StackCLIP: Clustering-Driven Stacked Prompt in Zero-Shot Industrial Anomaly Detection

TL;DR

StackCLIP introduces a novel clustering-driven stacked prompt approach that enhances zero-shot industrial anomaly detection by improving text-image alignment, generalization, and anomaly segmentation performance.

Contribution

The paper proposes a new stacked prompt framework with clustering and ensemble modules, significantly improving zero-shot anomaly detection and classification in industrial settings.

Findings

Achieves state-of-the-art results on seven datasets.

Improves training speed, stability, and convergence.

Enhances generalization across classification tasks.

Abstract

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.