Reading Anomalies as Types: Adapting Pretrained Models for Industrial Anomaly Diagnosis

Scarce and diverse defect examples make product-specific inspection models costly to train. Detecting a deviation from normal is only part of the task: diagnosis also requires identifying which product-specific inspection criterion it violates. We study defect diagnosis with frozen visual and vision-language models, combining normal-reference localization, targeted views, and textual inspection criteria. The target setting requires no defect-image exemplars at diagnosis time. Our experiments use descriptions derived from held-out defect images as a proxy for inspector knowledge; direct image access is tested only in separate controls. On public industrial benchmarks and semiconductor data, descriptions improve defect typing with model weights and visual inputs fixed. Correctly aligned descriptions outperform descriptions assigned to the wrong labels, supporting the importance of matching inspection criteria to defect types. Across separate controlled comparisons, visual marking primarily recovers missed defects, while descriptions can also correct type errors. Detector misses and uneven per-type performance limit deployment claims. These results identify textual inspection criteria as a means of adapting frozen models to product-specific diagnoses.