论文标题
从合成数据中学习：基于多任务网络合奏的面部表达分类
Learning from Synthetic Data: Facial Expression Classification based on Ensemble of Multi-task Networks
论文作者
论文摘要
野外表达对于各种交互式计算域至关重要。特别是，“从合成数据中学习”（LSD）是面部表达识别任务中的重要主题。在本文中，我们提出了一种基于多任务的面部表达识别方法，该方法由情感和外观学习分支组成，可以共享所有面部信息，并为第四次情感行为分析（ABAW）竞争中引入的LSD挑战提供初步结果。我们的方法达到的平均F1得分为0.71。
Facial expression in-the-wild is essential for various interactive computing domains. Especially, "Learning from Synthetic Data" (LSD) is an important topic in the facial expression recognition task. In this paper, we propose a multi-task learning-based facial expression recognition approach which consists of emotion and appearance learning branches that can share all face information, and present preliminary results for the LSD challenge introduced in the 4th affective behavior analysis in-the-wild (ABAW) competition. Our method achieved the mean F1 score of 0.71.
