用视觉提示与双教师机制提升异常检测在真实场景下的泛化能力
Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision
- 通过前景背景掩码实现物体隔离，增强对复杂场景的适应性
- 解冻教师模型并结合扩散生成数据，使检测性能提升3.5个百分点
- 适合需要高鲁棒性的工业缺陷检测场景
近期异常检测方法在MVTec等标准数据集上取得了完美检测与分割效果，但当物体尺度、视角、背景、光照或位置一致性等基础假设被打破时，性能显著下降，难以应用于真实场景。为此，本文提出三项关键贡献：(1) 基于前景-背景掩码的视觉提示流程，有效分离目标物体；(2) 解冻学生-教师模型中的教师网络以提升领域适应性；(3) 利用扩散模型生成合成图像进行数据增强。采用以Masked Multiscale Reconstruction（MMR）为骨干网络的方法，在挑战性强的AeBAD数据集上相较此前最优方法提升3.5个百分点。
原文摘要 · Abstract (English)
Recent Anomaly Detection methods achieve perfect detection and segmentation scores on well-established datasets, such as MVTec. However, many of these methods face challenges when foundational assumptions - such as consistent object scale, viewpoint, background, illumination, and centered placement - are violated. Those variations that occur render anomaly detection methods unusable in many real-world scenarios. To address these limitations, we introduce three key contributions: (1) a visual prompting pipeline that isolates objects using foreground-background masking; (2) a mechanism for unfreezing the teacher in student-teacher models to improve domain adaptability; and (3) a data augmentation strategy leveraging diffusion-generated synthetic images to enhance anomaly detection performance. We achieve a 3.5 percentage point improvement over the previous state-of-the-art on the challenging AeBAD dataset by using the Masked Multiscale Reconstruction (MMR) model as our backbone.
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