Exploring Facial Expression Recognition through Semi-Supervised Pretraining and Temporal Modeling
<|reference_start|>Exploring Facial Expression Recognition through Semi-Supervised Pretraining and Temporal Modeling: 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 neighbouring 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.<|reference_end|>
@article{yu2024exploring,
    title={Exploring Facial Expression Recognition through Semi-Supervised
  Pretraining and Temporal Modeling},
    author={Jun Yu, Zhihong Wei, Zhongpeng Cai, Gongpeng Zhao, Zerui Zhang, Yongqi
  Wang, Guochen Xie, Jichao Zhu, Wangyuan Zhu},
    journal={arXiv preprint arXiv:2403.11942},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11942},
    primaryClass={cs.CV cs.AI}
}