Feature Level Gradient Modulation Balances Multimodal Learning Across Modalities

Researchers have developed an adaptive gradient modulation strategy designed to resolve modality imbalance during the joint training of multimodal artificial intelligence models. In conventional multimodal learning systems that combine information across diverse modalities such as vision, speech, and text, dominant modalities possessing richer discriminative cues often overshadow weaker modalities during optimization. This dynamic typically leaves unimodal representations under-optimized and restricts overall model performance.

While earlier studies primarily examined imbalance occurring at the broad modality level, the investigative team identified that an imbalance also emerges within individual modalities at the feature level. Specifically, this discrepancy arises between modality-invariant features and modality-specific features. An examination of sample-wise variations in feature contribution distributions revealed significant heterogeneity across samples that previous optimization approaches failed to address.

To mitigate these feature-level disparities, the proposed framework disentangles modality-invariant and modality-specific representations during model training. It subsequently applies an adaptive gradient modulation mechanism to adjust the gradient contributions of each feature type dynamically.

Evaluation across two standard multimodal benchmark datasets demonstrated that the feature-level modulation strategy alleviated the imbalanced multimodal learning problem. The method achieved superior performance and robustness compared to existing approaches.

Subject of Research: Adaptive gradient modulation for feature-level rebalance in multimodal learning

Article Title: Towards feature-level rebalance in multimodal learning with adaptive gradient modulation

Article References: Xu, Y., Yin, H., Ding, N., Gu, J., & Xie, H. (2026). Towards feature-level rebalance in multimodal learning with adaptive gradient modulation. Cluster Computing, 29(16), Article 936. https://doi.org/10.1007/s10586-026-06781-8

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06781-8

Keywords: multimodal learning, gradient modulation, modality imbalance, feature-level rebalance, modality-invariant features, modality-specific features, joint training, unimodal representations, deep learning optimization, vision and language, speech processing, representation learning, benchmark evaluation, machine learning robustness, neural network training, Cluster Computing