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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2603.02964 |
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| _version_ | 1866911482349879296 |
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| author | Wu, Wensheng Lu, Zheming Lu, Ziqian He, Zewei Sun, Xuecheng Wang, Zhao Han, Jungong Yu, Yunlong |
| author_facet | Wu, Wensheng Lu, Zheming Lu, Ziqian He, Zewei Sun, Xuecheng Wang, Zhao Han, Jungong Yu, Yunlong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02964 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Improving Anomaly Detection with Foundation-Model Synthesis and Wavelet-Domain Attention Wu, Wensheng Lu, Zheming Lu, Ziqian He, Zewei Sun, Xuecheng Wang, Zhao Han, Jungong Yu, Yunlong Computer Vision and Pattern Recognition 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. |
| title | Improving Anomaly Detection with Foundation-Model Synthesis and Wavelet-Domain Attention |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.02964 |