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.

翻译：我们提出了一种新的零样本纹理异常定位方法。该任务旨在识别均匀图像中的异常区域。为了实现高保真定位，我们利用了基于一维Wasserstein距离导出的双射映射。与使用分布之间的整体距离不同，所提出的方法允许在局部上下文中以更高精度精确定位像素的不一致性。通过聚合像素对附近所有块误差的贡献，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，MVTec AD数据集的误差比先前最先进方法降低了40%以上。另见 https://reality.tf.fau.de/pub/ardelean2024highfidelity.html。