Log In Sign Up

Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition

by   Jiahui She, et al.

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.


page 7

page 13

page 14

page 15


Uncertainty-aware Label Distribution Learning for Facial Expression Recognition

Despite significant progress over the past few years, ambiguity is still...

Rethinking the Learning Paradigm for Facial Expression Recognition

Due to the subjective crowdsourcing annotations and the inherent inter-c...

Mid-level Representation Enhancement and Graph Embedded Uncertainty Suppressing for Facial Expression Recognition

Facial expression is an essential factor in conveying human emotional st...

Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition

Noisy label Facial Expression Recognition (FER) is more challenging than...

Understanding and Mitigating Annotation Bias in Facial Expression Recognition

The performance of a computer vision model depends on the size and quali...

Dynamic Adaptive Threshold based Learning for Noisy Annotations Robust Facial Expression Recognition

The real-world facial expression recognition (FER) datasets suffer from ...

Annotation Uncertainty in the Context of Grammatical Change

This paper elaborates on the notion of uncertainty in the context of ann...