用大模型生成真实异常样本，结合小波注意力提升检测精度
Improving Anomaly Detection with Foundation-Model Synthesis and Wavelet-Domain Attention
- 用基础模型无监督生成逼真异常图像，无需微调
- 小波域注意力模块使检测准确率显著提升，超越现有方法
- 适合工业异常检测场景，尤其缺乏标注数据时
工业异常检测面临异常样本稀缺和现实异常复杂性的挑战。本文提出基于基础模型的异常合成流水线（FMAS），可在不进行微调或类别特定训练的情况下生成高度逼真的异常样本。受异常在频域特征明显差异的启发，引入小波域注意力模块（WDAM），通过自适应子带处理增强异常特征提取能力。FMAS与WDAM结合显著提升了异常检测灵敏度，同时保持计算效率。在MVTec AD和VisA数据集上的全面实验表明，WDAM作为即插即用模块，相较于现有基线实现显著性能提升。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。