HIVE-AD: Adaptive Multilevel Feature Fusion for Training-Free Few-Shot Industrial Anomaly Detection

Few-shot industrial anomaly detection requires reliable characterization of normal patterns from only a handful of defect-free reference images, making repeated optimization for new products impractical for rapid deployment. Existing training-free methods often combine features from different layers using fixed rules and rely on a shared anomaly response for image-level detection and pixel-level localization. These strategies can miss complementary texture and structural cues, magnify harmless variations in normal samples, and produce fragmented anomaly maps. To overcome these limitations, we introduce HIVE-AD, a training-free framework that adaptively fuses multilevel features from a frozen DINOv2 backbone. HIVE-AD constructs an independent normal memory for each selected layer, determines each layer's contribution from spatial variation in normal support images, and adopts separate scoring procedures for image-level ranking and pixel-level localization. The visual pipeline requires no parameter updates, prompt tuning, or additional training for individual product categories. An optional frozen MLLM further translates localized anomaly regions into structured defect reports. In the one-shot setting, HIVE-AD achieves image-level and pixel-level AUROC scores of 97.8% and 97.6% on MVTec-AD, and 94.5% and 98.2% on VisA, respectively. Compared with a frozen DINOv2-G baseline, HIVE-AD improves image-level AUROC by 2.3 and 2.8 percentage points on MVTec-AD and VisA, demonstrating that adaptive multilevel feature fusion and task-specific scoring improve few-shot anomaly detection without additional training.