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.

翻译：面部情绪识别、年龄和性别识别等不同类别的行为分析是一个广泛的主题。许多研究侧重于个人任务,而多任务学习方法仍然是一个开放式研究问题,需要开展更多的研究。本文介绍了我们应对 " 多任务学习 " 挑战的解决方案和实验结果。挑战包括三项任务:行动单位检测、面部表情识别和价值激励估计。为了应对这一挑战,我们引入了一个交叉强化模块,以改善多任务学习绩效。此外,还应用了一张面部图来捕捉各行动单位之间的关联。结果,我们实现了对组织者提供的验证数据的128.8的评价尺度,这超过了30项基线结果。