{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:52:50Z","timestamp":1783529570447,"version":"3.55.0"},"reference-count":41,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100000148","name":"Canadian Anesthesiologists' Society","doi-asserted-by":"publisher","award":["YSBR-0122"],"award-info":[{"award-number":["YSBR-0122"]}],"id":[{"id":"10.13039\/501100000148","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.patcog.2026.113809","type":"journal-article","created":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T23:28:06Z","timestamp":1776814086000},"page":"113809","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":8,"special_numbering":"PC","title":["Causally Invariant Video Anomaly Detection via Counterfactual Reasoning and prototype intervention"],"prefix":"10.1016","volume":"179","author":[{"given":"Enhao","family":"Ning","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weixuan","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiwen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jicheng","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuyao","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7897-1673","authenticated-orcid":false,"given":"Xin","family":"Ning","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.patcog.2026.113809_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110877","article-title":"Fast video anomaly detection via context-aware shortcut exploration and abnormal feature distance learning","volume":"157","author":"Park","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113809_b2","doi-asserted-by":"crossref","first-page":"2213","DOI":"10.1109\/TIP.2024.3374070","article-title":"Toward video anomaly retrieval from video anomaly detection: New benchmarks and model","volume":"33","author":"Wu","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patcog.2026.113809_b3","series-title":"Deep learning for video anomaly detection: A review","author":"Wu","year":"2024"},{"key":"10.1016\/j.patcog.2026.113809_b4","first-page":"1","article-title":"Video anomaly detection in 10 years: A survey and outlook","author":"Abdalla","year":"2025","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.patcog.2026.113809_b5","article-title":"Self-supervised learning video anomaly detection based on time interval prediction and noise classification","author":"Liu","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113809_b6","series-title":"2023 IEEE International Conference on Systems, Man, and Cybernetics","first-page":"739","article-title":"A hybrid framework for video anomaly detection based on semantic consistency of motion and appearance","author":"Wang","year":"2023"},{"key":"10.1016\/j.patcog.2026.113809_b7","series-title":"ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"1","article-title":"Semi-supervised video anomaly detection with compact deformable 3D convolution","author":"Gao","year":"2025"},{"key":"10.1016\/j.patcog.2026.113809_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110500","article-title":"Dual groupgan: An unsupervised four-competitor (2v2) approach for video anomaly detection","volume":"153","author":"Sun","year":"2024","journal-title":"Pattern Recognit."},{"issue":"3","key":"10.1016\/j.patcog.2026.113809_b9","doi-asserted-by":"crossref","first-page":"1729","DOI":"10.1007\/s00371-023-02882-2","article-title":"DeMAAE: deep multiplicative attention-based autoencoder for identification of peculiarities in video sequences","volume":"40","author":"Aslam","year":"2024","journal-title":"Vis. Comput."},{"issue":"5","key":"10.1016\/j.patcog.2026.113809_b10","first-page":"2498","article-title":"Appearance-motion united auto-encoder framework for video anomaly detection","volume":"69","author":"Liu","year":"2022","journal-title":"IEEE Trans. Circuits Syst. II: Express Briefs"},{"key":"10.1016\/j.patcog.2026.113809_b11","article-title":"Rex: causal discovery based on machine learning and explainability techniques","author":"Renero","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113809_b12","article-title":"Causality-inspired learning semantic segmentation in unseen domain","author":"He","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113809_b13","unstructured":"I. Higgins, L. Matthey, A. Pal, C.P. Burgess, X. Glorot, M.M. Botvinick, S. Mohamed, A. Lerchner, beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework, in: International Conference on Learning Representations, ICLR, 2017."},{"key":"10.1016\/j.patcog.2026.113809_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.113674","article-title":"Occluded person re-identification in multi-scenarios: A synergistic interaction framework with perception-aware optimization","volume":"166","author":"Ning","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.patcog.2026.113809_b15","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1016\/j.neunet.2023.11.003","article-title":"Enhancement, integration, expansion: Activating representation of detailed features for occluded person re-identification","volume":"169","author":"Ning","year":"2024","journal-title":"Neural Netw."},{"issue":"7","key":"10.1016\/j.patcog.2026.113809_b16","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1007\/s10489-025-06485-3","article-title":"Multi-scale spatiotemporal normality learning for unsupervised video anomaly detection","volume":"55","author":"Liu","year":"2025","journal-title":"Appl. Intell."},{"key":"10.1016\/j.patcog.2026.113809_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110550","article-title":"Video anomaly detection guided by clustering learning","volume":"153","author":"Qiu","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113809_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122419","article-title":"Occluded person re-identification with deep learning: a survey and perspectives","volume":"239","author":"Ning","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.patcog.2026.113809_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111021","article-title":"Video anomaly detection via self-supervised and spatio-temporal proxy tasks learning","volume":"158","author":"Yang","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113809_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102863","article-title":"3D-guided multi-feature semantic enhancement network for person re-ID","volume":"117","author":"Ning","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2026.113809_b21","article-title":"DCTFormer: A dual-branch transformer with cloze tests for video anomaly detection","author":"Chen","year":"2025","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.patcog.2026.113809_b22","doi-asserted-by":"crossref","DOI":"10.1109\/JSEN.2025.3533613","article-title":"Time-efficient video anomaly detection with parallel computing and twice-reconstruction","author":"Qi","year":"2025","journal-title":"IEEE Sensors J."