工业图像异常检测模型中基于正常性检测的正常性添加
Normality Addition via Normality Detection in Industrial Image Anomaly Detection Models
AI总结：
针对工业图像异常检测中正常性标准变化的问题，提出基于视觉-语言模型的NAND方法，通过正常性检测调整决策边界以纳入新正常性，并在MVTec AD上验证其有效性。
摘要：
图像异常检测（IAD）的任务旨在识别图像数据中偏离正常性的情况。这些异常是显著偏离IAD模型在训练期间从数据中学习到的模式。然而，在现实场景中，关于什么构成正常性的标准经常发生变化，需要将先前异常的实例重新分类为正常。为了应对这一挑战，我们提出了一种称为“正常性添加”的新场景，涉及对决策边界进行训练后调整以纳入新的正常性。为了应对这一挑战，我们提出了一种名为Normality Addition via Normality Detection（NAND）的方法，利用视觉-语言模型。NAND执行正常性检测，基于文本描述检测图像中与预期正常性相关的模式。然后我们修改预训练IAD模型的结果以实现这种正常性添加。使用IAD中的基准数据集MVTec AD，我们为正常性添加任务建立了评估协议，并实证证明了NAND方法的有效性。
英文摘要：
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.
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