Unveiling Geometric Tension in Zero-Shot Anomaly Detection: Minimalist Calibration and Theoretical Bounds

Zero-shot anomaly detection (ZSAD) identifies defects in unseen categories by transferring knowledge from a labeled source domain. Current CLIP-based methods compare patch features against learned tokens via cosine similarity, compressing each high-dimensional feature vector to a single scalar. We find that this compression introduces three interdependent losses: high-frequency texture, spatial neighborhoods, and distribution shape, and that they cannot be recovered independently. Suppressing high-frequency content alone significantly degrades pixel-level accuracy, and neither spatial regularization nor distributional calibration alone reverses the damage. When both are active, the same suppression becomes beneficial. We call this phenomenon directional dependency and design Complementary Feature Calibration, a lightweight three-component framework that jointly calibrates frequency responses, isotropic structural priors, and score distributions. A full factorial ablation characterizes interactions that baseline-centered, one-factor-at-a-time evaluations cannot reveal. Across five paired runs, adding FreqEnhance to SC+MS improves Pixel AUROC by 0.5 points on average; the gain is positive in four runs and its 95% confidence interval excludes zero. On zero-shot transfer from VisA to MVTec-AD, our method improves Pixel AUROC from 90.8% to 91.7% and Sample AUROC from 92.2% to 94.7%. The same dependency structure replicates across the tested architectures, backbone scales, and token paradigms, indicating that non-additive component interactions recur across several CLIP-based ZSAD settings.