AnomalyDINO is out! Our latest project delves into the high-quality features of DINOv2 and how these can be adapted for one- or few-shot visual anomaly detection. AnomalyDINO leverages patch similarities and, despite being methodologically simple and training-free, achieves state-of-the-art performance across various settings.

This work is the product of an amazing collaboration with Simon and our supervisors Johannes and Asja. A big shoutout to all of you!

You can find a preprint of our work on arXiv.