Multilevel saliency-guided self-supervised learning for image anomaly detection

Anomaly detection (AD) is a fundamental task in computer vision. It aims to identify incorrect image data patterns which deviate from the normal ones. Conventional methods generally address AD by preparing augmented negative samples to enforce self-supervised learning. However, these techniques typi...

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Bibliographic Details
Published in:Signal, image and video processing image and video processing, 2024-09, Vol.18 (8-9), p.6339-6351
Main Authors: Qin, Jianjian, Gu, Chunzhi, Yu, Jun, Zhang, Chao
Format: Article
Language:English
Subjects:
Ablation
Anomalies
Centroids
Clustering
Computer Imaging
Computer Science
Computer vision
Data augmentation
Feature extraction
Image Processing and Computer Vision
Multimedia Information Systems
Original Paper
Pattern Recognition and Graphics
Salience
Self-supervised learning
Semantics
Signal,Image and Speech Processing
State-of-the-art reviews
Vision
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Summary:Anomaly detection (AD) is a fundamental task in computer vision. It aims to identify incorrect image data patterns which deviate from the normal ones. Conventional methods generally address AD by preparing augmented negative samples to enforce self-supervised learning. However, these techniques typically do not consider semantics during augmentation, leading to the generation of unrealistic or invalid negative samples. Consequently, the feature extraction network can be hindered from embedding critical features. In this study, inspired by visual attention learning approaches, we propose CutSwap, which leverages saliency guidance to incorporate semantic cues for augmentation. Specifically, we first employ LayerCAM to extract multilevel image features as saliency maps and then perform clustering to obtain multiple centroids. To fully exploit saliency guidance, on each map, we select a pixel pair from the cluster with the highest centroid saliency to form a patch pair. Such a patch pair includes highly similar context information with dense semantic correlations. The resulting negative sample is created by swapping the locations of the patch pair. Compared to prior augmentation methods, CutSwap generates more subtle yet realistic negative samples to facilitate quality feature learning. Extensive experimental and ablative evaluations demonstrate that our method achieves state-of-the-art AD performance on two mainstream AD benchmark datasets.
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-024-03320-z