Dual Low-Rank Multimodal Fusion
Tao Jin, Siyu Huang, Yingming Li, Zhongfei Mark Zhang · 2020
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 tensorbased fusion methods with a similar computational complexity.