2 papers
cs.CV2026
Spatial Autoregressive Modeling of DINOv3 Embeddings for Unsupervised Anomaly Detection
Ertunc Erdil, Nico Schulthess, Guney Tombak +1
DINO models provide rich patch-level representations that have recently enabled strong performance in unsupervised anomaly detection (UAD). Most existing methods extract patch embe…
cs.CV2025
Anomaly Detection by Clustering DINO Embeddings using a Dirichlet Process Mixture
Nico Schulthess, Ender Konukoglu
In this work, we leverage informative embeddings from foundational models for unsupervised anomaly detection in medical imaging. For small datasets, a memory-bank of normative feat…