Training Free Zero-Shot Visual Anomaly Localization via Diffusion Inversion

2026-01-12

AI Summary

This work proposes a training-free, zero-shot visual anomaly localization method that operates without access to normal samples of the target category or any textual prompts. By leveraging a pre-trained DDIM model, the approach performs diffusion inversion followed by partial denoising at intermediate timesteps to reconstruct the input image; anomalies are localized through discrepancies between the original and reconstructed images. As the first method to achieve high-precision spatial anomaly localization under purely visual zero-shot conditions, it eliminates reliance on language prompts or auxiliary modalities. The proposed technique attains state-of-the-art performance on the VISA dataset, significantly advancing the practicality and accuracy of zero-shot anomaly detection.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Zero-Shot image Anomaly Detection (ZSAD) aims to detect and localise anomalies without access to any normal training samples of the target data. While recent ZSAD approaches leverage additional modalities such as language to generate fine-grained prompts for localisation, vision-only methods remain limited to image-level classification, lacking spatial precision. In this work, we introduce a simple yet effective training-free vision-only ZSAD framework that circumvents the need for fine-grained prompts by leveraging the inversion of a pretrained Denoising Diffusion Implicit Model (DDIM). Specifically, given an input image and a generic text description (e.g.,"an image of an [object class]"), we invert the image to obtain latent representations and initiate the denoising process from a fixed intermediate timestep to reconstruct the image. Since the underlying diffusion model is trained solely on normal data, this process yields a normal-looking reconstruction. The discrepancy between the input image and the reconstructed one highlights potential anomalies. Our method achieves state-of-the-art performance on VISA dataset, demonstrating strong localisation capabilities without auxiliary modalities and facilitating a shift away from prompt dependence for zero-shot anomaly detection research. Code is available at https://github.com/giddyyupp/DIVAD.

Problem

• Zero-Shot Anomaly Detection
• Anomaly Localization
• Vision-only
• Training-free
• Diffusion Models

Innovation

• Diffusion Inversion
• Zero-Shot Anomaly Detection
• Training-Free
• Visual Anomaly Localization
• DDIM