Spatial Autoregressive Modeling of DINOv3 Embeddings for Unsupervised Anomaly Detection
DINO models provide rich patch-level representations that have recently enabled strong performance in unsupervised anomaly detection (UAD). Most existing methods extract patch embeddings from normal images and model them independently, ignoring spatial and neighborhood relationships between patches. This implicitly assumes that self-attention and positional encodings sufficiently encode contextual information within each patch embedding. In addition, the normative distribution is often modeled as memory banks or prototype-based representations, which require storing large numbers of features and performing costly comparisons at inference time, leading to substantial memory and computation...
| Authors | Ertunc Erdil, Nico Schulthess, Guney Tombak et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 March 2026 |
| Last updated at source | 1 September 2026 |
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