MATCH: Flow Matching for Multi-View Anomaly Detection

Detecting anomalies in industrial objects is an importanttopic for increasing production efficiency. More complex objects oftenrequire the analysis of several view points, which has led to the fieldof multi-view anomaly detection. We present MATCH , the first multi-view anomaly detection method based on Flow Matching (FM). Withthe ODE formulation of Flow Matching, we can estimate likelihoods andthereby derive an anomaly score to detect anomalies in multi-view imagedata at object, image, and pixel-level. The architectural flexibility of FMmodels allows us to efficiently transform features of different spatial sizesto the normal distribution. We evaluate thoroughly on the already estab-lished Real-IAD data set and are also the first to provide a comprehen-sive evaluation of popular anomaly detection methods for the MANTA-Tiny data set. MATCH achieves state-of-the-art performance in bothanomaly detection and segmentation, all while running on consumer-levelhardware. By omitting the costly divergence term needed for likelihoodestimation, we ensure that MATCH is usable in real-time productionscenarios. Lastly, several ablation studies are conducted to validate themethodological choices. Code: https://github.com/m-kruse98/MATCH