The task of image anomaly detection (IAD) aims to identify deviations from normality in image data. These anomalies are patterns that deviate significantly from what the IAD model has learned from the data during training. However, in real-world scenarios, the criteria for what constitutes normality often change, necessitating the reclassification of previously anomalous instances as normal. To address this challenge, we propose a new scenario termed "normality addition," involving the post-training adjustment of decision boundaries to incorporate new normalities. To address this challenge, we propose a method called Normality Addition via Normality Detection (NAND), leveraging a vision-language model. NAND performs normality detection which detect patterns related to the intended normality within images based on textual descriptions. We then modify the results of a pre-trained IAD model to implement this normality addition. Using the benchmark dataset in IAD, MVTec AD, we establish an evaluation protocol for the normality addition task and empirically demonstrate the effectiveness of the NAND method.

翻译：图像异常检测（IAD）任务旨在识别图像数据中偏离正态性的异常。这些异常模式与IAD模型在训练期间从数据中学到的模式存在显著偏差。然而，在实际应用场景中，构成正态性的标准常常发生变化，需要将先前被判定为异常的实例重新归类为正常。为应对这一挑战，我们提出了一种称为"正态性添加"的新场景，涉及在训练后调整决策边界以纳入新的正态性。为此，我们提出了一种名为"基于正态性检测的正态性添加"（NAND）的方法，该方法利用视觉-语言模型。NAND执行正态性检测，根据文本描述识别图像中与目标正态性相关的模式。随后，我们修改预训练IAD模型的结果以实现这种正态性添加。通过使用IAD领域的基准数据集MVTec AD，我们为"正态性添加"任务建立了评估协议，并通过实验验证了NAND方法的有效性。