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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2503.11115 |
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| _version_ | 1866912274292146176 |
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| author | Yu, Jun Zhang, Yunxiang Lu, Xilong Zheng, Yang Wang, Yongqi Zhu, Lingsi |
| author_facet | Yu, Jun Zhang, Yunxiang Lu, Xilong Zheng, Yang Wang, Yongqi Zhu, Lingsi |
| contents | In this report, we present our solution for the Action Unit (AU) Detection Challenge, in 8th Competition on Affective Behavior Analysis in-the-wild. In order to achieve robust and accurate classification of facial action unit in the wild environment, we introduce an innovative method that leverages audio-visual multimodal data. Our method employs ConvNeXt as the image encoder and uses Whisper to extract Mel spectrogram features. For these features, we utilize a Transformer encoder-based feature fusion module to integrate the affective information embedded in audio and image features. This ensures the provision of rich high-dimensional feature representations for the subsequent multilayer perceptron (MLP) trained on the Aff-Wild2 dataset, enhancing the accuracy of AU detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11115 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Solution for 8th Competition on Affective & Behavior Analysis in-the-wild Yu, Jun Zhang, Yunxiang Lu, Xilong Zheng, Yang Wang, Yongqi Zhu, Lingsi Computer Vision and Pattern Recognition In this report, we present our solution for the Action Unit (AU) Detection Challenge, in 8th Competition on Affective Behavior Analysis in-the-wild. In order to achieve robust and accurate classification of facial action unit in the wild environment, we introduce an innovative method that leverages audio-visual multimodal data. Our method employs ConvNeXt as the image encoder and uses Whisper to extract Mel spectrogram features. For these features, we utilize a Transformer encoder-based feature fusion module to integrate the affective information embedded in audio and image features. This ensures the provision of rich high-dimensional feature representations for the subsequent multilayer perceptron (MLP) trained on the Aff-Wild2 dataset, enhancing the accuracy of AU detection. |
| title | Solution for 8th Competition on Affective & Behavior Analysis in-the-wild |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.11115 |