  In the realm of practical Anomaly Detection (AD) tasks, manual labeling of
anomalous pixels proves to be a costly endeavor. Consequently, many AD methods
are crafted as one-class classifiers, tailored for training sets completely
devoid of anomalies, ensuring a more cost-effective approach. While some
pioneering work has demonstrated heightened AD accuracy by incorporating real
anomaly samples in training, this enhancement comes at the price of
labor-intensive labeling processes. This paper strikes the balance between AD
accuracy and labeling expenses by introducing ADClick, a novel Interactive
Image Segmentation (IIS) algorithm. ADClick efficiently generates
"ground-truth" anomaly masks for real defective images, leveraging innovative
residual features and meticulously crafted language prompts. Notably, ADClick
showcases a significantly elevated generalization capacity compared to existing
state-of-the-art IIS approaches. Functioning as an anomaly labeling tool,
ADClick generates high-quality anomaly labels (AP $= 94.1\%$ on MVTec AD) based
on only $3$ to $5$ manual click annotations per training image. Furthermore, we
extend the capabilities of ADClick into ADClick-Seg, an enhanced model designed
for anomaly detection and localization. By fine-tuning the ADClick-Seg model
using the weak labels inferred by ADClick, we establish the state-of-the-art
performances in supervised AD tasks (AP $= 86.4\%$ on MVTec AD and AP $=
78.4\%$, PRO $= 98.6\%$ on KSDD2).
