论文标题
使用变压器的多模式多标签面部动作单元检测
Multi-modal Multi-label Facial Action Unit Detection with Transformer
论文作者
论文摘要
面部动作编码系统是面部表达分析的重要方法。本文描述了我们对第三个情感行为分析（ABAW）2022竞争的提交。我们提出了一个基于转媒体的模型，以检测视频中的面部动作单元（FAU）。具体来说，我们首先训练了一个多模式模型来提取音频和视觉功能。之后，我们提出了一个动作单元相关模块，以了解每个动作单元标签与完善动作单元检测结果之间的关系。验证数据集的实验结果表明，我们的方法的性能要比基线模型更好，这证实了拟议网络的有效性。
Facial Action Coding System is an important approach of facial expression analysis.This paper describes our submission to the third Affective Behavior Analysis (ABAW) 2022 competition. We proposed a transfomer based model to detect facial action unit (FAU) in video. To be specific, we firstly trained a multi-modal model to extract both audio and visual feature. After that, we proposed a action units correlation module to learn relationships between each action unit labels and refine action unit detection result. Experimental results on validation dataset shows that our method achieves better performance than baseline model, which verifies that the effectiveness of proposed network.
