  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.
