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Weakly Supervised Video Anomaly Detection (WSVAD) is challenging because the binary anomaly label is only given on the video level, but the output requires snippet-level predictions.
Traffic monitoring and accident detection at intersections
Shunsuke Kamijo, Yasuyuki Matsushita, Katsushi Ikeuchi, and Masao Sakauchi · 2000
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Measuring differentiability: Unmasking pseudonymous authors
Moshe Koppel, Jonathan Schler, and Elisheva Bonchek-Dokow · 2007
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Robust real-time unusual event detection using multiple fixed-location monitors
Amit Adam, Ehud Rivlin, Ilan Shimshoni, and Daviv Reinitz · 2008
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Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models
Louis Kratz and Ko Nishino · 2009
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Abnormal crowd behavior detection using social force model
Ramin Mehran, Alexis Oyama, and Mubarak Shah · 2009
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Anomaly detection in crowded scenes
Vijay Mahadevan, Weixin Li, Viral Bhalodia, and Nuno Vasconcelos · 2010
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Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes
Shandong Wu, Brian E Moore, and Mubarak Shah · 2010
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Video parsing for abnormality detection
Borislav Antić and Björn Ommer · 2011
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Online detection of unusual events in videos via dynamic sparse coding
Bin Zhao, Li Fei-Fei, and Eric P Xing · 2011
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Anomaly detection and localization in crowded scenes
Weixin Li, Vijay Mahadevan, and Nuno Vasconcelos · 2013
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Abnormal event detection at 150 fps in matlab
Cewu Lu, Jianping Shi, and Jiaya Jia · 2013
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Real-time anomaly detection and localization in crowded scenes
Mohammad Sabokrou, Mahmood Fathy, Mojtaba Hoseini, and Reinhard Klette · 2015
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Convolutional lstm network: A machine learning approach for precipitation nowcasting
SHI Xingjian, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo · 2015
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Learning deep representations of appearance and motion for anomalous event detection
Dan Xu, Elisa Ricci, Yan Yan, Jingkuan Song, and Nicu Sebe · 2015
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A discriminative framework for anomaly detection in large videos
Allison Del Giorno, J Andrew Bagnell, and Martial Hebert · 2016
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Learning temporal regularity in video sequences
Mahmudul Hasan, Jonghyun Choi, Jan Neumann, Amit K Roy-Chowdhury, and Larry S Davis · 2016
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Angry crowds: Detecting violent events in videos
Sadegh Mohammadi, Alessandro Perina, Hamed Kiani, and Vittorio Murino · 2016
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Quo vadis, action recognition? a new model and the kinetics dataset
Joao Carreira and Andrew Zisserman · 2017
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A revisit of sparse coding based anomaly detection in stacked rnn framework
Weixin Luo, Wen Liu, and Shenghua Gao · 2017
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Unmasking the abnormal events in video
Radu Tudor Ionescu, Sorina Smeureanu, Bogdan Alexe, and Marius Popescu · 2017
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Self-ensembling for visual domain adaptation
Geoffrey French, Michal Mackiewicz, and Mark Fisher · 2018
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An anomaly-introduced learning method for abnormal event detection
Chengkun He, Jie Shao, and Jiayu Sun · 2018
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Motion-aware feature for improved video anomaly detection
Yi Zhu and Shawn Newsam · 2019
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Understanding self-training for gradual domain adaptation
Ananya Kumar, Tengyu Ma, and Percy Liang · 2020
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Global information guided video anomaly detection
Hui Lv, Chunyan Xu, and Zhen Cui · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel · 2020
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Not only look, but also listen: Learning multimodal violence detection under weak supervision
Peng Wu, Jing Liu, Yujia Shi, Yujia Sun, Fangtao Shao, Zhaoyang Wu, and Zhiwei Yang · 2020
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Interventional few-shot learning
Zhongqi Yue, Hanwang Zhang, Qianru Sun, and Xian-Sheng Hua · 2020
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Future frame prediction for anomaly detection–a new baseline
Wen Liu, Weixin Luo, Dongze Lian, and Shenghua Gao · 2018
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Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
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Plug-and-play cnn for crowd motion analysis: An application in abnormal event detection
Mahdyar Ravanbakhsh, Moin Nabi, Hossein Mousavi, Enver Sangineto, and Nicu Sebe · 2018
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Subspace support vector data description
Fahad Sohrab, Jenni Raitoharju, Moncef Gabbouj, and Alexandros Iosifidis · 2018
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Real-world anomaly detection in surveillance videos
Waqas Sultani, Chen Chen, and Mubarak Shah · 2018
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Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
Yang Zou, Zhiding Yu, BVK Kumar, and Jinsong Wang · 2018
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Cycle self-training for domain adaptation
Hong Liu, Jianmin Wang, and Mingsheng Long · 2021
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Learning normal dynamics in videos with meta prototype network
Hui Lv, Chen Chen, Zhen Cui, Chunyan Xu, Yong Li, and Jian Yang · 2021
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Localizing anomalies from weakly-labeled videos
Hui Lv, Chuanwei Zhou, Zhen Cui, Chunyan Xu, Yong Li, and Jian Yang · 2021
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Weakly-supervised video anomaly detection with robust temporal feature magnitude learning
Yu Tian, Guansong Pang, Yuanhong Chen, Rajvinder Singh, Johan W Verjans, and Gustavo Carneiro · 2021
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Transporting causal mechanisms for unsupervised domain adaptation
Zhongqi Yue, Qianru Sun, Xian-Sheng Hua, and Hanwang Zhang · 2021
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Ubnormal: New benchmark for supervised open-set video anomaly detection
Andra Acsintoae, Andrei Florescu, Mariana-Iuliana Georgescu, Tudor Mare, Paul Sumedrea, Radu Tudor Ionescu, Fahad Shahbaz Khan, and Mubarak Shah · 2022
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A survey on explainable anomaly detection for industrial internet of things
Zijie Huang and Yulei Wu · 2022
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Self-training multi-sequence learning with transformer for weakly supervised video anomaly detection
Shuo Li, Fang Liu, and Licheng Jiao · 2022
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Expanding language-image pretrained models for general video recognition
Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, and Haibin Ling · 2022
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Generative cooperative learning for unsupervised video anomaly detection
M Zaigham Zaheer, Arif Mahmood, M Haris Khan, Mattia Segu, Fisher Yu, and Seung-Ik Lee · 2022
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