Improving Anomaly Detection with Foundation-Model Synthesis and Wavelet-Domain Attention

• URL: http://arxiv.org/abs/2603.02964v1
• Date: Tue, 03 Mar 2026 13:17:08 GMT
• Title: Improving Anomaly Detection with Foundation-Model Synthesis and Wavelet-Domain Attention
• Authors: Wensheng Wu, Zheming Lu, Ziqian Lu, Zewei He, Xuecheng Sun, Zhao Wang, Jungong Han, Yunlong Yu,
• Abstract summary: anomaly synthesis pipeline (FMAS) generates highly realistic anomalous samples without fine-tuning or class-specific training.<n>Wavelet Domain Attention Module (WDAM) exploits adaptive sub-band processing to enhance anomaly feature extraction.
• Score: 54.73850941855912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Industrial anomaly detection faces significant challenges due to the scarcity of anomalous samples and the complexity of real-world anomalies. In this paper, we propose a foundation model-based anomaly synthesis pipeline (FMAS) that generates highly realistic anomalous samples without fine-tuning or class-specific training. Motivated by the distinct frequency-domain characteristics of anomalies, we introduce aWavelet Domain Attention Module (WDAM), which exploits adaptive sub-band processing to enhance anomaly feature extraction. The combination of FMAS and WDAM significantly improves anomaly detection sensitivity while maintaining computational efficiency. Comprehensive experiments on MVTec AD and VisA datasets demonstrate that WDAM, as a plug-and-play module, achieves substantial performance gains against existing baselines.

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