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.

本文提出了一种名为DMUE的方法来解决面部表情识别中令人困惑的注释模糊性问题，它从潜在分布挖掘和成对不确定性估计两个角度入手，引入了一个辅助的多分支学习框架来更好地挖掘和描述标签空间中的潜在分布，并充分利用不同实例之间的语义特征对成对模糊程度进行评估，而且该方法不依赖于主干架构，并且不会增加额外的推演负担。在流行的真实世界基准测试和合成嘈杂数据集上，DMUE稳定地实现了领先的性能。

深入研究模糊性：面部表情识别中的潜在分布挖掘与成对不确定性估计