{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T15:07:22Z","timestamp":1780931242117,"version":"3.54.1"},"reference-count":55,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100013804","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","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":[[2026,12]]},"DOI":"10.1016\/j.patcog.2026.113987","type":"journal-article","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:14:54Z","timestamp":1779380094000},"page":"113987","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["A2VAD: Attribute-augmented prompt learning for weakly supervised video anomaly detection"],"prefix":"10.1016","volume":"180","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1032-8201","authenticated-orcid":false,"given":"Chen","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingkuan","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrzej","family":"Cichocki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.patcog.2026.113987_b1","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, ICCV, 2013, pp. 2720\u20132727.","DOI":"10.1109\/ICCV.2013.338"},{"key":"10.1016\/j.patcog.2026.113987_b2","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.2026.113987_b3","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.2026.113987_b4","unstructured":"M.Z. Zaheer, J.-H. Lee, M. Astrid, S.-I. Lee, Old Is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2020, pp. 14183\u201314193."},{"key":"10.1016\/j.patcog.2026.113987_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109335","article-title":"Memory-augmented appearance-motion network for video anomaly detection","volume":"138","author":"Wang","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113987_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111759","article-title":"Unsupervised video anomaly detection by memory network with autoencoders in euclidean and non-euclidean spaces","volume":"167","author":"Kim","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113987_b7","doi-asserted-by":"crossref","unstructured":"W. Sultani, C. Chen, M. Shah, Real-World Anomaly Detection in Surveillance Videos, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2018, pp. 6479\u20136488.","DOI":"10.1109\/CVPR.2018.00678"},{"key":"10.1016\/j.patcog.2026.113987_b8","doi-asserted-by":"crossref","unstructured":"P. Wu, J. Liu, Y. Shi, Y. Sun, F. Shao, Z. Wu, Z. Yang, Not only Look, But Also Listen: Learning Multimodal Violence Detection Under Weak Supervision, in: Computer Vision \u2013 ECCV 2020, 2020, pp. 322\u2013339.","DOI":"10.1007\/978-3-030-58577-8_20"},{"key":"10.1016\/j.patcog.2026.113987_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109765","article-title":"Video anomaly detection with NTCN-ML: A novel TCN for multi-instance learning","volume":"143","author":"Shao","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113987_b10","doi-asserted-by":"crossref","unstructured":"J. Chen, L. Li, L. Su, Z.-j. Zha, Q. Huang, Prompt-Enhanced Multiple Instance Learning for Weakly Supervised Video Anomaly Detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2024, pp. 18319\u201318329.","DOI":"10.1109\/CVPR52733.2024.01734"},{"key":"10.1016\/j.patcog.2026.113987_b11","unstructured":"A. Radford, J.W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, I. Sutskever, Learning Transferable Visual Models From Natural Language Supervision, in: Proceedings of the 38th International Conference on Machine Learning, Vol. 139, 2021, pp. 8748\u20138763."},{"issue":"6","key":"10.1016\/j.patcog.2026.113987_b12","first-page":"6074","article-title":"VadCLIP: Adapting vision-language models for weakly supervised video anomaly detection","volume":"38","author":"Wu","year":"2024","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"9","key":"10.1016\/j.patcog.2026.113987_b13","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1007\/s11263-022-01653-1","article-title":"Learning to prompt for vision-language models","volume":"130","author":"Zhou","year":"2022","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.patcog.2026.113987_b14","doi-asserted-by":"crossref","unstructured":"Z. Yang, J. Liu, P. Wu, Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2024, pp. 18899\u201318908.","DOI":"10.1109\/CVPR52733.2024.01788"},{"key":"10.1016\/j.patcog.2026.113987_b15","doi-asserted-by":"crossref","first-page":"4923","DOI":"10.1109\/TIP.2024.3451935","article-title":"Learning prompt-enhanced context features for weakly-supervised video anomaly detection","volume":"33","author":"Pu","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patcog.2026.113987_b16","unstructured":"C. Huang, Y. Shi, J. Wen, W. Wang, Y. Xu, X. Cao, Ex-VAD: Explainable Fine-grained Video Anomaly Detection Based on Visual-Language Models, in: Proceedings of the 42nd International Conference on Machine Learning, Vol. 267, 2025, pp. 25750\u201325761."