| 标题 |
 Improving multimodal fake news detection by leveraging cross-modal content correlation 利用跨模态内容相关性改进多模态假新闻检测
相关领域
情态动词
相关性
计算机科学
内容（测量理论）
互相关
语音识别
人工智能
统计
数学
材料科学
数学分析
几何学
高分子化学
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| 网址 | |
| DOI | |
| 其它 | The widespread presence of multimodal fake news on social media platforms has severely impacted public order, making the automatic detection and filtering of such content a pressing issue. Although existing studies have attempted to integrate multimodal data for this task, they often struggle to effectively model cross-modal correlations. Most approaches focus on the global features of each modality and compute scalar similarities, which limits their capacity to learn and process comprehensive samples. To address this challenge, this paper introduces a novel cross-modal content correlation network. This method leverages salient objects from images and nouns from the text as the multimodal content, utilizing CLIP to extract generalizable features for similarity measurement, thereby enhancing cross-modal interaction. By applying convolution to the similarity matrix between nouns and image crops, |
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