论文标题
在第四次ABAW挑战中的情感描述估计的多任务学习
Multi-Task Learning for Emotion Descriptors Estimation at the fourth ABAW Challenge
论文作者
论文摘要
面价/唤醒，表达和动作单元是面部情感分析中的相关任务。但是，由于各种收集的条件，这些任务仅在野外的性能有限。野外情感行为分析的第四次竞争（ABAW）提供了价值/唤醒，表达和动作单元标签的图像。在本文中，我们介绍了多任务学习框架，以提高野外三个相关任务的性能。功能共享和标签融合用于利用它们的关系。我们对提供的培训和验证数据进行实验。
Facial valence/arousal, expression and action unit are related tasks in facial affective analysis. However, the tasks only have limited performance in the wild due to the various collected conditions. The 4th competition on affective behavior analysis in the wild (ABAW) provided images with valence/arousal, expression and action unit labels. In this paper, we introduce multi-task learning framework to enhance the performance of three related tasks in the wild. Feature sharing and label fusion are used to utilize their relations. We conduct experiments on the provided training and validating data.
