  Visual anomaly detection is critical in industrial manufacturing, but
traditional methods often rely on extensive normal datasets and custom models,
limiting scalability. Recent advancements in large-scale visual-language models
have significantly improved zero/few-shot anomaly detection. However, these
approaches may not fully utilize hierarchical features, potentially missing
nuanced details. We introduce a window self-attention mechanism based on the
CLIP model, combined with learnable prompts to process multi-level features
within a Soldier-Offier Window self-Attention (SOWA) framework. Our method has
been tested on five benchmark datasets, demonstrating superior performance by
leading in 18 out of 20 metrics compared to existing state-of-the-art
techniques.
