{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T17:17:07Z","timestamp":1787851027077,"version":"build-2784847793"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100007957","name":"Chongqing Municipal Education Commission","doi-asserted-by":"publisher","award":["KJQN202201109"],"award-info":[{"award-number":["KJQN202201109"]}],"id":[{"id":"10.13039\/501100007957","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005230","name":"Chongqing Natural Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.neucom.2026.133846","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T07:01:21Z","timestamp":1778137281000},"page":"133846","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Spatio-temporal adaptive diffusion network with improved autoencoder for video anomaly detection"],"prefix":"10.1016","volume":"694","author":[{"given":"Hongmin","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dingding","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianqian","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.neucom.2026.133846_bib0005","first-page":"14","article-title":"Overview of video based human abnormal behavior recognition and detection methods","volume":"37","author":"Zhang","year":"2022","journal-title":"Control Decis."},{"issue":"1","key":"10.1016\/j.neucom.2026.133846_bib0010","doi-asserted-by":"crossref","DOI":"10.1155\/2023\/7868415","article-title":"AD-graph: weakly supervised anomaly detection graph neural network","volume":"2023","author":"Ullah","year":"2023","journal-title":"Int. J. Intell. Syst."},{"key":"10.1016\/j.neucom.2026.133846_bib0015","doi-asserted-by":"crossref","first-page":"41403","DOI":"10.1109\/ACCESS.2022.3164711","article-title":"Skeleton-based ST-GCN for human action recognition with extended skeleton graph and partitioning strategy","volume":"10","author":"Wang","year":"2022","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.neucom.2026.133846_bib0020","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.3390\/s24051464","article-title":"Unsupervised conditional diffusion models in video anomaly detection for monitoring dust pollution","volume":"24","author":"Cai","year":"2024","journal-title":"Sensors"},{"key":"10.1016\/j.neucom.2026.133846_bib0025","series-title":"2023 IEEE International Conference on Robotics and Automation (ICRA)","first-page":"8246","article-title":"A generic diffusion-based approach for 3D human pose prediction in the wild","author":"Saadatnejad","year":"2023"},{"key":"10.1016\/j.neucom.2026.133846_bib0030","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"18000","article-title":"Executing your commands via motion diffusion in latent space","author":"Chen","year":"2023"},{"key":"10.1016\/j.neucom.2026.133846_bib0035","author":"KipfT N"},{"issue":"4","key":"10.1016\/j.neucom.2026.133846_bib0040","first-page":"1","article-title":"Survey of graph neural network","volume":"47","author":"Jianzong","year":"2021","journal-title":"Comput. Eng."},{"issue":"4","key":"10.1016\/j.neucom.2026.133846_bib0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2022.102983","article-title":"Spatio-temporal graph-based CNNs for anomaly detection in weakly-labeled videos","volume":"59","author":"Mu","year":"2022","journal-title":"Inf. Process. Manag."},{"key":"10.1016\/j.neucom.2026.133846_bib0050","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.neucom.2019.12.148","article-title":"Normal graph: spatial temporal graph convolutional networks based prediction network for skeleton based video anomaly detection","volume":"444","author":"Luo","year":"2021","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.neucom.2026.133846_bib0055","first-page":"48","article-title":"Video anomaly detection based on space-time fusion graph network learning","volume":"58","author":"Hang","year":"2021","journal-title":"J. Comput. Res. Dev."},{"issue":"20","key":"10.1016\/j.neucom.2026.133846_bib0060","first-page":"10","article-title":"Graph attention networks","volume":"1050","author":"Velickovic","year":"2017","journal-title":"Stat."},{"key":"10.1016\/j.neucom.2026.133846_bib0065","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","article-title":"Spatial temporal graph convolutional networks for skeleton-based action recognition","volume":"vol. 32","author":"Yan","year":"2018"},{"key":"10.1016\/j.neucom.2026.133846_bib0070","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"10539","article-title":"Graph embedded pose clustering for anomaly detection","author":"Markovitz","year":"2020"},{"key":"10.1016\/j.neucom.2026.133846_bib0075","first-page":"1","article-title":"AnomalyNet: a spatiotemporal motion-aware CNN approach for detecting anomalies in real-world autonomous surveillance","author":"Mumtaz","year":"2024","journal-title":"Vis. Comput."},{"key":"10.1016\/j.neucom.2026.133846_bib0080","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"650","article-title":"Anoddpm: anomaly detection with denoising diffusion probabilistic models using simplex noise","author":"Wyatt","year":"2022"},{"key":"10.1016\/j.neucom.2026.133846_bib0085","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"8472","article-title":"A diffusion-based framework for multi-class anomaly detection","volume":"vol. 38","author":"He","year":"2024"},{"key":"10.1016\/j.neucom.2026.133846_bib0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111387","article-title":"DiffTAD: denoising diffusion probabilistic models for vehicle trajectory anomaly detection","volume":"286","author":"Li","year":"2024","journal-title":"Knowl.-based Syst."