Combines visual prompting, dual-teacher supervision, and diffusion augmentation on an MMR backbone to gain 3.5 percentage points on the AeBAD anomaly detection dataset.
Yiyuan Yang, Chaoli Zhang, Tian Zhou, Qingsong Wen, and Liang Sun
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Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision
Combines visual prompting, dual-teacher supervision, and diffusion augmentation on an MMR backbone to gain 3.5 percentage points on the AeBAD anomaly detection dataset.