利用多视角信息增强生成模型，提升工业缺陷检测精度。
Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detection
- 设计跨视角消息传递机制，融合多相机数据信息。
- 在Real-IAD数据集上达到新最佳性能，图像级与样本级均优。
- 适合需要高精度工业质检的场景，尤其复杂产品缺陷识别。
随着众多高性能异常检测方法的出现，单视角任务已基本解决。然而，现实生产中复杂工业品的特性常无法仅由单一图像完全捕捉。尽管基于归一化流的方法在单摄像头场景下表现良好，但尚未充分利用多视角数据中的先验信息。为此，本文以归一化流为基底，提出Multi-Flow——一种新型多视角异常检测方法。该方法采用新颖的多视角架构，通过跨视角信息融合提升精确似然估计。为此，设计了一种新的跨视角消息传递机制，实现邻近视角间的信息流动。在真实多视角数据集Real-IAD上进行实证验证，结果表明其在图像级和样本级异常检测任务中均超越现有基线，达到新状态最优水平。
原文摘要 · Abstract (English)
With more well-performing anomaly detection methods proposed, many of the single-view tasks have been solved to a relatively good degree. However, real-world production scenarios often involve complex industrial products, whose properties may not be fully captured by one single image. While normalizing flow based approaches already work well in single-camera scenarios, they currently do not make use of the priors in multi-view data. We aim to bridge this gap by using these flow-based models as a strong foundation and propose Multi-Flow, a novel multi-view anomaly detection method. Multi-Flow makes use of a novel multi-view architecture, whose exact likelihood estimation is enhanced by fusing information across different views. For this, we propose a new cross-view message-passing scheme, letting information flow between neighboring views. We empirically validate it on the real-world multi-view data set Real-IAD and reach a new state-of-the-art, surpassing current baselines in both image-wise and sample-wise anomaly detection tasks.
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