计算机科学
稳健性(进化)
模式识别(心理学)
人工智能
正确性
特征(语言学)
基本事实
特征提取
面部表情
利用
机器学习
算法
生物化学
化学
语言学
哲学
计算机安全
基因
作者
Hao Sun,Chenchen Pi,Wei Xie
标识
DOI:10.1109/icme55011.2023.00048
摘要
Pseudo-labels are popular in semi-supervised facial expression recognition. Recent methods usually exploit the confidence as the criterion for pseudo-label generation, and utilize the high-confidence pseudo-labels as the ground-truth for training. However, high confidence cannot guarantee the correctness of pseudo-labels. False pseudo-labels can weaken the feature discrimination and degrade recognition performance. In this paper, we propose a Critical Feature Refinement Network (CFRN) to alleviate the interference of false pseudo-labels on the model performance. Specially, a feature dropout module and a feature emphasis module are proposed to improve the feature discrimination of CFRN. Then, a mean-absolute error loss is further exploited to improve the robustness against false pseudo-labels. Experimental results on three challenging datasets RAF-DB, SFEW and Affectnet demonstrate that the proposed CFRN outperforms the state-of-the-art methods.
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