arXiv:2605.28869v1 Announce Type: new 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,
This research addresses a fundamental challenge in multimodal AI, which is becoming increasingly prevalent across various applications.
Improving the efficiency and effectiveness of multimodal learning directly impacts the performance and reliability of advanced AI systems, influencing their broader adoption and capabilities.
This research proposes a new paradigm for balancing multimodal learning, potentially moving beyond existing compensation strategies to address the root causes of imbalance.
- · AI researchers
- · Multimodal AI developers
- · SaaS companies
- · Robotics
- · Inefficient multimodal AI systems
- · Developers reliant on heuristic balancing methods
Improved performance and reliability of multimodal AI models, particularly in complex data environments.
Accelerated development and deployment of sophisticated AI agents and autonomous systems.
Enhanced capabilities for AI to interpret and interact with the real world through diverse sensory inputs, leading to more robust applications across various sectors.
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Read at arXiv cs.LG