Facial expression recognition (FER) plays a significant role in our daily
life. However, annotation ambiguity in the datasets could greatly hinder the
performance. In this paper, we address FER task via label distribution learning
paradigm, and develop a dual-branch Adaptive Distribution Fusion (Ada-DF)
framework. One auxiliary branch is constructed to obtain the label
distributions of samples. The class distributions of emotions are then computed
through the label distributions of each emotion. Finally, those two
distributions are adaptively fused according to the attention weights to train
the target branch. Extensive experiments are conducted on three real-world
datasets, RAF-DB, AffectNet and SFEW, where our Ada-DF shows advantages over
the state-of-the-art works.

通过标签分布学习范例和双分支的自适应分布融合框架，以应对日常生活中面部表情识别（FER）任务中数据集中的注释模糊性，并通过辅助分支获得样本的标签分布，计算每种情绪的类别分布，然后根据注意权重自适应地融合这两个分布来训练目标分支，在 RAF-DB、AffectNet 和 SFEW 三个真实世界数据集上进行的大量实验证明了我们的 Ada-DF 相对于最先进的工作的优势。