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Recently, people tried to use a few anomalies for video anomaly detection (VAD) instead of only normal data during the training process.
Y. Zhao, B. Deng, C. Shen, Y. Liu, H. Lu, and X.-S. Hua, “Spatio-temporal autoencoder for video anomaly detection,” in Proceedings of the 25th ACM international conference on Multimedia , 2017, pp. 1933–1941
1941
Earlier work this paper cites.
V. Mahadevan, W. Li, V. Bhalodia, and N. Vasconcelos, “Anomaly detection in crowded scenes,” in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . IEEE, 2010, pp. 1975–1981
1981
Earlier work this paper cites.
Y. Chen, X. S. Zhou, and T. S. Huang, “One-class svm for learning in image retrieval,” in Proceedings 2001 International Conference on Image Processing (Cat. No. 01CH37205) , vol. 1. IEEE, 2001, pp. 34–37
2001
Earlier work this paper cites.
Y. Cong, J. Yuan, and J. Liu, “Sparse reconstruction cost for abnormal event detection,” in CVPR 2011 . IEEE, 2011, pp. 3449–3456
2011
Earlier work this paper cites.
S. Li, Z. Wang, G. Zhou, and S. Y. M. Lee, “Semi-supervised learning for imbalanced sentiment classification,” in Twenty-Second International Joint Conference on Artificial Intelligence . Citeseer, 2011
2011
Earlier work this paper cites.
J. Tian, H. Gu, and W. Liu, “Imbalanced classification using support vector machine ensemble,” Neural Computing and Applications , vol. 20, no. 2, pp. 203–209, 2011
2011
Earlier work this paper cites.
C. Lu, J. Shi, and J. Jia, “Abnormal event detection at 150 fps in matlab,” in Proceedings of the IEEE international conference on computer vision , 2013, pp. 2720–2727
2013
Earlier work this paper cites.
W. Li, V. Mahadevan, and N. Vasconcelos, “Anomaly detection and localization in crowded scenes,” IEEE transactions on pattern analysis and machine intelligence , vol. 36, no. 1, pp. 18–32, 2013
2013
Earlier work this paper cites.
B. Krawczyk, M. Woźniak, and G. Schaefer, “Cost-sensitive decision tree ensembles for effective imbalanced classification,” Applied Soft Computing , vol. 14, pp. 554–562, 2014
2014
Earlier work this paper cites.
K.-W. Cheng, Y.-T. Chen, and W.-H. Fang, “Video anomaly detection and localization using hierarchical feature representation and gaussian process regression,” in CVPR , 2015, pp. 2909–2917
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in MICCAI . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
L. v. d. Maaten and G. Hinton, “Visualizing data using t-sne,” Journal of machine learning research , vol. 9, no. Nov, pp. 2579–2605, 2008
2015
Earlier work this paper cites.
M. Hasan, J. Choi, J. Neumann, A. K. Roy-Chowdhury, and L. S. Davis, “Learning temporal regularity in video sequences,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 733–742
2016
Earlier work this paper cites.
W. Luo, W. Liu, and S. Gao, “A revisit of sparse coding based anomaly detection in stacked rnn framework,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 341–349
2017
Cited alongside, same era.
Q. Dong, S. Gong, and X. Zhu, “Class rectification hard mining for imbalanced deep learning,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 1851–1860
2017
Cited alongside, same era.
M. Ravanbakhsh, M. Nabi, E. Sangineto, L. Marcenaro, C. Regazzoni, and N. Sebe, “Abnormal event detection in videos using generative adversarial nets,” in 2017 IEEE International Conference on Image Processing (ICIP) . IEEE, 2017, pp. 1577–1581
2017
Cited alongside, same era.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017
2017
Cited alongside, same era.
R. T. Ionescu, F. S. Khan, M.-I. Georgescu, and L. Shao, “Object-centric auto-encoders and dummy anomalies for abnormal event detection in video,” in CVPR , 2019, pp. 7842–7851
2019
Later among the works it cites.
