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The rapid advancement of vision-language models (VLMs) has established a new paradigm in video anomaly detection (VAD): leveraging VLMs to simultaneously detect anomalies and provide comprehendible explanations for the decisions.
Digital image processing
Rafael C Gonzalez · 2009
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Abnormal event detection at 150 fps in matlab
Cewu Lu, Jianping Shi, and Jiaya Jia · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma · 2014
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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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Temporal segment networks: Towards good practices for deep action recognition
Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, and Luc Van Gool · 2016
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Deep hyperspherical learning
Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhiding Yu, Bo Dai, Tuo Zhao, and Le Song · 2017
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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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Real-world anomaly detection in surveillance videos
Waqas Sultani, Chen Chen, and Mubarak Shah · 2018
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Gods: Generalized one-class discriminative subspaces for anomaly detection
Jue Wang and Anoop Cherian · 2019
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Anopcn: Video anomaly detection via deep predictive coding network
Muchao Ye, Xiaojiang Peng, Weihao Gan, Wei Wu, and Yu Qiao · 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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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 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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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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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Mist: Multiple instance self-training framework for video anomaly detection
Jia-Chang Feng, Fa-Ting Hong, and Wei-Shi Zheng · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 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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Unbiased multiple instance learning for weakly supervised video anomaly detection
Hui Lv, Zhongqi Yue, Qianru Sun, Bin Luo, Zhen Cui, and Hanwang Zhang · 2023
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What does a platypus look like? generating customized prompts for zero-shot image classification
Sarah Pratt, Ian Covert, Rosanne Liu, and Ali Farhadi · 2023
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Controlling text-to-image diffusion by orthogonal finetuning
Zeju Qiu, Weiyang Liu, Haiwen Feng, Yuxuan Xue, Yao Feng, Zhen Liu, Dan Zhang, Adrian Weller, and Bernhard Schölkopf · 2023
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Unsupervised video anomaly detection with diffusion models conditioned on compact motion representations
Anil Osman Tur, Nicola Dall’Asen, Cigdem Beyan, and Elisa Ricci · 2023
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The surprising effectiveness of multimodal large language models for video moment retrieval
Meinardus Boris, Batra Anil, Rohrbach Anna, and Rohrbach Marcus · 2024
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Anomaly detection in autonomous driving: A survey
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Self-supervised sparse representation for video anomaly detection
Jhih-Ciang Wu, He-Yen Hsieh, Ding-Jie Chen, Chiou-Shann Fuh, and Tyng-Luh Liu · 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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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Mgfn: Magnitude-contrastive glance-and-focus network for weakly-supervised video anomaly detection
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Imagebind: One embedding space to bind them all
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Qiushan Guo, Shalini De Mello, Hongxu Yin, Wonmin Byeon, Ka Chun Cheung, Yizhou Yu, Ping Luo, and Sifei Liu · 2024
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Video anomaly detection and explanation via large language models
Hui Lv and Qianru Sun · 2024
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Jiaqi Tang, Hao Lu, Ruizheng Wu, Xiaogang Xu, Ke Ma, Cheng Fang, Bin Guo, Jiangbo Lu, Qifeng Chen, and Ying-Cong Chen · 2024
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