Anomaly inspection plays an important role in industrial manufacture. Existing anomaly
inspection methods are limited in their performance due to insufficient anomaly data …

Anomaly detection, the task of identifying unusual samples in data, often relies on a large set
of training samples. In this work, we consider the setting of few-shot anomaly detection in …

Existing anomaly detection paradigms overwhelmingly focus on training detection models
using exclusively normal data or unlabeled data (mostly normal samples). One notorious …

In industrial anomaly detection, data efficiency and the ability for fast migration across
products become the main concerns when developing detection algorithms. Existing …

Data augmentation is a promising technique for unsupervised anomaly detection in
industrial applications, where the availability of positive samples is often limited due to …

This paper considers few-shot anomaly detection (FSAD), a practical yet under-studied
setting for anomaly detection (AD), where only a limited number of normal images are …

We address the problem of anomaly detection in videos. The goal is to identify unusual
behaviours automatically by learning exclusively from normal videos. Most existing …

In the area of fewshot anomaly detection (FSAD), efficient visual feature plays an essential
role in memory bank M-based methods. However, these methods do not account for the …

Abstract We propose using Masked Auto-Encoder (MAE), a transformer model self-
supervisedly trained on image inpainting, for anomaly detection (AD). Assuming anomalous …

Reconstruction-based approaches have achieved remarkable outcomes in anomaly
detection. The exceptional image reconstruction capabilities of recently popular diffusion …