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

Recent advancements in large-scale visual-language pre-trained models have led to
significant progress in zero-/few-shot anomaly detection within natural image domains …

Graph anomaly detection (GAD), which aims to identify unusual graph instances (nodes,
edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its …

In this work, by re-examining the “matching” nature of Anomaly Detection (AD), we propose
a novel AD framework that simultaneously enjoys new records of AD accuracy and …

Humans recognize anomalies through two aspects: larger patch-wise representation
discrepancies and weaker patch-to-normal-patch correlations. However, the previous AD …

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

Visual Anomaly Detection (VAD) endeavors to pinpoint deviations from the concept of
normality in visual data, widely applied across diverse domains, eg, industrial defect …

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 synthesis strategies can effectively enhance unsupervised anomaly detection.
However, existing strategies have limitations in the coverage and controllability of anomaly …

Unified anomaly detection (AD) is one of the most valuable challenges for anomaly
detection, where one unified model is trained with normal samples from multiple classes …