什么能提升多模态虚假信息检测？大规模实证研究给出答案
原标题：What Improves Multimodal Misinformation Detection? Answers from a Large-Scale Empirical Study
AI 摘要
该论文对多模态虚假信息检测的设计选择进行了大规模实证研究，在三个基准数据集上开展了3375次实验，涵盖675次域内评估、1350次模态消融和1350次跨数据集迁移。研究系统比较了视觉编码器、文本编码器、融合策略和分类器等组件的影响，并回答了四个关键研究问题。结论为设计更可靠的多模态虚假信息检测系统提供了可复现的实践指导。
正文节选
What Improves Multimodal Misinformation Detection? Answers from a Large-Scale Empirical Study Abstract Multimodal misinformation is increasingly crafted to look convincing by pairing a textual claim with an image that appears to “prove” it. Yet in practice, building effective detectors often hinges on a small set of design choices that are rarely examined in a controlled way. In this paper, we conduct a large-scale study of multimodal design choices for misinformation detection with over 3,375 e