  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.
