4 papers
Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection
Yuhu Bai, Jiangning Zhang, Yunkang Cao +4
With the advent of vision-language models (e.g., CLIP) in zero- and few-shot settings, CLIP has been widely applied to zero-shot anomaly detection (ZSAD) in recent research, where…
Dual-path Frequency Discriminators for Few-shot Anomaly Detection
Yuhu Bai, Jiangning Zhang, Zhaofeng Chen +3
Few-shot anomaly detection (FSAD) plays a crucial role in industrial manufacturing. However, existing FSAD methods encounter difficulties leveraging a limited number of normal samp…
AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection
Yunkang Cao, Jiangning Zhang, Luca Frittoli +3
Zero-shot anomaly detection (ZSAD) targets the identification of anomalies within images from arbitrary novel categories. This study introduces AdaCLIP for the ZSAD task, leveragin…
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect
Yunkang Cao, Xiaohao Xu, Jiangning Zhang +4
Visual Anomaly Detection (VAD) endeavors to pinpoint deviations from the concept of normality in visual data, widely applied across diverse domains, e.g., industrial defect inspect…