{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:48:26Z","timestamp":1773773306539,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"41","license":[{"start":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T00:00:00Z","timestamp":1708128000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T00:00:00Z","timestamp":1708128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"publisher","award":["LH2021F004"],"award-info":[{"award-number":["LH2021F004"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-18551-y","type":"journal-article","created":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T08:02:24Z","timestamp":1708156944000},"page":"89501-89519","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Supervised abnormal event detection based on ChatGPT attention mechanism"],"prefix":"10.1007","volume":"83","author":[{"given":"Feng","family":"Tian","sequence":"first","affiliation":[]},{"given":"Yuanyuan","family":"Lu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8651-956X","authenticated-orcid":false,"given":"Fang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Guibao","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Neili","family":"Zong","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Ningbin","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Kaiguang","family":"Cao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,17]]},"reference":[{"key":"18551_CR1","doi-asserted-by":"crossref","unstructured":"Ladune T, Philippe P (2022) Aivc: artificial intelli-gence based video codec. arXiv:2202.04365","DOI":"10.1109\/ICIP46576.2022.9897240"},{"key":"18551_CR2","doi-asserted-by":"crossref","unstructured":"Liu Z, Ning J, Cao Y, Wei Y, Zhang Z, Lin S, Hu H (2022) Video swin transformer. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3202\u20133211","DOI":"10.1109\/CVPR52688.2022.00320"},{"key":"18551_CR3","doi-asserted-by":"crossref","unstructured":"Haris M, Shakhnarovich G, Ukita N (2020) Space-time-aware multi-resolution video enhancement. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2859\u20132868","DOI":"10.1109\/CVPR42600.2020.00293"},{"key":"18551_CR4","doi-asserted-by":"crossref","unstructured":"Geng Z, Liang L, Ding T, Zharkov I (2022) Rstt: real-time spatial temporal transformer for space-time video super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 17441\u201317451","DOI":"10.1109\/CVPR52688.2022.01692"},{"key":"18551_CR5","doi-asserted-by":"crossref","unstructured":"Lv H, Chen C, Cui Z, Xu C, Li Y, Yang J (2021) Learning normal dynamics in videos with meta prototype network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 15425\u201315434","DOI":"10.1109\/CVPR46437.2021.01517"},{"key":"18551_CR6","doi-asserted-by":"crossref","unstructured":"Zou X, Yang L, Liu D, Lee YJ (2021) Progressive temporal feature alignment network for video inpainting. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 16448\u201316457","DOI":"10.1109\/CVPR46437.2021.01618"},{"key":"18551_CR7","doi-asserted-by":"crossref","unstructured":"Heng L (2023) Rethinking human excellence in the AI age: the relationship between intellectual humility and attitudes toward ChatGPT. Personal Individ Differ 215","DOI":"10.1016\/j.paid.2023.112401"},{"key":"18551_CR8","doi-asserted-by":"crossref","unstructured":"Yang Z, Wu P, Liu J, Liu X (2022) Dynamic local aggregation network with adaptive clusterer for anomaly detection. In: Proceedings of the European conference on computer vision, pp 404\u2013421","DOI":"10.1007\/978-3-031-19772-7_24"},{"key":"18551_CR9","doi-asserted-by":"crossref","unstructured":"Wu P, Liu J, Shi Y, Sun Y, Shao F, Wu Z, Yang Z (2020) Not only look, but also listen: learning multimodal violence detection under weak supervision. In: Proceedings of the European conference on computer vision, pp 322\u2013339","DOI":"10.1007\/978-3-030-58577-8_20"},{"key":"18551_CR10","doi-asserted-by":"crossref","unstructured":"Bai X, Luo Z, Zhou L, Chen H, Li L, Hu Z, Fu H, Tai C-L (2021) Pointdsc: robust point cloud registration using deep spatial consistency. