Dominance aware multimodal rumor verification using reliability calibrated multi-expert inference
TL;DR
Results establish that dominance-aware control and missing-safe fusion are effective principles for robust multimodal recognition under structural modality imbalance.
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.