  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.
