PyramidFlow: High-Resolution Defect Contrastive Localization Using Pyramid Normalizing Flow

Jiarui Lei · Xiaobo Hu · Yue Wang · Dong Liu

West Building Exhibit Halls ABC 171
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Wed 21 Jun 4:30 p.m. PDT — 6 p.m. PDT


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.

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