This paper presents a novel model for multimodal learning based on gated neural networks. The Gated Multimodal Unit (GMU) model is intended to be used as an internal unit in a neural network architecture whose purpose is to find an intermediate representation based on a combination of data from different modalities. The GMU learns to decide how modalities influence the activation of the unit using multiplicative gates. It was evaluated on a multilabel scenario for genre classification of movies using the plot and the poster. The GMU improved the macro f-score performance of single-modality approaches and outperformed other fusion strategies, including mixture of experts models. Along with this work, the MM-IMDb dataset is released which, to the best of our knowledge, is the largest publicly available multimodal dataset for genre prediction on movies.
@article{arxiv.1702.01992,
title = {Gated Multimodal Units for Information Fusion},
author = {John Arevalo and Thamar Solorio and Manuel Montes-y-Gómez and Fabio A. González},
journal= {arXiv preprint arXiv:1702.01992},
year = {2017}
}