Multimodal Fusion Method with Spatiotemporal Sequences and Relationship Learning for Valence-Arousal Estimation

• URL: http://arxiv.org/abs/2403.12425v2
• Date: Wed, 20 Mar 2024 13:56:56 GMT
• Title: Multimodal Fusion Method with Spatiotemporal Sequences and Relationship Learning for Valence-Arousal Estimation
• Authors: Jun Yu, Gongpeng Zhao, Yongqi Wang, Zhihong Wei, Yang Zheng, Zerui Zhang, Zhongpeng Cai, Guochen Xie, Jichao Zhu, Wangyuan Zhu,
• Abstract summary: This paper presents our approach for the VA (Valence-Arousal) estimation task in the ABA6 competition. We devised a comprehensive model by preprocessing video frames and audio segments to extract visual and audio features. We employed a Transformer encoder structure to learn long-range dependencies, thereby enhancing the model's performance and generalization ability.
• Score: 9.93719767430551
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents our approach for the VA (Valence-Arousal) estimation task in the ABAW6 competition. We devised a comprehensive model by preprocessing video frames and audio segments to extract visual and audio features. Through the utilization of Temporal Convolutional Network (TCN) modules, we effectively captured the temporal and spatial correlations between these features. Subsequently, we employed a Transformer encoder structure to learn long-range dependencies, thereby enhancing the model's performance and generalization ability. Our method leverages a multimodal data fusion approach, integrating pre-trained audio and video backbones for feature extraction, followed by TCN-based spatiotemporal encoding and Transformer-based temporal information capture. Experimental results demonstrate the effectiveness of our approach, achieving competitive performance in VA estimation on the AffWild2 dataset.

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