Missing Modality meets Meta Sampling (M3S): An Efficient Universal Approach for Multimodal Sentiment Analysis with Missing Modality

TL;DR

This paper introduces M3S, a meta-sampling method that enhances multimodal sentiment analysis models to handle mixed missing modalities effectively, improving performance across multiple datasets.

Contribution

The paper proposes M3S, a novel meta-sampling approach integrated with MAML, to address the challenge of mixed missing modalities in multimodal sentiment analysis.

Findings

M3S significantly improves model performance on datasets with missing modalities.

The approach outperforms recent state-of-the-art methods.

M3S is an efficient add-on that enhances existing models.

Abstract

Multimodal sentiment analysis (MSA) is an important way of observing mental activities with the help of data captured from multiple modalities. However, due to the recording or transmission error, some modalities may include incomplete data. Most existing works that address missing modalities usually assume a particular modality is completely missing and seldom consider a mixture of missing across multiple modalities. In this paper, we propose a simple yet effective meta-sampling approach for multimodal sentiment analysis with missing modalities, namely Missing Modality-based Meta Sampling (M3S). To be specific, M3S formulates a missing modality sampling strategy into the modal agnostic meta-learning (MAML) framework. M3S can be treated as an efficient add-on training component on existing models and significantly improve their performances on multimodal data with a mixture of missing modalities. We conduct experiments on IEMOCAP, SIMS and CMU-MOSI datasets, and superior performance is achieved compared with recent state-of-the-art methods.