Balanced Multimodal Learning

Balanced multimodal learning aims to overcome the limitations of standard multimodal models, which often underutilize weaker modalities due to inherent data imbalances or dominant modalities overshadowing others. Current research focuses on developing algorithms that dynamically adjust the training process, for example through gradient modulation or knowledge distillation techniques, to ensure all modalities contribute equally to model performance. This improved balance leads to more robust and accurate models with broader applications across diverse fields like recommendation systems, medical diagnosis (e.g., cancer survival prediction), and active learning, ultimately enhancing the reliability and effectiveness of multimodal analysis.