Visual Anomaly Detection (VAD) endeavors to pinpoint deviations from the concept of
normality in visual data, widely applied across diverse domains, eg, industrial defect …

Zero-shot anomaly detection (ZSAD) targets the identification of anomalies within images
from arbitrary novel categories. This study introduces AdaCLIP for the ZSAD task, leveraging …

This work studies the recently proposed challenging and practical Multi-class Unsupervised
Anomaly Detection (MUAD) task, which only requires normal images for training while …

Diffusion models have shown superior performance on unsupervised anomaly detection
tasks. Since trained with normal data only, diffusion models tend to reconstruct normal …

Manufacturing wound rotor synchronous machines (WRSMs) for electric vehicle traction
systems necessitates rigorous quality inspection to ensure optimal product performance and …

Recent advancements in anomaly detection have seen the efficacy of CNN-and transformer-
based approaches. However, CNNs struggle with long-range dependencies, while …

We are exploring an emerging formulation in anomaly detection (AD) where multiple
instances of the same object are produced simultaneously and distinctly to address the …

Utilizing a unified model to detect multi-class anomalies is a promising solution to real-world
anomaly detection. Despite their appeal, such models typically suffer from large model …

Visual anomaly detection has emerged as a highly applicable solution in practical industrial
manufacturing, owing to its notable effectiveness and efficiency. However, it also presents …

Anomaly detection (AD) is a machine learning task that identifies anomalies by learning
patterns from normal training data. In many real-world scenarios, anomalies vary in severity …