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