论文标题
具有知识蒸馏和任务歧视者的多任务情感识别模型
Multitask Emotion Recognition Model with Knowledge Distillation and Task Discriminator
论文作者
论文摘要
由于收集了大数据和深度学习的发展，因此正在积极进行预测野外人类情绪的研究。我们设计了一个使用ABAW数据集的多任务模型，通过在现实世界中的音频数据和面对图像来预测价值，表达和动作单元。我们通过应用知识蒸馏技术从不完整的标签中训练了模型。教师模型是作为有监督的学习方法培训的，学生模型通过使用教师模型的输出作为软标签进行培训。结果，我们在多任务学习任务验证数据集中实现了2.40。
Due to the collection of big data and the development of deep learning, research to predict human emotions in the wild is being actively conducted. We designed a multi-task model using ABAW dataset to predict valence-arousal, expression, and action unit through audio data and face images at in real world. We trained model from the incomplete label by applying the knowledge distillation technique. The teacher model was trained as a supervised learning method, and the student model was trained by using the output of the teacher model as a soft label. As a result we achieved 2.40 in Multi Task Learning task validation dataset.
