Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classification
<|reference_start|>Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classification: With wearing masks becoming a new cultural norm, facial expression recognition (FER) while taking masks into account has become a significant challenge. In this paper, we propose a unified multi-branch vision transformer for facial expression recognition and mask wearing classification tasks. Our approach extracts shared features for both tasks using a dual-branch architecture that obtains multi-scale feature representations. Furthermore, we propose a cross-task fusion phase that processes tokens for each task with separate branches, while exchanging information using a cross attention module. Our proposed framework reduces the overall complexity compared with using separate networks for both tasks by the simple yet effective cross-task fusion phase. Extensive experiments demonstrate that our proposed model performs better than or on par with different state-of-the-art methods on both facial expression recognition and facial mask wearing classification task.<|reference_end|>
@article{zhu2024cross-task,
    title={Cross-Task Multi-Branch Vision Transformer for Facial Expression and
  Mask Wearing Classification},
    author={Armando Zhu, Keqin Li, Tong Wu, Peng Zhao, Bo Hong},
    journal={Journal of Computer Technology and Applied Mathematics, vol. 1,
  no. 1, Apr. 2024, pp. 46-53,},
    year={2024},
    doi={10.5281/zenodo.11083875},
    archivePrefix={arXiv},
    eprint={2404.14606},
    primaryClass={cs.CV cs.AI}
}