Human affective behavior analysis plays a vital role in human-computer interaction (HCI) systems. In this paper, we introduce our submission to the CVPR 2023 Competition on Affective Behavior Analysis in-the-wild (ABAW). We propose a single-stage trained AU detection framework. Specifically, in order to effectively extract facial local region features related to AU detection, we use a local region perception module to effectively extract features of different AUs. Meanwhile, we use a graph neural network-based relational learning module to capture the relationship between AUs. In addition, considering the role of the overall feature of the target face on AU detection, we also use the feature fusion module to fuse the feature information extracted by the backbone network and the AU feature information extracted by the relationship learning module. We also adopted some sampling methods, data augmentation techniques and post-processing strategies to further improve the performance of the model.

翻译：人类情感行为分析在人机交互（HCI）系统中起着至关重要的作用。本文介绍了我们提交至CVPR 2023野外情感行为分析竞赛（ABAW）的工作。我们提出了一种单阶段训练的动作单元（AU）检测框架。具体而言，为有效提取与AU检测相关的面部局部区域特征，我们采用局部区域感知模块对不同AU的特征进行高效提取。同时，我们利用基于图神经网络的关系学习模块来捕捉AU之间的关联关系。此外，考虑到目标面部整体特征对AU检测的作用，我们还通过特征融合模块将主干网络提取的特征信息与关系学习模块提取的AU特征信息进行融合。我们还采用了一些采样方法、数据增强技术及后处理策略，以进一步提升模型性能。