XPAD: Language-Free and Training-Free Zero-Shot Industrial Anomaly Detection via Cross-Dataset Prototypes

Zero-shot anomaly detection (ZSAD) detects anomalies of unseen categories without target-domain supervision. Existing ZSAD methods generally fine-tune on an auxiliary dataset to learn transferable textual prompts or visual representations, incurring considerable training costs and leaving the question of what is actually transferred unresolved. Across six industrial benchmarks, we observe that a frozen encoder already organizes normal and anomalous patch features into a shared, dataset-agnostic direction, which can be estimated without any training. This suggests that an effective scoring function can be derived directly from this direction. We instantiate this idea as XPAD (Cross-dataset Prototypes for Anomaly Detection), a language-free and training-free framework. XPAD aggregates a single annotated auxiliary dataset into cross-dataset prototypes, and scores test patches by prototype similarity. To sharpen this comparison, it further separates the normal prototype into foreground and background sub-prototypes, and calibrates the aggregation-degree mismatch between dataset-level prototypes and individual query patches. Without textual prompts, additional training, or target-domain data, XPAD achieves state-of-the-art average performance on six industrial benchmarks, and runs directly on pretrained vision backbones such as the CLIP image encoder and DINOv2.