论文标题
情感行为分析使用动作单位关系图和多任务交叉注意
Affective Behavior Analysis using Action Unit Relation Graph and Multi-task Cross Attention
论文作者
论文摘要
面部行为分析是一个广泛的话题，具有各种类别，例如面部情绪识别，年龄和性别认识。许多研究集中在单个任务上，而多任务学习方法仍然是一个开放的研究问题，需要更多的研究。在本文中，我们为情感行为分析内竞争的多任务学习挑战提供了解决方案和实验结果。挑战是三个任务的组合：动作单位检测，面部表达识别和偶像 - 公开估计。为了应对这一挑战，我们引入了一个跨指导模块，以提高多任务学习绩效。此外，还采用面部图来捕获动作单元之间的关联。结果，我们在组织者提供的验证数据上实现了128.8的评估措施，这表现优于30的基线结果。
Facial behavior analysis is a broad topic with various categories such as facial emotion recognition, age, and gender recognition. Many studies focus on individual tasks while the multi-task learning approach is still an open research issue and requires more research. In this paper, we present our solution and experiment result for the Multi-Task Learning challenge of the Affective Behavior Analysis in-the-wild competition. The challenge is a combination of three tasks: action unit detection, facial expression recognition, and valance-arousal estimation. To address this challenge, we introduce a cross-attentive module to improve multi-task learning performance. Additionally, a facial graph is applied to capture the association among action units. As a result, we achieve the evaluation measure of 128.8 on the validation data provided by the organizers, which outperforms the baseline result of 30.
