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low-quality multimodal data

Low-quality multimodal data refers to datasets combining multiple distinct data types, such as text, images, audio, or sensor signals, in which one or more constituent modalities are degraded, noisy, incomplete, or unreliable. Such quality impairments often stem from hardware limitations, transmission errors, environmental interference, missing values, or temporal and spatial misalignments between different input streams. In machine learning and data processing pipelines, these deficiencies introduce high levels of uncertainty, obscure meaningful cross-modal correlations, and reduce the overall accuracy and robustness of models that integrate information across modalities.

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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