用双网格重构提升细粒度异常检测精度
Bi-Grid Reconstruction for Image Anomaly Detection
- 双连续网格分别存储正常与异常特征，增强泛化能力
- 异常特征网格细化正常边界，显著提升细微缺陷识别率
- 特征块拼接模块快速合成异常，适配多类异常检测
在图像异常检测中，尽管无监督和自监督方法已取得进展，但对细粒度异常的检测仍存在困难。本文提出GRAD：基于双网格重构的图像异常检测方法，通过两个连续网格从正常与异常双重视角提升检测能力。第一，将网格作为特征仓库，提升模型泛化性并缓解相同捷径（IS）问题；第二，引入异常特征网格，精炼正常特征边界，增强对细粒度缺陷的识别能力；第三，设计特征块拼接（FBP）模块，在特征层面合成多样化异常，实现异常网格的快速部署。GRAD具备强鲁棒表征能力，可单模型处理多类别异常。在MVTecAD、VisA和GoodsAD等数据集上的实验表明，其在细粒度异常检测上性能显著优于现有方法，整体准确率更高，能更好区分细微差异。
原文摘要 · Abstract (English)
In image anomaly detection, significant advancements have been made using un- and self-supervised methods with datasets containing only normal samples. However, these approaches often struggle with fine-grained anomalies. This paper introduces \textbf{GRAD}: Bi-\textbf{G}rid \textbf{R}econstruction for Image \textbf{A}nomaly \textbf{D}etection, which employs two continuous grids to enhance anomaly detection from both normal and abnormal perspectives. In this work: 1) Grids as feature repositories that improve generalization and mitigate the Identical Shortcut (IS) issue; 2) An abnormal feature grid that refines normal feature boundaries, boosting detection of fine-grained defects; 3) The Feature Block Paste (FBP) module, which synthesizes various anomalies at the feature level for quick abnormal grid deployment. GRAD's robust representation capabilities also allow it to handle multiple classes with a single model. Evaluations on datasets like MVTecAD, VisA, and GoodsAD show significant performance improvements in fine-grained anomaly detection. GRAD excels in overall accuracy and in discerning subtle differences, demonstrating its superiority over existing methods.
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