H. Vu, T. D. Nguyen, T. Le, W. Luo, and D. Phung, “Robust anomaly detection in videos using multilevel representations,” in AAAI , vol. 33, 2019, pp. 5216–5223
2019
Later among the works it cites.
C. Huang, Y. Li, C. L. Chen, and X. Tang, “Deep imbalanced learning for face recognition and attribute prediction,” IEEE transactions on pattern analysis and machine intelligence , 2019
2019
Later among the works it cites.
T.-N. Nguyen and J. Meunier, “Anomaly detection in video sequence with appearance-motion correspondence,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 1273–1283
2019
Later among the works it cites.
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R. Tudor Ionescu, S. Smeureanu, B. Alexe, and M. Popescu, “Unmasking the abnormal events in video,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 2895–2903
2017
Cited alongside, same era.
W. Liu, W. Luo, D. Lian, and S. Gao, “Future frame prediction for anomaly detection–a new baseline,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 6536–6545
2018
Cited alongside, same era.
M. Ren, W. Zeng, B. Yang, and R. Urtasun, “Learning to reweight examples for robust deep learning,” in International Conference on Machine Learning , 2018, pp. 4334–4343
2018
Cited alongside, same era.
L. Ruff, R. Vandermeulen, N. Goernitz, L. Deecke, S. A. Siddiqui, A. Binder, E. Müller, and M. Kloft, “Deep one-class classification,” in International conference on machine learning , 2018, pp. 4393–4402
2018
Cited alongside, same era.
M. Sabokrou, M. Khalooei, M. Fathy, and E. Adeli, “Adversarially learned one-class classifier for novelty detection,” in CVPR , 2018, pp. 3379–3388
2018
Cited alongside, same era.
D. Gong, L. Liu, V. Le, B. Saha, M. R. Mansour, S. Venkatesh, and A. v. d. Hengel, “Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 1705–1714
2019
Cited alongside, same era.
W. Liu, W. Luo, Z. Li, P. Zhao, S. Gao et al. , “Margin learning embedded prediction for video anomaly detection with a few anomalies.” in IJCAI , 2019, pp. 3023–3030
2019
Cited alongside, same era.
L. Ruff, R. A. Vandermeulen, N. Görnitz, A. Binder, E. Müller, K.-R. Müller, and M. Kloft, “Deep semi-supervised anomaly detection,” in International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
H. Park, J. Noh, and B. Ham, “Learning memory-guided normality for anomaly detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 14 372–14 381
2020
Later among the works it cites.
M. Z. Zaheer, A. Mahmood, H. Shin, and S.-I. Lee, “A self-reasoning framework for anomaly detection using video-level labels,” IEEE Signal Processing Letters , vol. 27, pp. 1705–1709, 2020
2020
Later among the works it cites.
M. Z. Zaheer, A. Mahmood, M. Astrid, and S.-I. Lee, “Claws: Clustering assisted weakly supervised learning with normalcy suppression for anomalous event detection,” in ECCV . Springer, 2020, pp. 358–376
2020
Later among the works it cites.
B. Wan, Y. Fang, X. Xia, and J. Mei, “Weakly supervised video anomaly detection via center-guided discriminative learning,” in ICME . IEEE, 2020, pp. 1–6
2020
Later among the works it cites.
B. Ramachandra and M. Jones, “Street scene: A new dataset and evaluation protocol for video anomaly detection,” in WACV , 2020, pp. 2569–2578
2020
Later among the works it cites.
Z. Wang, Y. Zou, and Z. Zhang, “Cluster attention contrast for video anomaly detection,” in Proceedings of the 28th ACM International Conference on Multimedia , 2020, pp. 2463–2471
2020
Later among the works it cites.
C. Sun, Y. Jia, Y. Hu, and Y. Wu, “Scene-aware context reasoning for unsupervised abnormal event detection in videos,” in Proceedings of the 28th ACM International Conference on Multimedia , 2020, pp. 184–192
2020
Later among the works it cites.
M. Z. Zaheer, J.-h. Lee, M. Astrid, and S.-I. Lee, “Old is gold: Redefining the adversarially learned one-class classifier training paradigm,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 14 183–14 193
2020
Later among the works it cites.