Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classification
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909184846462976 |
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| author | Zhu, Armando Li, Keqin Wu, Tong Zhao, Peng Hong, Bo |
| author_facet | Zhu, Armando Li, Keqin Wu, Tong Zhao, Peng Hong, Bo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14606 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classification Zhu, Armando Li, Keqin Wu, Tong Zhao, Peng Hong, Bo Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classification |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2404.14606 |