  Multimodal learning, which integrates data from diverse sensory modes, plays
a pivotal role in artificial intelligence. However, existing multimodal
learning methods often struggle with challenges where some modalities appear
more dominant than others during multimodal learning, resulting in suboptimal
performance. To address this challenge, we propose MLA (Multimodal Learning
with Alternating Unimodal Adaptation). MLA reframes the conventional joint
multimodal learning process by transforming it into an alternating unimodal
learning process, thereby minimizing interference between modalities.
Simultaneously, it captures cross-modal interactions through a shared head,
which undergoes continuous optimization across different modalities. This
optimization process is controlled by a gradient modification mechanism to
prevent the shared head from losing previously acquired information. During the
inference phase, MLA utilizes a test-time uncertainty-based model fusion
mechanism to integrate multimodal information. Extensive experiments are
conducted on five diverse datasets, encompassing scenarios with complete
modalities and scenarios with missing modalities. These experiments demonstrate
the superiority of MLA over competing prior approaches. Our code is available
at
https://github.com/Cecile-hi/Multimodal-Learning-with-Alternating-Unimodal-Adaptation.
