{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:05:23Z","timestamp":1750309523158,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":60,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Foundation of China under Grants","award":["62101064, 62171057,62201072, U23B2001, 62001054, 62071067"],"award-info":[{"award-number":["62101064, 62171057,62201072, U23B2001, 62001054, 62071067"]}]},{"name":"Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center"},{"name":"the BUPT Excellent Ph.D. Students Foundation","award":["CX20241007"],"award-info":[{"award-number":["CX20241007"]}]},{"name":"the Ministry of Education and China Mobile Joint Fund","award":["MCM20200202, MCM20180101"],"award-info":[{"award-number":["MCM20200202, MCM20180101"]}]},{"name":"Project funded by China Postdoctoral Science Foundation","award":["2023TQ0039"],"award-info":[{"award-number":["2023TQ0039"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,28]]},"DOI":"10.1145\/3664647.3680871","type":"proceedings-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T06:59:27Z","timestamp":1729925967000},"page":"4719-4728","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Video Anomaly Detection via Progressive Learning of Multiple Proxy Tasks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9713-9938","authenticated-orcid":false,"given":"Menghao","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications &amp; E-BYTE Technology Co., Ltd., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2182-2228","authenticated-orcid":false,"given":"Jingyu","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Post and Telecommunication &amp; E-BYTE Technology Co., Ltd., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0829-4624","authenticated-orcid":false,"given":"Qi","family":"Qi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications &amp; E-BYTE Technology Co., Ltd., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1691-6457","authenticated-orcid":false,"given":"Pengfei","family":"Ren","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3072-7422","authenticated-orcid":false,"given":"Haifeng","family":"Sun","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3345-1732","authenticated-orcid":false,"given":"Zirui","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5733-2060","authenticated-orcid":false,"given":"Huazheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8795-5675","authenticated-orcid":false,"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1486-0573","authenticated-orcid":false,"given":"Jianxin","family":"Liao","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Fahad Shahbaz Khan, and Mubarak Shah.","author":"Acsintoae Andra","year":"2022","unstructured":"Andra Acsintoae, Andrei Florescu, Mariana-Iuliana Georgescu, Tudor Mare, Paul Sumedrea, Radu Tudor Ionescu, Fahad Shahbaz Khan, and Mubarak Shah. 2022. UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection. In CVPR. 20111--20121."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Qianyue Bao Fang Liu Yang Liu Licheng Jiao Xu Liu and Lingling Li. 2022. Hierarchical Scene Normality-Binding Modeling for Anomaly Detection in Surveillance Videos. In ACM Multimedia. 6103--6112.","DOI":"10.1145\/3503161.3548199"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Congqi Cao Yue Lu Peng Wang and Yanning Zhang. 2023. A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation. In CVPR. 20392--20401.","DOI":"10.1109\/CVPR52729.2023.01953"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Yunpeng Chang Zhigang Tu Wei Xie and Junsong Yuan. 2020. Clustering Driven Deep Autoencoder for Video Anomaly Detection. In ECCV. 329--345.","DOI":"10.1007\/978-3-030-58555-6_20"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Chengwei Chen Yuan Xie Shaohui Lin Angela Yao Guannan Jiang Wei Zhang Yanyun Qu Ruizhi Qiao Bo Ren and Lizhuang Ma. 2022. Comprehensive Regularization in a Bi-directional Predictive Network for Video Anomaly Detection. In AAAI. 230--238.","DOI":"10.1609\/aaai.v36i1.19898"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Liang-Chieh Chen Yukun Zhu George Papandreou Florian Schroff and Hartwig Adam. 2018. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. In ECCV. 833--851.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108703"},{"key":"e_1_3_2_1_8_1","volume-title":"MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection. In CVPR. 14009--14018.","author":"Feng Jia-Chang","year":"2021","unstructured":"Jia-Chang Feng, Fa-Ting Hong, and Wei-Shi Zheng. 2021. MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection. In CVPR. 14009--14018."