The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection
Abstract
Weakly supervised video anomaly detection (WS-VAD) is to distinguish anomalies from normal events based on discriminative representations. Most existing works are limited in insufficient video representations. In this work, we develop a multiple instance self-training framework (MIST) to efficiently refine task-specific discriminative representations with only video-level annotations. In particular, MIST is composed of 1) a multiple instance pseudo label generator, which adapts a sparse continuous sampling strategy to produce more reliable clip-level pseudo labels, and 2) a self-guided attention boosted feature encoder that aims to automatically focus on anomalous regions in frames while extracting task-specific representations. Moreover, we adopt a self-training scheme to optimize both components and finally obtain a task-specific feature encoder. Extensive experiments on two public datasets demonstrate the efficacy of our method, and our method performs comparably to or even better than existing supervised and weakly supervised methods, specifically obtaining a frame-level AUC 94.83% on ShanghaiTech.
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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513
Learning Spatiotemporal Features with 3D Convolutional Networks
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Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Carreira, J., & Zisserman, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.502 · Cited by 9,570
Unsupervised word sense disambiguation rivaling supervised methods
Yarowsky, D. (1995) · Proceedings of the 33rd annual meeting on Association for Computational Linguistics - · doi:10.3115/981658.981684 · Cited by 2,385
Real-World Anomaly Detection in Surveillance Videos
Sultani, W., Chen, C., & Shah, M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00678 · Cited by 2,140
Gong, D., Liu, L., Le, V., Saha, B., Mansour, M. R., Venkatesh, S., & Van Den Hengel, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00179 · Cited by 1,930
Semi-supervised Learning by Entropy Minimization
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Future Frame Prediction for Anomaly Detection - A New Baseline
Liu, W., Luo, W., Lian, D., & Gao, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00684 · Cited by 1,530
Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training
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Cited by
Weakly Supervised Video Anomaly Detection via Transformer-Enabled Temporal Relation Learning
Zhang, D., Huang, C., Liu, C., & Xu, Y. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3175092 · Cited by 58
Nazir, A., Mitra, R., Sulieman, H., & Kamalov, F. (2023) · Sensors · doi:10.3390/s23135811 · Cited by 30
Paulraj, S., & Vairavasundaram, S. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109496 · Cited by 27
Metadata
- Authors
- Feng, J.-C., Hong, F.-T., & Zheng, W.-S.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 14004-14013
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3168600998
- Cited by
- 355
- References
- 36
- FWCI
- 24.93
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Feng, J.-C., Hong, F.-T., & Zheng, W.-S. (2021). MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr46437.2021.01379
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