Understanding Missing-Modality Robustness through Target-Relevant Redundancy

Multimodal models often suffer substantial performance degradation when partial modalities become unavailable. Existing approaches primarily focus on improving performance under incomplete inputs, while leaving unclear what information property makes multimodal model inherently robust to modality incompleteness. In this work, we study missing-modality robustness from the perspective of cross-modal redundancy. We formulate cross-modal redundancy as the target-relevant information shared between modalities, and establish its theoretical connection to robustness under modality incompleteness. Our analysis shows that larger cross-modal redundancy leads to a more favorable bound on the average prediction error when either modality is unavailable. Guided by this analysis, we develop a Redundancy-Oriented Multimodal learning (ROM) framework. Specifically, we introduce two complementary objectives that strengthen target-mediated cross-modal dependence while suppressing residual class-conditional dependence, thereby encouraging the model to obtain more target-relevant redundancy. Extensive experiments across different modality availability settings demonstrate improved robustness to missing modalities. Further analyses reveal a consistent correspondence between increased target-relevant redundancy and improved missing-modality performance, supporting our theoretical predictions. Together, our results establish target-relevant cross-modal redundancy as a principled information perspective for understanding and improving multimodal robustness.