Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors
This addresses the problem of accurate anomaly detection with limited samples under category-agnostic conditions for industrial safety inspection applications, representing an incremental advancement.
The paper tackles few-shot anomaly detection for industrial safety inspection by leveraging foundation visual encoders, showing that anomaly amount correlates with embedding differences and designing FoundAD which learns a nonlinear projection operator onto the natural image manifold. The approach achieves competitive performance with substantially fewer parameters than prior methods, as demonstrated through extensive experiments with multiple foundation encoders including DINOv3.
Few-shot anomaly detection streamlines and simplifies industrial safety inspection. However, limited samples make accurate differentiation between normal and abnormal features challenging, and even more so under category-agnostic conditions. Large-scale pre-training of foundation visual encoders has advanced many fields, as the enormous quantity of data helps to learn the general distribution of normal images. We observe that the anomaly amount in an image directly correlates with the difference in the learnt embeddings and utilize this to design a few-shot anomaly detector termed FoundAD. This is done by learning a nonlinear projection operator onto the natural image manifold. The simple operator acts as an effective tool for anomaly detection to characterize and identify out-of-distribution regions in an image. Extensive experiments show that our approach supports multi-class detection and achieves competitive performance while using substantially fewer parameters than prior methods. Backed up by evaluations with multiple foundation encoders, including fresh DINOv3, we believe this idea broadens the perspective on foundation features and advances the field of few-shot anomaly detection.