Uncertainty-Aware Multimodal Fusion

Uncertainty-aware multimodal fusion studies how to combine signals such as images, audio, text, and sensor measurements when their quality or relevance varies. Common approaches estimate uncertainty with Bayesian or evidential models, predict variance or confidence scores, and use them to gate features, route experts, or combine evidence with methods such as Dempster-Shafer theory. Researchers evaluate these methods on tasks including medical imaging, sentiment analysis, robotics, and autonomous sensing, often testing missing modalities, noise, corruption, and sensor mismatch. A key open challenge is ensuring that estimated reliability is calibrated and actually predicts which inputs are useful, especially beyond controlled benchmarks.