Industrial anomaly detection plays a crucial role in ensuring product quality control.
Therefore, proposing an effective anomaly detection model is of great significance. While …

Anomaly detection is an important task for medical image analysis, which can alleviate the
reliance of supervised methods on large labelled datasets. Most existing methods use a …

This paper offers a comprehensive review of one-class classification (OCC), examining the
technologies and methodologies employed in its implementation. It delves into various …

The widespread proliferation of Internet of Things (IoT) devices has pushed for the
development of novel transformer-based Anomaly Detection (AD) tools for an accurate …

Visual anomaly detection plays a crucial role in manufacturing to ensure product quality by
identifying image patterns that deviate from the expected ones. Existing methods that rely on …

Transformer, as one of the most advanced neural network models in Natural Language
Processing (NLP), exhibits diverse applications in the field of anomaly detection. To inspire …

Advancements in computer vision have fueled rapid developments in unsupervised
anomaly detection, but current methods often encounter limitations when addressing …

Weakly-supervised learning approaches have gained significant attention due to their ability
to reduce the effort required for human annotations in training neural networks. This paper …

The quality of polyester yarn is mainly affected by its dyeing uniformity. As a result, textile
manufacturers need to inspect the dyeing uniformity of polyester yarn. The existing …

Current unsupervised industrial product surface anomaly detection methods suffer from poor
reconstructed image quality and difficulty in detecting low-contrast anomalies, resulting in …