用于高效零样本纹理异常检测的全局正则化邻域回归
Global-Regularized Neighborhood Regression for Efficient Zero-Shot Texture Anomaly Detection
AI总结：
本文提出零样本GRNR方法，利用测试图像的局部邻域与全局正常性先验进行自重建回归，无需训练即可在八个基准上实现优越的开集纹理异常检测。
摘要：
纹理表面异常检测在工业环境中具有广泛应用。然而，现有方法通常需要收集大量样本用于模型训练。此外，它们主要在闭集检测框架内运行，限制了其识别训练数据集之外异常的能力。为应对这些挑战，本文提出了一种名为全局正则化邻域回归（Global-Regularized Neighborhood Regression，GRNR）的新型零样本纹理异常检测方法。与传统方法不同，GRNR无需任何训练数据或训练成本即可检测任意纹理表面上的异常。借鉴人类视觉认知，GRNR直接从测试纹理图像中获得两种内在先验支撑：以连贯相似性为特征的局部邻域先验，以及以典型正常模式为特征的全局正常性先验。GRNR的基本原理是利用提取出的两种内在支撑先验，对查询样本进行自重建回归。该过程采用由局部邻域支撑促成的变换，同时由全局正常性支撑进行正则化，旨在不仅获得视觉一致的重建结果，而且保持正常性属性。我们使用八个基准数据集在多种工业场景中验证了GRNR的有效性，证明其在无需训练数据的情况下具有优越的检测性能。值得注意的是，我们的方法适用于开集纹理缺陷检测，甚至可以超越需要大量训练的现有普通方法。
英文摘要：
Texture surface anomaly detection finds widespread applications in industrial settings. However, existing methods often necessitate gathering numerous samples for model training. Moreover, they predominantly operate within a close-set detection framework, limiting their ability to identify anomalies beyond the training dataset. To tackle these challenges, this paper introduces a novel zero-shot texture anomaly detection method named Global-Regularized Neighborhood Regression (GRNR). Unlike conventional approaches, GRNR can detect anomalies on arbitrary textured surfaces without any training data or cost. Drawing from human visual cognition, GRNR derives two intrinsic prior supports directly from the test texture image: local neighborhood priors characterized by coherent similarities and global normality priors featuring typical normal patterns. The fundamental principle of GRNR involves utilizing the two extracted intrinsic support priors for self-reconstructive regression of the query sample. This process employs the transformation facilitated by local neighbor support while being regularized by global normality support, aiming to not only achieve visually consistent reconstruction results but also preserve normality properties. We validate the effectiveness of GRNR across various industrial scenarios using eight benchmark datasets, demonstrating its superior detection performance without the need for training data. Remarkably, our method is applicable for open-set texture defect detection and can even surpass existing vanilla approaches that require extensive training.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
