Balancing Multimodal Learning through Label Space Reshaping

The paper addresses modality imbalance in multimodal learning, where some modalities dominate optimization while others remain undertrained. It proposes that this discrepancy stems from differing mapping difficulties between modality-specific feature space and the shared label space, introducing BMLR to equalize this difficulty.