CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection

ArXi:2508.03447v2 Announce Type: replace Recently, large pre-trained vision-language models have shown remarkable performance in zero-shot anomaly detection (ZSAD). With fine-tuning on a single auxiliary dataset, the model enables cross-category anomaly detection on diverse datasets covering industrial defects and medical lesions. Compared to manually designed prompts, prompt learning eliminates the need for expert knowledge and trial-and-error.