| 标题 |
 Dynamic hierarchical memory improved mixture-of-experts for multimodal fake news detection 用于多模态假新闻检测的动态分层记忆改进专家混合
相关领域
计算机科学
假新闻
人工智能
特征（语言学）
钥匙（锁）
动态随机存取存储器
计算机视觉
噪音（视频）
模式识别（心理学）
遮罩（插图）
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| 网址 | |
| DOI | |
| 其它 |
Abstract The explosive growth of social media information has exacerbated the risk of fake news dissemination. This paper proposes a Dynamic Hierarchical Memory Improved Mixture-of-Experts for Multimodal Fake News Detection, named MemiMoE-FND. Specifically, MemiMoE-FND first utilizes advanced feature encoders to extract multi-scale feature representations of modalities. Then, the Memory Improved Mixture-of-Experts is introduced to enhance the feature refinement ability. Next, the Dynamic Hierarchical Memory Improved Mixture-of-Experts is designed, using a two-level expert network to progressively refine and fuse features, achieving collaborative optimization of multi-scale features. Finally, a lightweight Fusion_Gate is designed to dynamically adjust feature contributions while ensuring transparency in the decision-making process. Experimental results on three benchmark datasets |
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(2025-6-4)
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