提出量化版FCA方法，实现纹理异常检测实时化。
Quantized FCA: Efficient Zero-Shot Texture Anomaly Detection
- 用量化特征对应分析替代原始算法，提升效率
- 速度提升10倍，准确率几乎不变
- 适合工业生产线等实时监控场景
零样本异常定位是计算机视觉的前沿方向，近年来进展显著。本文聚焦于纹理异常的检测与定位，异常被定义为偏离整体统计特性的区域，违反平稳性假设。现有方法主要瓶颈在于运行时间过长，难以部署于真实场景（如产线监控）。为此，我们提出QFCA方法，实现特征对应分析（FCA）的量化版本。通过将补丁统计比较适配至量化值的直方图，实现10倍速度提升，且精度损失极小。此外，引入基于主成分分析的特征预处理，增强正常与异常特征的对比度，显著提升复杂纹理下的检测精度。方法在多个基准上评估，性能优于现有主流方法。
原文摘要 · Abstract (English)
Zero-shot anomaly localization is a rising field in computer vision research, with important progress in recent years. This work focuses on the problem of detecting and localizing anomalies in textures, where anomalies can be defined as the regions that deviate from the overall statistics, violating the stationarity assumption. The main limitation of existing methods is their high running time, making them impractical for deployment in real-world scenarios, such as assembly line monitoring. We propose a real-time method, named QFCA, which implements a quantized version of the feature correspondence analysis (FCA) algorithm. By carefully adapting the patch statistics comparison to work on histograms of quantized values, we obtain a 10x speedup with little to no loss in accuracy. Moreover, we introduce a feature preprocessing step based on principal component analysis, which enhances the contrast between normal and anomalous features, improving the detection precision on complex textures. Our method is thoroughly evaluated against prior art, comparing favorably with existing methods. Project page: https://reality.tf.fau.de/pub/ardelean2025quantized.html
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