  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.
