Industrial image anomaly detection under the setting of one-class classification has significant practical value. However, most existing models struggle to extract separable feature representations when performing feature embedding and struggle to build compact descriptions of normal features when performing one-class classification. One direct consequence of this is that most models perform poorly in detecting logical anomalies which violate contextual relationships. Focusing on more effective and comprehensive anomaly detection, we propose a network based on self-supervised learning and self-attentive graph convolution (SLSG) for anomaly detection. SLSG uses a generative pre-training network to assist the encoder in learning the embedding of normal patterns and the reasoning of position relationships. Subsequently, SLSG introduces the pseudo-prior knowledge of anomaly through simulated abnormal samples. By comparing the simulated anomalies, SLSG can better summarize the normal features and narrow down the hypersphere used for one-class classification. In addition, with the construction of a more general graph structure, SLSG comprehensively models the dense and sparse relationships among elements in the image, which further strengthens the detection of logical anomalies. Extensive experiments on benchmark datasets show that SLSG achieves superior anomaly detection performance, demonstrating the effectiveness of our method.

翻译：在单分类设置下的工业图像异常检测具有重要的实际价值。然而，现有大多数模型在进行特征嵌入时难以提取可分离的特征表示，且在单分类过程中难以构建正常特征的紧致描述。这直接导致多数模型在检测违反上下文关系的逻辑异常时表现不佳。针对更有效且更全面的异常检测需求，我们提出一种基于自监督学习与自注意力图卷积的网络（SLSG）。SLSG利用生成式预训练网络辅助编码器学习正常模式的嵌入与位置关系的推理。随后，通过模拟异常样本引入异常的伪先验知识。通过对比模拟异常，SLSG能够更好总结正常特征并缩紧用于单分类的超球面。此外，通过构建更通用的图结构，SLSG全面建模图像元素间的密集与稀疏关系，进一步强化对逻辑异常的检测。在基准数据集上的大量实验表明，SLSG取得了优越的异常检测性能，验证了本方法的有效性。