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In multimodal tasks, we find that the importance of text and image modalinformation is different for different input cases, and for this motivation, wepropose a high-performance and highly general Dual-Router Dynamic Framework(DRDF), consisting of Dual-Router, MWF-Layer, experts and expert fusion unit.The text router and image router in Dual-Router accept text modal informationand image modal information, and use MWF-Layer to determine the importance ofmodal information. Based on the result of the determination, MWF-Layergenerates fused weights for the fusion of experts. Experts are model backbonesthat match the current task. DRDF has high performance and high generality, andwe have tested 12 backbones such as Visual BERT on multimodal dataset Hatefulmemes, unimodal dataset CIFAR10, CIFAR100, and TinyImagenet. Our DRDFoutperforms all the baselines. We also verified the components of DRDF indetail by ablations, compared and discussed the reasons and ideas of DRDFdesign.

DRDF: Determining the Importance of Different Multimodal Information with Dual-Router Dynamic Framework