论文标题
CUBEMLP：基于MLP的多模式情感分析和抑郁估计的模型
CubeMLP: An MLP-based Model for Multimodal Sentiment Analysis and Depression Estimation
论文作者
论文摘要
多模式情感分析和抑郁估计是两个重要的研究主题，旨在使用多模式数据预测人类精神状态。先前的研究重点是制定有效的融合策略，以交换和整合不同方式的与思想相关的信息。一些基于MLP的技术最近在各种计算机视觉任务中取得了巨大的成功。受此启发的启发，我们探索了本研究中具有混合视角的多模式方法。为此，我们介绍了完全基于MLP的多模式特征处理框架CubeMLP。 CUBEMLP由三个独立的MLP单元组成，每个单元都有两个仿射转换。 CUBEMLP接受所有相关模态特征作为输入，并在三个轴上混合它们。使用CubeMLP提取特性后，将混合的多模式特征扁平以进行任务预测。我们的实验是在情感分析数据集上进行的：CMU-MOSI和CMU-MOSEI，以及抑郁估计数据集：AVEC2019。结果表明，CUBEMLP可以以低得多的计算成本来实现最先进的性能。
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.
