论文标题
多任务情绪识别不完整的标签
Multitask Emotion Recognition with Incomplete Labels
论文作者
论文摘要
我们训练一个统一模型执行三个任务：面部动作单元检测，表达分类和价值估计。我们解决了学习这三个任务的两个主要挑战。首先，大多数现有的数据集高度不平衡。其次，大多数现有的数据集不包含所有三个任务的标签。为了应对第一个挑战，我们将数据平衡技术应用于实验数据集。为了应对第二个挑战，我们为多任务模型提出了一种算法，以从缺失（不完整）标签中学习。该算法有两个步骤。我们首先训练一个教师模型执行所有三个任务，每个实例都受其相应任务的地面真相标签培训。其次，我们将教师模型的输出称为软标签。我们使用软标签和地面真相来训练学生模型。我们发现，大多数学生模型在所有三个任务上都优于他们的教师模型。最后，我们使用模型结合来进一步提高三个任务的性能。
We train a unified model to perform three tasks: facial action unit detection, expression classification, and valence-arousal estimation. We address two main challenges of learning the three tasks. First, most existing datasets are highly imbalanced. Second, most existing datasets do not contain labels for all three tasks. To tackle the first challenge, we apply data balancing techniques to experimental datasets. To tackle the second challenge, we propose an algorithm for the multitask model to learn from missing (incomplete) labels. This algorithm has two steps. We first train a teacher model to perform all three tasks, where each instance is trained by the ground truth label of its corresponding task. Secondly, we refer to the outputs of the teacher model as the soft labels. We use the soft labels and the ground truth to train the student model. We find that most of the student models outperform their teacher model on all the three tasks. Finally, we use model ensembling to boost performance further on the three tasks.
