Plug, Play, and Fortify: A Low-Cost Module for Robust Multimodal Image Understanding Models

Siqi Lu, Wanying XU, Yongbin Zheng, Wenting Luan, Peng Sun, Jianhang Yao

Keywords: Modality Missing, Plug-and-play module, multimodal imbalanced learning

Abstract: Missing modalities present a fundamental challenge in multimodal models, often causing catastrophic performance degradation. Our observations suggest that this fragility stems from an imbalanced learning process, where the model develops an implicit preference for certain modalities, leading to the under-optimization of others. We propose a simple yet efficiency method to address this challenge. The central insight of our work is that the dominance relationship between modalities can be effectively discerned and quantified in the frequency domain. To leverage this principle, we first introduce a **F**requency **R**atio **M**etric (FRM) to quantify modality preference by analyzing features in the frequency domain. Guided by FRM, we then propose a **M**ultimodal **W**eight **A**llocation **M**odule, a plug-and-play component that dynamically re-balances the contribution of each branch during training, promoting a more holistic learning paradigm. Extensive experiments demonstrate that MWAM can be seamlessly integrated into diverse architectural backbones, such as those based on CNNs and ViTs. Furthermore, MWAM delivers consistent performance gains across a wide range of tasks and modality combinations. This advancement extends beyond merely optimizing the performance of the base model; it also manifests as further performance improvements to state-of-the-art methods addressing the missing modality problem.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 2549