Capacity-Constrained Global Normality Association with Transport-Conditioned Residual Calibration for Few-Shot Industrial Anomaly Detection

Few-shot industrial anomaly detection (FS-IAD) identifies defects from a few normal reference images. Despite advances in pretrained representations, locally plausible matches need not form a jointly consistent normal explanation, while residuals conflate defects with association ambiguity and normal appearance drift. We propose Global Optimal-Transport Memory Calibration for Anomaly Detection (GOMC-AD), a training-free framework for capacity-constrained normality explanation. Optimal-Transport Guided Global Memory Association (OT-GMA) couples query patches through prescribed normal capacities and a fixed rejection budget, making reference allocation part of the anomaly criterion. Transport-Residual-Aware Test-Time Gaussian Calibration (TR-TGC) incorporates transport-derived association variance into heteroscedastic Gaussian scoring and removes its modeled contribution from historical second-moment updates. Asymmetric feedback reallocates rejection mass while retaining the first-pass normal moments used to define residuals. Across MVTec AD, VisA, and Real-IAD with 1-, 2-, and 4-shot support, GOMC-AD outperforms the compared methods in mean image- and pixel-level area under the receiver operating characteristic curve (AUROC). Image-level AUROC improves by up to 5.3 percentage points and pixel-level AUROC by up to 1.3 points over the strongest compared methods. An anonymous project page is available at https://anonymous.4open.science/r/GOMC-AD-C25C/.