3 papers
cs.CV2025
A Single Image Is All You Need: Zero-Shot Anomaly Localization Without Training Data
Mehrdad Moradi, Shengzhe Chen, Hao Yan +1
Anomaly detection in images is typically addressed by learning from collections of training data or relying on reference samples. In many real-world scenarios, however, such traini…
cs.CV2025
RDDPM: Robust Denoising Diffusion Probabilistic Model for Unsupervised Anomaly Segmentation
Mehrdad Moradi, Kamran Paynabar
Recent advancements in diffusion models have demonstrated significant success in unsupervised anomaly segmentation. For anomaly segmentation, these models are first trained on norm…
cs.CV2025
Single-Step Reconstruction-Free Anomaly Detection and Segmentation via Diffusion Models
Mehrdad Moradi, Marco Grasso, Bianca Maria Colosimo +1
Generative models have demonstrated significant success in anomaly detection and segmentation over the past decade. Recently, diffusion models have emerged as a powerful alternativ…