I2MoE: Interpretable Multimodal Interaction-aware Mixture-of-Experts
Journal:
arXiv
Published Date:
May 25, 2025
Abstract
Modality fusion is a cornerstone of multimodal learning, enabling information
integration from diverse data sources. However, vanilla fusion methods are
limited by (1) inability to account for heterogeneous interactions between
modalities and (2) lack of interpretability in uncovering the multimodal
interactions inherent in the data. To this end, we propose I2MoE (Interpretable
Multimodal Interaction-aware Mixture of Experts), an end-to-end MoE framework
designed to enhance modality fusion by explicitly modeling diverse multimodal
interactions, as well as providing interpretation on a local and global level.
First, I2MoE utilizes different interaction experts with weakly supervised
interaction losses to learn multimodal interactions in a data-driven way.
Second, I2MoE deploys a reweighting model that assigns importance scores for
the output of each interaction expert, which offers sample-level and
dataset-level interpretation. Extensive evaluation of medical and general
multimodal datasets shows that I2MoE is flexible enough to be combined with
different fusion techniques, consistently improves task performance, and
provides interpretation across various real-world scenarios. Code is available
at https://github.com/Raina-Xin/I2MoE.
