In this work, by re-examining the “matching” nature of Anomaly Detection (AD), we propose
a novel AD framework that simultaneously enjoys new records of AD accuracy and …

Surface anomaly detection is a vital component in manufacturing inspection. Current
discriminative methods follow a two-stage architecture composed of a reconstructive …

Medical imaging often contains critical fine-grained features, such as tumors or
hemorrhages, which are crucial for diagnosis yet potentially too subtle for detection with …

Unsupervised anomaly detection (UAD) aims to locate anomalies in images without using
annotated defective data. Normalizing flow is inherently suitable for the UAD task because it …

In the industrial production process, unsupervised visual inspection methods have obvious
advantages over supervised visual inspection methods due to the scarcity of defect samples …

Existing unsupervised industry anomaly detection methods often rely on convolutional
operations to capture fine-grained details in images. However, they may overlook crucial …

The unsupervised detection and localization of image anomalies hold significant importance
across various domains, particularly in industrial quality inspection. Despite its widespread …

Anomaly segmentation is a valuable computer vision task for safety-critical applications that
need to be aware of unexpected events. Current state-of-the-art (SOTA) scene-level …

NASA's forthcoming Lunar Gateway space station, which will be uncrewed most of the time,
will need to operate with an unprecedented level of autonomy. Enhancing autonomy on the …

In the anomaly detection field, the scarcity of anomalous samples has directed the current
research emphasis towards unsupervised anomaly detection. While these unsupervised …