Tri-Mem: A Triple-Memory Framework for Online Industrial Anomaly Detection

Industrial anomaly detection in practical inspection systems often relies on only a few normal reference images and must accommodate appearance variations encountered during testing. Most existing memory-based few-shot methods use a fixed repository of normal features, which limits their ability to incorporate information from the test stream and to use anomaly-related evidence. We propose Tri-Mem, a training-free framework for online few-shot anomaly detection that consists of an independent frozen core memory, an adaptive normal memory, and an anomaly memory. The core memory preserves support-derived normal prototypes, while the other two memories are updated periodically with rank-selected test features and compressed independently. Tri-Mem further combines multi-layer representations from a frozen DINOv3 encoder with support-time illumination augmentation and capacity-triggered coreset compression of the adaptive memories. We evaluate the proposed method under 1-, 2-, 4-, 8-, and full-shot protocols on four industrial anomaly detection benchmarks. On MVTec AD 2, Tri-Mem yields consistently strong pixel-level results across the evaluated few-shot settings. In the full-shot setting, it obtains 91.2% P-AUROC, 64.6% P-AUPRO, and 72.6% I-AUROC. Competitive results are also observed on MVTec AD, MPDD, and BTAD, particularly for pixel-level localization. Ablation experiments indicate that the three memory components provide complementary information: compared with the static core-memory baseline, the complete configuration increases the average P-AUPRO and I-AUROC on MVTec AD 2 by 4.6 and 3.2 percentage points, respectively.