理论揭示多模态学习中细粒度特征如何提升模型泛化能力
Quantifying Multimodal Capabilities: Formal Generalization Guarantees in Pairwise Metric Learning
- 构建多模态子集的函数类层级关系，分析不同模态组合对模型复杂度的影响
- 推导出包含模态数量与粒度的泛化误差上界和下界
- 适合研究多模态系统理论机制或优化模型设计的研究者
多模态学习通过融合多种数据模态提升复杂任务性能，但在真实场景中常面临模态缺失或冗余问题。本文对多模态度量学习模型的泛化性质进行细粒度理论分析，填补了模态选择与算法性能之间关系的理解空白。建立了不同模态子集对应函数类之间的层级关系，量化了学习映射与真实映射间的差异。通过严格分析多模态框架内的成对复杂度，推导出新的泛化误差边界，揭示模态数量与粒度对模型性能的联合影响。上下界分析表明，引入细粒度模态特征能通过增强模态互补性降低假设空间复杂度。本工作为提升多模态学习系统的收敛速度与准确率提供了理论基础与实践启示。
原文摘要 · Abstract (English)
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 multimodal learning framework, we derive novel generalization error bounds that reveal the joint impact of modality quantity and granularity on model performance. Our theoretical findings on both upper and lower bounds demonstrate that incorporating fine-grained modality features reduces the complexity of the hypothesis space by enhancing modality complementarity. This work offers both theoretical foundations and practical implications for improving convergence rates and accuracy in multimodal learning systems.
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