Multimodal Prompt Learning with Missing Modalities for Sentiment Analysis and Emotion Recognition
This work addresses a practical issue in multimodal AI for sentiment and emotion analysis, offering an incremental improvement in handling missing data.
The paper tackles the problem of performance degradation in multimodal sentiment analysis and emotion recognition due to missing modalities in real-world applications, proposing a multimodal Transformer framework with prompt learning that reduces trainable parameters and outperforms other methods across all evaluation metrics.
The development of multimodal models has significantly advanced multimodal sentiment analysis and emotion recognition. However, in real-world applications, the presence of various missing modality cases often leads to a degradation in the model's performance. In this work, we propose a novel multimodal Transformer framework using prompt learning to address the issue of missing modalities. Our method introduces three types of prompts: generative prompts, missing-signal prompts, and missing-type prompts. These prompts enable the generation of missing modality features and facilitate the learning of intra- and inter-modality information. Through prompt learning, we achieve a substantial reduction in the number of trainable parameters. Our proposed method outperforms other methods significantly across all evaluation metrics. Extensive experiments and ablation studies are conducted to demonstrate the effectiveness and robustness of our method, showcasing its ability to effectively handle missing modalities.