HaloAE：一种基于HaloNet的局部Transformer自编码器，用于异常检测与定位
HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization
AI总结：
针对无监督异常检测与定位中全局自注意力计算成本过高的问题，提出首个基于HaloNet的局部2D Transformer自编码器HaloAE，结合卷积与分块自注意力实现联合检测与分割，在MVTec数据集上取得竞争力结果。
摘要：
无监督异常检测与定位是一项关键任务，因为收集并标注所有可能的异常是不现实的。许多研究强调了整合局部与全局信息以实现异常精准分割的重要性。为此，允许建模长距离内容交互的Transformer引起了越来越多的关注。然而，对于大多数图像尺度而言，通过自注意力机制进行全局交互通常计算成本过高。在本研究中，我们引入了HaloAE，这是首个基于带有HaloNet的局部2D版本Transformer的自编码器。借助HaloAE，我们创建了一个混合模型，它结合了卷积与局部2D分块自注意力层，并通过单一模型联合执行异常检测与分割。我们在MVTec数据集上取得了具有竞争力的结果，这表明融合Transformer的视觉模型能够从自注意力操作的局部计算中获益，并为其他应用铺平了道路。
英文摘要：
Unsupervised anomaly detection and localization is a crucial task as it is impossible to collect and label all possible anomalies. Many studies have emphasized the importance of integrating local and global information to achieve accurate segmentation of anomalies. To this end, there has been a growing interest in Transformer, which allows modeling long-range content interactions. However, global interactions through self attention are generally too expensive for most image scales. In this study, we introduce HaloAE, the first auto-encoder based on a local 2D version of Transformer with HaloNet. With HaloAE, we have created a hybrid model that combines convolution and local 2D block-wise self-attention layers and jointly performs anomaly detection and segmentation through a single model. We achieved competitive results on the MVTec dataset, suggesting that vision models incorporating Transformer could benefit from a local computation of the self-attention operation, and pave the way for other applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
