论文标题
在ABAW4挑战中，用于面部影响识别的混合CNN转换模型
Hybrid CNN-Transformer Model For Facial Affect Recognition In the ABAW4 Challenge
论文作者
论文摘要
本文描述了我们对第四个情感行为分析（ABAW）竞争的提交。我们提出了一个用于多任务学习（MTL）的混合CNN转换模型，并从合成数据（LSD）任务中学习。验证数据集的实验结果表明，我们的方法的性能要比基线模型更好，这证实了拟议网络的有效性。
This paper describes our submission to the fourth Affective Behavior Analysis (ABAW) competition. We proposed a hybrid CNN-Transformer model for the Multi-Task-Learning (MTL) and Learning from Synthetic Data (LSD) task. Experimental results on validation dataset shows that our method achieves better performance than baseline model, which verifies that the effectiveness of proposed network.
