论文标题
面部影响分析：从合成数据和多任务学习挑战中学习
Facial Affect Analysis: Learning from Synthetic Data & Multi-Task Learning Challenges
论文作者
论文摘要
面部影响分析仍然是一项艰巨的任务，其设置从实验室控制到野外情况。在本文中，我们提出了新的框架，以应对第四次情感行为分析（ABAW）竞争：i）多任务学习（MTL）挑战和II）从合成数据（LSD）挑战中学习。对于MTL挑战，我们采用了具有更好的特征向量策略的SMM-EmotionNet。对于LSD挑战，我们建议采用各自的方法来应对单个标签，不平衡分布，微调限制和模型体系结构的选择的问题。竞争的官方验证集的实验结果表明，我们提出的方法的表现优于基线。该代码可在https://github.com/sylyoung/abaw4-hust-ant上找到。
Facial affect analysis remains a challenging task with its setting transitioned from lab-controlled to in-the-wild situations. In this paper, we present novel frameworks to handle the two challenges in the 4th Affective Behavior Analysis In-The-Wild (ABAW) competition: i) Multi-Task-Learning (MTL) Challenge and ii) Learning from Synthetic Data (LSD) Challenge. For MTL challenge, we adopt the SMM-EmotionNet with a better ensemble strategy of feature vectors. For LSD challenge, we propose respective methods to combat the problems of single labels, imbalanced distribution, fine-tuning limitations, and choice of model architectures. Experimental results on the official validation sets from the competition demonstrated that our proposed approaches outperformed baselines by a large margin. The code is available at https://github.com/sylyoung/ABAW4-HUST-ANT.
