Built independently by an author, for readers. Read the story and support ChapterPal
ChapterPal Logo
Log inSign up

keyword

quality-aware multimodal fusion

Quality-aware multimodal fusion is a machine learning paradigm that integrates information from diverse modalities by dynamically evaluating and accounting for the reliability, confidence, or quality of each input stream. Rather than assigning fixed or uniform importance to all data sources, this approach leverages uncertainty estimation and quality assessment techniques to dynamically adjust the contribution of individual modalities during the fusion process. By reducing the influence of noisy, corrupted, or missing inputs and emphasizing high-quality, informative signals, quality-aware multimodal fusion enhances overall model robustness and predictive accuracy in environments with imperfect or variable data conditions.

1 item

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