Multimodal Representation Learning by Alternating Unimodal Adaptation

Abstract

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 Uni-modal Adaptation). MLA reframes the conventional joint multimodal learning process by transforming it into an al-ternating 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 modi-fication 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, encom-passing 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/MLA.