仅需几次点击和描述，即可高效生成缺陷像素标注，提升工业质检模型性能。
Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization
- 通过少量点击+文本描述自动生成像素级异常标注。
- 在MVTec AD数据集上达到AP 96.1%，显著提升检测效果。
- 融合视觉与语言提示，适合缺乏标注数据的工业场景使用。
工业产品检测通常基于仅包含正常样本的异常检测（AD）框架。尽管可收集缺陷样本，但其利用需像素级标注，限制了可扩展性。为此，我们提出ADClick，一种用于工业异常检测的交互式图像分割（IIS）算法。ADClick仅需少量用户点击和简短文本描述，即可生成像素级异常标注，显著提升AD模型性能（如在MVTec AD上达到AP=96.1%）。我们进一步提出ADClick-Seg，一种跨模态框架，通过原型方法对齐视觉特征与文本提示，实现异常检测与定位。结合像素级先验与语言引导线索，ADClick-Seg在挑战性的“多类”异常检测任务上达到最先进水平（在MVTec AD上AP=80.0%，PRO=97.5%，Pixel-AUROC=99.1%）。
原文摘要 · Abstract (English)
Industrial product inspection is often performed using Anomaly Detection (AD) frameworks trained solely on non-defective samples. Although defective samples can be collected during production, leveraging them usually requires pixel-level annotations, limiting scalability. To address this, we propose ADClick, an Interactive Image Segmentation (IIS) algorithm for industrial anomaly detection. ADClick generates pixel-wise anomaly annotations from only a few user clicks and a brief textual description, enabling precise and efficient labeling that significantly improves AD model performance (e.g., AP = 96.1\% on MVTec AD). We further introduce ADClick-Seg, a cross-modal framework that aligns visual features and textual prompts via a prototype-based approach for anomaly detection and localization. By combining pixel-level priors with language-guided cues, ADClick-Seg achieves state-of-the-art results on the challenging ``Multi-class'' AD task (AP = 80.0\%, PRO = 97.5\%, Pixel-AUROC = 99.1\% on MVTec AD).
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