Summary of Vmid: a Multimodal Fusion Llm Framework For Detecting and Identifying Misinformation Of Short Videos, by Weihao Zhong et al.
VMID: A Multimodal Fusion LLM Framework for Detecting and Identifying Misinformation of Short Videos
by Weihao Zhong, Yinhao Xiao, Minghui Xu, Xiuzhen Cheng
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel method for detecting fake news on short video platforms. The existing methods mainly rely on single-modal information or apply basic fusion techniques, which limits their ability to handle complex, multi-layered information inherent in short videos. To address this limitation, the proposed approach utilizes multimodal representations to generate a unified textual description, which is then fed into a large language model for comprehensive evaluation. The framework integrates multimodal features within videos, enhancing the accuracy and reliability of fake news detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The method can detect misinformation through a multi-level analysis of video content, utilizing different modal representations such as text, images, and audio. This approach outperforms existing models in terms of accuracy, robustness, and utilization of multimodal information, achieving an accuracy of 90.93%. The case studies provide additional evidence of the effectiveness of the approach in accurately distinguishing between fake news, debunking content, and real incidents. |
Keywords
» Artificial intelligence » Large language model
