论文标题
情感表达分析使用多任务时间统计深度学习模型在野外
Affective Expression Analysis in-the-wild using Multi-Task Temporal Statistical Deep Learning Model
论文作者
论文摘要
情感行为分析在人类计算机互动，客户营销，健康监测中起着重要作用。 Abaw Challenge and Aff-Wild2数据集提出了在野外环境下对基本情绪和回归价值估计值进行分类的新挑战。在本文中，我们提出了一种情感表达分析模型，该模型应对上述挑战。我们的方法包括用于再次调整面部特征模型的统计和时间模块。我们在AFF-WILD2数据集上进行了实验，这是一个大规模数据集，用于ABAW挑战，并具有分类和价值情感的注释。我们在验证集上实现了表达得分0.543和价值分数0.534。
Affective behavior analysis plays an important role in human-computer interaction, customer marketing, health monitoring. ABAW Challenge and Aff-Wild2 dataset raise the new challenge for classifying basic emotions and regression valence-arousal value under in-the-wild environments. In this paper, we present an affective expression analysis model that deals with the above challenges. Our approach includes STAT and Temporal Module for fine-tuning again face feature model. We experimented on Aff-Wild2 dataset, a large-scale dataset for ABAW Challenge with the annotations for both the categorical and valence-arousal emotion. We achieved the expression score 0.543 and valence-arousal score 0.534 on the validation set.
