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arXiv · 2007.02038

Low Rank Fusion based Transformers for Multimodal Sequences

Saurav Sahay·Eda Okur·Shachi H Kumar·Lama Nachman
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Abstract

Our senses individually work in a coordinated fashion to express our emotional intentions. In this work, we experiment with modeling modality-specific sensory signals to attend to our latent multimodal emotional intentions and vice versa expressed via low-rank multimodal fusion and multimodal transformers. The low-rank factorization of multimodal fusion amongst the modalities helps represent approximate multiplicative latent signal interactions. Motivated by the work of~\cite{tsai2019MULT} and~\cite{Liu_2018}, we present our transformer-based cross-fusion architecture without any over-parameterization of the model. The low-rank fusion helps represent the latent signal interactions while the modality-specific attention helps focus on relevant parts of the signal. We present two methods for the Multimodal Sentiment and Emotion Recognition results on CMU-MOSEI, CMU-MOSI, and IEMOCAP datasets and show that our models have lesser parameters, train faster and perform comparably to many larger fusion-based architectures.

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Saurav Sahay, Eda Okur, Shachi H Kumar, Lama Nachman. 2020-07-04. Low Rank Fusion based Transformers for Multimodal Sequences. https://arxiv.org/abs/2007.02038

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