IA-CLIP: A Single-Source Industrial Anomaly Detection Method for Multi-Target Domain Generalization

In industrial manufacturing, ensuring product quality is of paramount importance. A key component of this process is anomaly detection, which aims to promptly identify defective products to reduce operational losses. However, practical industrial environments are characterized by complexity, including limited availability of labeled data, a wide variety of defect categories, and frequent changes in these categories. Such factors pose significant challenges to the effective cross-domain generalization of anomaly detection methods. To address this limitation, IA-CLIP, a novel framework that enhances cross-domain generalization for industrial anomaly detection, is proposed. IA-CLIP integrates global and local prompts with contrastive learning to overcome the limitations of existing approaches. The proposed class-agnostic global-local semantic prompts enable the model to capture general patterns of normality and anomaly without relying on object-specific semantics. We further introduce a Similarity-aware Triplet Contrastive Learning strategy to facilitate complementary learning between global and local prompts, and an Adaptive Focal Contrastive Learning scheme to help the model focus more effectively on hard-to-identify anomalous regions. Extensive experiments on nine real-world target-domain datasets, covering 50 categories of industrial products, demonstrate that IA-CLIP achieves impressive cross-domain generalization performance in realistic industrial settings. Code and data will be released upon publication.