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Article | Open Access

TACFN: Transformer-Based Adaptive Cross-Modal Fusion Network for Multimodal Emotion Recognition

Feng Liu1( ), Ziwang Fu2, Yunlong Wang3, Qijian Zheng1
School of Computer Science and Technology, East China Normal University, Shanghai 200062, China
MTlab, Meitu (China) Limited, Beijing 100876, China
Institute of Acoustics, University of Chinese Academy of Sciences, Beijing 100084, China
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Abstract

The fusion technique is the key to the multimodal emotion recognition task. Recently, cross-modal attention-based fusion methods have demonstrated high performance and strong robustness. However, cross-modal attention suffers from redundant features and does not capture complementary features well. We find that it is not necessary to use the entire information of one modality to reinforce the other during cross-modal interaction, and the features that can reinforce a modality may contain only a part of it. To this end, we design an innovative Transformer-based Adaptive Cross-modal Fusion Network (TACFN). Specifically, for the redundant features, we make one modality perform intra-modal feature selection through a self-attention mechanism, so that the selected features can adaptively and efficiently interact with another modality. To better capture the complementary information between the modalities, we obtain the fused weight vector by splicing and use the weight vector to achieve feature reinforcement of the modalities. We apply TCAFN to the RAVDESS and IEMOCAP datasets. For fair comparison, we use the same unimodal representations to validate the effectiveness of the proposed fusion method. The experimental results show that TACFN brings a significant performance improvement compared to other methods and reaches the state-of-the-art performance. All code and models could be accessed from https://github.com/shuzihuaiyu/TACFN.

Keywords

Transformermultimodal fusionmultimodal emotion recognitionadaptive cross-modal blockscomputational perception

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CAAI Artificial Intelligence Research
Volume 2,
2023
Article number: 9150019
DOI: 10.26599/AIR.2023.9150019
Cite this article:
Liu F, Fu Z, Wang Y, et al. TACFN: Transformer-Based Adaptive Cross-Modal Fusion Network for Multimodal Emotion Recognition. CAAI Artificial Intelligence Research, 2023, 2: 9150019. https://doi.org/10.26599/AIR.2023.9150019
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Received: 07 July 2023
Accepted: 31 August 2023
Published: 27 October 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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