},{"key":"e_1_3_2_1_9_1","volume-title":"Stefano D'Arrigo, Bardh Prenkaj, and Fabio Galasso.","author":"Flaborea Alessandro","year":"2023","unstructured":"Alessandro Flaborea, Luca Collorone, Guido Maria D'Amely di Melendugno, Stefano D'Arrigo, Bardh Prenkaj, and Fabio Galasso. 2023. Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection. In (ICCV). 10318--10329."},{"key":"e_1_3_2_1_10_1","volume-title":"Fahad Shahbaz Khan, Marius Popescu, and Mubarak Shah.","author":"Georgescu Mariana-Iuliana","year":"2021","unstructured":"Mariana-Iuliana Georgescu, Antonio Barbalau, Radu Tudor Ionescu, Fahad Shahbaz Khan, Marius Popescu, and Mubarak Shah. 2021. Anomaly Detection in Video via Self-Supervised and Multi-Task Learning. In CVPR. 12742--12752."},{"key":"e_1_3_2_1_11_1","first-page":"4505","article-title":"A Background-Agnostic Framework With Adversarial Training for Abnormal Event Detection in Video","volume":"44","author":"Georgescu Mariana-Iuliana","year":"2022","unstructured":"Mariana-Iuliana Georgescu, Radu Tudor Ionescu, Fahad Shahbaz Khan, Marius Popescu, and Mubarak Shah. 2022. A Background-Agnostic Framework With Adversarial Training for Abnormal Event Detection in Video. IEEE Trans. Pattern Anal. Mach. Intell., Vol. 44, 9 (2022), 4505--4523.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"e_1_3_2_1_12_1","volume-title":"Svetha Venkatesh, and Anton van den Hengel.","author":"Gong Dong","year":"2019","unstructured":"Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, and Anton van den Hengel. 2019. Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection. In ICCV. 1705--1714."},{"key":"e_1_3_2_1_13_1","volume-title":"Svetha Venkatesh, and Anton van den Hengel.","author":"Gong Dong","year":"2019","unstructured":"Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, and Anton van den Hengel. 2019. Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection. In ICCV. 1705--1714."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Michelle Guo Albert Haque De-An Huang Serena Yeung and Li Fei-Fei. 2018. Dynamic Task Prioritization for Multitask Learning. In ECCV (16). 282--299.","DOI":"10.1007\/978-3-030-01270-0_17"},{"key":"e_1_3_2_1_15_1","volume-title":"Davis","author":"Hasan Mahmudul","year":"2016","unstructured":"Mahmudul Hasan, Jonghyun Choi, Jan Neumann, Amit K. Roy-Chowdhury, and Larry S. Davis. 2016. Learning Temporal Regularity in Video Sequences. In CVPR. 733--742."},{"key":"e_1_3_2_1_16_1","volume-title":"Learning and Transfer of Modulated Locomotor Controllers. CoRR","author":"Heess Nicolas","year":"2016","unstructured":"Nicolas Heess, Gregory Wayne, Yuval Tassa, Timothy P. Lillicrap, Martin A. Riedmiller, and David Silver. 2016. Learning and Transfer of Modulated Locomotor Controllers. CoRR, Vol. abs\/1610.05182 (2016)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Jinlei Hou Yingying Zhang Qiaoyong Zhong Di Xie Shiliang Pu and Hong Zhou. 2021. Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection. In ICCV. 8771--8780.","DOI":"10.1109\/ICCV48922.2021.00867"},{"key":"e_1_3_2_1_18_1","unstructured":"Yuzheng Hu Ruicheng Xian Qilong Wu Qiuling Fan Lang Yin and Han Zhao. 2023. Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective. In NeurIPS."},{"key":"e_1_3_2_1_19_1","volume-title":"Mariana-Iuliana Georgescu, and Ling Shao.","author":"Ionescu Radu Tudor","year":"2019","unstructured":"Radu Tudor Ionescu, Fahad Shahbaz Khan, Mariana-Iuliana Georgescu, and Ling Shao. 2019. Object-Centric Auto-Encoders and Dummy Anomalies for Abnormal Event Detection in Video. In CVPR. 7842--7851."},{"key":"e_1_3_2_1_20_1","unstructured":"Parker Knight and Rui Duan. 2023. Multi-task learning with summary statistics. In NeurIPS."},{"key":"e_1_3_2_1_21_1","volume-title":"Ngo Van Linh, and Thien Huu Nguyen.","author":"Le Thanh-Thien","year":"2024","unstructured":"Thanh-Thien Le, Manh Nguyen, Tung Thanh Nguyen, Ngo Van Linh, and Thien Huu Nguyen. 2024. Continual Relation Extraction via Sequential Multi-Task Learning. In AAAI. 18444--18452."},{"key":"e_1_3_2_1_22_1","unstructured":"Wenrui Liu Hong Chang Bingpeng Ma Shiguang Shan and Xilin Chen. 2023. Diversity-measurable anomaly detection. In CVPR. 12147--12156."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Wen Liu Weixin Luo Dongze Lian and Shenghua Gao. 2018. Future Frame Prediction for Anomaly Detection - A New Baseline. In CVPR. 6536--6545.","DOI":"10.1109\/CVPR.2018.00684"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Yang Liu Zhaoyang Xia Mengyang Zhao Donglai Wei Yuzheng Wang Siao Liu Bobo Ju Gaoyun Fang Jing Liu and Liang Song. 2023. Learning Causality-inspired Representation Consistency for Video Anomaly Detection. In ACM Multimedia. 