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dynamic multimodal fusion

Dynamic multimodal fusion is a machine learning approach that adaptively integrates information from multiple data modalities, such as text, images, audio, and sensor streams, by adjusting each modality's contribution on a per-sample basis. Unlike static fusion techniques that apply fixed combination weights or uniform architectures across all inputs, dynamic fusion evaluates instance-specific conditions, such as modality reliability, noise, missing values, or predictive uncertainty. By dynamically modulating cross-modal interactions, assigning input-dependent weights, or routing representations through adaptive computational pathways during inference, this methodology enhances model robustness, generalization, and performance when handling heterogeneous or variable-quality data.

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Provable Dynamic Fusion for Low-Quality Multimodal Data

Provable Dynamic Fusion for Low-Quality Multimodal Data

Qingyang Zhang, Haitao Wu, Changqing Zhang, Qinghua Hu, Huazhu Fu, Joey Tianyi Zhou, Xi Peng

Why you should read this

Establishes theoretical generalization bounds for dynamic decision-level multimodal integration and introduces Quality-aware Multimodal Fusion to prevent performance degradation on noisy and low-quality inputs.

The inherent challenge of multimodal fusion is to precisely capture the cross-modal correlation and flexibly conduct cross-modal interaction. To fully release the value of each modality and mitigate the influence of low-quality multimodal data, dynamic multimodal fusion emerges as a promising learning paradigm. Despite its widespread use, theoretical justifications in this field are still notably lacking. Can we design a provably robust multimodal fusion method? This paper provides theoretical understandings to answer this question under a most popular multimodal fusion framework from the generalization perspective. We proceed to reveal that several uncertainty estimation solutions are naturally available to achieve robust multimodal fusion. Then a novel multimodal fusion framework termed Quality-aware Multimodal Fusion (QMF) is proposed, which can improve the performance in terms of classification accuracy and model robustness. Extensive experimental results on multiple benchmarks can support our findings.

Added

2026-09-26