发表机构
Dartmouth College; Korea University（达特茅斯学院; 高丽大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对工业异常检测的分布偏移问题，提出SPARC小样本校准方法，利用少量经验证的正常图像通过逐单元子空间投影修正特征，提升了7类检测器在偏移基准上的性能。
AI 中文摘要
基于视觉的工业异常检测器在某一分布上进行校准后，可能会部署到光照、夹具位置或传感器特性不同的另一分布中，这会导致原本准确的检测器性能大幅下降。适应新批次数据是自然的应对方式，但标注异常样本稀缺。因此，我们考虑仅使用在对其余批次数据评分前可用的少量经验证的正常图像进行校准。现有修正方法需要反向传播、特定于检测器的调优，或对特征方向做出选择，而少量校准样本无法为这些选择提供依据。我们提出SPARC，一种小样本校准方法，它在编码器与检测器之间拦截 patch 特征，并通过逐单元子空间投影去除部署时间干扰项的闭式、空间索引估计。该方法仅需k≤8张经验证的正常图像，且在编码器的原生 patch 网格上使用代数饱和秩r=k-1。修正过程无需梯度或权重更新，可与 memory-bank、密度、原型和互信息检测器配合使用。在易发生偏移的基准上，对于所有7个依赖修正后 patch 特征进行图像评分的检测器，SPARC将合并的Image AUROC和AU-PRO₀.₃分别提升13.8和3.5个百分点（pp）；在无刻意偏移的基准上，变化较小且无统一趋势。将竞争修正方法置于相同校准图像条件下的控制实验表明，这些增益源于逐单元子空间结构，而非仅图像本身。进一步的 ablation 实验支持饱和秩的选择，并表征了对骨干网络和校准条件的敏感性。
英文摘要
Vision-based industrial anomaly detectors are calibrated on one distribution but may be deployed on another that differs in illumination, fixture placement, or sensor characteristics, sharply degrading an otherwise accurate detector. Adapting to the incoming lot is a natural response, but labeled anomalies are scarce. We therefore consider calibration using only a handful of verified-normal images available before scoring the rest of the lot. Existing fixes require backpropagation, detector-specific tuning, or choices about feature directions that few calibration samples cannot justify. We present SPARC, a few-shot calibration method that intercepts patch features between encoder and detector and removes a closed-form, spatially indexed estimate of deployment-time nuisance through per-cell subspace projection. It needs only $k \le 8$ verified-normal images and uses the algebraic saturation rank $r{=}k{-}1$ on the encoder's native patch grid. The correction requires no gradient or weight updates and works with memory-bank, density, prototype, and mutual detectors. On the shift-prone benchmarks, SPARC improves pooled Image AUROC and AU-PRO$_{0.3}$ for all seven detectors whose image scores depend on corrected patch features by $+13.8$ and $+3.5$ percentage points (pp), respectively; on benchmarks without engineered shift, the changes are small and mixed. Controls that give competing corrections the same calibration images attribute these gains to the per-cell subspace structure rather than the images alone. Further ablations support the saturation-rank choice and characterize sensitivity to backbone and calibration conditions.
