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Anomaly detection with weakly supervised video-level labels is typically formulated as a multiple instance learning (MIL) problem, in which we aim to identify snippets containing abnormal events, with each video represented as a bag of video snippets.
Support vector method for novelty detection
Bernhard Schölkopf, Robert C Williamson, Alex J Smola, John Shawe-Taylor, and John C Platt · 2000
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Event detection and analysis from video streams
Gérard Medioni, Isaac Cohen, François Brémond, Somboon Hongeng, and Ramakant Nevatia · 2001
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Learning object motion patterns for anomaly detection and improved object detection
Arslan Basharat, Alexei Gritai, and Mubarak Shah · 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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Learning semantic scene models by object classification and trajectory clustering
Tianzhu Zhang, Hanqing Lu, and Stan Z Li · 2009
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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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Large-scale video classification with convolutional neural networks
Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Li Fei-Fei · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Learning fine-grained image similarity with deep ranking
Jiang Wang, Yang Song, Thomas Leung, Chuck Rosenberg, Jingbin Wang, James Philbin, Bo Chen, and Ying Wu · 2014
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Video anomaly detection based on a hierarchical activity discovery within spatio-temporal contexts
Dan Xu, Rui Song, Xinyu Wu, Nannan Li, Wei Feng, and Huihuan Qian · 2014
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Video anomaly detection and localization using hierarchical feature representation and gaussian process regression
Kai-Wen Cheng, Yie-Tarng Chen, and Wen-Hsien Fang · 2015
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Multiple instance learning for soft bags via top instances
Weixin Li and Nuno Vasconcelos · 2015
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Unsupervised behavior-specific dictionary learning for abnormal event detection
Huamin Ren, Weifeng Liu, Søren Ingvor Olsen, Sergio Escalera, and Thomas B Moeslund · 2015
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Learning spatiotemporal features with 3d convolutional networks
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri · 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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Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 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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Video anomaly detection based on locality sensitive hashing filters
Ying Zhang, Huchuan Lu, Lihe Zhang, Xiang Ruan, and Shun Sakai · 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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Joint detection and recounting of abnormal events by learning deep generic knowledge
Ryota Hinami, Tao Mei, and Shin’ichi Satoh · 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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The kinetics human action video dataset
Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al · 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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Abnormal event detection in videos using generative adversarial nets
Mahdyar Ravanbakhsh, Moin Nabi, Enver Sangineto, Lucio Marcenaro, Carlo Regazzoni, and Nicu Sebe · 2017
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Deep-cascade: Cascading 3d deep neural networks for fast anomaly detection and localization in crowded scenes
Mohammad Sabokrou, Mohsen Fayyaz, Mahmood Fathy, and Reinhard Klette · 2017
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Deep appearance features for abnormal behavior detection in video
Sorina Smeureanu, Radu Tudor Ionescu, Marius Popescu, and Bogdan Alexe · 2017
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Deep anomaly detection using geometric transformations
Izhak Golan and Ran El-Yaniv · 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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Temporal attention network for action proposal
C. Liu, X. Xu, and Y. Zhang · 2018
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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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Learning representations of ultrahigh-dimensional data for random distance-based outlier detection
Guansong Pang, Longbing Cao, Ling Chen, and Huan Liu · 2018
Gods: Generalized one-class discriminative subspaces for anomaly detection
Jue Wang and Anoop Cherian · 2019
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Temporal convolutional network with complementary inner bag loss for weakly supervised anomaly detection
J. Zhang, L. Qing, and J. Miao · 2019
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Graph convolutional label noise cleaner: Train a plug-and-play action classifier for anomaly detection
