DiCLIP: Integrating DINOv2 and CLIP for Zero-Shot and Few-Shot Anomaly Detection with Versatile Combination Prompts

Industrial Anomaly Detection (AD) encounters a significant cold-start challenge due to the requirement of a large number of labeled normal samples, which are often difficult to obtain in new production lines. Although Zero-Shot Anomaly Detection (ZSAD) and Few-Shot Anomaly Detection (FSAD) have been proposed as potential solutions, existing methods suffer from limitations in generalization ability and are prone to contamination by anomalies in few-shot scenarios. To address these issues, we propose DiCLIP, a unified framework that integrates ZSAD and FSAD through three key innovations. First, Versatile Combination Prompts Learning combines static, dynamic, and anomaly-sensitive prompts to leverage textual anomaly cues together with image features for accurate anomaly localization in images. Second, the Anomaly-Aware Memory Bank utilizes ZSAD priors to filter contaminated features, enabling anomaly detection based on a small number of anomaly samples. Third, Adaptive Threshold Optimization integrates semantic alignment from ZSAD with feature matching from FSAD to release the constraint of a uniform threshold for test images, thereby achieving higher-precision segmentation and localization performance. Extensive experiments on the standard MVTec and VisA benchmark datasets demonstrate the superior performance of DiCLIP, highlighting its effectiveness and practical value for industrial deployment.