{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:16:36Z","timestamp":1761808596533,"version":"3.40.3"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031250552"},{"type":"electronic","value":"9783031250569"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-25056-9_46","type":"book-chapter","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T12:09:56Z","timestamp":1676376596000},"page":"726-742","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Learning 3D Semantics From\u00a0Pose-Noisy 2D Images with\u00a0Hierarchical Full Attention Network"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7679-9339","authenticated-orcid":false,"given":"Yuhang","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9839-476X","authenticated-orcid":false,"given":"Lin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4812-6273","authenticated-orcid":false,"given":"Junkun","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4925-0572","authenticated-orcid":false,"given":"Long","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,15]]},"reference":[{"issue":"12","key":"46_CR1","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"46_CR2","doi-asserted-by":"crossref","unstructured":"Behley, J., et al.: SemanticKITTI: a dataset for semantic scene understanding of LiDAR sequences. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00939"},{"issue":"4","key":"46_CR3","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"46_CR4","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Yang, Y., Wang, J., Xu, W., Yuille, A.L.: Attention to scale: scale-aware semantic image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3640\u20133649 (2016)","DOI":"10.1109\/CVPR.2016.396"},{"key":"46_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","volume-title":"Computer Vision \u2013 ECCV 2018","author":"L-C Chen","year":"2018","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with Atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833\u2013851. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49"},{"key":"46_CR6","doi-asserted-by":"crossref","unstructured":"Chen, L., He, Y., Chen, J., Li, Q., Zou, Q.: Transforming a 3-D lidar point cloud into a 2-D dense depth map through a parameter self-adaptive framework. IEEE Trans. Intell. Transp. Syst. 16, 165\u2013 176 (2017)","DOI":"10.1109\/TITS.2016.2564640"},{"key":"46_CR7","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"46_CR8","doi-asserted-by":"crossref","unstructured":"Dai, A., Ritchie, D., Bokeloh, M., Reed, S., Sturm, J., Nie\u00dfner, M.: Scancomplete: large-scale scene completion and semantic segmentation for 3D scans. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4578\u20134587 (2018)","DOI":"10.1109\/CVPR.2018.00481"},{"key":"46_CR9","doi-asserted-by":"crossref","unstructured":"Furukawa, Y., Ponce, J.: Accurate, dense, and Robust multi-view stereopsis. In: Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR) (2007)","DOI":"10.1109\/CVPR.2007.383246"},{"key":"46_CR10","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3354\u20133361 (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"46_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1007\/978-3-319-46487-9_32","volume-title":"Computer Vision \u2013 ECCV 2016","author":"G Ghiasi","year":"2016","unstructured":"Ghiasi, G., Fowlkes, C.C.: Laplacian pyramid reconstruction and refinement for semantic segmentation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 519\u2013534. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_32"},{"key":"46_CR12","doi-asserted-by":"crossref","unstructured":"Graham, B., Engelcke, M., Van Der Maaten, L.: 3D semantic segmentation with submanifold sparse convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9224\u20139232 (2018)","DOI":"10.1109\/CVPR.2018.00961"},{"key":"46_CR13","doi-asserted-by":"crossref","unstructured":"Guerry, J., Boulch, A., Le Saux, B., Moras, J., Plyer, A., Filliat, D.: SnapNet-r: consistent 3D multi-view semantic labeling for robotics. