choi, C.: Titan: future forecast using action priors. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01120"},{"key":"29_CR30","unstructured":"Tanke, J., Weber, A., Gall, J.: Human motion anticipation with symbolic label. CoRR abs\/1912.06079 (2019)"},{"key":"29_CR31","doi-asserted-by":"crossref","unstructured":"Liang, J., Jiang, L., Niebles, J.C., Hauptmann, A.G., Fei-Fei, L.: Peeking into the future: predicting future person activities and locations in videos. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00587"},{"key":"29_CR32","doi-asserted-by":"crossref","unstructured":"Liang, J., Jiang, L., Murphy, K., Yu, T., Hauptmann, A.: The garden of forking paths: towards multi-future trajectory prediction. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01052"},{"key":"29_CR33","doi-asserted-by":"crossref","unstructured":"Liang, J., Jiang, L., Hauptmann, A.: SimAug: learning robust representations from 3d simulation for pedestrian trajectory prediction in unseen cameras. arXiv preprint arXiv:2004.02022 (2020)","DOI":"10.1007\/978-3-030-58601-0_17"},{"key":"29_CR34","doi-asserted-by":"crossref","unstructured":"Makansi, O., Cicek, O., Buchicchio, K., Brox, T.: Multimodal future localization and emergence prediction for objects in egocentric view with a reachability prior. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00441"},{"key":"29_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Hassan, M., Neumann, H., Black, M.J., Tang, S.: Generating 3d people in scenes without people. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6194\u20136204 (2020)","DOI":"10.1109\/CVPR42600.2020.00623"},{"key":"29_CR36","unstructured":"Hong, S., Yan, X., Huang, T.S., Lee, H.: Learning hierarchical semantic image manipulation through structured representations. In: Advances in Neural Information Processing Systems, pp. 2708\u20132718 (2018)"},{"key":"29_CR37","doi-asserted-by":"crossref","unstructured":"Chien, J.T., Chou, C.J., Chen, D.J., Chen, H.T.: Detecting nonexistent pedestrians. In: CVPR (2017)","DOI":"10.1109\/ICCVW.2017.30"},{"key":"29_CR38","doi-asserted-by":"crossref","unstructured":"Li, X., Liu, S., Kim, K., Wang, X., Yang, M.H., Kautz, J.: Putting humans in a scene: learning affordance in 3d indoor environments. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12368\u201312376 (2019)","DOI":"10.1109\/CVPR.2019.01265"},{"key":"29_CR39","doi-asserted-by":"crossref","unstructured":"Lee, D., Pfister, T., Yang, M.H.: Inserting videos into videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10061\u201310070 (2019)","DOI":"10.1109\/CVPR.2019.01030"},{"key":"29_CR40","doi-asserted-by":"crossref","unstructured":"Wang, B., Adeli, E., Chiu, H.K., Huang, D.A., Niebles, J.C.: Imitation learning for human pose prediction. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 7123\u20137132 (2019)","DOI":"10.1109\/ICCV.2019.00722"},{"key":"29_CR41","unstructured":"Wei, M., Miaomiao, L., Mathieu, S., Hongdong, L.: Learning trajectory dependencies for human motion prediction. In: ICCV (2019)"},{"key":"29_CR42","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.1109\/LRA.2019.2895266","volume":"4","author":"X Du","year":"2019","unstructured":"Du, X., Vasudevan, R., Johnson-Roberson, M.: Bio-LSTM: a biomechanically inspired recurrent neural network for 3-d pedestrian pose and gait prediction. IEEE Robot. Autom. Lett. 4, 1501\u20131508 (2019)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"29_CR43","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1007\/978-3-030-58452-8_23","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Z Cao","year":"2020","unstructured":"Cao, Z., Gao, H., Mangalam, K., Cai, Q.-Z., Vo, M., Malik, J.: Long-term human motion prediction with scene context. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 387\u2013404. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_23"},{"key":"29_CR44","doi-asserted-by":"publisher","first-page":"6033","DOI":"10.1109\/LRA.2020.3010742","volume":"5","author":"V Adeli","year":"2020","unstructured":"Adeli, V., Adeli, E., Reid, I., Niebles, J.C., Rezatofighi, H.: Socially and contextually aware human motion and pose forecasting. IEEE Robot. Autom. Lett. 5, 6033\u20136040 (2020)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"29_CR45","unstructured":"Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)"},{"key":"29_CR46","unstructured":"Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn. 8, 229\u2013256 (1992)"},{"key":"29_CR47","unstructured":"Hodgins, J.: CMU graphics lab motion capture database (2015)"},{"key":"29_CR48","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, 1325\u20131339 (2013)","DOI":"10.1109\/TPAMI.2013.248"},{"key":"29_CR49","doi-asserted-by":"crossref","unstructured":"Joo, H., et al.: Panoptic studio: a massively multiview system for social motion capture. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.381"},{"key":"29_CR50","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"29_CR51","doi-asserted-by":"crossref","unstructured":"Holden, D., Komura, T., Saito, J.: Phase-functioned neural networks for character control. ACM Trans. Graph. 36, 42:1\u201342:13 (2017)","DOI":"10.1145\/3072959.3073663"},{"key":"29_CR52","unstructured":"Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289 (2015)"},{"key":"29_CR53","unstructured":"Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, Savannah, GA, USA, November 2\u20134, 2016 (2016)"},{"key":"29_CR54","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)"},{"key":"29_CR55","doi-asserted-by":"publisher","first-page":"1696","DOI":"10.1109\/LRA.2020.2969925","volume":"5","author":"C Sch\u00f6ller","year":"2020","unstructured":"Sch\u00f6ller, C., Aravantinos, V., Lay, F., Knoll, A.: What the constant velocity model can teach us about pedestrian motion prediction. IEEE Robot. Autom. Lett. 5, 1696\u20131703 (2020)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"29_CR56","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1016\/j.sbspro.2013.11.160","volume":"104","author":"S Chandra","year":"2013","unstructured":"Chandra, S., Bharti, A.K.: Speed distribution curves for pedestrians during walking and crossing. Procedia-Soc. Behav. Sci. 104, 660\u2013667 (2013)","journal-title":"Procedia-Soc. Behav. Sci."},{"key":"29_CR57","doi-asserted-by":"crossref","unstructured":"Everett, M., Chen, Y.F., How, J.P.: Motion planning among dynamic, decision-making agents with deep reinforcement learning. In: IROS (2018)","DOI":"10.1109\/IROS.2018.8593871"},{"key":"29_CR58","unstructured":"Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: an open urban driving simulator. In: CoRL (2017)"},{"key":"29_CR59","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"29_CR60","doi-asserted-by":"crossref","unstructured":"Nilsson, D., Sminchisescu, C.: Semantic video segmentation by gated recurrent flow propagation. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00713"},{"key":"29_CR61","doi-asserted-by":"crossref","unstructured":"Schonberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.445"},{"key":"29_CR62","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"29_CR63","doi-asserted-by":"crossref","unstructured":"Zhou, B., et al.: Semantic understanding of scenes through the ADE20K dataset. IJCV 127, 302\u2013321 (2018)","DOI":"10.1007\/s11263-018-1140-0"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2020"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-69532-3_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T02:40:17Z","timestamp":1671417617000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-69532-3_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030695316","9783030695323"],"references-count":63,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-69532-3_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"27 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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":"30 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/accv2020.kyoto\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"768","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":"254","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":"33% - 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":"3","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.","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)"}}]}}