Meta-Learning for Incomplete Multimodal Sentiment Analysis

Modality incompleteness is a critical yet underexplored challenge in multimodal sentiment analysis (MSA). Existing efforts, trained and evaluated under fixed missing rates, struggle to adapt to real-world scenarios with varying missing rates. To address this, we propose the Missing Modality Adaptation Framework (M2AF), leveraging model-agnostic meta-learning to enhance robustness against different levels of modality incompleteness. M2AF operates in two stages: meta-training and meta-testing. In the meta-training stage, a pre-trained MSA model, initially optimized for fixed missing rates, is further adapted to different levels of missing rates-low, moderate, and high. In the meta-testing stage, the model rapidly updates its parameters using minimal training data to handle target missing scenarios. Experiments on two popular datasets demonstrate that M2AF significantly improves the performance and generalization of various MSA models, ensuring more robust sentiment analysis in real-world settings with different modality incompleteness.