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).

翻译：在实际异常检测任务中，对异常像素进行人工标注是一项成本高昂的工作。因此，许多异常检测方法被设计为单分类器，专门针对完全不含异常的训练集进行训练，以确保更具成本效益的方案。尽管一些开创性工作通过在训练中引入真实异常样本展示了更高的异常检测精度，但这种提升是以劳动密集型的标注过程为代价的。本文通过提出ADClick——一种新颖的交互式图像分割算法，在异常检测精度与标注成本之间取得了平衡。ADClick利用创新的残差特征和精心设计的语言提示，高效地为真实缺陷图像生成"真实"异常掩码。值得注意的是，与现有最先进的交互式图像分割方法相比，ADClick展现出显著提升的泛化能力。作为一种异常标注工具，ADClick仅需每张训练图像3至5次手动点击标注，即可生成高质量的异常标签（在MVTec AD数据集上AP $= 94.1\%$）。此外，我们将ADClick扩展为ADClick-Seg，这是一种专为异常检测与定位设计的增强模型。通过使用ADClick推断的弱标签对ADClick-Seg模型进行微调，我们在监督式异常检测任务中取得了最先进的性能（在MVTec AD数据集上AP $= 86.4\%$，在KSDD2数据集上AP $= 78.4\%$，PRO $= 98.6\%$）。