MATCH: Flow Matching for Multi-View Anomaly Detection
Detecting anomalies in industrial objects is an important topic for increasing production efficiency. More complex objects often require the analysis of several view points, which has led to the field of multi-view anomaly detection. We present MATCH, the first multi-view anomaly detection method based on Flow Matching (FM). With the ODE formulation of Flow Matching, we can estimate likelihoods and thereby derive an anomaly score to detect anomalies in multi-view image data at object, image, and pixel-level. The architectural flexibility of FM models allows us to efficiently transform features of different spatial sizes to the normal distribution. We evaluate thoroughly on the already est...
| Authors | Mathis Kruse, Melissa Schween, Bodo Rosenhahn |
|---|---|
| Primary category | cs.CV |
| Published | 23 June 2026 |
| Last updated at source | 23 June 2026 |
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