SOWA: Adapting Hierarchical Frozen Window Self-Attention to Visual-Language Models for Better Anomaly Detection
<|reference_start|>SOWA: Adapting Hierarchical Frozen Window Self-Attention to Visual-Language Models for Better Anomaly Detection: 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.<|reference_end|>
@article{hu2024sowa:,
    title={SOWA: Adapting Hierarchical Frozen Window Self-Attention to
  Visual-Language Models for Better Anomaly Detection},
    author={Zongxiang Hu, Zhaosheng Zhang},
    journal={arXiv preprint arXiv:2407.03634},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03634},
    primaryClass={cs.CV}
}