用截断ConvNeXt+自注意力提升表情识别准确率
A Visual Self-attention Mechanism Facial Expression Recognition Network beyond Convnext
- 截断ConvNeXt提取特征，搭配细节提取块
- 在RAF-DB和FERPlus上达最新水平
- 适合处理相似表情与数据不均衡场景
面部表情识别是人工智能领域的重要方向。尽管近年取得进展，但数据集分布不均、不同表情间相似度高、同一表情在不同个体间差异大等问题仍存在。本文提出基于截断ConvNeXt（Conv-cut）的视觉表情信号特征处理网络，以提升复杂条件下的识别准确率。网络采用截断的ConvNeXt-Base作为特征提取器，设计细节提取块捕捉细微特征，并引入自注意力机制增强特征学习能力。在RAF-DB和FERPlus数据集上的实验表明，该模型达到当前最优性能。代码已开源。
原文摘要 · Abstract (English)
Facial expression recognition is an important research direction in the field of artificial intelligence. Although new breakthroughs have been made in recent years, the uneven distribution of datasets and the similarity between different categories of facial expressions, as well as the differences within the same category among different subjects, remain challenges. This paper proposes a visual facial expression signal feature processing network based on truncated ConvNeXt approach(Conv-cut), to improve the accuracy of FER under challenging conditions. The network uses a truncated ConvNeXt-Base as the feature extractor, and then we designed a Detail Extraction Block to extract detailed features, and introduced a Self-Attention mechanism to enable the network to learn the extracted features more effectively. To evaluate the proposed Conv-cut approach, we conducted experiments on the RAF-DB and FERPlus datasets, and the results show that our model has achieved state-of-the-art performance. Our code could be accessed at Github.
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