We present a facial emotion recognition framework, built upon Swin vision Transformers jointly with squeeze and excitation block (SE). A transformer model based on an attention mechanism has been presented recently to address vision tasks. Our method uses a vision transformer with a Squeeze excitation block (SE) and sharpness-aware minimizer (SAM). We have used a hybrid dataset, to train our model and the AffectNet dataset to evaluate the result of our model

翻译：我们提出了一种基于Swin视觉Transformer与压缩激励模块（SE）相结合的面部情绪识别框架。近期，基于注意力机制的Transformer模型已被用于解决视觉任务。我们的方法采用视觉Transformer，并融合了压缩激励模块（SE）和锐度感知最小化器（SAM）。我们使用混合数据集对模型进行训练，并采用AffectNet数据集评估模型性能。