203--212.","DOI":"10.1145\/3581783.3612393"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Zhian Liu Yongwei Nie Chengjiang Long Qing Zhang and Guiqing Li. 2021. A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction. In ICCV. 13568--13577.","DOI":"10.1109\/ICCV48922.2021.01333"},{"volume-title":"Generating Anomalies for Video Anomaly Detection with Prompt-based Feature Mapping","author":"Liu Zuhao","key":"e_1_3_2_1_26_1","unstructured":"Zuhao Liu, Xiao-Ming Wu, Dian Zheng, Kun-Yu Lin, and Wei-Shi Zheng. 2023. Generating Anomalies for Video Anomaly Detection with Prompt-based Feature Mapping. In CVPR. IEEE, 24500--24510."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Cewu Lu Jianping Shi and Jiaya Jia. 2013. Abnormal event detection at 150 FPS in MATLAB. In ICCV. 2720--2727.","DOI":"10.1109\/ICCV.2013.338"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Weixin Luo Wen Liu and Shenghua Gao. 2017. A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework. In ICCV. 341--349.","DOI":"10.1109\/ICCV.2017.45"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Hui Lv Chen Chen Zhen Cui Chunyan Xu Yong Li and Jian Yang. 2021. Learning Normal Dynamics in Videos With Meta Prototype Network. In CVPR. 15425--15434.","DOI":"10.1109\/CVPR46437.2021.01517"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Hui Lv Zhongqi Yue Qianru Sun Bin Luo Zhen Cui and Hanwang Zhang. 2023. Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection. In CVPR. 8022--8031.","DOI":"10.1109\/CVPR52729.2023.00775"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Trong-Nguyen Nguyen and Jean Meunier. 2019. Anomaly Detection in Video Sequence With Appearance-Motion Correspondence. In ICCV. 1273--1283.","DOI":"10.1109\/ICCV.2019.00136"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Ravikiran Parameshwara Ibrahim Radwan Akshay Asthana Iman Abbasnejad Ramanathan Subramanian and Roland Goecke. 2023. Efficient Labelling of Affective Video Datasets via Few-Shot & Multi-Task Contrastive Learning. In ACM Multimedia. 6161--6170.","DOI":"10.1145\/3581783.3613784"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Hyunjong Park Jongyoun Noh and Bumsub Ham. 2020. Learning Memory-Guided Normality for Anomaly Detection. In CVPR. 14360--14369.","DOI":"10.1109\/CVPR42600.2020.01438"},{"key":"e_1_3_2_1_34_1","volume-title":"YOLOv3: An Incremental Improvement. CoRR","author":"Redmon Joseph","year":"2018","unstructured":"Joseph Redmon and Ali Farhadi. 2018. YOLOv3: An Incremental Improvement. CoRR, Vol. abs\/1804.02767 (2018)."},{"key":"e_1_3_2_1_35_1","volume-title":"Kamal Nasrollahi, Fahad Shahbaz Khan, Thomas B. Moeslund, and Mubarak Shah.","author":"Ristea Nicolae-Catalin","year":"2022","unstructured":"Nicolae-Catalin Ristea, Neelu Madan, Radu Tudor Ionescu, Kamal Nasrollahi, Fahad Shahbaz Khan, Thomas B. Moeslund, and Mubarak Shah. 2022. Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection. In CVPR. 13566--13576."},{"key":"e_1_3_2_1_36_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 15984--15995","author":"Croitoru Florinel-Alin","year":"2024","unstructured":"Florinel-Alin Croitoru, Radu Tudor Ionescu, Marius Popescu, Fahad Shahbaz Khan, and Mubarak Shah. 2024. Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 15984--15995."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Florian Schroff Dmitry Kalenichenko and James Philbin. 2015. FaceNet: A unified embedding for face recognition and clustering. In CVPR. 815--823.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"e_1_3_2_1_38_1","unstructured":"Ozan Sener and Vladlen Koltun. 2018. Multi-Task Learning as Multi-Objective Optimization. In NeurIPS. 525--536."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Chenrui Shi Che Sun Yuwei Wu and Yunde Jia. 2023. Video Anomaly Detection via Sequentially Learning Multiple Pretext Tasks. In ICCV. 10296--10306.","DOI":"10.1109\/ICCV51070.2023.00948"},{"key":"e_1_3_2_1_40_1","volume-title":"EVAL: Explainable Video Anomaly Localization. In CVPR. 18717--18726.","author":"Singh Ashish","year":"2023","unstructured":"Ashish Singh, Michael J Jones, and Erik G Learned-Miller. 2023. EVAL: Explainable Video Anomaly Localization. In CVPR. 18717--18726."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Waqas Sultani Chen Chen and Mubarak Shah. 2018. Real-World Anomaly Detection in Surveillance Videos. In CVPR. 6479--6488.","DOI":"10.1109\/CVPR.2018.00678"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"crossref","unstructured":"Che Sun Chenrui Shi Yunde Jia and Yuwei Wu. 2023. Learning Event-Relevant Factors for Video Anomaly Detection. In AAAI. 2384--2392.","DOI":"10.1609\/aaai.v37i2.25334"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Shengyang Sun and Xiaojin Gong. 2023. Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection. In CVPR. 22846--22856.","DOI":"10.1109\/CVPR52729.2023.02188"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"crossref","unstructured":"Yu Tian Guansong Pang Yuanhong Chen Rajvinder Singh Johan W. Verjans and Gustavo Carneiro. 2021. Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning. In ICCV. 4955--4966.","DOI":"10.1109\/ICCV48922.2021.00493"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"crossref","unstructured":"Guodong Wang Yunhong Wang Jie Qin Dongming Zhang Xiuguo Bao and Di Huang. 2022. Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles. In ECCV. 494--511.","DOI":"10.1007\/978-3-031-20080-9_29"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3083152"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"crossref","unstructured":"Ziming Wang Yuexian Zou and Zeming Zhang. 2020. Cluster Attention Contrast for Video Anomaly Detection. In ACM Multimedia. 2463--2471.","DOI":"10.1145\/3394171.3413529"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"crossref","unstructured":"Jie Wu Wei Zhang Guanbin Li Wenhao Wu Xiao Tan Yingying Li Errui Ding and Liang Lin. 2021. Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Video. In IJCAI. 1172--1178.","DOI":"10.24963\/ijcai.2021\/162"},{"key":"e_1_3_2_1_49_1","volume-title":"Open-Vocabulary Video Anomaly Detection. CoRR","author":"Wu Peng","year":"2023","unstructured":"Peng Wu, Xuerong Zhou, Guansong Pang, Yujia Sun, Jing Liu, Peng Wang, and Yanning Zhang. 2023. Open-Vocabulary Video Anomaly Detection. CoRR, Vol. abs\/2311.07042 (2023)."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"crossref","unstructured":"Zhiwei Yang Jing Liu Zhaoyang Wu Peng Wu and Xiaotao Liu. 2023. Video Event Restoration Based on Keyframes for Video Anomaly Detection. In CVPR. 14592--14601.","DOI":"10.1109\/CVPR52729.2023.01402"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"crossref","unstructured":"Zhiwei Yang Peng Wu Jing Liu and Xiaotao Liu. 2022. Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection. In ECCV. 404--421.","DOI":"10.1007\/978-3-031-19772-7_24"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"crossref","unstructured":"Guang Yu Siqi Wang Zhiping Cai En Zhu Chuanfu Xu Jianping Yin and Marius Kloft. 2020. Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events. In ACM Multimedia. 583--591.","DOI":"10.1145\/3394171.3413973"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3053563"},{"key":"e_1_3_2_1_54_1","volume-title":"Mattia Seg\u00f9, Fisher Yu, and Seung-Ik Lee.","author":"Zaheer Muhammad Zaigham","year":"2022","unstructured":"Muhammad Zaigham Zaheer, Arif Mahmood, Muhammad Haris Khan, Mattia Seg\u00f9, Fisher Yu, and Seung-Ik Lee. 2022. Generative Cooperative Learning for Unsupervised Video Anomaly Detection. In CVPR. 14724--14734."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3134410"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"crossref","unstructured":"Chen Zhang Guorong Li Yuankai Qi Shuhui Wang Laiyun Qing Qingming Huang and Ming-Hsuan Yang. 2023. Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection. In CVPR. 16271--16280.","DOI":"10.1109\/CVPR52729.2023.01561"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01646"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"crossref","unstructured":"Yiru Zhao Bing Deng Chen Shen Yao Liu Hongtao Lu and Xian-Sheng Hua. 2017. Spatio-Temporal AutoEncoder for Video Anomaly Detection. In ACM Multimedia. 1933--1941.","DOI":"10.1145\/3123266.3123451"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3190539"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"crossref","unstructured":"Hang Zhou Junqing Yu and Wei Yang. 2023. Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection. In AAAI. AAAI 3769--3777.","DOI":"10.1609\/aaai.v37i3.25489"}],"event":{"name":"MM '24: The 32nd ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Melbourne VIC Australia","acronym":"MM '24"},"container-title":["Proceedings of the 32nd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3680871","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3664647.3680871","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:08Z","timestamp":1750295888000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3680871"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,28]]},"references-count":60,"alternative-id":["10.1145\/3664647.3680871","10.1145\/3664647"],"URL":"https:\/\/doi.org\/10.1145\/3664647.3680871","relation":{},"subject":[],"published":{"date-parts":[[2024,10,28]]},"assertion":[{"value":"2024-10-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}