CDGP: Contrastive Dual Gaussian Processes for Weakly Supervised Anomaly Segmentation

• URL: http://arxiv.org/abs/2608.18614v1
• Date: Wed, 19 Aug 2026 07:02:00 GMT
• Title: CDGP: Contrastive Dual Gaussian Processes for Weakly Supervised Anomaly Segmentation
• Abstract summary: Contrastive Dual Gaussian Processes is a weakly supervised framework that models normal and anomaly inducing predictive distributions over dense tokens.<n>Its posterior-dominance statistic standardizes their predictive-mean difference by the joint predictive uncertainty.<n>Across MVTec AD2, KSDD2, and VisA, CDGP ranks first among the evaluated methods on all MVTec AD2 localization metrics.
• Score: 4.214241374885902
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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, can both deviate substantially and receive similarly high scores. We propose Contrastive Dual Gaussian Processes (CDGP), a weakly supervised framework that models normal and anomaly inducing-variable predictive distributions over dense tokens. Its posterior-dominance statistic standardizes their predictive-mean difference by the joint predictive uncertainty, providing both spatial evidence and image-level confidence. This evidence complements hierarchical normal-reconstruction residuals for fine localization. All calibration uses training data only, without human pixel annotations or test-time fitting. Across MVTec AD~2, KSDD2, and VisA, CDGP ranks first among the evaluated methods on all MVTec AD~2 localization metrics and is first-place or competitive on KSDD2 and VisA. Factorized and matched linear-head controls delimit the contribution and scope of the linear-kernel Gaussian process (GP) formulation.

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