Imbalanced Multi-Domain Multi-Modal Learning with Expert Collaboration and Dynamic Fusion Mechanism for Fake News Detection

Imbalance in multi-domain multi-modal settings remains a significant challenge in real-world applications, such as fake news detection. Although existing methods have enhanced semantic representations and employed complex architectures to improve recognition performance, most of them focus on domain heterogeneity and modality sensitivity. However, these methods neglect the class imbalance, which severely impacts model robustness and generalization in open-world scenarios. Learning for such imbalanced multi-domain multi-modal data is crucial but rarely studied, particularly when the domain and modal of samples are both imbalanced. To address these challenges, we propose a novel Imbalanced multi-domain multi-modal learning method with Expert Collaboration and Dynamic Fusion, named IECDF. Specifically, to alleviate inter-domain imbalance, we design an expert collaboration module with a domain-shared specific embedding structure and an improved gating strategy to enhance the discriminative power of the cross domain features. Besides, a dynamic fusion mechanism based on Mamba-Former is designed to learn adaptive weights for each modality. Moreover, to tackle the intra-class imbalance problem, we adopt asymmetric re-weighted loss –DLINEX– to increase the contributions of minority class samples and learn the unbiased decision boundary. Extensive experimental results on various datasets validate the superiority of our proposed IECDF as compared to state-of-the-art methods. Our code will be available at https://github.com/Yuchen-zh/IECDF .