基于极线约束跨视图融合的多视图工业异常检测
Multi-View Industrial Anomaly Detection with Epipolar Constrained Cross-View Fusion
AI总结：
针对多相机工业异常检测中跨视图几何信息利用不足的问题，提出极线几何约束注意力融合模块及多视图感知负样本预训练策略，在先进基准上取得优于现有方法的性能。
摘要：
多相机系统为工业异常检测提供了更丰富的上下文信息。然而，传统方法独立处理每个视图，忽视了不同视点之间的互补信息。现有的多视图异常检测方法通常采用数据驱动的跨视图注意力进行特征融合，但未能利用多相机设置所特有的几何属性。在本工作中，我们引入了一个极线几何约束注意力模块来引导跨视图融合，从而确保更有效的信息聚合。为进一步释放跨视图注意力的潜力，我们提出了一种受基于记忆库的异常检测启发的预训练策略。该方法促使正常特征表示形成多个局部簇，并结合具有多视图感知能力的负样本合成来正则化预训练过程。我们证明，在最先进的多视图异常检测数据集上，我们的极线引导多视图异常检测框架优于现有方法。
英文摘要：
Multi-camera systems provide richer contextual information for industrial anomaly detection. However, traditional methods process each view independently, disregarding the complementary information across viewpoints. Existing multi-view anomaly detection approaches typically employ data-driven cross-view attention for feature fusion but fail to leverage the unique geometric properties of multi-camera setups. In this work, we introduce an epipolar geometry-constrained attention module to guide cross-view fusion, ensuring more effective information aggregation. To further enhance the potential of cross-view attention, we propose a pretraining strategy inspired by memory bank-based anomaly detection. This approach encourages normal feature representations to form multiple local clusters and incorporate multi-view aware negative sample synthesis to regularize pretraining. We demonstrate that our epipolar guided multi-view anomaly detection framework outperforms existing methods on the state-of-the-art multi-view anomaly detection dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
