论文标题
在第三ABAW竞赛中基于注意的动作单元检测方法
An Attention-based Method for Action Unit Detection at the 3rd ABAW Competition
论文作者
论文摘要
面部动作编码系统是建模人类情感表达复杂性的一种方法。自动动作单元（AU）检测是人类计算机相互作用的关键研究领域。本文描述了我们对2022年野外野外行为分析（ABAW）竞赛的提交。我们提出了一种检测视频中面部动作单元的方法。在第一阶段，使用基于CNN的轻质功能提取器来从每个视频框架中提取特征图。然后，应用注意模块来完善注意力图。注意编码的向量是使用特征图的加权和后来得出的。最后，在输出层使用Sigmoid函数，以使预测适用于多标签AUS检测。我们在ABAW挑战验证集中达到了0.48的宏F1得分，而基线模型的宏观得分为0.39。
Facial Action Coding System is an approach for modeling the complexity of human emotional expression. Automatic action unit (AU) detection is a crucial research area in human-computer interaction. This paper describes our submission to the third Affective Behavior Analysis in-the-wild (ABAW) competition 2022. We proposed a method for detecting facial action units in the video. At the first stage, a lightweight CNN-based feature extractor is employed to extract the feature map from each video frame. Then, an attention module is applied to refine the attention map. The attention encoded vector is derived using a weighted sum of the feature map and the attention scores later. Finally, the sigmoid function is used at the output layer to make the prediction suitable for multi-label AUs detection. We achieved a macro F1 score of 0.48 on the ABAW challenge validation set compared to 0.39 from the baseline model.
