Balancing Multimodal Learning through Label Space Reshaping

📰 ArXiv cs.AI

Learn to balance multimodal learning by reshaping label spaces to mitigate modality imbalance and improve optimization capacity

advanced Published 29 May 2026

Action Steps

Identify modalities with disparate convergence rates in your multimodal model
Apply label space reshaping to balance optimization across modalities
Evaluate the impact of label space reshaping on model performance and modality balance
Adjust optimization gradients to further mitigate modality imbalance if necessary
Test the robustness of the balanced model on various datasets and tasks

Who Needs to Know This

Researchers and engineers working on multimodal learning models can benefit from this approach to improve model performance and prevent modality dominance

Key Insight

💡 Label space reshaping can help mitigate modality imbalance in multimodal learning by adjusting the optimization capacity of each modality

Full Article

Title: Balancing Multimodal Learning through Label Space Reshaping

Abstract:
arXiv:2605.28869v1 Announce Type: cross Abstract: Multimodal learning often suffers from modality imbalance, where modalities that converge faster dominate optimization while others remain undertrained. Existing approaches typically mitigate this issue by strengthening the weak modality or adjusting optimization gradients. However, such strategies mainly compensate for optimization rate discrepancies, often at the expense of the strong modality's optimization capacity, without analyzing how thes

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