Feature-Based Dual Visual Feature Extraction Model for Compound Multimodal Emotion Recognition
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908277511553024 |
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| author | Liu, Ran Zhang, Fengyu Yu, Cong Yang, Longjiang Wen, Zhuofan Zhang, Siyuan Yao, Hailiang Chen, Shun Lian, Zheng Liu, Bin |
| author_facet | Liu, Ran Zhang, Fengyu Yu, Cong Yang, Longjiang Wen, Zhuofan Zhang, Siyuan Yao, Hailiang Chen, Shun Lian, Zheng Liu, Bin |
| contents | This article presents our results for the eighth Affective Behavior Analysis in-the-wild (ABAW) competition.Multimodal emotion recognition (ER) has important applications in affective computing and human-computer interaction. However, in the real world, compound emotion recognition faces greater issues of uncertainty and modal conflicts. For the Compound Expression (CE) Recognition Challenge,this paper proposes a multimodal emotion recognition method that fuses the features of Vision Transformer (ViT) and Residual Network (ResNet). We conducted experiments on the C-EXPR-DB and MELD datasets. The results show that in scenarios with complex visual and audio cues (such as C-EXPR-DB), the model that fuses the features of ViT and ResNet exhibits superior performance.Our code are avalible on https://github.com/MyGitHub-ax/8th_ABAW |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17453 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Feature-Based Dual Visual Feature Extraction Model for Compound Multimodal Emotion Recognition Liu, Ran Zhang, Fengyu Yu, Cong Yang, Longjiang Wen, Zhuofan Zhang, Siyuan Yao, Hailiang Chen, Shun Lian, Zheng Liu, Bin Computer Vision and Pattern Recognition This article presents our results for the eighth Affective Behavior Analysis in-the-wild (ABAW) competition.Multimodal emotion recognition (ER) has important applications in affective computing and human-computer interaction. However, in the real world, compound emotion recognition faces greater issues of uncertainty and modal conflicts. For the Compound Expression (CE) Recognition Challenge,this paper proposes a multimodal emotion recognition method that fuses the features of Vision Transformer (ViT) and Residual Network (ResNet). We conducted experiments on the C-EXPR-DB and MELD datasets. The results show that in scenarios with complex visual and audio cues (such as C-EXPR-DB), the model that fuses the features of ViT and ResNet exhibits superior performance.Our code are avalible on https://github.com/MyGitHub-ax/8th_ABAW |
| title | Feature-Based Dual Visual Feature Extraction Model for Compound Multimodal Emotion Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.17453 |