  Multi-modal learning aims to enhance performance by unifying models from
various modalities but often faces the "modality imbalance" problem in real
data, leading to a bias towards dominant modalities and neglecting others,
thereby limiting its overall effectiveness. To address this challenge, the core
idea is to balance the optimization of each modality to achieve a joint
optimum. Existing approaches often employ a modal-level control mechanism for
adjusting the update of each modal parameter. However, such a global-wise
updating mechanism ignores the different importance of each parameter. Inspired
by subnetwork optimization, we explore a uniform sampling-based optimization
strategy and find it more effective than global-wise updating. According to the
findings, we further propose a novel importance sampling-based, element-wise
joint optimization method, called Adaptively Mask Subnetworks Considering Modal
Significance(AMSS). Specifically, we incorporate mutual information rates to
determine the modal significance and employ non-uniform adaptive sampling to
select foreground subnetworks from each modality for parameter updates, thereby
rebalancing multi-modal learning. Additionally, we demonstrate the reliability
of the AMSS strategy through convergence analysis. Building upon theoretical
insights, we further enhance the multi-modal mask subnetwork strategy using
unbiased estimation, referred to as AMSS+. Extensive experiments reveal the
superiority of our approach over comparison methods.
