基于特征对应分析的高保真零样本文理异常定位
High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis
AI总结：
本文提出一种基于一维Wasserstein距离双射映射的零样本文理异常定位方法，通过局部特征对应分析聚合像素误差贡献，在MVTec AD上实现误差降低超40%。
摘要：
我们提出了一种用于纹理零样本异常定位的新方法。该任务旨在识别原本均匀图像中的异常区域。为了实现高保真定位，我们利用由一维Wasserstein距离导出的双射映射。与使用分布之间的整体距离不同，所提出的方法能够以更高的精度在局部上下文中定位像素的不一致性。通过聚合该像素对所有邻近块误差的贡献，我们获得了可靠的异常分数估计。我们在多个数据集上验证了我们的解决方案，在零样本设置下，在MVTec AD数据集上的误差相比之前的最先进方法减少了40%以上。另见https://reality.tf.fau.de/pub/ardelean2024highfidelity.html。
英文摘要：
We propose a novel method for Zero-Shot Anomaly Localization on textures. The task refers to identifying abnormal regions in an otherwise homogeneous image. To obtain a high-fidelity localization, we leverage a bijective mapping derived from the 1-dimensional Wasserstein Distance. As opposed to using holistic distances between distributions, the proposed approach allows pinpointing the non-conformity of a pixel in a local context with increased precision. By aggregating the contribution of the pixel to the errors of all nearby patches we obtain a reliable anomaly score estimate. We validate our solution on several datasets and obtain more than a 40% reduction in error over the previous state of the art on the MVTec AD dataset in a zero-shot setting. Also see https://reality.tf.fau.de/pub/ardelean2024highfidelity.html.
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。
