English(EN) When More References Hurt: Contamination-Aware DINOv2 Memory Banks for Few-Shot Steel Defect Detection

DINOv2 记忆库针对钢材缺陷检测进行了改进

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-25 04:00

研究人员开发了一种污染感知的 DINOv2 记忆库方法，以改进少样本钢材缺陷检测。该方法解决了未经工业图像验证引入异常到参考库的问题。通过在将可疑斑块与干净种子库合并之前对其进行评分和过滤，所提出的方法与朴素扩展或随机移除技术相比，显著减少了残留污染并提高了检测准确性。 AI

影响引入了一种利用现有视觉模型改进工业环境中异常检测的新颖技术。

排序理由在 arXiv 上发表的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Hannaneh Kalantari, Javad Khoramdel · 2026-08-25 04:00

当更多参考物适得其反时：用于少样本钢材缺陷检测的污染感知 DINOv2 内存库

arXiv:2608.22082v1 Announce Type: new Abstract: Patch-memory anomaly detectors assume that their reference bank is normal, an assumption that is difficult to guarantee when additional industrial images are unverified. We study whether a few trusted normal images can safely recove…