Modality Imbalance

Modality imbalance, a pervasive challenge in multimodal learning, arises when different data modalities (e.g., text, images, audio) contribute unevenly to model training and performance. Current research focuses on developing methods to rebalance this disparity, often employing techniques like adaptive weighting of modalities, prototype-based approaches, and subnetwork optimization to ensure fair representation and prevent dominance by a single modality. Addressing modality imbalance is crucial for improving the accuracy and robustness of multimodal systems across diverse applications, including medical image analysis, video understanding, and autonomous driving, where reliable integration of multiple data sources is essential.