ROSA: A Robust Self-Adaptive Model for Multimodal Emotion Recognition With Uncertain Missing Modalities

Abstract

The rapid development of online media has heightened the importance of multimodal emotion recognition (MER) in video analysis. However, practical applications often encounter challenges due to missing modalities caused by various interferences. It is difficult to predict the specific missing situations, such as the number and types of missing modalities. Current approaches to modality missing typically apply a uniform method to address various missing cases, which are insufficiently adaptive to dynamic conditions. For example, translation-based methods can efficiently complete missing text from audio, but generating audio or video features that retain the original emotional information from other modalities is challenging and may introduce additional noise. In this paper, we introduce ROSA, a novel robust self-adaptive model designed to address various missing cases with tailored approaches, leveraging available modalities effectively and reducing the introduction of additional noise. Specifically, the A-T Completion module based on the encoder-decoder architecture enables ROSA to generate missing raw text from audio rather than mere embedding representations, capturing more nuanced modal features. Additionally, we design the T-V Fusion module based on a vision-language large model for deep extraction and fusion of textual and visual features. Comprehensive experiments conducted on three widely used public datasets demonstrate the superiority and effectiveness of our model. ROSA outperforms other models in both fixed missing rate and fixed missing modality cases. The ablation studies further highlights the contribution of each designed module.