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        <title>Dual Low-Rank Multimodal Fusion</title>
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    <name type="personal">
        <namePart type="given">Tao</namePart>
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    <name type="personal">
        <namePart type="given">Siyu</namePart>
        <namePart type="family">Huang</namePart>
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    <name type="personal">
        <namePart type="given">Yingming</namePart>
        <namePart type="family">Li</namePart>
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    <name type="personal">
        <namePart type="given">Zhongfei</namePart>
        <namePart type="family">Zhang</namePart>
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            <namePart type="given">Trevor</namePart>
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        <name type="personal">
            <namePart type="given">Yang</namePart>
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    <abstract>Tensor-based fusion methods have been proven effective in multimodal fusion tasks. However, existing tensor-based methods make a poor use of the fine-grained temporal dynamics of multimodal sequential features. Motivated by this observation, this paper proposes a novel multimodal fusion method called Fine-Grained Temporal Low-Rank Multimodal Fusion (FT-LMF). FT-LMF correlates the features of individual time steps between multiple modalities, while it involves multiplications of high-order tensors in its calculation. This paper further proposes Dual Low-Rank Multimodal Fusion (Dual-LMF) to reduce the computational complexity of FT-LMF through low-rank tensor approximation along dual dimensions of input features. Dual-LMF is conceptually simple and practically effective and efficient. Empirical studies on benchmark multimodal analysis tasks show that our proposed methods outperform the state-of-the-art tensor-based fusion methods with a similar computational complexity.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.35</identifier>
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        <url>https://aclanthology.org/2020.findings-emnlp.35</url>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T Dual Low-Rank Multimodal Fusion
%A Jin, Tao
%A Huang, Siyu
%A Li, Yingming
%A Zhang, Zhongfei
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F jin-etal-2020-dual
%X Tensor-based fusion methods have been proven effective in multimodal fusion tasks. However, existing tensor-based methods make a poor use of the fine-grained temporal dynamics of multimodal sequential features. Motivated by this observation, this paper proposes a novel multimodal fusion method called Fine-Grained Temporal Low-Rank Multimodal Fusion (FT-LMF). FT-LMF correlates the features of individual time steps between multiple modalities, while it involves multiplications of high-order tensors in its calculation. This paper further proposes Dual Low-Rank Multimodal Fusion (Dual-LMF) to reduce the computational complexity of FT-LMF through low-rank tensor approximation along dual dimensions of input features. Dual-LMF is conceptually simple and practically effective and efficient. Empirical studies on benchmark multimodal analysis tasks show that our proposed methods outperform the state-of-the-art tensor-based fusion methods with a similar computational complexity.
%R 10.18653/v1/2020.findings-emnlp.35
%U https://aclanthology.org/2020.findings-emnlp.35
%U https://doi.org/10.18653/v1/2020.findings-emnlp.35
%P 377-387

Markdown (Informal)

[Dual Low-Rank Multimodal Fusion](https://aclanthology.org/2020.findings-emnlp.35) (Jin et al., Findings 2020)

• Dual Low-Rank Multimodal Fusion (Jin et al., Findings 2020)

ACL

• Tao Jin, Siyu Huang, Yingming Li, and Zhongfei Zhang. 2020. Dual Low-Rank Multimodal Fusion. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 377–387, Online. Association for Computational Linguistics.