Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detection
This addresses the challenge of detecting anomalies in real-world industrial production where single-view data is insufficient, representing an incremental improvement over existing flow-based methods.
The paper tackled the problem of anomaly detection for complex industrial products by proposing Multi-Flow, a multi-view method that enhances normalizing flows with cross-view information fusion, achieving state-of-the-art results on the Real-IAD dataset in both image-wise and sample-wise tasks.
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.