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原文摘要

Few-Shot Industrial Visual Anomaly Detection (FS-IVAD) is a critical task in modern manufacturing, where automated product inspection systems must identify rare defects using only a handful of normal, defect-free training samples. Existing approaches typically model normality by assessing each patch largely through its individual appearance, e.g., through similarity to stored normal features, reconstruction from support samples, or distance from a learned normal representation. This paper introduces GATE-AD, whose key contribution is to instead model few-shot normality through local contextual relations: a patch is considered normal when it can be consistently recovered from its learned dependencies with its spatial neighbors. To realize this formulation, frozen self-supervised ViT patch tokens are organized on a spatial neighbor graph and processed by a Graph Attention Network (GAT), while representation alignment and a Scaled Cosine Error (SCE) objective enable reconstruction-based anomaly scoring. Node masking and attention dropout further regularize learning in the low-shot regime. On MVTec AD, VisA, and MPDD, GATE-AD achieves strong detection and localization performance across $1$- to $8$-shot settings, attaining the highest image-level AUROC in $10$ of $12$ settings and the highest image-level AP in $8$ of $12$ settings under a unified evaluation protocol, while also achieving the lowest inference latency among the compared methods. In order to facilitate reproducibility and further research, the source code of GATE-AD is available at https://github.com/gthpapadopoulos/GATE-AD.