论文标题
域中纹理表面异常检测
Domain-Generalized Textured Surface Anomaly Detection
论文作者
论文摘要
异常检测旨在识别异常数据与正常数据偏离，同时通常需要足够数量的正常数据来训练模型执行此任务。尽管最近的异常检测方法取得了成功，但在看不见的域中进行异常检测仍然是一项艰巨的任务。在本文中，我们介绍了域将纹理的表面异常检测的任务。通过观察到跨多个源域的正常表面数据，我们的模型有望被推广到未见的纹理质感表面，其中在测试过程中只能观察到少数正常数据。尽管只有在训练数据中观察到的图像级标签，但我们的基于斑块的元学习模型具有有希望的概括能力：它不仅可以推广到看不见的图像域，而且还可以在查询图像中定位异常区域。我们的实验验证了我们的模型在各种设置中的最新异常检测和域泛化方法方面表现出色。
Anomaly detection aims to identify abnormal data that deviates from the normal ones, while typically requiring a sufficient amount of normal data to train the model for performing this task. Despite the success of recent anomaly detection methods, performing anomaly detection in an unseen domain remain a challenging task. In this paper, we address the task of domain-generalized textured surface anomaly detection. By observing normal and abnormal surface data across multiple source domains, our model is expected to be generalized to an unseen textured surface of interest, in which only a small number of normal data can be observed during testing. Although with only image-level labels observed in the training data, our patch-based meta-learning model exhibits promising generalization ability: not only can it generalize to unseen image domains, but it can also localize abnormal regions in the query image. Our experiments verify that our model performs favorably against state-of-the-art anomaly detection and domain generalization approaches in various settings.
