Exchanging-based Multimodal Fusion with Transformer

TL;DR

This paper introduces MuSE, a novel Transformer-based multimodal fusion model that exchanges embeddings between text and vision modalities, improving performance on multimodal tasks.

Contribution

The paper proposes MuSE, a new exchanging-based multimodal fusion model using Transformers, capable of handling sequential data and effectively capturing cross-modal correlations.

Findings

MuSE outperforms existing methods on NER and sentiment analysis tasks.

The model effectively exchanges information between text and vision modalities.

Experimental results demonstrate the superiority of MuSE over competitors.

Abstract

We study the problem of multimodal fusion in this paper. Recent exchanging-based methods have been proposed for vision-vision fusion, which aim to exchange embeddings learned from one modality to the other. However, most of them project inputs of multimodalities into different low-dimensional spaces and cannot be applied to the sequential input data. To solve these issues, in this paper, we propose a novel exchanging-based multimodal fusion model MuSE for text-vision fusion based on Transformer. We first use two encoders to separately map multimodal inputs into different low-dimensional spaces. Then we employ two decoders to regularize the embeddings and pull them into the same space. The two decoders capture the correlations between texts and images with the image captioning task and the text-to-image generation task, respectively. Further, based on the regularized embeddings, we present CrossTransformer, which uses two Transformer encoders with shared parameters as the backbone model to exchange knowledge between multimodalities. Specifically, CrossTransformer first learns the global contextual information of the inputs in the shallow layers. After that, it performs inter-modal exchange by selecting a proportion of tokens in one modality and replacing their embeddings with the average of embeddings in the other modality. We conduct extensive experiments to evaluate the performance of MuSE on the Multimodal Named Entity Recognition task and the Multimodal Sentiment Analysis task. Our results show the superiority of MuSE against other competitors. Our code and data are provided at https://github.com/RecklessRonan/MuSE.