Multimodal Learning on Low-Quality Data with Conformal Predictive Self-Calibration

ArXi:2605.03820v1 Announce Type: cross Multimodal learning often grapples with the challenge of low-quality data, which predominantly manifests as two facets: modality imbalance and noisy corruption. While these issues are often studied in isolation, we argue that they share a common root in the predictive uncertainty towards the reliability of individual modalities and instances during learning.