Rethinking Normal Memory Readout for Training-Free Anomaly Detection

Normal patch memories support anomaly detection by comparing test features with stored normal references. Nearest-neighbor scoring measures proximity to one reference, whereas nearby references can jointly provide a local estimate of normal appearance. We propose ProCon, a training-free anomaly detector that retrieves normal neighbors, reconstructs a query by their distance-weighted average, and uses the reconstruction residual as its anomaly score. Independent memories at different DINOv2 layers preserve the normal neighborhood of each feature space; a median across memory banks followed by a mean across layers combines their residual maps. With the encoder and memory fixed, replacing nearest-neighbor distance with local reconstruction improves pixel AP by 1.4 and 2.5 percentage points on MVTec-AD and VisA. Under the Dinomaly evaluation protocol, ProCon achieves pixel AP of 75.3% and 57.0%, respectively, outperforming the compared Dinomaly and INP-Former configurations on this metric. At matched memory storage, independent layer reconstruction improves pixel AP and AUPRO over feature concatenation in all 27 categories. These results show that a normal patch memory can serve as an effective local reconstruction model without training a decoder.