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Video anomaly detection (VAD) aims to temporally locate abnormal events in a video.
Isolation-based anomaly detection
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou · 2012
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Abnormal event detection at 150 fps in matlab
Cewu Lu, Jianping Shi, and Jiaya Jia · 2013
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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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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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Traffic anomaly detection via perspective map based on spatial-temporal information matrix
Shuai Bai, Zhiqun He, Yu Lei, Wei Wu, Chengkai Zhu, Ming Sun, and Junjie Yan · 2019
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Anomaly candidate identification and starting time estimation of vehicles from traffic videos
Gaoang Wang, Xinyu Yuan, Aotian Zheng, Hung-Min Hsu, and Jenq-Neng Hwang · 2019
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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
Jiangong Zhang, Laiyun Qing, and Jun 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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Learning memory-guided normality for anomaly detection
Hyunjong Park, Jongyoun Noh, and Bumsub Ham · 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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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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A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction
Zhian Liu, Yongwei Nie, Chengjiang Long, Qing Zhang, and Guiqing Li · 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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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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Imagebind: One embedding space to bind them all
Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra · 2023
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Anomalygpt: Detecting industrial anomalies using large vision-language models
Zhaopeng Gu, Bingke Zhu, Guibo Zhu, Yingying Chen, Ming Tang, and Jinqiao Wang · 2023
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Mistral 7b
Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al · 2023
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Survey on video anomaly detection in dynamic scenes with moving cameras
Runyu Jiao, Yi Wan, Fabio Poiesi, and Yiming Wang · 2023
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Clip-tsa: Clip-assisted temporal self-attention for weakly-supervised video anomaly detection
Hyekang Kevin Joo, Khoa Vo, Kashu Yamazaki, and Ngan Le · 2023
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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
Cited alongside, same era.
Learning causal temporal relation and feature discrimination for anomaly detection
Peng Wu and Jing Liu · 2021
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
Mgfn: Magnitude-contrastive glance-and-focus network for weakly-supervised video anomaly detection
Yingxian Chen, Zhengzhe Liu, Baoheng Zhang, Wilton Fok, Xiaojuan Qi, and Yik-Chung Wu · 2023
Cited alongside, same era.
Semantic anomaly detection with large language models
Amine Elhafsi, Rohan Sinha, Christopher Agia, Edward Schmerling, Issa AD Nesnas, and Marco Pavone · 2023
Cited alongside, same era.
Scale-aware spatio-temporal relation learning for video anomaly detection
Guoqiu Li, Guanxiong Cai, Xingyu Zeng, and Rui Zhao
Cited in the paper.
Jaehyun Kim, Seongwook Yoon, Taehyeon Choi, and Sanghoon Sull · 2023
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Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi · 2023
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Improved baselines with visual instruction tuning
Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee · 2023
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Hierarchical semantic contrast for scene-aware video anomaly detection
Shengyang Sun and Xiaojin Gong · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Feature prediction diffusion model for video anomaly detection
Cheng Yan, Shiyu Zhang, Yang Liu, Guansong Pang, and Wenjun Wang · 2023
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