Multimodal Robustness

Multimodal robustness asks whether systems that combine images, text, audio, medical data, or sensors can still make reliable predictions when some inputs are missing, corrupted, or in conflict. Common approaches include modality dropout, reliability-aware gating and expert routing, uncertainty estimation, and methods that separate shared information from modality-specific or spurious features. Researchers evaluate these methods on tasks such as medical segmentation, sentiment analysis, autonomous driving, and multimodal language-model reasoning, using corruption, missing-input, domain-shift, and adversarial benchmarks. A central tension is that adding modalities can improve performance when evidence is complementary but can also introduce interference or overreliance, and gains on controlled benchmarks do not always carry over to real deployment conditions.