English(EN) MSCR: Jointly Balancing Utilization Across Modalities and Discovering Synergistic Information

新的MSCR方法通过平衡模态和协同作用来增强多模态学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员推出了一种新颖的多模态协同竞争正则化器（MSCR）方法，旨在解决多模态学习中的模态不平衡问题。该方法旨在通过联合促进模态利用的平衡和协同信息的发现来增强模型利用跨模态交互的能力。MSCR采用反主导目标来抑制过度的单模态影响，并采用协同目标来提升融合预测器在单模态参考之上的性能，同时通过自适应门控进一步优化特定样本的正则化。在五个基准测试上的实验表明，MSCR的性能持续优于MCR等现有方法，尤其是在基于Transformer的融合模块中，能够改善条件恢复以及预测归因于跨模态交互的能力。 AI

影响通过提高多模态AI系统整合和利用不同数据类型信息的能力来增强其性能。

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arXiv cs.AI TIER_1 English(EN) · Xinyu Chen, Liangjian Wen, Jiang Duan, Dongkai Wang, Yong Dai, Jiayu Bai, Jianzhuang Liu, Zhen Tian, Guoping Qiu, Zhao Kan · 2026-11-11 05:00

MSCR：联合平衡跨模态利用并发现协同信息

arXiv:2610.24395v2 Announce Type: replace Abstract: In multimodal learning, modality imbalance often lets one modality dominate optimization and keeps the model from fully exploiting cross-modal interactions. Balance-oriented methods such as the Multimodal Competition Regularizer…

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MSCR：联合平衡跨模态利用并发现协同信息

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