AI RESEARCH
HLGFA: High-Low Resolution Guided Feature Alignment for Unsupervised Anomaly Detection
arXiv CS.CV
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ArXi:2602.09524v3 Announce Type: replace 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.