Multi-Flow：用于工业异常检测的多视图增强归一化流
Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detection
AI总结：
针对单视图无法完全捕捉复杂工业产品属性的问题，提出多视图异常检测方法 Multi-Flow，通过跨视图消息传递方案融合不同视图信息以增强精确似然估计，在 Real-IAD 数据集上达到最先进水平。
摘要：
随着更多性能优异的异常检测方法被提出，许多单视图任务已在一定程度上得到较好解决。然而，现实生产场景通常涉及复杂的工业产品，其属性可能无法仅凭单张图像完全捕捉。尽管基于归一化流的方法在单相机场景中已表现良好，但它们目前尚未利用多视图数据中的先验信息。我们旨在通过将这些基于流的方法作为坚实基础来弥合这一差距，并提出了一种新颖的多视图异常检测方法 Multi-Flow。Multi-Flow 采用了一种新颖的多视图架构，通过融合不同视图间的信息来增强其精确似然估计。为此，我们提出了一种新的跨视图消息传递方案，使信息能在相邻视图间流动。我们在真实世界多视图数据集 Real-IAD 上进行了实证验证，并达到了新的最先进水平，在图像级和样本级异常检测任务中均超越了当前基线。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
