发表机构
Southeast University（东南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出受神经崩溃启发的NC-TFAD框架，通过冻结骨干网络、对齐特征至ETF空间、生成合成异常样本及引入正则化与FNCC损失，在无任务持续异常检测任务中优于现有方法，为工业应用提供鲁棒方案。
AI 中文摘要
近年来，工业视觉检测领域的持续异常检测研究日益受到关注。然而，现实制造环境中数据分布会出现不可预测的变化，这使得依赖任务的持续学习假设难以适用。为解决这一局限，本文将工业异常检测问题建模为无任务的持续学习问题，并提出NC-TFAD——一种受神经崩溃（Neural Collapse）启发、基于几何驱动的框架，用于从无任务边界的非平稳数据流中学习。NC-TFAD冻结预训练骨干网络，将流特征对齐到单纯形等角紧框架（ETF）原型空间，以在非平稳流下稳定表示几何结构。为在无真实异常的情况下满足神经崩溃启发的几何构造，我们在训练过程中生成合成异常样本作为辅助锚点。基于该几何结构，我们进一步引入类间与类内正则化，结合焦点神经崩溃对比（FNCC）损失，以抑制表示漂移并提升正常-异常样本的可分性。最后，一个基于正常补丁原型的定位分支从正常训练样本构建校准的逐补丁偏差图，并与弱自注意力先验融合，生成无需像素级标注的异常热图。在MVTec AD和VisA数据集上的大量实验表明，在无任务持续学习协议下，NC-TFAD在图像级检测和像素级定位任务中，均优于从通用视觉方法适配而来的代表性无任务持续学习方法，以及统一异常检测基线。这些结果表明，几何驱动建模为现实工业应用中的无任务持续异常检测提供了有效且鲁棒的解决方案。
英文摘要
Recent years have witnessed growing interest in continual anomaly detection for industrial visual inspection. However, real-world manufacturing environments exhibit unpredictable shifts in data distributions, rendering task-dependent continual learning assumptions impractical. To address this limitation, we formulate industrial anomaly detection as a task-free continual learning problem and propose NC-TFAD, a neural-collapse-inspired, geometry-driven framework for learning from non-stationary data streams without task boundaries. NC-TFAD freezes a pretrained backbone and aligns streaming features to a simplex Equiangular Tight Frame (ETF) prototype space to stabilize representation geometry under non-stationary streams. To satisfy the NC-inspired geometric construction in the absence of real anomalies, we generate synthetic anomaly samples as auxiliary anchors during training. Building on this geometry, we further introduce inter- and intra-class regularization together with a Focal Neural Collapse Contrastive (FNCC) loss to suppress representation drift and improve normal-anomaly separability. Finally, a normal-patch-prototype-guided localization branch constructs calibrated patch-wise deviation maps from normal training samples and fuses them with a weak self-attention prior, producing anomaly heatmaps without pixel-level annotations. Extensive experiments on MVTec AD and VisA show that NC-TFAD consistently outperforms representative task-free continual learning methods adapted from general vision, as well as unified anomaly detection baselines, in both image-level detection and pixel-level localization under the task-free continual learning protocol. These results highlight that geometry-driven modeling offers an effective and robust solution for task-free continual anomaly detection in real-world industrial applications.
