  Current deep learning approaches for multimodal fusion rely on bottom-up
fusion of high and mid-level latent modality representations (late/mid fusion)
or low level sensory inputs (early fusion). Models of human perception
highlight the importance of top-down fusion, where high-level representations
affect the way sensory inputs are perceived, i.e. cognition affects perception.
These top-down interactions are not captured in current deep learning models.
In this work we propose a neural architecture that captures top-down
cross-modal interactions, using a feedback mechanism in the forward pass during
network training. The proposed mechanism extracts high-level representations
for each modality and uses these representations to mask the sensory inputs,
allowing the model to perform top-down feature masking. We apply the proposed
model for multimodal sentiment recognition on CMU-MOSEI. Our method shows
consistent improvements over the well established MulT and over our strong late
fusion baseline, achieving state-of-the-art results.
