Detecting anomalies in images is an important task, especially in real-time computer vision
applications. In this work, we focus on computational efficiency and propose a lightweight …

Abstract Unsupervised image Anomaly Detection (UAD) aims to learn robust and
discriminative representations of normal samples. While separate solutions per class endow …

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 …

Industrial anomaly detection (IAD) has garnered significant attention and experienced rapid
development. However the recent development of IAD approach has encountered certain …

The vision-language model has brought great improvement to few-shot industrial anomaly
detection which usually needs to design of hundreds of prompts through prompt …

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

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

3D anomaly detection is an emerging and vital computer vision task in industrial
manufacturing (IM). Recently many advanced algorithms have been published, but most of …

Recently, deep learning-based methods have been widely applied in identifying and
detecting surface defects in industrial products. However, in real industrial scenarios, there …

Traditional reconstruction-based methods have struggled to achieve competitive
performance in anomaly detection. In this paper, we introduce Denoising Diffusion Anomaly …