},{"key":"10.1016\/j.patcog.2026.113987_b17","doi-asserted-by":"crossref","unstructured":"X. Tian, S. Zou, Z. Yang, J. Zhang, ArGue: Attribute-Guided Prompt Tuning for Vision-Language Models, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2024, pp. 28578\u201328587.","DOI":"10.1109\/CVPR52733.2024.02700"},{"issue":"4","key":"10.1016\/j.patcog.2026.113987_b18","first-page":"3518","article-title":"Prompt tuning in a compact attribute space","volume":"39","author":"Hou","year":"2025","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.patcog.2026.113987_b19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TCSVT.2024.3482007","article-title":"ReFLIP-VAD: Towards weakly supervised video anomaly detection via vision-language model","author":"Dev","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.patcog.2026.113987_b20","doi-asserted-by":"crossref","unstructured":"J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, in: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019, pp. 4171\u20134186.","DOI":"10.18653\/v1\/N19-1423"},{"key":"10.1016\/j.patcog.2026.113987_b21","doi-asserted-by":"crossref","unstructured":"G. Wang, Y. Wang, J. Qin, D. Zhang, X. Bao, D. Huang, Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles, in: Computer Vision \u2013 ECCV 2022, 2022, pp. 494\u2013511.","DOI":"10.1007\/978-3-031-20080-9_29"},{"key":"10.1016\/j.patcog.2026.113987_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108213","article-title":"Video anomaly detection with spatio-temporal dissociation","volume":"122","author":"Chang","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113987_b23","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, ICCV, 2019, pp. 1705\u20131714.","DOI":"10.1109\/ICCV.2019.00179"},{"issue":"9","key":"10.1016\/j.patcog.2026.113987_b24","first-page":"4505","article-title":"A background-agnostic framework with adversarial training for abnormal event detection in video","volume":"44","author":"Georgescu","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.113987_b25","doi-asserted-by":"crossref","unstructured":"W. Liu, W. Luo, D. Lian, S. Gao, Future Frame Prediction for Anomaly Detection \u2013 A New Baseline, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2018, pp. 6536\u20136545.","DOI":"10.1109\/CVPR.2018.00684"},{"key":"10.1016\/j.patcog.2026.113987_b26","doi-asserted-by":"crossref","unstructured":"Z. Yang, J. Liu, Z. Wu, P. Wu, X. Liu, Video Event Restoration Based on Keyframes for Video Anomaly Detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2023, pp. 14592\u201314601.","DOI":"10.1109\/CVPR52729.2023.01402"},{"key":"10.1016\/j.patcog.2026.113987_b27","doi-asserted-by":"crossref","unstructured":"Y. Tian, G. Pang, Y. Chen, R. Singh, J.W. Verjans, G. Carneiro, Weakly-Supervised Video Anomaly Detection With Robust Temporal Feature Magnitude Learning, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV, 2021, pp. 4975\u20134986.","DOI":"10.1109\/ICCV48922.2021.00493"},{"issue":"1","key":"10.1016\/j.patcog.2026.113987_b28","first-page":"387","article-title":"MGFN: Magnitude-contrastive glance-and-focus network for weakly-supervised video anomaly detection","volume":"37","author":"Chen","year":"2023","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"3","key":"10.1016\/j.patcog.2026.113987_b29","first-page":"3769","article-title":"Dual memory units with uncertainty regulation for weakly supervised video anomaly detection","volume":"37","author":"Zhou","year":"2023","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.patcog.2026.113987_b30","doi-asserted-by":"crossref","unstructured":"J.-X. Zhong, N. Li, W. Kong, S. Liu, T.H. Li, G. Li, Graph Convolutional Label Noise Cleaner: Train a Plug-And-Play Action Classifier for Anomaly Detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2019, pp. 1237\u20131246.","DOI":"10.1109\/CVPR.2019.00133"},{"key":"10.1016\/j.patcog.2026.113987_b31","doi-asserted-by":"crossref","unstructured":"J.-C. Feng, F.-T. Hong, W.-S. Zheng, MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2021, pp. 14009\u201314018.","DOI":"10.1109\/CVPR46437.2021.01379"},{"issue":"2","key":"10.1016\/j.patcog.2026.113987_b32","first-page":"1395","article-title":"Self-training multi-sequence learning with transformer for weakly supervised video anomaly detection","volume":"36","author":"Li","year":"2022","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.patcog.2026.113987_b33","doi-asserted-by":"crossref","unstructured":"C. Zhang, G. Li, Y. Qi, S. Wang, L. Qing, Q. Huang, M.-H. Yang, Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2023, pp. 16271\u201316280.","DOI":"10.1109\/CVPR52729.2023.01561"},{"key":"10.1016\/j.patcog.2026.113987_b34","doi-asserted-by":"crossref","first-page":"10342","DOI":"10.1109\/TMM.2024.3407664","article-title":"Estimating the semantics via sector embedding for image-text retrieval","volume":"26","author":"Wang","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.patcog.2026.113987_b35","doi-asserted-by":"crossref","first-page":"2226","DOI":"10.1109\/TIP.2024.3374111","article-title":"Semantics disentangling for cross-modal retrieval","volume":"33","author":"Wang","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patcog.2026.113987_b36","first-page":"1","article-title":"Progressively alleviating noise for unsupervised cross-domain image retrieval","author":"Wang","year":"2026","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.patcog.2026.113987_b37","doi-asserted-by":"crossref","first-page":"5907","DOI":"10.1109\/TIP.2024.3477351","article-title":"Injecting text clues for improving anomalous event detection from weakly labeled videos","volume":"33","author":"Liu","year":"2024","journal-title":"IEEE Trans. Image Process."