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arXiv · 1903.06496

MFAS: Multimodal Fusion Architecture Search

Juan-Manuel Pérez-Rúa·Valentin Vielzeuf·Stéphane Pateux·Moez Baccouche·Frédéric Jurie
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Abstract

We tackle the problem of finding good architectures for multimodal classification problems. We propose a novel and generic search space that spans a large number of possible fusion architectures. In order to find an optimal architecture for a given dataset in the proposed search space, we leverage an efficient sequential model-based exploration approach that is tailored for the problem. We demonstrate the value of posing multimodal fusion as a neural architecture search problem by extensive experimentation on a toy dataset and two other real multimodal datasets. We discover fusion architectures that exhibit state-of-the-art performance for problems with different domain and dataset size, including the NTU RGB+D dataset, the largest multi-modal action recognition dataset available.

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Juan-Manuel Pérez-Rúa, Valentin Vielzeuf, Stéphane Pateux, Moez Baccouche, Frédéric Jurie. 2019-03-15. MFAS: Multimodal Fusion Architecture Search. https://arxiv.org/abs/1903.06496

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