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PL-SCEA: Reconfiguring Pretrained Attention for Few-Shot Industrial Anomaly Detection

arXiv · AI, language, vision and robotics · article · Sep 3, 2026 · UTC

Vision Foundation Models (VFMs) provide transferable patch representations for few-shot industrial anomaly detection, but their attention computation is typically inherited from pretraining objectives centered on semantic aggregation. This creates a potential mismatch: token relations that support semantic recognition may not adequately expose the localized texture and structural deviations required for anomaly localization. We therefore investigate the hypothesis that the attention computation of a frozen VFM can be reconfigured as a task-relevant component of anomaly detection. We instantiat

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