},{"issue":"11","key":"10.1016\/j.patcog.2026.113987_b38","doi-asserted-by":"crossref","first-page":"9994","DOI":"10.1109\/TPAMI.2025.3590242","article-title":"Multilingual-prompt-guided directional feature learning for weakly supervised video anomaly detection","volume":"47","author":"Xiao","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.113987_b39","doi-asserted-by":"crossref","unstructured":"P. Wu, X. Zhou, G. Pang, Y. Sun, J. Liu, P. Wang, Y. Zhang, Open-Vocabulary Video Anomaly Detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2024, pp. 18297\u201318307.","DOI":"10.1109\/CVPR52733.2024.01732"},{"issue":"6","key":"10.1016\/j.patcog.2026.113987_b40","doi-asserted-by":"crossref","first-page":"5925","DOI":"10.1109\/TCSVT.2025.3528108","article-title":"PLOVAD: Prompting vision-language models for open vocabulary video anomaly detection","volume":"35","author":"Xu","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.patcog.2026.113987_b41","doi-asserted-by":"crossref","unstructured":"F. Li, W. Liu, J. Chen, R. Zhang, Y. Wang, X. Zhong, Z. Wang, Anomize: Better Open Vocabulary Video Anomaly Detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2025, pp. 29203\u201329212.","DOI":"10.1109\/CVPR52734.2025.02719"},{"key":"10.1016\/j.patcog.2026.113987_b42","doi-asserted-by":"crossref","unstructured":"Z. Li, Y. Song, M.-M. Cheng, X. Li, J. Yang, Advancing Textual Prompt Learning with Anchored Attributes, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV, 2025, pp. 3618\u20133627.","DOI":"10.1109\/ICCV51701.2025.00345"},{"key":"10.1016\/j.patcog.2026.113987_b43","doi-asserted-by":"crossref","unstructured":"H. Yao, R. Zhang, C. Xu, TCP:Textual-based Class-aware Prompt tuning for Visual-Language Model, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2024, pp. 23438\u201323448.","DOI":"10.1109\/CVPR52733.2024.02212"},{"key":"10.1016\/j.patcog.2026.113987_b44","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.115218","article-title":"MoBA: Motion memory-augmented deblurring autoencoder for video anomaly detection","volume":"335","author":"Lyu","year":"2026","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.patcog.2026.113987_b45","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.112010","article-title":"Bidirectional skip-frame prediction for video anomaly detection with intra-domain disparity-driven attention","volume":"170","author":"Lyu","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113987_b46","doi-asserted-by":"crossref","unstructured":"H. Hu, W. Du, P. Liao, B. Wang, S. Fan, Noise-Resistant Video Anomaly Detection via RGB Error-Guided Multiscale Predictive Coding and Dynamic Memory, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2025, pp. 19109\u201319119.","DOI":"10.1109\/CVPR52734.2025.01780"},{"key":"10.1016\/j.patcog.2026.113987_b47","doi-asserted-by":"crossref","unstructured":"J. Zhang, L. Qing, J. Miao, Temporal Convolutional Network with Complementary Inner Bag Loss for Weakly Supervised Anomaly Detection, in: 2019 IEEE International Conference on Image Processing, ICIP, 2019, pp. 4030\u20134034.","DOI":"10.1109\/ICIP.2019.8803657"},{"key":"10.1016\/j.patcog.2026.113987_b48","doi-asserted-by":"crossref","unstructured":"C. Xu, C. Li, H. Xing, Discriminative Score Suppression for Weakly Supervised Video Anomaly Detection, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, WACV, 2025, pp. 9569\u20139578.","DOI":"10.1109\/WACV61041.2025.00928"},{"key":"10.1016\/j.patcog.2026.113987_b49","series-title":"Representation learning with contrastive predictive coding","author":"Oord","year":"2018"},{"key":"10.1016\/j.patcog.2026.113987_b50","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.112288","article-title":"FADMB: Fully attention-based dual memory bank network for weakly supervised video anomaly detection","volume":"172","author":"Luo","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113987_b51","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.112656","article-title":"DSCIL: Dynamic selected contrastive instance learning for weakly supervised video anomaly detection","volume":"172","author":"Zeng","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113987_b52","first-page":"1","article-title":"Dynamic erasing network with adaptive temporal modeling for weakly supervised video anomaly detection","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.patcog.2026.113987_b53","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2026.113201","article-title":"MG-TVMF: Multi-grained text-video matching and fusing for weakly supervised video anomaly detection","volume":"176","author":"He","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113987_b54","doi-asserted-by":"crossref","unstructured":"J. Carreira, A. Zisserman, Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2017, pp. 6299\u20136308.","DOI":"10.1109\/CVPR.2017.502"},{"issue":"Nov","key":"10.1016\/j.patcog.2026.113987_b55","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326009520?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326009520?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T14:54:25Z","timestamp":1780930465000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326009520"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":55,"alternative-id":["S0031320326009520"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113987","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A2VAD: Attribute-augmented prompt learning for weakly supervised video anomaly detection","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113987","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":"113987"}}