{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T14:49:31Z","timestamp":1783608571496,"version":"3.55.0"},"reference-count":31,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,1,10]]},"DOI":"10.1109\/icpr48806.2021.9412114","type":"proceedings-article","created":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T22:15:54Z","timestamp":1620252954000},"page":"2551-2558","source":"Crossref","is-referenced-by-count":52,"title":["DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting"],"prefix":"10.1109","author":[{"given":"Alessio","family":"Monti","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessia","family":"Bertugli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simone","family":"Calderara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rita","family":"Cucchiara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref31","year":"0","journal-title":"SportVU - STATS Perform"},{"key":"ref30","article-title":"Trajnet: Towards a benchmark for human trajectory prediction","author":"sadeghian","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref10","article-title":"Auto-encoding variational bayes","volume":"abs 1312 6114","author":"kingma","year":"2013","journal-title":"CoRR"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.51.4282"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2005.09.006"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/1141911.1142008"},{"key":"ref14","first-page":"1441","article-title":"Gaussian Process Dynamical Models","author":"wang","year":"2006","journal-title":"Advances in Neural Information Processing Systems 18"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2018.09.002"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.233"},{"key":"ref17","first-page":"0","article-title":"Social ways: Learning multimodal distributions of pedestrian trajectories with gans","author":"javad","year":"0","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)"},{"key":"ref18","article-title":"STGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction","author":"yingfan","year":"0","journal-title":"The IEEE International Conference on Computer Vision (ICCV)"},{"key":"ref19","article-title":"Graph Attention Networks","author":"veli?kovi?","year":"0","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref28","article-title":"Conditional generative neural system for probabilistic trajectory prediction","author":"jiachen","year":"0","journal-title":"in 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) IEEE"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2016.2581216"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01252-6_45"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2016.7487768"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206641"},{"key":"ref29","article-title":"Forecasting Social Navigation in Crowded Complex Scenes","volume":"abs 1601 998","author":"robicquet","year":"2016","journal-title":"CoRR"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995586"},{"key":"ref8","article-title":"Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks","author":"kosaraju","year":"0","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00240"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01240"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.493"},{"key":"ref20","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"vaswani","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref22","first-page":"1278","article-title":"Stochastic Backpropagation and Approximate Inference in Deep Generative Models","volume":"32","author":"rezende","year":"0","journal-title":"Proceedings of the 31st International Conference on Machine Learning Cycle 2 ser JMLR Proceedings"},{"key":"ref21","article-title":"Generating multi-agent trajectories using programmatic weak supervision","author":"zhan","year":"0","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref24","first-page":"3581","article-title":"Semi-supervised learning with deep generative models","volume":"2","author":"kingma","year":"2014","journal-title":"Proceedings of the 27th International Conference on Neural Information Processing Systems"},{"key":"ref23","first-page":"2980","article-title":"A Recurrent Latent Variable Model for Sequential Data","author":"chung","year":"2015","journal-title":"Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2 ser NIPS'15"},{"key":"ref26","article-title":"Conditional flow variational autoencoders for structured sequence prediction","volume":"abs 1908 9008","author":"bhattacharyya","year":"2019","journal-title":"ArXiv"},{"key":"ref25","first-page":"3483","article-title":"Learning structured output representation using deep conditional generative models","volume":"2","author":"sohn","year":"2015","journal-title":"Proceedings of the 28th International Conference on Neural Information Processing Systems"}],"event":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","location":"Milan, Italy","start":{"date-parts":[[2021,1,10]]},"end":{"date-parts":[[2021,1,15]]}},"container-title":["2020 25th International Conference on Pattern Recognition (ICPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9411940\/9411911\/09412114.pdf?arnumber=9412114","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T11:40:49Z","timestamp":1652182849000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9412114\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,10]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/icpr48806.2021.9412114","relation":{},"subject":[],"published":{"date-parts":[[2021,1,10]]}}}