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 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 instantiate this idea with Power-Law Self-Correlation Enhanced Attention (PL-SCEA), which retains the seman...
| Authors | Xiaoyu Yang, Qixing Wu, Huixian Zhao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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