Image Anomaly Detection has been a challenging task in Computer Vision field.
The advent of Vision-Language models, particularly the rise of CLIP-based
frameworks, has opened new avenues for zero-shot anomaly detection. Recent
studies have explored the use of CLIP by aligning images with normal and prompt
descriptions. However, the exclusive dependence on textual guidance often falls
short, highlighting the critical importance of additional visual references. In
this work, we introduce a Dual-Image Enhanced CLIP approach, leveraging a joint
vision-language scoring system. Our methods process pairs of images, utilizing
each as a visual reference for the other, thereby enriching the inference
process with visual context. This dual-image strategy markedly enhanced both
anomaly classification and localization performances. Furthermore, we have
strengthened our model with a test-time adaptation module that incorporates
synthesized anomalies to refine localization capabilities. Our approach
significantly exploits the potential of vision-language joint anomaly detection
and demonstrates comparable performance with current SOTA methods across
various datasets.

通过引入双图增强 CLIP 方法，结合视觉 - 语言评分系统，对图像异常检测进行了增强，包括利用图像进行相互参考以增强推理过程的视觉环境，以及在测试时引入合成异常来提高定位能力。该方法充分利用了视觉 - 语言联合异常检测的潜力，并在各种数据集上展现了与当前最先进方法相媲美的性能。