用小波分析增强文本提示，提升零样本异常检测能力
WMoE-CLIP: Wavelet-Enhanced Mixture-of-Experts Prompt Learning for Zero-Shot Anomaly Detection
- 引入小波分解提取多频图像特征，动态优化文本嵌入
- 通过变分自编码器融合全局语义，增强提示适应性
- 适合工业与医疗领域中的细微异常检测任务
视觉-语言模型在零样本异常检测（ZSAD）中展现出强大泛化能力，可在无特定任务监督下检测未见异常。然而，现有方法通常依赖固定文本提示，难以捕捉复杂语义，且仅关注空间域特征，限制了对细微异常的检测能力。为此，我们提出一种小波增强的专家混合提示学习方法用于ZSAD。具体而言，采用变分自编码器建模全局语义表示，并将其融入提示以提升对多样异常模式的适应性；小波分解提取多频图像特征，通过跨模态交互动态优化文本嵌入；此外，引入语义感知的专家混合模块聚合上下文信息。在14个工业与医学数据集上的大量实验验证了该方法的有效性。
原文摘要 · Abstract (English)
Vision-language models have recently shown strong generalization in zero-shot anomaly detection (ZSAD), enabling the detection of unseen anomalies without task-specific supervision. However, existing approaches typically rely on fixed textual prompts, which struggle to capture complex semantics, and focus solely on spatial-domain features, limiting their ability to detect subtle anomalies. To address these challenges, we propose a wavelet-enhanced mixture-of-experts prompt learning method for ZSAD. Specifically, a variational autoencoder is employed to model global semantic representations and integrate them into prompts to enhance adaptability to diverse anomaly patterns. Wavelet decomposition extracts multi-frequency image features that dynamically refine textual embeddings through cross-modal interactions. Furthermore, a semantic-aware mixture-of-experts module is introduced to aggregate contextual information. Extensive experiments on 14 industrial and medical datasets demonstrate the effectiveness of the proposed method.
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