Recently, large vision and language models have shown their success when
adapting them to many downstream tasks. In this paper, we present a unified
framework named CLIP-ADA for Anomaly Detection by Adapting a pre-trained CLIP
model. To this end, we make two important improvements: 1) To acquire unified
anomaly detection across industrial images of multiple categories, we introduce
the learnable prompt and propose to associate it with abnormal patterns through
self-supervised learning. 2) To fully exploit the representation power of CLIP,
we introduce an anomaly region refinement strategy to refine the localization
quality. During testing, the anomalies are localized by directly calculating
the similarity between the representation of the learnable prompt and the
image. Comprehensive experiments demonstrate the superiority of our framework,
e.g., we achieve the state-of-the-art 97.5/55.6 and 89.3/33.1 on MVTec-AD and
VisA for anomaly detection and localization. In addition, the proposed method
also achieves encouraging performance with marginal training data, which is
more challenging.

我们提出了一个统一的框架 CLIP-ADA 用于通过适应预训练 CLIP 模型进行异常检测，在这个框架中，我们通过引入可学习的提示，并通过自监督学习将其与异常模式关联起来，从而获得多类别工业图像上的统一异常检测，并且通过引入异常区域细化策略来充分利用 CLIP 的表示能力来提高定位质量，我们的框架在 MVTec-AD 和 VisA 上取得了 97.5/55.6 和 89.3/33.1 的最新成果，以及在极小的训练数据下取得了令人鼓舞的性能，这更具挑战性。