Facial Expression Recognition (FER) plays a crucial role in computer vision
and finds extensive applications across various fields. This paper aims to
present our approach for the upcoming 6th Affective Behavior Analysis
in-the-Wild (ABAW) competition, scheduled to be held at CVPR2024.. In the
facial expression recognition task, The limited size of the FER dataset poses a
challenge to the expression recognition model's generalization ability,
resulting in subpar recognition performance. To address this problem, we employ
a semi-supervised learning technique to generate expression category
pseudo-labels for unlabeled face data. At the same time, we uniformly sampled
the labeled facial expression samples and implemented a debiased feedback
learning strategy to address the problem of category imbalance in the dataset
and the possible data bias in semi-supervised learning. Moreover, , to further
compensate for the limitation and bias of features obtained only from static
images, we introduced a Temporal Encoder to learn and capture temporal
relationships between neighboring expression image features. In the 6th ABAW
competition, our method achieved outstanding results on the official validation
set, a result that fully confirms the effectiveness and competitiveness of our
proposed method.

我们提出了一种半监督学习技术来生成未标记面部数据的表情类别伪标签以解决有限 FER 数据集的泛化能力问题，并采用均匀抽样和去偏反馈学习策略来应对数据集中的类别不平衡问题和半监督学习中的数据偏差问题。此外，引入了时间编码器来学习和捕捉静态图像之间的临近表情特征的时间关系，并在第 6 届 ABAW 竞赛中在官方验证集上取得了优秀的成绩，充分证实了我们提出方法的有效性和竞争力。