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 15859\u201315869","DOI":"10.1109\/CVPR46437.2021.01560"},{"key":"18551_CR11","doi-asserted-by":"crossref","unstructured":"Brachmann E, Rother C (2019) Neural-guided ransac: learning where to sample model hypotheses. In ICCV","DOI":"10.1109\/ICCV.2019.00442"},{"key":"18551_CR12","doi-asserted-by":"crossref","unstructured":"Zhang X, Zeng H, Guo S et al (2022) Efficient long-range attention network for image super-resolution. Computer Vision-ECCV2022:17th European Conference, Tel Aviv, lsrael, Proceedings, Part XVII. Cham: Springer Nature Switzerland, 2022:649-667. Accessed 23\u201327 Oct 2022","DOI":"10.1007\/978-3-031-19790-1_39"},{"key":"18551_CR13","doi-asserted-by":"crossref","unstructured":"Kaiming H, Xiangyu Z, Shaoqing R et al (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904\u20131916","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"18551_CR14","doi-asserted-by":"crossref","unstructured":"Chen W, Li H, Nie Q, Liu Y-H (2022) Deterministic point cloud registration via novel transformation decomposition. In: CVPR, 2022. Chen Z, Sun K, Yang F, Tao W. Sc2-pcr: a second order spatial compatibility for efficient and robust point cloud registration. In CVPR","DOI":"10.1109\/CVPR52688.2022.01287"},{"key":"18551_CR15","doi-asserted-by":"crossref","unstructured":"Biao Z, Weiqiang J, Javier SD et al (2023) ChatAgri: exploring potentials of ChatGPT on cross-linguistic agricultural text classification. Neurocomputing, 557","DOI":"10.1016\/j.neucom.2023.126708"},{"key":"18551_CR16","doi-asserted-by":"crossref","unstructured":"Dang Z, Wang L, Guo Y, Salzmann M (2022) Learning-based point cloud registration for 6d object pose estimation in the real world. In ECCV","DOI":"10.1007\/978-3-031-19769-7_2"},{"key":"18551_CR17","doi-asserted-by":"crossref","unstructured":"Deng H, Birdal T, Ilic S (2018) Ppf-foldnet: unsupervised learning of rotation invariant 3d local descriptors. In: Proceedings of the European conference on computer vision (ECCV), pp 602\u2013618","DOI":"10.1007\/978-3-030-01228-1_37"},{"key":"18551_CR18","doi-asserted-by":"crossref","unstructured":"Fu K, Liu S, Luo X, Wang M (2021) Robust point cloud registration framework based on deep graph matching. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8893\u20138902","DOI":"10.1109\/CVPR46437.2021.00878"},{"key":"18551_CR19","doi-asserted-by":"crossref","unstructured":"Huang S, Gojcic Z, Usvyatsov M, Wieser A, Schindler K (2021) Predator: registration of 3d point clouds with low overlap. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4267\u20134276","DOI":"10.1109\/CVPR46437.2021.00425"},{"key":"18551_CR20","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S et al (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904\u20131916","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"18551_CR21","unstructured":"Lee J, Kim S, Cho M, Park J (2022) Deep hough voting for robust global registration. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 15994\u201316003"},{"key":"18551_CR22","doi-asserted-by":"crossref","unstructured":"Li J, Zhang C, Xu Z, Zhou H, Zhang C (2020) Iterative distance-aware similarity matrix convolution with mutual-supervised point elimination for efficient point cloud registration. In ECCV","DOI":"10.1007\/978-3-030-58586-0_23"},{"key":"18551_CR23","unstructured":"Xiaoru H, Dannya E, Xuening L et al (2023) Evaluating the performance of ChatGPT in clinical pharmacy: a comparative study of ChatGPT and clinical pharmacists. British journal of clinical pharmacology"},{"key":"18551_CR24","doi-asserted-by":"crossref","unstructured":"Quan S, Yang J (2020) Compatibility-guided sampling consensus for 3-d point cloud registration. IEEE Trans Geosci Remote Sen","DOI":"10.1109\/TGRS.2020.2982221"},{"key":"18551_CR25","doi-asserted-by":"crossref","unstructured":"Shen Y, Hui L, Jiang H, Xie J, Yang J (2022) Reliable inlier evaluation for unsupervised point cloud registration. arXiv:2202.11292","DOI":"10.1609\/aaai.v36i2.20117"},{"key":"18551_CR26","doi-asserted-by":"crossref","unstructured":"Yang H, Shi J, Carlone L (2020) Teaser: fast and certifiable point cloud registration. IEEE Trans Robot","DOI":"10.1109\/TRO.2020.3033695"},{"key":"18551_CR27","doi-asserted-by":"crossref","unstructured":"Yew ZJ, Lee GH (2022) Regtr: end-to-end point cloud correspondences with transformers. In CVPR","DOI":"10.1109\/CVPR52688.2022.00656"},{"key":"18551_CR28","doi-asserted-by":"crossref","unstructured":"Yuanhong Z, Xia C, Yongting H et al (2022) Bidirectional spatio-temporal feature learning with