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Our senses individually work in a coordinated fashion to express ouremotional intentions. In this work, we experiment with modelingmodality-specific sensory signals to attend to our latent multimodal emotionalintentions and vice versa expressed via low-rank multimodal fusion andmultimodal transformers. The low-rank factorization of multimodal fusionamongst the modalities helps represent approximate multiplicative latent signalinteractions. Motivated by the work of~\cite{tsai2019MULT} and~\cite{Liu_2018},we present our transformer-based cross-fusion architecture without anyover-parameterization of the model. The low-rank fusion helps represent thelatent signal interactions while the modality-specific attention helps focus onrelevant parts of the signal. We present two methods for the MultimodalSentiment and Emotion Recognition results on CMU-MOSEI, CMU-MOSI, and IEMOCAPdatasets and show that our models have lesser parameters, train faster andperform comparably to many larger fusion-based architectures.

Low Rank Fusion based Transformers for Multimodal Sequences