Affective Behavior Analysis is an important part in human-computer interaction. Existing multi-task affective behavior recognition methods suffer from the problem of incomplete labeled datasets. To tackle this problem, this paper presents a semi-supervised model with a mean teacher framework to leverage additional unlabeled data. To be specific, a multi-task model is proposed to learn three different kinds of facial affective representations simultaneously. After that, the proposed model is assigned to be student and teacher networks. When training with unlabeled data, the teacher network is employed to predict pseudo labels for student network training, which allows it to learn from unlabeled data. Experimental results showed that our proposed method achieved much better performance than baseline model and ranked 4th in both competition track 1 and track 2, and 6th in track 3, which verifies that the proposed network can effectively learn from incomplete datasets.

翻译：视觉行为分析是人与计算机互动的一个重要部分。 现有的多任务感知行为识别方法存在标签不全的数据集问题。 为了解决这一问题,本文件提出了一个半监督模型, 其教师框架为利用额外的无标签数据提供了平均的教师框架。 具体地说, 提议一个多任务模型同时学习三种不同的面部感知表情。 在此之后, 拟议的模型被指定为学生和教师网络。 在使用无标签数据进行培训时, 教师网络被用来预测学生网络培训的假标签, 从而使其能够从未标签数据中学习。 实验结果显示,我们提出的方法取得了比基线模型更好的业绩, 在竞争轨道1和轨道2排在第4位, 在轨道3排在第6位, 这证实了拟议网络能够有效地从不完整的数据集中学习。