  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.