multiscale evaluation for video anomaly detection. IEEE Transon Circ Syst Vid Technol 32(12): 8285\u20138296","DOI":"10.1109\/TCSVT.2022.3190539"},{"key":"18551_CR29","doi-asserted-by":"crossref","unstructured":"Sijia Z, Maoguo G, Yu X et al (2022) Influence-aware attention networks for anomaly detection in surveillance videos. IEEE Trans on Circ Syst Vid Technol 32(8):5427\u20135437","DOI":"10.1109\/TCSVT.2022.3148392"},{"key":"18551_CR30","unstructured":"Iaoru H, Dannya E, Xuening L et al (2023) Evaluating the performance of ChatGPT in clinical pharmacy: a comparative study of ChatGPT and clinical pharmacists. Br J Clin Pharmacol"},{"key":"18551_CR31","unstructured":"Xinyang F, Dongjin S, Yuncong C et al (2021) Convolutional transformer based dual discriminator generative adversarial networks for video anomaly detection. Proc of the 29th ACM international conference on multimedia. New York: ACM Press, 2021: 5546\u20135554"},{"key":"18551_CR32","doi-asserted-by":"crossref","unstructured":"Yew ZJ, Lee GH (2020) RPM-Net: robust point matching using learned features. In CVPR","DOI":"10.1109\/CVPR42600.2020.01184"},{"key":"18551_CR33","doi-asserted-by":"crossref","unstructured":"Mahadevan V, Li WX (2010) Anomaly detection in crowded scenes. The twenty-third IEEE Conference on Computer Vision and Pattern Recognition, IEEE Comput Soc 2010:1975\u20131981","DOI":"10.1109\/CVPR.2010.5539872"},{"key":"18551_CR34","doi-asserted-by":"crossref","unstructured":"Zhu J, Fang Y (2020) Reference grid-assisted network for 3D point signature learning from point clouds. In WACV","DOI":"10.1109\/WACV45572.2020.9093270"},{"key":"18551_CR35","unstructured":"RJB, SJCLD, WMK et al (2023) BPPV Information on Google Versus AI (ChatGPT). Otolaryngology\u2013head and neck surgery: official journal of American Academy of Otolaryngology-Head and Neck Surgery"},{"key":"18551_CR36","doi-asserted-by":"crossref","unstructured":"Sultani W, Chen C, Shah M (2018) Real-world anomaly detection in surveillance videos. 2018 IEEE\/CVF Conf Comput Vis Pattern Recogn, Salt Lake City, UT, USA. New York:IEEE Press, 2018:6479\u20136488. Accessed 18\u201323 June 2018","DOI":"10.1109\/CVPR.2018.00678"},{"key":"18551_CR37","doi-asserted-by":"crossref","unstructured":"Carreira J, Zisserman A (2017) Quo Vadis,action recognition?A new model and Kinetics dataset. 2017 IEEE Conf Comput Vis Pattern Recogn (CVPR), Honolulu, HI, USA. New York:IEEE Press, 2017:4724\u20134733. Accessed 21\u201326 July 2017","DOI":"10.1109\/CVPR.2017.502"},{"key":"18551_CR38","doi-asserted-by":"crossref","unstructured":"Hasan M, Choi J, Neumann J et al (2016) Learning temporal regularity in video sequences. 2016 IEEE Conf Comput Vis Pattern Recogn (CVPR), Las Vegas, NV, USA. New York: IEEE Press, 2016:733\u2013742. Accessed 27\u201330 June 2016","DOI":"10.1109\/CVPR.2016.86"},{"key":"18551_CR39","doi-asserted-by":"crossref","unstructured":"Guo RY, Jin J, Liu GH et al (2020) Improved humanaction recognition algorithm based on two-stream faster region convolutional neural network. Laser & Optoelectronics Progress, 57(24):241506","DOI":"10.3788\/LOP57.241506"},{"key":"18551_CR40","doi-asserted-by":"crossref","unstructured":"Zhang T, Jia WJ, Yang BQ et al (2017) MoWLD: arobust motion image descriptor for violence detection. Multimed Tools Appl 76(1):1419\u20131438","DOI":"10.1007\/s11042-015-3133-0"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18551-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18551-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18551-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T12:09:55Z","timestamp":1734955795000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18551-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,17]]},"references-count":40,"journal-issue":{"issue":"41","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["18551"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18551-y","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,17]]},"assertion":[{"value":"20 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 January 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"The authors declare that they have no known competing financialinterests or personal relatianships that could have appeared to influencethe work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}]}}