论文标题
Tal Emotionet挑战2020重新考虑多任务学习中选择的模型问题
TAL EmotioNet Challenge 2020 Rethinking the Model Chosen Problem in Multi-Task Learning
论文作者
论文摘要
本文介绍了我们对2020年情绪挑战的方法。我们将au识别问题作为多任务学习问题，在该问题中，非韧性面部肌肉运动（主要是第一个17 AU）和刚性头运动（最后6个AUS）分别建模。探索了表达特征和头部姿势特征的同时出现。我们观察到不同的AUS以各种速度收敛。通过为每个AU选择最佳检查点，可以改善识别结果。我们能够在验证集中获得0.746的最终分数，在挑战的测试集中获得0.7306。
This paper introduces our approach to the EmotioNet Challenge 2020. We pose the AU recognition problem as a multi-task learning problem, where the non-rigid facial muscle motion (mainly the first 17 AUs) and the rigid head motion (the last 6 AUs) are modeled separately. The co-occurrence of the expression features and the head pose features are explored. We observe that different AUs converge at various speed. By choosing the optimal checkpoint for each AU, the recognition results are improved. We are able to obtain a final score of 0.746 in validation set and 0.7306 in the test set of the challenge.
