Re-M3Dr is a multimodal regression framework using adaptive-margin supervised contrastive learning and sharpness-aware gradient modulation to stabilize training and reduce MSE by 29% versus SOTA multimodal methods on clinical eye-imaging datasets for mean deviation prediction.
Balancebench- mark: A survey for multimodal imbalance learning,
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BMLR reshapes the cross-modal label space to equalize mapping difficulty and balance optimization across modalities in multimodal learning.
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Re-M3Dr: Rebalanced MultiModal Mean Deviation Regression
Re-M3Dr is a multimodal regression framework using adaptive-margin supervised contrastive learning and sharpness-aware gradient modulation to stabilize training and reduce MSE by 29% versus SOTA multimodal methods on clinical eye-imaging datasets for mean deviation prediction.