Jia-Xing Zhong, Nannan Li, Weijie Kong, Shan Liu, Thomas H Li, and Ge Li · 2019
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Motion-aware feature for improved video anomaly detection
Yi Zhu and Shawn Newsam · 2019
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Classification-based anomaly detection for general data
Liron Bergman and Yedid Hoshen · 2020
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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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Deep one-class classification
Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft · 2018
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Adversarially learned one-class classifier for novelty detection
Mohammad Sabokrou, Mohammad Khalooei, Mahmood Fathy, and Ehsan Adeli · 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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Non-local neural networks
Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He · 2018
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Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2020
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Anomaly detection with bidirectional consistency in videos
Zhiwen Fang, Jiafei Liang, Joey Tianyi Zhou, Yang Xiao, and Feng Yang · 2020
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Anomaly detection in video via self-supervised and multi-task learning
Mariana-Iuliana Georgescu, Antonio Barbalau, Radu Tudor Ionescu, Fahad Shahbaz Khan, Marius Popescu, and Mubarak Shah · 2020
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Photoshopping colonoscopy video frames
Yuyuan Liu, Yu Tian, Gabriel Maicas, Leonardo Zorron Cheng Tao Pu, Rajvinder Singh, Johan W Verjans, and Gustavo Carneiro · 2020
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Graph embedded pose clustering for anomaly detection
Amir Markovitz, Gilad Sharir, Itamar Friedman, Lihi Zelnik-Manor, and Shai Avidan · 2020
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Self-trained deep ordinal regression for end-to-end video anomaly detection
Guansong Pang, Cheng Yan, Chunhua Shen, Anton van den Hengel, and Xiao Bai · 2020
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Learning memory-guided normality for anomaly detection
Hyunjong Park, Jongyoun Noh, and Bumsub Ham · 2020
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Spotnet: Self-attention multi-task network for object detection
Hughes Perreault, Guillaume-Alexandre Bilodeau, Nicolas Saunier, and Maguelonne Héritier · 2020
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A survey of single-scene video anomaly detection
Bharathkumar Ramachandra, Michael Jones, and Ranga Raju Vatsavai · 2020
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Discriminative clip mining for video anomaly detection
Li Sun, Yanjun Chen, Wu Luo, Haiyan Wu, and Chongyang Zhang · 2020
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Few-shot anomaly detection for polyp frames from colonoscopy
Yu Tian, Gabriel Maicas, Leonardo Zorron Cheng Tao Pu, Rajvinder Singh, Johan W Verjans, and Gustavo Carneiro · 2020
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Weakly supervised video anomaly detection via center-guided discriminative learning
B. Wan, Y. Fang, X. Xia, and J. Mei · 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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Cloze test helps: Effective video anomaly detection via learning to complete video events
Guang Yu, Siqi Wang, Zhiping Cai, En Zhu, Chuanfu Xu, Jianping Yin, and Marius Kloft · 2020
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Claws: Clustering assisted weakly supervised learning with normalcy suppression for anomalous event detection
Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid, and Seung-Ik Lee · 2020
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A self-reasoning framework for anomaly detection using video-level labels
Muhammad Zaigham Zaheer, Arif Mahmood, Hochul Shin, and Seung-Ik Lee · 2020
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Exploring self-attention for image recognition
Hengshuang Zhao, Jiaya Jia, and Vladlen Koltun · 2020
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Encoding structure-texture relation with p-net for anomaly detection in retinal images
Kang Zhou, Yuting Xiao, Jianlong Yang, Jun Cheng, Wen Liu, Weixin Luo, Zaiwang Gu, Jiang Liu, and Shenghua Gao · 2020
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Unsupervised anomaly detection with multi-scale interpolated gaussian descriptors
Yuanhong Chen, Yu Tian, Guansong Pang, and Gustavo Carneiro · 2021
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Deep learning for anomaly detection: A review
Guansong Pang, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel · 2021
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Yu Tian, Guansong Pang, Fengbei Liu, Seon Ho Shin, Johan W Verjans, Rajvinder Singh, Gustavo Carneiro, et al · 2021
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Cleaning label noise with clusters for minimally supervised anomaly detection
Muhammad Zaigham Zaheer, Jin-ha Lee, Marcella Astrid, Arif Mahmood, and Seung-Ik Lee · 2021
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