Quantifying Multimodal Capabilities: Formal Generalization Guarantees in Pairwise Metric Learning
Multimodal learning leverages the integration of diverse data modalities to enhance performance in complex tasks. Yet, it frequently encounters incomplete or redundant modality data in real-world scenarios. This paper presents a fine-grained theoretical analysis of the generalization properties of multimodal metric learning models, addressing critical gaps in understanding the relationship between modality selection and algorithmic performance. We establish hierarchical relationships between function classes corresponding to different modality subsets and quantify the discrepancy between learned mappings and ground truth. Through rigorous analysis of pairwise complexity within the multimo...
| Authors | Richeng Zhou, Xuelin Zhang, Liyuan Liu |
|---|---|
| Primary category | cs.LG |
| Published | 2 May 2026 |
| Last updated at source | 2 May 2026 |
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