{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T13:26:45Z","timestamp":1740144405695,"version":"3.37.3"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&D Program of China","award":["2018YFC2001600","2018YFC2001602"],"award-info":[{"award-number":["2018YFC2001600","2018YFC2001602"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61473150"],"award-info":[{"award-number":["61473150"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Comput. Intell. Mag."],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1109\/mci.2023.3245690","type":"journal-article","created":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T17:34:46Z","timestamp":1681407286000},"page":"94-103","source":"Crossref","is-referenced-by-count":0,"title":["Deep Pixel Restoration Loop Coding Network [Application Notes]"],"prefix":"10.1109","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4618-7299","authenticated-orcid":false,"given":"Tianxiang","family":"Wang","sequence":"first","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qun","family":"Dai","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muxuan","family":"Yuan","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00937"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00910"},{"article-title":"Unsupervised learning of visual structure using predictive generative networks","year":"2015","author":"Lotter","key":"ref3"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref6","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Shi"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-018-1395-8"},{"key":"ref8","first-page":"1957","article-title":"Attention in convolutional LSTM for gesture recognition","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2020.0209"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2012.2228597"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(81)90024-2"},{"issue":"1","key":"ref12","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"article-title":"Deep predictive coding networks for video prediction and unsupervised learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Lotter","key":"ref13"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"article-title":"UCF101: A dataset of 101 human actions classes from videos in the wild","year":"2012","author":"Soomro","key":"ref15"},{"key":"ref16","first-page":"5622","article-title":"Deep learning for precipitation nowcasting: A benchmark and a new model","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Shi"},{"key":"ref17","first-page":"667","article-title":"Dynamic filter networks","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"De Brabandere"},{"article-title":"Decomposing motion and content for natural video sequence prediction","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Villegas","key":"ref18"},{"key":"ref19","first-page":"879","article-title":"PredRNN: Recurrent neural networks for predictive learning using spatiotemporal LSTM","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref20","first-page":"5110","article-title":"PredRNN++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning","volume-title":"Proc. Int. Conf. Int. Conf. Mach. Learn.","author":"Wang"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6819"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICME46284.2020.9102876"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2004.1334462"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206631"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.155"},{"article-title":"Efficient and information-preserving future frame prediction and beyond","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Yu","key":"ref26"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00191"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3240857"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1002\/wcs.142"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1038\/4580"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2004.11.006"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1038\/nature03689"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1098\/rstb.2014.0169"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s00429-014-0942-2"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-24673-2_3"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s10851-010-0239-x"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1023\/A:1011286029287"},{"article-title":"Deep multi-scale video prediction beyond mean square error","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Mathieu","key":"ref38"},{"article-title":"Stochastic adversarial video prediction","year":"2018","author":"Lee","key":"ref39"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"}],"container-title":["IEEE Computational Intelligence Magazine"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10207\/10102373\/10102376.pdf?arnumber=10102376","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T02:06:07Z","timestamp":1709258767000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10102376\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5]]},"references-count":40,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/mci.2023.3245690","relation":{},"ISSN":["1556-603X","1556-6048"],"issn-type":[{"type":"print","value":"1556-603X"},{"type":"electronic","value":"1556-6048"}],"subject":[],"published":{"date-parts":[[2023,5]]}}}