CubeMLP: An MLP-based Model for Multimodal Sentiment Analysis and Depression Estimation
This work addresses the problem of predicting human mental states for applications in sentiment analysis and depression detection, presenting an incremental improvement in multimodal fusion methods.
The paper tackles multimodal sentiment analysis and depression estimation by introducing CubeMLP, an MLP-based framework that mixes features across three axes, achieving state-of-the-art performance with lower computing cost on datasets like CMU-MOSI, CMU-MOSEI, and AVEC2019.
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.