},{"key":"10.1016\/j.neucom.2026.133846_bib0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124013","article-title":"Diffusion-based normality pre-training for weakly supervised video anomaly detection","volume":"251","author":"Basak","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.133846_bib0100","series-title":"European Conference on Computer Vision","first-page":"91","article-title":"R3D-AD: reconstruction via diffusion for 3D anomaly detection","author":"Zhou","year":"2025"},{"issue":"9","key":"10.1016\/j.neucom.2026.133846_bib0105","doi-asserted-by":"crossref","first-page":"8398","DOI":"10.1109\/TCSVT.2024.3382633","article-title":"VADiffusion: compressed domain information guided conditional diffusion for video anomaly detection","volume":"34","author":"Liu","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"21","key":"10.1016\/j.neucom.2026.133846_bib0110","doi-asserted-by":"crossref","first-page":"35928","DOI":"10.1109\/JSEN.2024.3453437","article-title":"Video anomaly detection via motion completion diffusion for intelligent surveillance system","volume":"24","author":"Xue","year":"2024","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.neucom.2026.133846_bib0115","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"10318","article-title":"Multimodal motion conditioned diffusion model for skeleton-based video anomaly detection","author":"Flaborea","year":"2023"},{"issue":"2","key":"10.1016\/j.neucom.2026.133846_bib0120","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1109\/TETCI.2024.3358103","article-title":"Skeletal video anomaly detection using deep learning: survey, challenges, and future directions","volume":"8","author":"Mishra","year":"2024","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"issue":"4","key":"10.1016\/j.neucom.2026.133846_bib0125","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.1007\/s00530-022-00915-9","article-title":"Predicting skeleton trajectories using a skeleton-transformer for video anomaly detection","volume":"28","author":"Pang","year":"2022","journal-title":"Multimed. Syst."},{"key":"10.1016\/j.neucom.2026.133846_bib0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2023.106093","article-title":"RGDAN: a random graph diffusion attention network for traffic prediction","volume":"172","author":"Fan","year":"2024","journal-title":"Neural Netw."},{"key":"10.1016\/j.neucom.2026.133846_bib0135","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"5","key":"10.1016\/j.neucom.2026.133846_bib0140","first-page":"1","article-title":"Improved spatio-temporal graph convolutional networks for video anomaly detection","volume":"51","author":"Zhang","year":"2024","journal-title":"Opto-electron. Eng."},{"key":"10.1016\/j.neucom.2026.133846_bib0145","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"11209","article-title":"Space-time-separable graph convolutional network for pose forecasting","author":"Sofianos","year":"2021"},{"issue":"2","key":"10.1016\/j.neucom.2026.133846_bib0150","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1007\/s10278-023-00954-2","article-title":"Polyp segmentation using a hybrid vision transformer and a hybrid loss function","volume":"37","author":"Goceri","year":"2024","journal-title":"J. Imaging Inform. Med."},{"key":"10.1016\/j.neucom.2026.133846_bib0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.127445","article-title":"Nuclei segmentation using attention aware and adversarial networks","volume":"579","author":"Goceri","year":"2024","journal-title":"Neurocomputing"},{"issue":"9","key":"10.1016\/j.neucom.2026.133846_bib0160","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1007\/s10462-024-10897-x","article-title":"GAN based augmentation using a hybrid loss function for dermoscopy images","volume":"57","author":"Goceri","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.neucom.2026.133846_bib0165","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"2720","article-title":"Abnormal event detection at 150 fps in MATLAB","author":"Lu","year":"2013"},{"key":"10.1016\/j.neucom.2026.133846_bib0170","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"341","article-title":"A revisit of sparse coding based anomaly detection in stacked RNN framework","author":"Luo","year":"2017"},{"key":"10.1016\/j.neucom.2026.133846_bib0175","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"20143","article-title":"Ubnormal: new benchmark for supervised open-set video anomaly detection","author":"Acsintoae","year":"2022"},{"key":"10.1016\/j.neucom.2026.133846_bib0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110817","article-title":"Contracting skeletal kinematics for human-related video anomaly detection","volume":"156","author":"Flaborea","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.neucom.2026.133846_bib0185","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.neucom.2021.12.023","article-title":"Human-related anomalous event detection via spatial-temporal graph convolutional autoencoder with embedded long short-term memory network","volume":"490","author":"Li","year":"2022","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133846_bib0190","series-title":"2020 25th International Conference on Pattern Recognition (ICPR)","first-page":"2927","article-title":"Posecvae: anomalous human activity detection","author":"Jain","year":"2021"},{"key":"10.1016\/j.neucom.2026.133846_bib0195","author":"Kanu-Asiegbu"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226012439?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226012439?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T16:19:11Z","timestamp":1787847551000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226012439"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":39,"alternative-id":["S0925231226012439"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133846","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Spatio-temporal adaptive diffusion network with improved autoencoder for video anomaly detection","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133846","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"133846"}}