DeltaAD: Visual Delta Modeling for Zero-Shot Anomaly Detection

Zero-shot anomaly detection (ZSAD) aims to identify anomalies in unseen domains without access to target-specific training data. To alleviate cross-domain visual discrepancies, existing ZSAD methods typically leverage pretrained vision or vision-language models to characterize visual distributions or learn text prompts from auxiliary data. However, their cross-domain generalization remains limited under substantial visual distribution shifts. To address this challenge, we propose DeltaAD, a novel framework that detects anomalies by amplifying anomaly-related differences while suppressing visual variations shared by normal and anomalous responses through differential similarity. Our train-free variant, DeltaAD-TF, freezes the CLIP image encoder and constructs separate memory banks of normal and anomalous region-pooled features from auxiliary data. During inference, it contrasts retrieval evidence from the two memory banks under shared visual contexts, highlighting relative differences between normal and anomalous responses while suppressing their shared variations. Building on this principle, our training-based variant, DeltaAD-LE, learns a residual relation that characterizes anomalies relative to retrieved normal visual explanations. Extensive experiments on 15 industrial and medical benchmarks demonstrate the strong zero-shot generalization of DeltaAD for both image-level anomaly detection and pixel-level anomaly localization under train-free and training-based settings.