Balancing Multimodal Learning through Label Space Reshaping
Multimodal learning often suffers from modality imbalance, where faster-converging modalities dominate optimization. Existing methods typically strengthen weak modalities or adjust gradients, but may compromise strong modalities. This paper proposes Balanced Multimodal Label Reshaping (BMLR), the first label-side approach to promote balance. BMLR reshapes the cross-modal label space to equalize mapping difficulty across modalities, enhancing interaction and injecting rich inter-class information. Extensive experiments show consistent improvement and compatibility.
Article intelligence
Key points
- Modality imbalance arises from differences in mapping difficulty from feature spaces to the shared label space.
- BMLR is the first method to address multimodal balance from the label side.
- BMLR reshapes the label space to equalize mapping difficulty, improving modality interaction.
- Experiments across architectures demonstrate consistent performance gains and strong compatibility.
Why it matters
This matters because modality imbalance arises from differences in mapping difficulty from feature spaces to the shared label space.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.28869] Balancing Multimodal Learning through Label Space Reshaping
[Submitted on 22 May 2026]
Title:Balancing Multimodal Learning through Label Space Reshaping
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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 these discrepancies arise at the modality level. Based on theoretical insights and empirical observations, we argue that the discrepancy of learning pace arises from differences in the mapping difficulty between modality-specific feature space and the shared label space. To address this issue, we propose Balanced Multimodal Label Reshaping (BMLR), the first method that promotes multimodal balance from the label-side design. BMLR reshapes the cross-modal label space to equalize mapping difficulty across modalities, thereby facilitating modality interaction and injecting richer inter-class information into each modality. Extensive experiments across multiple architectures demonstrate that BMLR consistently improves multimodal performance and exhibits strong compatibility with diverse model designs. The source code will be released soon.
Comments: In process
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.28869 [cs.LG]
(or arXiv:2605.28869v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.28869
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xiaoyu Ma [view email] [v1] Fri, 22 May 2026 08:22:31 UTC (4,057 KB)
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