HLGFA: High-Low Resolution Guided Feature Alignment for Unsupervised Anomaly Detection

TL;DR

HLGFA introduces a novel cross-resolution feature alignment framework for unsupervised industrial anomaly detection, leveraging multi-level feature consistency without pixel reconstruction, and achieves state-of-the-art results.

Contribution

The paper proposes HLGFA, a high-low resolution guided feature alignment method that models cross-resolution feature consistency for anomaly detection, avoiding pixel-level reconstruction.

Findings

Achieves 97.9% pixel-level AUROC on MVTec AD

Achieves 97.5% image-level AUROC on MVTec AD

Outperforms existing reconstruction-based and feature-based methods

Abstract

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.