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Attention-guided MIL weakly supervised visual anomaly detection
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DOI：10.1016/j.measurement.2023.112500.png)
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摘要
En 中文
The deep learning anomaly detection method that employs visual sensors as the original signal input suffers from the over-boundary problem caused by a lack of labeled datasets, resulting in unsupervised training. This paper proposes an anomaly detection method based on weakly supervised learning. First, this paper proposes a pseudo -label generation technique based on a multi-instance ranking algorithm to generate pseudo-labels, thereby transforming the weakly supervised learning problem into a fully supervised learning problem. The C3D network extracts video temporal-spatial features as the initial data stream input for the pseudo-label generator/anomaly detection model. Finally, the attention and guidance augmentation modules are combined to make the network focus on the anomaly region, improving the model's spatial localization capability. A series of experimental results on two datasets with varied scales and scene complexity demonstrate that our method can reach a frame -level AUC of 81.48% in UCF-Crime and 94.01% in ShanghaiTech.
Keyword:
Visual anomaly detection
Weak supervision
Attention mechanism
Multi -instance learning
期刊
IF:
5.6
论文数:
2.0W
被引数:
5.4W
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