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C2MOE：基于一致性与互补性的专家混合模型用于不完整多模态情感学习
原标题：C$^2$MOE: Consistency and Complementarity-guided Mixture of Experts for Incomplete Multimodal Emotion Learning
AI 摘要
arXiv 上发表了一篇关于多模态情感识别的新研究，提出了 C2MOE 框架，通过一致性和互补性引导的专家混合模型来处理不完整的多模态输入。该方法在多个基准测试上超越了现有方法，提升了鲁棒性和泛化能力。
以上摘要由 AI 生成，可能存在误差。事实请以原文为准。
正文节选
Computer Science > Machine Learning Title:C$^2$MOE: Consistency and Complementarity-guided Mixture of Experts for Incomplete Multimodal Emotion Learning View PDF HTML (experimental) Abstract:Recent advances in Multimodal Emotion Recognition in Conversations (MERC) highlight its reliance on complete multimodal inputs. However, real-world data often suffer from missing modalities due to transmission errors or user behavior, severely degrading model performance. Existing methods enhance
发布时间：2026-08-06 12:00
抓取时间：2026-08-06 20:49
来源机构：arXiv