Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detection

• URL: http://arxiv.org/abs/2504.03306v1
• Date: Fri, 04 Apr 2025 09:32:01 GMT
• Title: Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detection
• Authors: Mathis Kruse, Bodo Rosenhahn,
• Abstract summary: We propose Multi-Flow, a novel multi-view anomaly detection method.<n>It makes use of a novel multi-view architecture, whose exact likelihood estimation is enhanced by fusing information across different views.<n>We empirically validate it on the real-world multi-view data set Real-IAD and reach a new state-of-the-art.
• Score: 20.499874396491347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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