Uncertainty-aware Label Distribution Learning for Facial Expression Recognition. (arXiv:2209.10448v1 [cs.CV])

Despite significant progress over the past few years, ambiguity is still a
key challenge in Facial Expression Recognition (FER). It can lead to noisy and
inconsistent annotation, which hinders the performance of deep learning models
in real-world scenarios. In this paper, we propose a new uncertainty-aware
label distribution learning method to improve the robustness of deep models
against uncertainty and ambiguity. We leverage neighborhood information in the
valence-arousal space to adaptively construct emotion distributions for
training samples. We also consider …

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