ReCal-CLIP: Patch-Token Recalibration for Cross-Domain Zero-Shot Anomaly Detection

Cross-domain zero-shot anomaly detection aims to identify and localize abnormal regions in unseen target domains without using target-domain anomalous samples, labels, or masks for optimization. Existing CLIP-based methods benefit from vision-language priors, but their patch-level representations are often not calibrated for context-dependent defects: a patch may look ambiguous in isolation, while its abnormality becomes clear only when compared with surrounding normal patterns. This limitation is particularly problematic for industrial inspection, where defects can be small, low-contrast, spatially fragmented, or visually defined by deviations from normal texture. We propose ReCal-CLIP, a patch-token recalibration framework that addresses this issue before anomaly scoring. The core idea is to recalibrate image-side CLIP tokens by routing anomaly-suspect evidence, contrasting it with normal-context cues, and injecting the resulting update through confidence-gated residual fusion. With the recalibrated tokens, ReCal-CLIP uses a lightweight token-space consistency step and a training-only Sampled Discriminative Patch Alignment (SDPA) objective that applies a mask-guided similarity-margin constraint during source-domain training. Experiments on industrial benchmarks, with supplementary medical-domain evaluation, show competitive image-level detection and pixel-level localization performance.