ArcAD is a plug-and-play push-pull calibration method that projects limited normal samples onto a hypersphere for compact clustering while synthesizing pseudo-anomalies and using real anomalies to refine the decision boundary for reconstruction-based IAD under data scarcity.
arXiv preprint arXiv:2510.17611 (2025)
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GroundingAnomaly uses a Spatial Conditioning Module and Gated Self-Attention in a frozen diffusion U-Net to synthesize spatially accurate few-shot anomalies, reaching SOTA on MVTec AD and VisA for detection, segmentation, and instance detection.
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ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection
ArcAD is a plug-and-play push-pull calibration method that projects limited normal samples onto a hypersphere for compact clustering while synthesizing pseudo-anomalies and using real anomalies to refine the decision boundary for reconstruction-based IAD under data scarcity.
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GroundingAnomaly: Spatially-Grounded Diffusion for Few-Shot Anomaly Synthesis
GroundingAnomaly uses a Spatial Conditioning Module and Gated Self-Attention in a frozen diffusion U-Net to synthesize spatially accurate few-shot anomalies, reaching SOTA on MVTec AD and VisA for detection, segmentation, and instance detection.