用流匹配实现多视角异常检测，实时高效且精度领先。
MATCH: Flow Matching for Multi-View Anomaly Detection
- 基于流匹配的ODE建模，统一处理多视角图像特征
- 在Real-IAD和MANTA-Tiny上均达顶尖性能，支持像素级定位
- 无需耗时的发散项，适合工业实时生产场景
工业物体异常检测对提升生产效率至关重要。复杂物体常需多视角分析，推动了多视角异常检测的发展。本文提出MATCH，首个基于流匹配（Flow Matching, FM）的多视角异常检测方法。通过流匹配的常微分方程（ODE）形式，可估计似然并生成异常分数，实现对象、图像及像素级别的异常检测。FM模型的架构灵活性使其能高效将不同空间尺寸的特征映射至标准正态分布。我们在已建立的Real-IAD数据集上进行了全面评估，并首次对MANTA-Tiny数据集上的主流异常检测方法进行了系统性对比。MATCH在异常检测与分割任务中均达到当前最优表现，且可在消费级硬件上运行。通过省去代价高昂的发散项，确保了方法在实时生产环境中的可用性。此外，我们还进行了多项消融实验，验证了方法设计的合理性。
原文摘要 · Abstract (English)
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 established Real-IAD data set and are also the first to provide a comprehensive evaluation of popular anomaly detection methods for the MANTA-Tiny data set. MATCH achieves state-of-the-art performance in both anomaly detection and segmentation, all while running on consumer-level hardware. By omitting the costly divergence term needed for likelihood estimation, we ensure that MATCH is usable in real-time production scenarios. Lastly, several ablation studies are conducted to validate the methodological choices.
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