面向无监督视觉缺陷检测的Patch感知向量量化码本学习
Patch-aware Vector Quantized Codebook Learning for Unsupervised Visual Defect Detection
AI总结：
针对无监督视觉缺陷检测中表征能力与紧凑性难平衡的问题，提出引入patch感知动态码分配的增强VQ-VAE框架，优化空间表征以提升检测精度，在多个数据集上达SOTA性能。
摘要：
无监督视觉缺陷检测在工业应用中至关重要，其需要一个既能捕捉正常数据特征又能检测偏差的表征空间。在表征能力与紧凑性之间取得平衡颇具挑战：表征能力过强的空间存在效率低下和模式崩溃的风险，会损害检测精度。我们提出一种采用增强型VQ-VAE（向量量化变分自编码器）框架的新方法，该框架针对无监督缺陷检测进行了优化。我们的模型引入了patch感知动态码分配机制，可实现上下文敏感的码本分配以优化空间表征。该策略增强了正常样本与缺陷的区分度，并提升了推理阶段的检测精度。在MVTecAD、BTAD和MTSD数据集上的实验表明，我们的方法取得了当前最优性能。
英文摘要：
Unsupervised visual defect detection is critical in industrial applications, requiring a representation space that captures normal data features while detecting deviations. Achieving a balance between expressiveness and compactness is challenging; an overly expressive space risks inefficiency and mode collapse, impairing detection accuracy. We propose a novel approach using an enhanced VQ-VAE framework optimized for unsupervised defect detection. Our model introduces a patch-aware dynamic code assignment scheme, enabling context-sensitive code allocation to optimize spatial representation. This strategy enhances normal-defect distinction and improves detection accuracy during inference. Experiments on MVTecAD, BTAD, and MTSD datasets show our method achieves state-of-the-art performance.
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。