},{"key":"10.1016\/j.patcog.2026.113809_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.displa.2023.102467","article-title":"Pedestrian Re-ID based on feature consistency and contrast enhancement","volume":"79","author":"Ning","year":"2023","journal-title":"Displays"},{"key":"10.1016\/j.patcog.2026.113809_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.112016","article-title":"A video anomaly detection framework based on semantic consistency and multi-attribute feature complementarity","volume":"170","author":"Wang","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113809_b25","series-title":"International Conference on Machine Learning","first-page":"2649","article-title":"Disentangling by factorising","author":"Kim","year":"2018"},{"key":"10.1016\/j.patcog.2026.113809_b26","series-title":"International Conference on Machine Learning","first-page":"4114","article-title":"Challenging common assumptions in the unsupervised learning of disentangled representations","author":"Locatello","year":"2019"},{"key":"10.1016\/j.patcog.2026.113809_b27","unstructured":"M. Kocaoglu, C. Snyder, A.G. Dimakis, S. Vishwanath, CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training, in: International Conference on Learning Representations, ICLR, 2018."},{"key":"10.1016\/j.patcog.2026.113809_b28","doi-asserted-by":"crossref","unstructured":"T. Wang, J. Huang, H. Zhang, Q. Sun, Visual Commonsense R-CNN, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2020, pp. 10760\u201310770.","DOI":"10.1109\/CVPR42600.2020.01077"},{"issue":"1","key":"10.1016\/j.patcog.2026.113809_b29","first-page":"18","article-title":"Anomaly detection and localization in crowded scenes","volume":"36","author":"Li","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.113809_b30","doi-asserted-by":"crossref","unstructured":"C. Lu, J. Shi, J. Jia, Abnormal event detection at 150 fps in matlab, in: Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 2720\u20132727.","DOI":"10.1109\/ICCV.2013.338"},{"key":"10.1016\/j.patcog.2026.113809_b31","doi-asserted-by":"crossref","unstructured":"W. Luo, W. Liu, S. Gao, A revisit of sparse coding based anomaly detection in stacked rnn framework, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 341\u2013349.","DOI":"10.1109\/ICCV.2017.45"},{"key":"10.1016\/j.patcog.2026.113809_b32","doi-asserted-by":"crossref","unstructured":"M. Hasan, J. Choi, J. Neumann, A.K. Roy-Chowdhury, L.S. Davis, Learning temporal regularity in video sequences, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 733\u2013742.","DOI":"10.1109\/CVPR.2016.86"},{"key":"10.1016\/j.patcog.2026.113809_b33","doi-asserted-by":"crossref","unstructured":"D. Gong, L. Liu, V. Le, B. Saha, M.R. Mansour, S. Venkatesh, A.v.d. Hengel, Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp. 1705\u20131714.","DOI":"10.1109\/ICCV.2019.00179"},{"key":"10.1016\/j.patcog.2026.113809_b34","doi-asserted-by":"crossref","unstructured":"H. Park, J. Noh, B. Ham, Learning memory-guided normality for anomaly detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 14372\u201314381.","DOI":"10.1109\/CVPR42600.2020.01438"},{"key":"10.1016\/j.patcog.2026.113809_b35","doi-asserted-by":"crossref","unstructured":"H. Lv, C. Chen, Z. Cui, C. Xu, Y. Li, J. Yang, Learning normal dynamics in videos with meta prototype network, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 15425\u201315434.","DOI":"10.1109\/CVPR46437.2021.01517"},{"key":"10.1016\/j.patcog.2026.113809_b36","doi-asserted-by":"crossref","unstructured":"W. Liu, W. Luo, D. Lian, S. Gao, Future frame prediction for anomaly detection\u2013a new baseline, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 6536\u20136545.","DOI":"10.1109\/CVPR.2018.00684"},{"key":"10.1016\/j.patcog.2026.113809_b37","doi-asserted-by":"crossref","first-page":"2395","DOI":"10.1109\/TIP.2019.2948286","article-title":"BMAN: Bidirectional multi-scale aggregation networks for abnormal event detection","volume":"29","author":"Lee","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patcog.2026.113809_b38","first-page":"938","article-title":"Appearance-motion memory consistency network for video anomaly detection","volume":"vol. 35","author":"Cai","year":"2021"},{"key":"10.1016\/j.patcog.2026.113809_b39","doi-asserted-by":"crossref","unstructured":"Z. Liu, Y. Nie, C. Long, Q. Zhang, G. Li, A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 13588\u201313597.","DOI":"10.1109\/ICCV48922.2021.01333"},{"key":"10.1016\/j.patcog.2026.113809_b40","series-title":"2022 IEEE International Conference on Multimedia and Expo","first-page":"1","article-title":"Learning appearance-motion normality for video anomaly detection","author":"Liu","year":"2022"},{"key":"10.1016\/j.patcog.2026.113809_b41","doi-asserted-by":"crossref","DOI":"10.1109\/TPAMI.2026.3683792","article-title":"Neural network optimization reimagined: Decoupled techniques for scratch and fine-tuning","author":"Ning","year":"2026","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326007740?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326007740?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T01:46:39Z","timestamp":1783043199000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326007740"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":41,"alternative-id":["S0031320326007740"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113809","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Causally Invariant Video Anomaly Detection via Counterfactual Reasoning and prototype intervention","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113809","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"113809"}}