Problem
Research questions and friction points this paper is trying to address.
few-shot industrial anomaly detection
attention computation
pretrained vision foundation models
anomaly localization
PL-SCEA: Reconfiguring Pretrained Attention for Few-Shot Industrial Anomaly Detection
🤖 AI Summary
本文提出PL-SCEA方法，通过重新配置预训练模型的注意力机制来解决少量样本工业异常检测中的局部纹理和结构偏差问题。
📝 Abstract
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 semantic context of pretrained query-key attention while constructing token-adaptive self-correlations over contextualized value features. Positive-correlation filtering and power-law reweighting then emphasize relations that are salient relative to each token's relational background, without introducing additional trainable attention projections. The resulting features are modeled by a lightweight variational autoencoder that provides a fixed-size reconstruction-based representation of category-specific normality. The two stages serve complementary roles: attention reconfiguration shapes how local relational deviations are represented, while reconstruction-based modeling converts deviations from learned normality into anomaly scores. Across MVTec AD and VisA, the complete framework achieves competitive image-level detection and consistently strong pixel-level localization across the evaluated few-shot settings. Ablations further show that PL-SCEA improves localization with either the VAE or a memory bank under the tested setting. These results support the view that task-aligned attention reconfiguration can improve the anomaly-localization capability of frozen pretrained representations.
Innovation
Methods, ideas, or system contributions that make the work stand out.
PL-SCEA
attention reconfiguration
few-shot industrial anomaly detection
self-correlation
variational autoencoder
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