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 leads to the phenomenon of optimization imbalance%2C thus struggling to achieve satisfactory performance. Recently%2C some representative methods have been proposed to boost the performance%2C mainly focusing on adaptive adjusting the optimization of each modality to rebalance the learning speed of dominant and non-dominant modalities. To better facilitate the interaction of model information in multimodal learning%2C in this paper%2C we propose a novel multimodal learning method%2C called modal-aware interactive enhancement (MIE). Specifically%2C we first utilize an optimization strategy based on sharpness aware minimization (SAM) to smooth the learning objective during the forward phase. Then%2C with the help of the geometry property of SAM%2C we propose a gradient modification strategy to impose the influence between different modalities during the backward phase. Therefore%2C we can improve the generalization ability and alleviate the modality forgetting phenomenon simultaneously for multimodal learning. Extensive experiments on widely used datasets demonstrate that our proposed method can outperform various state-of-the-art baselines to achieve the best performance.?quality=80&w=800)
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