Ada-DF: An Adaptive Label Distribution Fusion Network For Facial Expression Recognition
arxiv(2024)
摘要
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.
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