Balanced Multimodal Learning via Mutual Information

2025-11-02

AI Summary

To address modality imbalance and conflict in biomedical multimodal data analysis—arising from data scarcity and heterogeneous quality—we propose a mutual information-driven unified learning framework. Methodologically, we design cross-modal knowledge distillation and dynamic gradient calibration: in Stage I, strong modalities guide representation learning for weak modalities; in Stage II, gradient updates are dynamically weighted based on both task performance and inter-modal mutual information, enabling collaborative optimization. Our key contribution is the first differentiable integration of mutual information as a principled, learnable metric for modality interaction, embedded within a two-stage balancing strategy. Extensive experiments on multiple biomedical multimodal benchmarks demonstrate significant improvements over state-of-the-art fusion methods, validating the framework’s effectiveness, robustness to modality degradation, and strong generalization across diverse tasks and datasets.

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
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Multimodal learning has increasingly become a focal point in research, primarily due to its ability to integrate complementary information from diverse modalities. Nevertheless, modality imbalance, stemming from factors such as insufficient data acquisition and disparities in data quality, has often been inadequately addressed. This issue is particularly prominent in biological data analysis, where datasets are frequently limited, costly to acquire, and inherently heterogeneous in quality. Conventional multimodal methodologies typically fall short in concurrently harnessing intermodal synergies and effectively resolving modality conflicts. In this study, we propose a novel unified framework explicitly designed to address modality imbalance by utilizing mutual information to quantify interactions between modalities. Our approach adopts a balanced multimodal learning strategy comprising two key stages: cross-modal knowledge distillation (KD) and a multitask-like training paradigm. During the cross-modal KD pretraining phase, stronger modalities are leveraged to enhance the predictive capabilities of weaker modalities. Subsequently, our primary training phase employs a multitask-like learning mechanism, dynamically calibrating gradient contributions based on modality-specific performance metrics and intermodal mutual information. This approach effectively alleviates modality imbalance, thereby significantly improving overall multimodal model performance.

Problem

• Addressing modality imbalance in multimodal learning systems
• Quantifying intermodal interactions using mutual information
• Enhancing weaker modalities through cross-modal knowledge distillation

Innovation

• Mutual information quantifies cross-modal interactions
• Cross-modal knowledge distillation enhances weaker modalities
• Multitask training dynamically calibrates gradient contributions