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dc.contributor.authorZhu, Hu
dc.contributor.authorWang, Ze
dc.contributor.authorShi, Yu
dc.contributor.authorHua, Yingying
dc.contributor.authorXu, Guoxia
dc.contributor.authorDeng, Lizhen
dc.description.abstractMultimodal fusion is one of the popular research directions of multimodal research, and it is also an emerging research field of artificial intelligence. Multimodal fusion is aimed at taking advantage of the complementarity of heterogeneous data and providing reliable classification for the model. Multimodal data fusion is to transform data from multiple single-mode representations to a compact multimodal representation. In previous multimodal data fusion studies, most of the research in this field used multimodal representations of tensors. As the input is converted into a tensor, the dimensions and computational complexity increase exponentially. In this paper, we propose a low-rank tensor multimodal fusion method with an attention mechanism, which improves efficiency and reduces computational complexity. We evaluate our model through three multimodal fusion tasks, which are based on a public data set: CMU-MOSI, IEMOCAP, and POM. Our model achieves a good performance while flexibly capturing the global and local connections. Compared with other multimodal fusions represented by tensors, experiments show that our model can achieve better results steadily under a series of attention mechanisms.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleMultimodal Fusion Method Based on Self-Attention Mechanismen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.source.journalWireless Communications & Mobile Computingen_US
dc.description.localcodeCopyright © 2020 Hu Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal