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Towards open-ended Video Anomaly Detection (VAD), existing methods often exhibit biased detection when faced with challenging or unseen events and lack interpretability.
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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Observe locally, infer globally: a space-time mrf for detecting abnormal activities with incremental updates
Jaechul Kim and Kristen Grauman · 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 crowd scene
Shu Wang and Zhenjiang Miao · 2010
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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
Earlier work this paper cites.
Anomaly detection and localization in crowded scenes
Weixin Li, Vijay Mahadevan, and Nuno Vasconcelos · 2013
Earlier work this paper cites.
Learning temporal regularity in video sequences
Mahmudul Hasan, Jonghyun Choi, Jan Neumann, Amit K Roy-Chowdhury, and Larry S Davis · 2016
Earlier work this paper cites.
Spot on: Action localization from pointly-supervised proposals
Pascal Mettes, Jan C Van Gemert, and Cees GM Snoek · 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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Detecting anomalous events in videos by learning deep representations of appearance and motion
Dan Xu, Yan Yan, Elisa Ricci, and Nicu Sebe · 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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Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection
Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, and Anton van den Hengel · 2019
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Anomaly locality in video surveillance
Federico Landi, Cees GM Snoek, and Rita Cucchiara · 2019
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Exploring background-bias for anomaly detection in surveillance videos
Kun Liu and Huadong Ma · 2019
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Gods: Generalized one-class discriminative subspaces for anomaly detection
Jue Wang and Anoop Cherian · 2019
Earlier work this paper cites.
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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Sf-net: Single-frame supervision for temporal action localization
Fan Ma, Linchao Zhu, Yi Yang, Shengxin Zha, Gourab Kundu, Matt Feiszli, and Zheng Shou · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Mist: Multiple instance self-training framework for video anomaly detection
Jia-Chang Feng, Fa-Ting Hong, and Wei-Shi Zheng · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Cited alongside, same era.
Learning action completeness from points for weakly-supervised temporal action localization
Pilhyeon Lee and Hyeran Byun · 2021
Cited alongside, same era.
Clip-tsa: Clip-assisted temporal self-attention for weakly-supervised video anomaly detection
Hyekang Kevin Joo, Khoa Vo, Kashu Yamazaki, and Ngan Le · 2023
Later among the works it cites.
Improved baselines with visual instruction tuning
Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee · 2023
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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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Learning prompt-enhanced context features for weakly-supervised video anomaly detection
Yujiang Pu, Xiaoyu Wu, and Shengjin Wang · 2023
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Dyannet: A scene dynamicity guided self-trained video anomaly detection network
Kamalakar Vijay Thakare, Yash Raghuwanshi, Debi Prosad Dogra, Heeseung Choi, and Ig-Jae Kim · 2023
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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
Cited alongside, same era.
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
Cited alongside, same era.
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.
Video moment retrieval from text queries via single frame annotation
Ran Cui, Tianwen Qian, Pai Peng, Elena Daskalaki, Jingjing Chen, Xiaowei Guo, Huyang Sun, and Yu-Gang Jiang · 2022
Cited alongside, same era.
Self-training multi-sequence learning with transformer for weakly supervised video anomaly detection
Shuo Li, Fang Liu, and Licheng Jiao · 2022
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.
Later among the works it cites.
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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Exploring diffusion models for unsupervised video anomaly detection
Anil Osman Tur, Nicola Dall’Asen, Cigdem Beyan, and Elisa Ricci · 2023
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mplug-owl: Modularization empowers large language models with multimodality
Qinghao Ye, Haiyang Xu, Guohai Xu, Jiabo Ye, Ming Yan, Yiyang Zhou, Junyang Wang, Anwen Hu, Pengcheng Shi, Yaya Shi, et al · 2023
Later among the works it cites.
Towards surveillance video-and-language understanding: New dataset, baselines, and challenges, 2023
Tongtong Yuan, Xuange Zhang, Kun Liu, Bo Liu, Chen Chen, Jian Jin, and Zhenzhen Jiao · 2023
Later among the works it cites.
Dual memory units with uncertainty regulation for weakly supervised video anomaly detection
Hang Zhou, Junqing Yu, and Wei Yang · 2023
Later among the works it cites.
Llama 3 model card
AI@Meta · 2024
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Instructblip: Towards general-purpose vision-language models with instruction tuning
Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale N Fung, and Steven Hoi · 2024
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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 · 2024
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Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2024
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Video anomaly detection and explanation via large language models
Hui Lv and Qianru Sun · 2024
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Text prompt with normality guidance for weakly supervised video anomaly detection
Zhiwei Yang, Jing Liu, and Peng Wu · 2024
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Harnessing large language models for training-free video anomaly detection
Luca Zanella, Willi Menapace, Massimiliano Mancini, Yiming Wang, and Elisa Ricci · 2024
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Glancevad: Exploring glance supervision for label-efficient video anomaly detection
Huaxin Zhang, Xiang Wang, Xiaohao Xu, Xiaonan Huang, Chuchu Han, Yuehuan Wang, Changxin Gao, Shanjun Zhang, and Nong Sang · 2024
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