Defect detection from images is a crucial and challenging topic of industry scenarios due to the scarcity and unpredictability of anomalous samples. However, existing defect detection methods exhibit low detection performance when it comes to small-size defects. In this work, we propose a Cross-Attention Regression Flow (CARF) framework to model a compact distribution of normal visual patterns for separating outliers. To retain rich scale information of defects, we build an interactive cross-attention pattern flow module to jointly transform and align distributions of multi-layer features, which is beneficial for detecting small-size defects that may be annihilated in high-level features. To handle the complexity of multi-layer feature distributions, we introduce a layer-conditional autoregression module to improve the fitting capacity of data likelihoods on multi-layer features. By transforming the multi-layer feature distributions into a latent space, we can better characterize normal visual patterns. Extensive experiments on four public datasets and our collected industrial dataset demonstrate that the proposed CARF outperforms state-of-the-art methods, particularly in detecting small-size defects.

从图像中检测缺陷是工业场景中的一个关键且具有挑战性的课题，由于异常样本的稀少性和不可预测性。然而，现有的缺陷检测方法在处理小尺寸缺陷时表现较低。在这项工作中，我们提出了一种交叉注意力回归流（CARF）框架，用于建模正常视觉模式的紧凑分布以区分异常值。为了保留缺陷的丰富尺度信息，我们构建了一个交互式交叉注意模式流动模块，共同转换和对齐多层特征的分布，这对于检测可能在高级特征中被湮灭的小尺寸缺陷是有益的。为了解决多层特征分布的复杂性，我们引入了一种条件自回归模块以提高数据似然度在多层特征上的拟合能力。通过将多层特征分布转换到潜在空间中，我们可以更好地描述正常视觉模式。我们在四个公开的数据集和我们收集的工业数据集上进行了广泛的实验，结果表明提出的CARF方法优于现有的最先进的方法，特别是在检测小尺寸缺陷方面表现出色。