论文标题
ABAW4多任务挑战的两种环境信息融合模型
Two-Aspect Information Fusion Model For ABAW4 Multi-task Challenge
论文作者
论文摘要
在本文中，我们提出了第四次情感行为分析（ABAW）竞争的多任务学习（MTL）挑战的解决方案。 ABAW的任务是从视频中预测框架级的情感描述：离散的情绪状态；价和唤醒；和行动单位。尽管研究人员提出了几种方法，并在ABAW中取得了有希望的结果，但目前在此任务中的作品很少考虑不同的情感描述符之间的相互作用。为此，我们提出了一种新颖的端到端体系结构，以实现不同类型的信息的完整集成。实验结果证明了我们提出的解决方案的有效性。
In this paper, we propose the solution to the Multi-Task Learning (MTL) Challenge of the 4th Affective Behavior Analysis in-the-wild (ABAW) competition. The task of ABAW is to predict frame-level emotion descriptors from videos: discrete emotional state; valence and arousal; and action units. Although researchers have proposed several approaches and achieved promising results in ABAW, current works in this task rarely consider interactions between different emotion descriptors. To this end, we propose a novel end to end architecture to achieve full integration of different types of information. Experimental results demonstrate the effectiveness of our proposed solution.
