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 …

Across many industries, visual quality assurance has transitioned from a manual, labor-
intensive, and error-prone task to a fully automated and precise assessment of industrial …

We present a new method for the unsupervised detection of geometric anomalies in high-
resolution 3D point clouds. In particular, we propose an adaptation of the established …

Anomalies are ubiquitous in all scientific fields and can express an unexpected event due to
incomplete knowledge about the data distribution or an unknown process that suddenly …

Recently 3D anomaly detection a crucial problem involving fine-grained geometry
discrimination is getting more attention. However the lack of abundant real 3D anomaly data …

The last two decades have seen an incessant growth in the use of Unmanned Aerial
Vehicles (UAVs) equipped with HD cameras for developing aerial vision-based systems to …

Anomaly detection on high-dimensional data is crucial for real-world industrial applications.
Recent works adopt the Knowledge Distillation (KD) technique to improve the accuracy of …

Expert interpretation of anatomical images of the human brain is the central part of
neuroradiology. Several machine learning-based techniques have been proposed to assist …

Abnormal behaviors in industrial systems may be early warnings on critical events that may
cause severe damages to facilities and security. Thus, it is important to detect abnormal …

Anomaly detection (AD) is an enduring topic, and it has been used in various fields, such as
fraud detection, industrial fault diagnosis, and medical image diagnosis. With the continuous …