FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection
Journal:
arXiv
Published Date:
Jun 26, 2025
Abstract
Few-shot industrial anomaly detection (FS-IAD) presents a critical challenge
for practical automated inspection systems operating in data-scarce
environments. While existing approaches predominantly focus on deriving
prototypes from limited normal samples, they typically neglect to
systematically incorporate query image statistics to enhance prototype
representativeness. To address this issue, we propose FastRef, a novel and
efficient prototype refinement framework for FS-IAD. Our method operates
through an iterative two-stage process: (1) characteristic transfer from query
features to prototypes via an optimizable transformation matrix, and (2)
anomaly suppression through prototype alignment. The characteristic transfer is
achieved through linear reconstruction of query features from prototypes, while
the anomaly suppression addresses a key observation in FS-IAD that unlike
conventional IAD with abundant normal prototypes, the limited-sample setting
makes anomaly reconstruction more probable. Therefore, we employ optimal
transport (OT) for non-Gaussian sampled features to measure and minimize the
gap between prototypes and their refined counterparts for anomaly suppression.
For comprehensive evaluation, we integrate FastRef with three competitive
prototype-based FS-IAD methods: PatchCore, FastRecon, WinCLIP, and AnomalyDINO.
Extensive experiments across four benchmark datasets of MVTec, ViSA, MPDD and
RealIAD demonstrate both the effectiveness and computational efficiency of our
approach under 1/2/4-shots.
