Facial expression recognition (FER) has received considerable attention in computer vision, with "in-the-wild" environments such as human-computer interaction. However, FER images contain uncertainties such as occlusion, low resolution, pose variation, illumination variation, and subjectivity, which includes some expressions that do not match the target label. Consequently, little information is obtained from a noisy single image and it is not trusted. This could significantly degrade the performance of the FER task. To address this issue, we propose a batch transformer (BT), which consists of the proposed class batch attention (CBA) module, to prevent overfitting in noisy data and extract trustworthy information by training on features reflected from several images in a batch, rather than information from a single image. We also propose multi-level attention (MLA) to prevent overfitting the specific features by capturing correlations between each level. In this paper, we present a batch transformer network (BTN) that combines the above proposals. Experimental results on various FER benchmark datasets show that the proposed BTN consistently outperforms the state-ofthe-art in FER datasets. Representative results demonstrate the promise of the proposed BTN for FER.

提出了一种批处理转换器网络（BTN），它包含了类批处理注意（CBA）模块和多级注意（MLA）模块来提取可信赖信息并防止过拟合，在各种表情识别基准数据集上实验结果显示，BTN相对于FER领域的最新成果有着持续的优越表现。

批量Transformer：在批次中寻找注意力