  Multimodal fusion is crucial in joint decision-making systems for rendering
holistic judgments. Since multimodal data changes in open environments, dynamic
fusion has emerged and achieved remarkable progress in numerous applications.
However, most existing dynamic multimodal fusion methods lack theoretical
guarantees and easily fall into suboptimal problems, yielding unreliability and
instability. To address this issue, we propose a Predictive Dynamic Fusion
(PDF) framework for multimodal learning. We proceed to reveal the multimodal
fusion from a generalization perspective and theoretically derive the
predictable Collaborative Belief (Co-Belief) with Mono- and Holo-Confidence,
which provably reduces the upper bound of generalization error. Accordingly, we
further propose a relative calibration strategy to calibrate the predicted
Co-Belief for potential uncertainty. Extensive experiments on multiple
benchmarks confirm our superiority. Our code is available at
https://github.com/Yinan-Xia/PDF.
