Geometric Gradient Divergence Modulation for Imbalanced Multimodal Learning

Multimodal learning, which has been given great significance recently, may face the challenge of the imbalanced multimodal phenomenon, which leads to the insufficient optimization of both multimodal and unimodal objectives. The core problem lies in the optimization conflicts between the above optimization objectives, resulting in the diverse updating directions and strengths that cause antagonism between them. In this paper, we mathematically analyze the optimization processes of imbalanced multimodal learning in the hyperspaces from a novel geometric perspective. Additionally, based on our theoretical analysis, we defined the volumes of the gradients constructed parallel polyhedron in the hyperspace to quantify the misalignment between the optimization objectives. Subsequently, we proposed the Geometric Gradient Divergence Modulation (GGDM), which leverages the volumes of gradient polyhedron to perform gradient modulation, encouraging alignment among gradients and promoting a synergistic optimization effect. Lastly, we evaluate our GGDM on five widely used multimodal benchmarks, where RGB image, optical flow, text, image, video and audio are involved. Our method achieved state-of-the-art performance compared to other imbalanced multimodal learning methods. Our code is available at: https://github.com/ConstantineWayne/GGDM.