Summary of Gamed: Knowledge Adaptive Multi-experts Decoupling For Multimodal Fake News Detection, by Lingzhi Shen et al.
GAMED: Knowledge Adaptive Multi-Experts Decoupling for Multimodal Fake News Detection
by Lingzhi Shen, Yunfei Long, Xiaohao Cai, Imran Razzak, Guanming Chen, Kang Liu, Shoaib Jameel
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 introduces a novel approach, GAMED, for multimodal fake news detection. The existing methods rely on fusion effectiveness and cross-modal consistency, making it difficult to understand how each modality affects prediction accuracy. GAMED focuses on generating distinctive features through modal decoupling to enhance cross-modal synergies, optimizing overall performance. It leverages parallel expert networks to refine features, pre-embed semantic knowledge to improve information selection and viewpoint sharing, and adaptively adjusts feature distribution based on experts’ opinions. The paper also introduces a novel classification technique for dynamically managing contributions from different modalities and improving explainability of decisions. Experimental results on the Fakeddit and Yang datasets demonstrate GAMED’s performance superiority over state-of-the-art models. Keywords: multimodal fake news detection, GAMED, parallel expert networks, cross-modal synergies, feature distribution, classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to detect fake news that uses information from multiple sources like pictures and words. Right now, most methods try to combine these different types of data in a way that’s hard to understand. The new approach, called GAMED, tries to make each type of data better by working separately on each one. Then it combines the results to make a more accurate prediction about whether something is fake or real. The researchers tested this method and found it worked better than other methods they looked at. This could help us spot fake news more easily and stop it from spreading. |
Keywords
» Artificial intelligence » Classification
