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Dual Low-Rank Multimodal Fusion
2020
Findings of the Association for Computational Linguistics: EMNLP 2020
unpublished
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
doi:10.18653/v1/2020.findings-emnlp.35
fatcat:m4uwzw3abrf2pcu5ibkgzmdkpu