StackCLIP：零样本工业异常检测中的聚类驱动堆叠提示
StackCLIP: Clustering-Driven Stacked Prompt in Zero-Shot Industrial Anomaly Detection
AI总结：
StackCLIP通过聚类驱动堆叠提示、集成特征对齐和调节提示学习三个模块，解决CLIP在零样本工业异常检测中的类别过拟合问题，在七个数据集上实现检测与分割的最先进性能。
摘要：
在零样本工业异常检测任务中，增强CLIP模型中文本与图像特征之间的对齐是一个关键挑战。近期研究主要在预训练期间使用特定类别提示，这可能导致对训练类别的过拟合，并限制模型的泛化能力。为解决这一问题，我们提出一种通过多类别名称堆叠来转换类别名称以生成堆叠提示的方法，并以此构建了我们的StackCLIP模型。我们的方法引入了两个关键组件。聚类驱动堆叠提示（CSP）模块通过堆叠语义上相似的类别来构建通用提示，同时利用多目标文本特征融合来放大相似物体之间的判别性异常。集成特征对齐（EFA）模块为每个堆叠簇训练知识特定的线性层，并根据测试类别的属性自适应地集成它们。这些模块协同工作，提供了优越的训练速度、稳定性和收敛性，显著提升了异常分割性能。此外，我们的堆叠提示框架在分类任务中提供了稳健的泛化能力。为了进一步提高性能，我们引入了调节提示学习（RPL）模块，该模块利用堆叠提示的泛化能力来优化提示学习，从而提升异常检测分类任务的结果。在七个工业异常检测数据集上的广泛测试表明，我们的方法在零样本异常检测和分割任务中均达到了最先进的性能。
英文摘要：
Enhancing the alignment between text and image features in the CLIP model is a critical challenge in zero-shot industrial anomaly detection tasks. Recent studies predominantly utilize specific category prompts during pretraining, which can cause overfitting to the training categories and limit model generalization. To address this, we propose a method that transforms category names through multicategory name stacking to create stacked prompts, forming the basis of our StackCLIP model. Our approach introduces two key components. The Clustering-Driven Stacked Prompts (CSP) module constructs generic prompts by stacking semantically analogous categories, while utilizing multi-object textual feature fusion to amplify discriminative anomalies among similar objects. The Ensemble Feature Alignment (EFA) module trains knowledge-specific linear layers tailored for each stack cluster and adaptively integrates them based on the attributes of test categories. These modules work together to deliver superior training speed, stability, and convergence, significantly boosting anomaly segmentation performance. Additionally, our stacked prompt framework offers robust generalization across classification tasks. To further improve performance, we introduce the Regulating Prompt Learning (RPL) module, which leverages the generalization power of stacked prompts to refine prompt learning, elevating results in anomaly detection classification tasks. Extensive testing on seven industrial anomaly detection datasets demonstrates that our method achieves state-of-the-art performance in both zero-shot anomaly detection and segmentation tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
