面向工业异常检测的注意力引导扰动网络
Attention-Guided Perturbation Network for Industrial Anomaly Detection
AI总结：
针对无监督图像异常检测中重构方法易漏检异常的问题，提出AGPNet框架，通过样本感知注意力掩码引导扰动，在MVTec系列数据集上取得优异性能。
摘要：
在无监督图像异常检测中，基于重构的方法学习数据重构的正常模式，但在推理时往往会不合需要地重构异常区域，导致漏检。为缓解该问题，现有方法以与样本无关的方式通过均匀注入噪声来扰动正常样本，却忽略了前景区域对于鲁棒重构更为关键。为解决这一局限，我们提出注意力引导扰动网络（AGPNet），这是一种用于工业异常检测的新型重构框架。AGPNet引入样本感知注意力掩码来引导扰动，强化重要位置不变正常模式的学习。AGPNet由两个分支构成：重构分支和基于注意力的辅助扰动分支。重构分支专注于学习重构正常样本，而辅助分支生成注意力掩码以引导噪声扰动。通过对显著区域施加更强的扰动，重构分支能以更全面、鲁棒的方式学习内在正常模式。在MVTec-AD、VisA和MVTec-3D上开展的大量实验表明，AGPNet在少样本、单类和多类设置下均取得了具竞争力或领先的性能，尤其在少样本异常定位中表现强劲。
英文摘要：
In unsupervised image anomaly detection, reconstruction-based methods learn normal patterns for data reconstruction, but often undesirably reconstruct anomalous regions at inference, resulting in missed detections. To alleviate this, existing approaches perturb normal samples in a sample-agnostic manner by uniformly injecting noise, ignoring that foreground regions are more critical for robust reconstruction. To address this limitation, we propose Attention-Guided Perturbation Network (AGPNet), a novel reconstruction framework for industrial anomaly detection. AGPNet introduces sample-aware attention masks to guide perturbations, enhancing the learning of invariant normal patterns at important locations. AGPNet consists of two branches, a reconstruction branch and an auxiliary attention-based perturbation one. The reconstruction branch focuses on learning to reconstruct normal samples, while the auxiliary branch generates attention masks to guide noise perturbation. By applying stronger perturbations to salient regions, the reconstruction branch learns intrinsic normal patterns in a more comprehensive and robust manner. Extensive experiments on MVTec-AD, VisA, and MVTec-3D show that AGPNet achieves competitive or leading performance across few-shot, one-class, and multi-class settings, with particularly strong performance in few-shot anomaly localization.
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。
