mlsanityData to 14 Oct 2026

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

arXiv 2603.02964 ↗First posted 3 Mar 2026cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 52 references that are in the map. First posted on arXiv 3 Mar 2026.

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32 of 52 references.

References · 32

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20 more
  1. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  2. 2021Attention Mechanisms in Computer Vision: A Surveycited by 232
  3. 2018Multi-level Wavelet-CNN for Image Restorationcited by 184
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  5. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  6. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 117
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  11. 2024Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detectioncited by 93
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  13. 2024Wavelet Convolutions for Large Receptive Fieldscited by 74
  14. 2024A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localizationcited by 74
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  18. 2024GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detectioncited by 42
  19. 2022Multi-stage image denoising with the wavelet transformcited by 37
  20. 2022Wavelet-Attention CNN for Image Classificationcited by 8

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