Meta Fusion: A Unified Framework For Multimodality Fusion with Mutual Learning
This work addresses the need for effective fusion methods in applications such as autonomous driving and medical diagnosis, presenting an incremental improvement over existing strategies.
The paper tackles the problem of multimodal data fusion by introducing Meta Fusion, a unified framework that outperforms traditional fusion strategies in simulation studies and real-world applications like Alzheimer's disease detection and neural decoding.
Developing effective multimodal data fusion strategies has become increasingly essential for improving the predictive power of statistical machine learning methods across a wide range of applications, from autonomous driving to medical diagnosis. Traditional fusion methods, including early, intermediate, and late fusion, integrate data at different stages, each offering distinct advantages and limitations. In this paper, we introduce Meta Fusion, a flexible and principled framework that unifies these existing strategies as special cases. Motivated by deep mutual learning and ensemble learning, Meta Fusion constructs a cohort of models based on various combinations of latent representations across modalities, and further boosts predictive performance through soft information sharing within the cohort. Our approach is model-agnostic in learning the latent representations, allowing it to flexibly adapt to the unique characteristics of each modality. Theoretically, our soft information sharing mechanism reduces the generalization error. Empirically, Meta Fusion consistently outperforms conventional fusion strategies in extensive simulation studies. We further validate our approach on real-world applications, including Alzheimer's disease detection and neural decoding.