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.- Anthology ID:
- 2020.findings-emnlp.35
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 377–387
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.35
- DOI:
- 10.18653/v1/2020.findings-emnlp.35
- Cite (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.
- Cite (Informal):
- Dual Low-Rank Multimodal Fusion (Jin et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.findings-emnlp.35.pdf