Multimodal Emotion Recognition with Missing Modality via a Unified Multi-task Pre-training Framework

Multimodal emotion recognition based on physiological signals faces the challenge of missing modality due to issues such as inaccurate signal synchronization and inadequate device contact. Existing methods either require additional generative modules to handle missing modalities, leading to extra computational overhead, or fail to effectively capture both modality-specific and joint representation. In contrast, we propose a Unified Multi-task Pre-training (UMAP) framework based on the mixture of experts structure. Our approach offers two key advantages: (1) It retains a joint structure while flexibly handling both unimodal and multimodal inputs by selecting lightweight modality experts and shared experts, thus preserving both modality-specific features and joint multimodal information. 2) Three pre-training tasks-contrastive learning, modality matching, and modality generation-are integrated into UMAP through different attention masks, enhancing the model's ability to adapt to both complete and incomplete modalities during fine-tuning. Comprehensive experiments conducted on three benchmark datasets demonstrate that UMAP achieves state-of-the-art (SOTA) performance, both in multimodal scenarios and in cases where any modality is missing. The code is available at https://github.com/iiieeeve/UMAP.