Quantifying Multimodal Capabilities: Formal Generalization Guarantees in Pairwise Metric Learning

ArXi:2605.01424v1 Announce Type: new 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.