HLGFA：高-低分辨率引导的特征对齐用于无监督异常检测
HLGFA: High-Low Resolution Guided Feature Alignment for Unsupervised Anomaly Detection
AI总结：
本文提出HLGFA框架，通过高-低分辨率特征一致性学习正常样本的正常性，不依赖像素级重建，利用双分辨率输入提取多级特征，并通过条件调制和门控残差修正引导低分辨率特征细化，有效识别异常区域。
摘要：
无监督工业异常检测（UAD）对现代制造检测至关重要，其中缺陷样本稀缺且需要可靠检测。本文提出HLGFA，一种高-低分辨率引导的特征对齐框架，通过建模正常样本高分辨率和低分辨率表示之间的跨分辨率特征一致性来学习正常性，而非依赖像素级重建。双分辨率输入通过共享冻结的主干网络提取多级特征，高分辨率表示被分解为结构和细节先验，通过条件调制和门控残差修正引导低分辨率特征的细化。在推理过程中，异常自然地被识别为跨分辨率对齐失效的区域。此外，引入了一种噪声感知的数据增强策略，以抑制工业环境中常见的干扰响应。在标准基准上的广泛实验表明，HLGFA的有效性，其在MVTec AD数据集上实现了97.9%的像素级AUROC和97.5%的图像级AUROC，优于代表性的重建和特征方法。
英文摘要：
Unsupervised industrial anomaly detection (UAD) is essential for modern manufacturing inspection, where defect samples are scarce and reliable detection is required. In this paper, we propose HLGFA, a high-low resolution guided feature alignment framework that learns normality by modeling cross-resolution feature consistency between high-resolution and low-resolution representations of normal samples, instead of relying on pixel-level reconstruction. Dual-resolution inputs are processed by a shared frozen backbone to extract multi-level features, and high-resolution representations are decomposed into structure and detail priors to guide the refinement of low-resolution features through conditional modulation and gated residual correction. During inference, anomalies are naturally identified as regions where cross-resolution alignment breaks down. In addition, a noise-aware data augmentation strategy is introduced to suppress nuisance-induced responses commonly observed in industrial environments. Extensive experiments on standard benchmarks demonstrate the effectiveness of HLGFA, achieving 97.9% pixel-level AUROC and 97.5% image-level AUROC on the MVTec AD dataset, outperforming representative reconstruction-based and feature-based methods.
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