This paper explores the problem of Generalist Anomaly Detection (GAD) aiming to train one
single detection model that can generalize to detect anomalies in diverse datasets from …

High-precision point cloud anomaly detection is the gold standard for identifying the defects
of advancing machining and precision manufacturing. Despite some methodological …

Anomaly detection is crucial to the advanced identification of product defects such as
incorrect parts, misaligned components, and damages in industrial manufacturing. Due to …

Self-supervised feature reconstruction methods have shown promising advances in
industrial image anomaly detection and localization. Despite this progress these methods …

Open-set supervised anomaly detection (OSAD)-a recently emerging anomaly detection
area-aims at utilizing a few samples of anomaly classes seen during training to detect …

Anomaly detection (AD) is a fundamental research problem in machine learning and
computer vision with practical applications in industrial inspection video surveillance and …

This work studies the recently proposed challenging and practical Multi-class Unsupervised
Anomaly Detection (MUAD) task, which only requires normal images for training while …

Image anomaly localization is a pivotal technique in industrial inspection, often manifesting
as a supervised task where abundant normal samples coexist with rare abnormal samples …

Logical anomalies (LA) refer to data violating underlying logical constraints eg, the quantity,
arrangement, or composition of components within an image. Detecting accurately such …

Anomaly detection (AD) aims to identify defective images and localize their defects (if any).
Ideally AD models should be able to detect defects over many image classes; without relying …