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Learning discriminative features for effectively separating abnormal events from normality is crucial for weakly supervised video anomaly detection (WS-VAD) tasks.
Look, Listen and Pay More Attention: Fusing Multi-Modal Information for Video Violence Detection
Wei, D.; Liu, C.; Liu, Y.; Liu, J.; Zhu, X.; and Zeng, X. 2022 · 1984
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Abnormal Event Detection at 150 FPS in MATLAB
Lu, C.; Shi, J.; and Jia, J. 2013 · 2013
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Learning Spatiotemporal Features with 3D Convolutional Networks
Tran, D.; Bourdev, L. D.; Fergus, R.; Torresani, L.; and Paluri, M. 2015 · 2015
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Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Carreira, J.; and Zisserman, A. 2017 · 2017
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Attention is All you Need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; and Polosukhin, I. 2017 · 2017
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Attention-based deep multiple instance learning
Ilse, M.; Tomczak, J. M.; and Welling, M. 2018 · 2018
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Future Frame Prediction for Anomaly Detection - A New Baseline
Liu, W.; Luo, W.; Lian, D.; and Gao, S. 2018 · 2018
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Weakly Supervised Action Localization by Sparse Temporal Pooling Network
Nguyen, P.; Liu, T.; Prasad, G.; and Han, B. 2018 · 2018
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Real-World Anomaly Detection in Surveillance Videos
Sultani, W.; Chen, C.; and Shah, M. 2018 · 2018
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Videos as Space-Time Region Graphs
Wang, X.; and Gupta, A. 2018 · 2018
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Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection
Gong, D.; Liu, L.; Le, V.; Saha, B.; Mansour, M. R.; Venkatesh, S.; and Hengel, A. V. D. 2019 · 2019
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Temporal Convolutional Network with Complementary Inner Bag Loss for Weakly Supervised Anomaly Detection
Zhang, J.; Qing, L.; and Miao, J. 2019 · 2019
Cited alongside, same era.
Graph Convolutional Label Noise Cleaner: Train a Plug-And-Play Action Classifier for Anomaly Detection
Zhong, J.; Li, N.; Kong, W.; Liu, S.; Li, T. H.; and Li, G. 2019 · 2019
Cited alongside, same era.
Motion-Aware Feature for Improved Video Anomaly Detection
Zhu, Y.; and Newsam, S. D. 2019 · 2019
Cited alongside, same era.
Confidence Regularized Self-Training
Zou, Y.; Yu, Z.; Liu, X.; and Kumar, J., B.V.K. Vijaya adn Wang. 2019 · 2019
Cited alongside, same era.
Data Uncertainty Learning in Face Recognition
Chang, J.; Lan, Z.; Cheng, C.; and Wei, Y. 2020 · 2020
Cited alongside, same era.
Background Suppression Network for Weakly-Supervised Temporal Action Localization
MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection
Feng, J.; Hong, F.; and Zheng, W. 2021 · 2021
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Weakly-supervised Temporal Action Localization by Uncertainty Modeling
Lee, P.; Wang, J.; Lu, Y.; and Byun, H. 2021 · 2021
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Action Unit Memory Network for Weakly Supervised Temporal Action Localization
Luo, W.; Zhang, T.; Yang, W.; Liu, J.; Mei, T.; Wu, F.; and Zhang, Y. 2021 · 2021
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Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
Tian, Y.; Pang, G.; Chen, Y.; Singh, R.; Verjans, J. W.; and Carneiro, G. 2021 · 2021
Later among the works it cites.
Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Video
Wu, J.; Zhang, W.; Li, G.; Wu, H.; Tan, X.; Li, Y.; Ding, E.; and Lin, L. 2021 · 2021
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Lee, P.; Uh, Y.; and Byun, H. 2020 · 2020
Cited alongside, same era.
Self-Trained Deep Ordinal Regression for End-to-End Video Anomaly Detection
Pang, G.; Yan, C.; Shen, C.; Hengel, A. V. D.; and Bai, X. 2020 · 2020
Cited alongside, same era.
Learning Memory-guided Normality for Anomaly Detection
Stephen, K.; and Menon, V. 2020 · 2020
Cited alongside, same era.
Not only Look, But Also Listen: Learning Multimodal Violence Detection Under Weak Supervision
Wu, P.; Liu, J.; Shi, Y.; Shao, F.; Wu, Z.; and Yang, Z. 2020 · 2020
Cited alongside, same era.
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection
Zaheer, M. Z.; Mahmood, A.; Astrid, M.; and Lee, S.-I. 2020 · 2020
Cited alongside, same era.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; Uszkoreit, J.; and Houlsby, N. 2021 · 2021
Cited alongside, same era.
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z.; Lin, Y.; Cao, Y.; Hu, H.; Wei, Y.; Zhang, Z.; Lin, S.; and Guo, B. 2021a
Cited in the paper.
Wu, P.; and Liu, J. 2021 · 2021
Later among the works it cites.
Self-Training Multi-Sequence Learning with Transformer for Weakly Supervised Video Anomaly Detection
Li, S.; Liu, F.; and Jiao, L. 2022 · 2022
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Uncertainty Modeling for Out-of-Distribution Generalization
Li, X.; Dai, Y.; Ge, Y.; Liu, J.; Shan, Y.; and Duan, L. 2022 · 2022
Later among the works it cites.
Anomaly Detection Based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation
Riveram, A. R.; Khan, A.; Bekkouch, I. E. I.; and Sheikh, T. S. 2022 · 2022
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ACGNet: Action Complement Graph Network for Weakly-Supervised Temporal Action Localization
Yang, Z.; Qin, J.; and Huang, D. 2022 · 2022
Later among the works it cites.