Dominance aware multimodal rumor verification using reliability calibrated multi-expert inference

Abstract

Modality imbalance undermines multimodal pattern recognition when auxiliary inputs are missing, noisy, or weakly aligned, causing fusion models to collapse toward quasi-unimodal shortcuts and produce unreliable confidence. We propose DOMINO, a dominance-aware framework that mitigates this failure through three mechanisms: (1) a closed-loop dominance controller that regulates auxiliary objectives during training, (2) a missing-safe residual fusion module that enables stable fallback under degraded modalities, and (3) reliability-aware inference via temperature scaling and weighted expert aggregation. Experiments on Weibo and MediaEval2015 demonstrate state-of-the-art performance, achieving 0.9785 macro-F1 on Weibo and 0.9559 macro-F1 on MediaEval2015, while substantially reducing expected calibration error compared with that of strong baselines. These results establish that dominance-aware control and missing-safe fusion are effective principles for robust multimodal recognition under structural modality imbalance.