Papers › How Far Are We from Predicting Missing Modalities with Foundation Models?
How Far Are We from Predicting Missing Modalities with Foundation Models?
Guanzhou Ke, Yi Xie, Xiaoli Wang, Guoqing Chao, Bo wang, Shengfeng He
Multimodal foundation models have demonstrated impressive capabilities across diverse tasks. However, their potential as plug-and-play solutions for missing modality prediction remains underexplored. To investigate this, we categorize existing approaches into three representative paradigms, encompassing a total of 42 model variants, and conduct a comprehensive evaluation in terms of prediction accuracy and adaptability to downstream tasks. Our analysis reveals that current foundation models often fall short in two critical aspects: (i) fine-grained semantic extraction from the available modalities, and (ii) robust validation of generated modalities. These limitations lead to suboptimal and, at times, misaligned predictions. To address these challenges, we propose an agentic framework tailored for missing modality prediction. This framework dynamically formulates modality-aware mining strategies based on the input context, facilitating the extraction of richer and more discriminative semantic features. In addition, we introduce a \textit{self-refinement mechanism}, which iteratively verifies and enhances the quality of generated modalities through internal feedback. Experimental results show that our method reduces FID for missing image prediction by at least 14% and MER for missing text prediction by at least 10% compared to baselines.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections