ProtoDeCo: Decoupling Anomaly Ranking and Normality Measurement for Cross-Domain Zero-Shot Anomaly Detection

Cross-domain zero-shot anomaly detection must rank target images without target-domain anomaly annotations. However, the representation that supports anomaly ranking need not provide a suitable metric for measuring target normality. We present ProtoDeCo, a post-hoc transductive calibration method that separates these roles: a fixed host detector selects low-score target images as pseudo-normal candidates, while frozen Contrastive Language-Image Pre-training (CLIP) patch features construct category-specific prototypes and measure deviations from them. The deviation score is fused with the host image score; the host pixel map remains unchanged. The method uses no target labels or masks and updates no model parameters. In the matched comparison, Detector Proto and Frozen Proto use the same host ranking, pseudo-normal candidates, memory construction, query reduction, and score fusion; they differ only in the representation used for metric measurement. Across 12 industrial host–target pairs, Frozen Proto improves the uncalibrated host by an average of 0.88 area under the receiver operating characteristic curve (AUROC) points, with positive gains on all pairs. On eight distinct-stream pairs, it improves over Detector Proto by 0.23 points; the 95% confidence interval is 0.14 to 0.36 points. On four medical image-level targets, applying ProtoDeCo to the uncalibrated BD-Host increases mean AUROC and average precision (AP) from 80.53 and 85.57 to 81.17 and 85.85, with gains on three targets. These results support separating anomaly ranking from target-side metric measurement under the evaluated transductive protocol.