Adaptive Context Matters: Towards Provable Multi-Modality Guidance for Super-Resolution

2026-05-11

AI Summary

Multimodal super-resolution faces significant challenges due to its ill-posed nature and inadequate modality fusion mechanisms, resulting in weak semantic alignment and limited generalization capability. This work proposes the first theoretical framework tailored to this task, establishing a formal generalization error bound and introducing a spatially dynamic modality weighting scheme coupled with a temporally adaptive temperature scheduling mechanism to enable provably optimal modality fusion. Built upon a Multimodal Mixture-of-Experts architecture (M³ESR), the method effectively regulates modality-specific weights and ensures consistency between their contributions, thereby reducing representational complexity while enhancing generalization. Experimental results demonstrate that the proposed approach substantially improves semantic consistency and cross-dataset generalization performance.

Abstract

Super-resolution (SR) is a severely ill-posed problem with inherent ambiguity, as widely recognized in both empirical and theoretical studies. Although recent semantic-guided and multi-modal SR methods exploit large models or external priors to enhance semantic alignment, the fusion of heterogeneous modalities remains insufficiently understood in practice and theory. In this work, we provide the first theoretical modeling of multi-modal SR, revealing that prior methods are bottlenecked by sub-optimal modality utilization. Our analysis shows that the generalization risk bound can be improved by strengthening the alignment between modality weights and their effective contributions, while reducing representation complexity. This theoretical insight inspires us to propose the novel Multi-Modal Mixture-of-Experts Super-Resolution framework (M$^3$ESR) that employs generalization-oriented dynamic modality fusion for accurate risk control and modality contribution optimization. In detail, we propose a novel spatially dynamic modality weighting module and a temporally adaptive modality temperature scheduling mechanism, enabling flexible and adaptive spatial-temporal modality weighting for effective risk control. Extensive experiments demonstrate that our M$^3$ESR significantly boosts generalization and semantic consistency performances, which confirms our superiority.

Problem

• super-resolution
• multi-modality
• modality fusion
• generalization risk
• semantic alignment

Innovation

• multi-modal super-resolution
• generalization risk bound
• dynamic modality fusion
• Mixture-of-Experts
• adaptive context weighting