  Multimodal sentiment analysis and depression estimation are two important
research topics that aim to predict human mental states using multimodal data.
Previous research has focused on developing effective fusion strategies for
exchanging and integrating mind-related information from different modalities.
Some MLP-based techniques have recently achieved considerable success in a
variety of computer vision tasks. Inspired by this, we explore multimodal
approaches with a feature-mixing perspective in this study. To this end, we
introduce CubeMLP, a multimodal feature processing framework based entirely on
MLP. CubeMLP consists of three independent MLP units, each of which has two
affine transformations. CubeMLP accepts all relevant modality features as input
and mixes them across three axes. After extracting the characteristics using
CubeMLP, the mixed multimodal features are flattened for task predictions. Our
experiments are conducted on sentiment analysis datasets: CMU-MOSI and
CMU-MOSEI, and depression estimation dataset: AVEC2019. The results show that
CubeMLP can achieve state-of-the-art performance with a much lower computing
cost.
