GATE-AD: Graph Attention Network Encoding for Few-Shot Industrial Visual Anomaly Detection

Abstract

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. This paper introduces GATE-AD, a reconstruction-based framework that casts few-shot normality modeling as a masked, representation-aligned graph reconstruction problem on a $k$-nearest-neighbor graph, built from frozen self-supervised ViT patch tokens. Since defects typically disrupt the contextual consistency between a patch and its spatial neighbors, GATE-AD emphasizes such neighborhood relations by attending anisotropically over each patch's local neighborhood using a Graph Attention Network (GAT) encoder. To prevent GAT over-smoothing in the low-shot setting, the encoder output is aligned with the input ViT tokens through a learnable latent space, where reconstruction inconsistency is scored with a Scaled Cosine Error (SCE) objective. On the MVTec AD, VisA, and MPDD benchmarks, GATE-AD attains state-of-the-art image-level AUROC in the wide majority of $1$- to $8$-shot settings, with low per-image inference cost that does not scale with the size of the support set. Code is included in the supplement material and will be publicly released upon acceptance.