Author

Seung-Jun Chu

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Preprint Aug 2026

CDGP: Contrastive Dual Gaussian Processes for Weakly Supervised Anomaly Segmentation

Industrial visual inspection must both decide whether a product is defective and localize the defect, yet pixel-level masks are costly to collect at scale. Most anomaly-segmentation methods learn only from defect-free images and score deviations from normality. A true defect and an unusual-but-normal region, however, c...

Seung-Jun Chu, Seokhee Han, Mateusz Nowak et al. · 0 citations

Preprint Aug 2026

SPARC: Subspace Position-Aware Robust Few-Shot Calibration for Distribution-Shifted Industrial Anomaly Detection

Vision-based industrial anomaly detectors are calibrated on one distribution but may be deployed on another that differs in illumination, fixture placement, or sensor characteristics, sharply degrading an otherwise accurate detector. Adapting to the incoming lot is a natural response, but labeled anomalies are scarce....

Seokhee Han, Seungjun Chu, Mateusz Nowak et al. · 1 citation