Multimodal emotion recognition (MER) extracts emotions from multimodal data, including visual, speech, and text inputs, playing a key role in human-computer interaction. Attention-based fusion methods dominate MER research, achieving strong classification performance. However, two key challenges remain: effectively extracting modality-specific features and capturing cross-modal similarities despite distribution differences caused by modality heterogeneity. To address these, we propose a gated interactive attention mechanism to adaptively extract modality-specific features while enhancing emotional information through pairwise interactions. Additionally, we introduce a modality-invariant generator to learn modality-invariant representations and constrain domain shifts by aligning cross-modal similarities. Experiments on IEMOCAP demonstrate that our method outperforms state-of-the-art MER approaches, achieving WA 80.7% and UA 81.3%.
@article{arxiv.2506.00865,
title = {GIA-MIC: Multimodal Emotion Recognition with Gated Interactive Attention and Modality-Invariant Learning Constraints},
author = {Jiajun He and Jinyi Mi and Tomoki Toda},
journal= {arXiv preprint arXiv:2506.00865},
year = {2025}
}