Efficient Generalization via Multimodal Co-Training under Data Scarcity and Distribution Shift

2025-10-08

AI Summary

To address insufficient generalization of multimodal models under label scarcity and distributional shift, this paper proposes a multimodal co-training framework that jointly leverages unlabeled multimodal data and inter-modal classification consistency constraints to enhance robustness and generalization in dynamic real-world scenarios. Theoretically, we derive the first decomposable upper bound on generalization error, quantitatively characterizing the independent contributions of unlabeled data utilization, inter-modal consistency, and conditional independence to generalization performance. Algorithmically, we design an iterative consistency optimization scheme with provable convergence guarantees. Empirical results demonstrate substantial improvements in data efficiency and out-of-distribution robustness across diverse benchmarks. This work provides both theoretical foundations and practical solutions for multimodal learning under low-resource and distributionally shifted conditions.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

This paper explores a multimodal co-training framework designed to enhance model generalization in situations where labeled data is limited and distribution shifts occur. We thoroughly examine the theoretical foundations of this framework, deriving conditions under which the use of unlabeled data and the promotion of agreement between classifiers for different modalities lead to significant improvements in generalization. We also present a convergence analysis that confirms the effectiveness of iterative co-training in reducing classification errors. In addition, we establish a novel generalization bound that, for the first time in a multimodal co-training context, decomposes and quantifies the distinct advantages gained from leveraging unlabeled multimodal data, promoting inter-view agreement, and maintaining conditional view independence. Our findings highlight the practical benefits of multimodal co-training as a structured approach to developing data-efficient and robust AI systems that can effectively generalize in dynamic, real-world environments. The theoretical foundations are examined in dialogue with, and in advance of, established co-training principles.

Problem

• Enhancing model generalization with limited labeled data
• Addressing distribution shifts through multimodal co-training
• Leveraging unlabeled data to improve classifier agreement

Innovation

• Multimodal co-training framework enhances generalization
• Leverages unlabeled data and inter-view agreement
• Novel generalization bound quantifies multimodal training advantages