This paper considers few-shot anomaly detection (FSAD), a practical yet under-studied setting for anomaly detection (AD), where only a limited number of normal images are provided for each category at training. So far, existing FSAD studies follow the one-model-per-category learning paradigm used for standard AD, and the inter-category commonality has not been explored. Inspired by how humans detect anomalies, i.e., comparing an image in question to normal images, we here leverage registration, an image alignment task that is inherently generalizable across categories, as the proxy task, to train a category-agnostic anomaly detection model. During testing, the anomalies are identified by comparing the registered features of the test image and its corresponding support (normal) images. As far as we know, this is the first FSAD method that trains a single generalizable model and requires no re-training or parameter fine-tuning for new categories. Experimental results have shown that the proposed method outperforms the state-of-the-art FSAD methods by 3%-8% in AUC on the MVTec and MPDD benchmarks.
翻译:本文考虑了几发异常点检测(FSAD),这是一个实际的、但研究不足的异常点检测(AD)环境,对异常点检测(AD)而言,培训中每个类别仅提供数量有限的正常图像。到目前为止,现有的FSAD研究遵循了标准AD使用的每类一模学习模式,而且没有探索类别间共性。受人类如何检测异常点(即将有关图像与正常图像进行比较)的启发,我们在这里利用了作为代理任务的图象登记,这一图像调整任务在类别之间必然可以普遍适用,以培训一个类别不可知异常点检测模型。测试期间,通过比较测试图像的登记特征及其相应的支持(正常)图像,确定了异常点。据我们所知,这是FSAD第一种方法,即培训一个单一通用模型,不需要对新类别进行再培训或参数微调。实验结果表明,拟议方法在MVTec和MPDD基准上比AUC的FSAD方法高出3%-8%。