Human-Free Automated Prompting for Vision-Language Anomaly Detection: Prompt Optimization with Meta-guiding Prompt Scheme
<|reference_start|>Human-Free Automated Prompting for Vision-Language Anomaly Detection: Prompt Optimization with Meta-guiding Prompt Scheme: Pre-trained vision-language models (VLMs) are highly adaptable to various downstream tasks through few-shot learning, making prompt-based anomaly detection a promising approach. Traditional methods depend on human-crafted prompts that require prior knowledge of specific anomaly types. Our goal is to develop a human-free prompt-based anomaly detection framework that optimally learns prompts through data-driven methods, eliminating the need for human intervention. The primary challenge in this approach is the lack of anomalous samples during the training phase. Additionally, the Vision Transformer (ViT)-based image encoder in VLMs is not ideal for pixel-wise anomaly segmentation due to a locality feature mismatch between the original image and the output feature map. To tackle the first challenge, we have developed the Object-Attention Anomaly Generation Module (OAGM) to synthesize anomaly samples for training. Furthermore, our Meta-Guiding Prompt-Tuning Scheme (MPTS) iteratively adjusts the gradient-based optimization direction of learnable prompts to avoid overfitting to the synthesized anomalies. For the second challenge, we propose Locality-Aware Attention, which ensures that each local patch feature attends only to nearby patch features, preserving the locality features corresponding to their original locations. This framework allows for the optimal prompt embeddings by searching in the continuous latent space via backpropagation, free from human semantic constraints. Additionally, the modified locality-aware attention improves the precision of pixel-wise anomaly segmentation.<|reference_end|>
@article{chen2024human-free,
    title={Human-Free Automated Prompting for Vision-Language Anomaly Detection:
  Prompt Optimization with Meta-guiding Prompt Scheme},
    author={Pi-Wei Chen, Jerry Chun-Wei Lin, Jia Ji, Feng-Hao Yeh, Zih-Ching Chen,
  Chao-Chun Chen},
    journal={arXiv preprint arXiv:2406.18197},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18197},
    primaryClass={cs.CV}
}