Authors: Simon Damm, Mike Laszkiewicz, Johannes Lederer, Asja Fischer

Published on: May 23, 2024

Impact Score: 8.2

Arxiv code: Arxiv:2405.14529

Summary

• What is new: The paper introduces AnomalyDINO, a vision-only approach using DINOv2 that pushes one-shot anomaly detection performance from 93.1% to 96.6% AUROC.
• Why this is important: The challenge was to determine if high-quality visual features can compete with state-of-the-art vision-language models in anomaly detection.
• What the research proposes: Adapting DINOv2 to create AnomalyDINO, a method based on patch similarities for one-shot and few-shot anomaly detection without additional training.
• Results: AnomalyDINO outperformed current state-of-the-art models in one-shot and few-shot anomaly detection, particularly improving one-shot detection performance on MVTec-AD substantially.

Technical Details

Technological frameworks used: DINOv2

Models used: nan

Data used: MVTec-AD dataset

Potential Impact

Industrial sectors requiring anomaly detection like manufacturing could benefit significantly from adopting this approach.

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