Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

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

Unsupervised anomaly detection with localization has many practical applications when
labeling is infeasible and, moreover, when anomaly examples are completely missing in the …

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 …

Abstract 2D-based Industrial Anomaly Detection has been widely discussed, however,
multimodal industrial anomaly detection based on 3D point clouds and RGB images still has …

Anomaly detection is an important application in large-scale industrial manufacturing.
Recent methods for this task have demonstrated excellent accuracy but come with a latency …

Visual anomaly detection, an important problem in computer vision, is usually formulated as
a one-class classification and segmentation task. The student-teacher (ST) framework has …

Currently, deep learning-based visual inspection has been highly successful with the help of
supervised learning methods. However, in real industrial scenarios, the scarcity of defect …

Detecting anomalies in images is an important task, especially in real-time computer vision
applications. In this work, we focus on computational efficiency and propose a lightweight …

The recent rapid development of deep learning has laid a milestone in industrial image
anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning …