  Due to the subjective annotation and the inherent interclass similarity of
facial expressions, one of key challenges in Facial Expression Recognition
(FER) is the annotation ambiguity. In this paper, we proposes a solution, named
DMUE, to address the problem of annotation ambiguity from two perspectives: the
latent Distribution Mining and the pairwise Uncertainty Estimation. For the
former, an auxiliary multi-branch learning framework is introduced to better
mine and describe the latent distribution in the label space. For the latter,
the pairwise relationship of semantic feature between instances are fully
exploited to estimate the ambiguity extent in the instance space. The proposed
method is independent to the backbone architectures, and brings no extra burden
for inference. The experiments are conducted on the popular real-world
benchmarks and the synthetic noisy datasets. Either way, the proposed DMUE
stably achieves leading performance.
