论文标题
带有多模型的面部动作单元识别
Facial Action Unit Recognition With Multi-models Ensembling
论文作者
论文摘要
情感行为分析（ABAW）2022竞争使情感计算具有较大的促销。在本文中，我们在这项比赛中介绍了我们对au挑战的方法。我们将改进的IRESNET100用作骨干。然后，我们分别由私有AU和表达数据集和GLINT360K预测的三个相关模型训练AU数据集。最后，我们整合了模型的结果。我们在AU验证集上实现了F1分数（宏）0.731。
The Affective Behavior Analysis in-the-wild (ABAW) 2022 Competition gives Affective Computing a large promotion. In this paper, we present our method of AU challenge in this Competition. We use improved IResnet100 as backbone. Then we train AU dataset in Aff-Wild2 on three pertained models pretrained by our private au and expression dataset, and Glint360K respectively. Finally, we ensemble the results of our models. We achieved F1 score (macro) 0.731 on AU validation set.