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 669\u2013678 (2017)","DOI":"10.1109\/ICCVW.2017.85"},{"key":"46_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual Learning for Image Recognition. In: Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"46_CR15","unstructured":"He, Y., et al.: Deep learning based 3D segmentation: a survey. arXiv preprint arXiv:2103.05423 (2021)"},{"key":"46_CR16","doi-asserted-by":"crossref","unstructured":"He, Y., Chen, L., Li, M.: Sparse depth map upsampling with RGB image and anisotropic diffusion tensor. In: 2015 IEEE Intelligent Vehicles Symposium (IV) (2015)","DOI":"10.1109\/IVS.2015.7225687"},{"key":"46_CR17","doi-asserted-by":"crossref","unstructured":"Hu, Q., et al.: Randla-net: efficient semantic segmentation of large-scale point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11108\u201311117 (2020)","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"46_CR18","doi-asserted-by":"crossref","unstructured":"Hu, Q., et al.: Randla-Net: efficient semantic segmentation of large-scale point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"46_CR19","doi-asserted-by":"crossref","unstructured":"Hua, B.S., Tran, M.K., Yeung, S.K.: Pointwise convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 984\u2013993 (2018)","DOI":"10.1109\/CVPR.2018.00109"},{"key":"46_CR20","unstructured":"Huang, J., You, S.: Point cloud labeling using 3D convolutional neural network. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2670\u20132675. IEEE (2016)"},{"key":"46_CR21","doi-asserted-by":"crossref","unstructured":"Huang, Z., et al.: CCNet: Criss-Cross attention for semantic segmentation. IEEE Trans. Pattern Analy. Mach. Intell. (TPAMI) (2020)","DOI":"10.1109\/ICCV.2019.00069"},{"key":"46_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/978-3-319-64689-3_8","volume-title":"Computer Analysis of Images and Patterns","author":"FJ Lawin","year":"2017","unstructured":"Lawin, F.J., Danelljan, M., Tosteberg, P., Bhat, G., Khan, F.S., Felsberg, M.: Deep projective 3D semantic segmentation. In: Felsberg, M., Heyden, A., Kr\u00fcger, N. (eds.) CAIP 2017. LNCS, vol. 10424, pp. 95\u2013107. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-64689-3_8"},{"key":"46_CR23","unstructured":"Lee, J., Lee, Y., Kim, J., Kosiorek, A., Choi, S., Teh, Y.W.: Set transformer: a framework for attention-based permutation-invariant neural networks. In: Proceedings of the 36th International Conference on Machine Learning (ICML), pp. 3744\u20133753 (2019)"},{"key":"46_CR24","doi-asserted-by":"crossref","unstructured":"Li, G., Muller, M., Thabet, A., Ghanem, B.: Deepgcns: Can GCNs go as deep as CNNS? In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (CVPR), pp. 9267\u20139276 (2019)","DOI":"10.1109\/ICCV.2019.00936"},{"key":"46_CR25","doi-asserted-by":"crossref","unstructured":"Liang, Z., Yang, M., Deng, L., Wang, C., Wang, B.: Hierarchical depthwise graph convolutional neural network for 3d semantic segmentation of point clouds. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 8152\u20138158. IEEE (2019)","DOI":"10.1109\/ICRA.2019.8794052"},{"key":"46_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"key":"46_CR27","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"46_CR28","doi-asserted-by":"crossref","unstructured":"Meng, H.Y., Gao, L., Lai, Y.K., Manocha, D.: Vv-Net: Voxel VAE net with group convolutions for point cloud segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 8500\u20138508 (2019)","DOI":"10.1109\/ICCV.2019.00859"},{"key":"46_CR29","doi-asserted-by":"crossref","unstructured":"Milioto, A., Vizzo, I., Behley, J., Stachniss, C.: RangeNet++: fast and accurate LiDAR semantic segmentation. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (2019)","DOI":"10.1109\/IROS40897.2019.8967762"},{"key":"46_CR30","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1520\u20131528 (2015)","DOI":"10.1109\/ICCV.2015.178"},{"key":"46_CR31","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2017)"},{"key":"46_CR32","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. In: Conference on Neural Information Processing Systems (NeurIPS) (2017)"},{"key":"46_CR33","unstructured":"Ren, S., He, K., Girshich, R., Sun, J.: Faster r-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (NeurIPS) (2015)"},{"key":"46_CR34","doi-asserted-by":"crossref","unstructured":"Riegler, G., Osman Ulusoy, A., Geiger, A.: Octnet: learning deep 3D representations at high resolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3577\u20133586 (2017)","DOI":"10.1109\/CVPR.2017.701"},{"key":"46_CR35","unstructured":"Sutskever, I., Vinyals, O., V. Le, Q.: Sequence to sequence learning with neural networks. In: Conference on Neural Information Processing Systems (NeurIPS) (2014)"},{"key":"46_CR36","doi-asserted-by":"crossref","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., Guibas, L.J.: Kpconv: flexible and deformable convolution for point clouds. In: Proceedings of the IEEE International Conference on Computer Vision (CVPR) (2019)","DOI":"10.1109\/ICCV.2019.00651"},{"key":"46_CR37","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Conference on Advances in Neural Information Processing Systems (NeurIPS) (2017)"},{"key":"46_CR38","doi-asserted-by":"crossref","unstructured":"Wang, S., Suo, S., Ma, W.C., Pokrovsky, A., Urtasun, R.: Deep parametric continuous convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2589\u20132597 (2018)","DOI":"10.1109\/CVPR.2018.00274"},{"key":"46_CR39","doi-asserted-by":"crossref","unstructured":"Wang, Y., Li, J., Metze, F.: A comparison of five multiple instance learning pooling functions for sound event detection with weak labeling. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2019)","DOI":"10.1109\/ICASSP.2019.8682847"},{"key":"46_CR40","doi-asserted-by":"crossref","unstructured":"Wu, W., Qi, Z., Fuxin, L.: Pointconv: deep convolutional networks on 3D point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9621\u20139630 (2019)","DOI":"10.1109\/CVPR.2019.00985"},{"issue":"4","key":"46_CR41","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1109\/MGRS.2019.2937630","volume":"8","author":"Y Xie","year":"2020","unstructured":"Xie, Y., Tian, J., Zhu, X.X.: Linking points with labels in 3D: a review of point cloud semantic segmentation. IEEE Geosc. Remote Sens Mag. 8(4), 38\u201359 (2020)","journal-title":"IEEE Geosc. Remote Sens Mag."},{"key":"46_CR42","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.neucom.2020.03.086","volume":"402","author":"Z Xie","year":"2020","unstructured":"Xie, Z., Chen, J., Peng, B.: Point clouds learning with attention-based graph convolution networks. Neurocomputing 402, 245\u2013255 (2020)","journal-title":"Neurocomputing"},{"key":"46_CR43","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/978-3-030-01234-2_25","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Ye","year":"2018","unstructured":"Ye, X., Li, J., Huang, H., Du, L., Zhang, X.: 3D recurrent neural networks with context fusion for point cloud semantic segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 415\u2013430. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_25"},{"key":"46_CR44","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1007\/978-3-030-11015-4_24","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"W Zeng","year":"2019","unstructured":"Zeng, W., Gevers, T.: 3DContextNet: K-d tree guided hierarchical learning of point clouds using local and global contextual cues. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11131, pp. 314\u2013330. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11015-4_24"},{"key":"46_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: PolarNet: an improved grid representation for online lidar point clouds semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9601\u20139610 (2020)","DOI":"10.1109\/CVPR42600.2020.00962"},{"key":"46_CR46","doi-asserted-by":"crossref","unstructured":"Zhao, H., Jiang, L., Fu, C.W., Jia, J.: Pointweb: enhancing local neighborhood features for point cloud processing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5565\u20135573 (2019)","DOI":"10.1109\/CVPR.2019.00571"},{"key":"46_CR47","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"46_CR48","doi-asserted-by":"crossref","unstructured":"Zhu, Y., et al.: Improving semantic segmentation via video propagation and label relaxation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00906"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25056-9_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:38:33Z","timestamp":1710268713000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25056-9_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250552","9783031250569"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25056-9_46","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"15 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1645","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.21","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.91","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"From the workshops, 367 reviewed full papers have been selected for publication","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}