{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T09:27:45Z","timestamp":1774430865880,"version":"3.50.1"},"reference-count":76,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100004052","name":"King Abdullah University of Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004052","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2023,6,1]]},"DOI":"10.1109\/tpami.2021.3074057","type":"journal-article","created":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T01:18:43Z","timestamp":1618881523000},"page":"6923-6939","source":"Crossref","is-referenced-by-count":112,"title":["DeepGCNs: Making GCNs Go as Deep as CNNs"],"prefix":"10.1109","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0260-5129","authenticated-orcid":false,"given":"Guohao","family":"Li","sequence":"first","affiliation":[{"name":"Visual Computing Center, KAUST, Thuwal, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5249-8734","authenticated-orcid":false,"given":"Matthias","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"Visual Computing Center, KAUST, Thuwal, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2935-8570","authenticated-orcid":false,"given":"Guocheng","family":"Qian","sequence":"additional","affiliation":[{"name":"Visual Computing Center, KAUST, Thuwal, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6518-0933","authenticated-orcid":false,"given":"Itzel C.","family":"Delgadillo","sequence":"additional","affiliation":[{"name":"Visual Computing Center, KAUST, Thuwal, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0901-8333","authenticated-orcid":false,"given":"Abdulellah","family":"Abualshour","sequence":"additional","affiliation":[{"name":"Visual Computing Center, KAUST, Thuwal, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7513-0748","authenticated-orcid":false,"given":"Ali","family":"Thabet","sequence":"additional","affiliation":[{"name":"Visual Computing Center, KAUST, Thuwal, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5534-587X","authenticated-orcid":false,"given":"Bernard","family":"Ghanem","sequence":"additional","affiliation":[{"name":"Visual Computing Center, KAUST, Thuwal, Saudi Arabia"}]}],"member":"263","reference":[{"key":"ref13","article-title":"Joint 2D-3D-Semantic data for indoor scene understanding","author":"armeni","year":"2017"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00952"},{"key":"ref12","article-title":"Multi-scale context aggregation by dilated convolutions","author":"yu","year":"2016"},{"key":"ref56","first-page":"4917","article-title":"Towards deeper graph neural networks with differentiable group normalization","volume":"33","author":"zhou","year":"2020"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00936"},{"key":"ref59","article-title":"SpiderCNN: Deep learning on point sets with parameterized convolutional filters","author":"xu","year":"0"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00100"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00169"},{"key":"ref53","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc Int Conf Med Image Comput Comput -Assisted Intervention"},{"key":"ref52","article-title":"How powerful are graph neural networks?","author":"xu","year":"2019"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00651"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1159"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1209"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.330"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.556"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/78.157290"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-75988-8_28"},{"key":"ref46","article-title":"Gated graph sequence neural networks","author":"li","year":"2016"},{"key":"ref45","first-page":"2224","article-title":"Convolutional networks on graphs for learning molecular fingerprints","author":"duvenaud","year":"2015","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.11"},{"key":"ref47","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume":"37","author":"ioffe","year":"2015"},{"key":"ref42","first-page":"1024","article-title":"Inductive representation learning on large graphs","author":"hamilton","year":"2017","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref41","first-page":"5453","article-title":"Representation learning on graphs with jumping knowledge networks","volume":"80","author":"xu","year":"2018"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00049"},{"key":"ref43","article-title":"Graph attention networks","author":"veli?kovi?","year":"2018"},{"key":"ref49","article-title":"Learning localized generative models for 3D point clouds via graph convolution","author":"valsesia","year":"2019"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"ref4","first-page":"3697","article-title":"Geometric matrix completion with recurrent multi-graph neural networks","author":"monti","year":"2017","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-007-0103-5"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3326362"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1187"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2017.90"},{"key":"ref34","first-page":"5099","article-title":"PointNet++: Deep hierarchical feature learning on point sets in a metric space","author":"qi","year":"2017","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_25"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00278"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.701"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.609"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.16"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2017.00067"},{"key":"ref76","article-title":"Rethinking atrous convolution for semantic image segmentation","author":"chen","year":"2017"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx252"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557109"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10851"},{"key":"ref38","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"0"},{"key":"ref71","article-title":"GaAN: Gated attention networks for learning on large and spatiotemporal graphs","author":"zhang","year":"2018"},{"key":"ref70","first-page":"942","article-title":"Stochastic training of graph convolutional networks with variance reduction","volume":"80","author":"chen","year":"2018"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00163"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014424"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.573"},{"key":"ref68","article-title":"Fast graph representation learning with PyTorch Geometric","author":"fey","year":"2019"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"ref67","first-page":"105","article-title":"Flex-convolution - million-scale point-cloud learning beyond grid-worlds","author":"groh","year":"2018","journal-title":"Proc 14th Asian Conf Comput Vis"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2017.85"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.114"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925"},{"key":"ref20","first-page":"670","article-title":"Graph R-CNN for scene graph generation","author":"yang","year":"2018","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201301"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00979"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00133"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1145\/3272127.3275110"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_21"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01225-0_4"},{"key":"ref28","first-page":"541","article-title":"LSTM-CF: Unifying context modeling and fusion with LSTMs for RGB-D scene labeling","author":"li","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref27","first-page":"17","article-title":"Unstructured point cloud semantic labeling using deep segmentation networks","author":"boulch","year":"2017","journal-title":"Proceedings of the ACM Workshop on 3D Object Retrieval"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.261"},{"key":"ref60","first-page":"828","article-title":"PointCNN: Convolution on X-transformed points","author":"li","year":"2018","journal-title":"Proc 32nd Int Conf Neural Inf Process Syst"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2850061"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58592-1_20"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10120646\/09408381.pdf?arnumber=9408381","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T17:36:20Z","timestamp":1685381780000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9408381\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,1]]},"references-count":76,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2021.3074057","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,1]]}}}