CroMe：基于跨模态三变换器和度量学习的多模态假新闻检测
CroMe: Multimodal Fake News Detection using Cross-Modal Tri-Transformer and Metric Learning
AI总结：
CroMe通过跨模态三变换器和度量学习方法，提升多模态假新闻检测的准确性和有效性。
摘要：
多模态假新闻检测近年来受到越来越多的关注。现有方法依赖于独立编码的单模态数据，忽视了利用先进技术捕捉模态内关系和整合模态间相似性的优势。为了解决这些问题，提出了用于多模态假新闻检测的跨模态三变换器和度量学习（CroMe）。CroMe利用Bootstrapping语言-图像预训练结合冻结图像编码器和大语言模型（BLIP2）作为编码器，以捕捉详细的文本、图像和结合图像-文本表示。度量学习模块采用代理锚点方法捕捉模态内关系，而特征融合模块使用跨模态和三变换器进行有效整合。最终的假新闻检测器通过分类器处理融合特征以预测内容的真实性。在数据集上的实验表明，CroMe在多模态假新闻检测中表现优异。
英文摘要：
Multimodal Fake News Detection has received increasing attention recently. Existing methods rely on independently encoded unimodal data and overlook the advantages of capturing intra-modality relationships and integrating inter-modal similarities using advanced techniques. To address these issues, Cross-Modal Tri-Transformer and Metric Learning for Multimodal Fake News Detection (CroMe) is proposed. CroMe utilizes Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models (BLIP2) as encoders to capture detailed text, image and combined image-text representations. The metric learning module employs a proxy anchor method to capture intra-modality relationships while the feature fusion module uses a Cross-Modal and Tri-Transformer for effective integration. The final fake news detector processes the fused features through a classifier to predict the authenticity of the content. Experiments on datasets show that CroMe excels in multimodal fake news detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
