论文标题
通过辅助作用单元图进行不确定的标签校正，以识别面部表达
Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial Expression Recognition
论文作者
论文摘要
高质量的注释图像对于深层面部表达识别（FER）方法很重要。但是，不确定的标签（主要存在于大型公共数据集中）通常会误导培训过程。在本文中，我们使用称为ULC-AG的辅助作用单元（AU）图实现了不确定的标签校正面部表情。具体而言，引入了加权正规化模块，以突出显示有效的样本并抑制每个批次中的类别失衡。基于情绪和AUS之间的潜在依赖性，添加了使用图卷积层的辅助分支来从图形拓扑中提取语义信息。最后，重新标记的策略通过将其特征相似性与语义模板进行比较来纠正模棱两可的注释。实验表明，我们的ULC-AG分别在RAF-DB和AfternNet数据集上达到了89.31％和61.57％的精度，表现优于基线和最新方法。
High-quality annotated images are significant to deep facial expression recognition (FER) methods. However, uncertain labels, mostly existing in large-scale public datasets, often mislead the training process. In this paper, we achieve uncertain label correction of facial expressions using auxiliary action unit (AU) graphs, called ULC-AG. Specifically, a weighted regularization module is introduced to highlight valid samples and suppress category imbalance in every batch. Based on the latent dependency between emotions and AUs, an auxiliary branch using graph convolutional layers is added to extract the semantic information from graph topologies. Finally, a re-labeling strategy corrects the ambiguous annotations by comparing their feature similarities with semantic templates. Experiments show that our ULC-AG achieves 89.31% and 61.57% accuracy on RAF-DB and AffectNet datasets, respectively, outperforming the baseline and state-of-the-art methods.
