During industrial processing, unforeseen defects may arise in products due to uncontrollable factors. Although unsupervised methods have been successful in defect localization, the usual use of pre-trained models results in low-resolution outputs, which damages visual performance. To address this issue, we propose PyramidFlow, the first fully normalizing flow method without pre-trained models that enables high-resolution defect localization. Specifically, we propose a latent template-based defect contrastive localization paradigm to reduce intra-class variance, as the pre-trained models do. In addition, PyramidFlow utilizes pyramid-like normalizing flows for multi-scale fusing and volume normalization to help generalization. Our comprehensive studies on MVTecAD demonstrate the proposed method outperforms the comparable algorithms that do not use external priors, even achieving state-of-the-art performance in more challenging BTAD scenarios.

提出的PyramidFlow方法是第一个无需预训练模型就能实现高分辨率瑕疵定位的全归一化流法，该方法利用金字塔状的归一化流进行多尺度融合和体积归一化来帮助泛化，并提出一种基于潜变量模板的瑕疵对比定位范式来降低类内方差。综合研究MVTecAD的研究表明，该方法表现优于不使用外部prior的可比算法，甚至在更具挑战性的BTAD场景中达到了最先进的表现。

金字塔归一化流在高分辨率缺陷对比定位中的应用