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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.12258 |
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| _version_ | 1866910543018721280 |
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| author | Shen, Kang Liu, Xuxiong Wang, Boyan Yao, Jun Liu, Xin Guan, Yujie Wang, Yu Li, Gengchen Sun, Xiao |
| author_facet | Shen, Kang Liu, Xuxiong Wang, Boyan Yao, Jun Liu, Xin Guan, Yujie Wang, Yu Li, Gengchen Sun, Xiao |
| contents | In this paper, we present our approach to addressing the challenges of the 7th ABAW competition. The competition comprises three sub-challenges: Valence Arousal (VA) estimation, Expression (Expr) classification, and Action Unit (AU) detection. To tackle these challenges, we employ state-of-the-art models to extract powerful visual features. Subsequently, a Transformer Encoder is utilized to integrate these features for the VA, Expr, and AU sub-challenges. To mitigate the impact of varying feature dimensions, we introduce an affine module to align the features to a common dimension. Overall, our results significantly outperform the baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12258 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Facial Affect Recognition based on Multi Architecture Encoder and Feature Fusion for the ABAW7 Challenge Shen, Kang Liu, Xuxiong Wang, Boyan Yao, Jun Liu, Xin Guan, Yujie Wang, Yu Li, Gengchen Sun, Xiao Computer Vision and Pattern Recognition In this paper, we present our approach to addressing the challenges of the 7th ABAW competition. The competition comprises three sub-challenges: Valence Arousal (VA) estimation, Expression (Expr) classification, and Action Unit (AU) detection. To tackle these challenges, we employ state-of-the-art models to extract powerful visual features. Subsequently, a Transformer Encoder is utilized to integrate these features for the VA, Expr, and AU sub-challenges. To mitigate the impact of varying feature dimensions, we introduce an affine module to align the features to a common dimension. Overall, our results significantly outperform the baselines. |
| title | Facial Affect Recognition based on Multi Architecture Encoder and Feature Fusion for the ABAW7 Challenge |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.12258 |