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arXiv · 1702.01992

Gated Multimodal Units for Information Fusion

John Arevalo·Thamar Solorio·Manuel Montes-y-Gómez·Fabio A. González
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Abstract

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.

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John Arevalo, Thamar Solorio, Manuel Montes-y-Gómez, Fabio A. González. 2017-02-07. Gated Multimodal Units for Information Fusion. https://arxiv.org/abs/1702.01992

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