用注意力机制+新激活函数提升人脸表情识别准确率
Emotion Recognition with Facial Attention and Objective Activation Functions
- 引入通道与空间注意力模块增强特征提取
- 结合新激活函数使模型性能进一步提升
- 适合关注图像识别优化与情绪分析的研究者
本文研究在基于卷积神经网络的面部表情识别模型（如VGGNet、ResNet、ResNetV2）中引入通道注意力（SE-Net）、高效通道注意力（ECA-Net）和协同注意力（CBAM）机制的效果。实验表明，注意力机制能显著提升模型表现，且与新型激活函数结合后，性能进一步提高。该方法为提升视觉模型在情感识别任务中的表现提供了有效路径。
原文摘要 · Abstract (English)
In this paper, we study the effect of introducing channel and spatial attention mechanisms, namely SEN-Net, ECA-Net, and CBAM, to existing CNN vision-based models such as VGGNet, ResNet, and ResNetV2 to perform the Facial Emotion Recognition task. We show that not only attention can significantly improve the performance of these models but also that combining them with a different activation function can further help increase the performance of these models.
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