The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

The comprehensive intelligent development of the manufacturing industry puts forward new
requirements for the quality inspection of industrial products. This paper summarizes the …

Being able to spot defective parts is a critical component in large-scale industrial
manufacturing. A particular challenge that we address in this work is the cold-start problem …

Visual surface anomaly detection aims to detect local image regions that significantly
deviate from normal appearance. Recent surface anomaly detection methods rely on …

Visual anomaly detection addresses the problem of classification or localization of regions in
an image that deviate from their normal appearance. A popular approach trains an auto …

In this paper, we address the problem of image anomaly detection and segmentation.
Anomaly detection involves making a binary decision as to whether an input image contains …

Despite the rapid advance of unsupervised anomaly detection, existing methods require to
train separate models for different objects. In this work, we present UniAD that accomplishes …

Anomaly detection is a challenging task and usually formulated as an one-class learning
problem for the unexpectedness of anomalies. This paper proposes a simple yet powerful …

We present a transformer-based image anomaly detection and localization network. Our
proposed model is a combination of a reconstruction-based approach and patch …

Machine learning models often encounter samples that are diverged from the training
distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently …