Unsupervised anomaly detection enables the identification of potential
pathological areas by juxtaposing original images with their pseudo-healthy
reconstructions generated by models trained exclusively on normal images.
However, the clinical interpretation of resultant anomaly maps presents a
challenge due to a lack of detailed, understandable explanations. Recent
advancements in language models have shown the capability of mimicking
human-like understanding and providing detailed descriptions. This raises an
interesting question: \textit{How can language models be employed to make the
anomaly maps more explainable?} To the best of our knowledge, we are the first
to leverage a language model for unsupervised anomaly detection, for which we
construct a dataset with different questions and answers. Additionally, we
present a novel multi-image visual question answering framework tailored for
anomaly detection, incorporating diverse feature fusion strategies to enhance
visual knowledge extraction. Our experiments reveal that the framework,
augmented by our new Knowledge Q-Former module, adeptly answers questions on
the anomaly detection dataset. Besides, integrating anomaly maps as inputs
distinctly aids in improving the detection of unseen pathologies.

通过使用语言模型以增强未监督异常检测的解释性，我们能够构建一个包含问题和答案的数据集，并提出一种新的多图像视觉问答框架来应对异常检测，其中融合了多样的特征融合策略以增强视觉知识提取。