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Learning joint embedding space for various modalities is of vital importancefor multimodal fusion. Mainstream modality fusion approaches fail to achievethis goal, leaving a modality gap which heavily affects cross-modal fusion. Inthis paper, we propose a novel adversarial encoder-decoder-classifier frameworkto learn a modality-invariant embedding space. Since the distributions ofvarious modalities vary in nature, to reduce the modality gap, we translate thedistributions of source modalities into that of target modality via theirrespective encoders using adversarial training. Furthermore, we exertadditional constraints on embedding space by introducing reconstruction lossand classification loss. Then we fuse the encoded representations usinghierarchical graph neural network which explicitly explores unimodal, bimodaland trimodal interactions in multi-stage. Our method achieves state-of-the-artperformance on multiple datasets. Visualization of the learned embeddingssuggests that the joint embedding space learned by our method isdiscriminative. code is available at:\url{https://github.com/TmacMai/ARGF_multimodal_fusion}

Modality to Modality Translation: An Adversarial Representation Learning and Graph Fusion Network for Multimodal Fusion