Summary of Cross-modal Augmentation For Few-shot Multimodal Fake News Detection, by Ye Jiang et al.
Cross-Modal Augmentation for Few-Shot Multimodal Fake News Detection
by Ye Jiang, Taihang Wang, Xiaoman Xu, Yimin Wang, Xingyi Song, Diana Maynard
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty Summary: This paper addresses the pressing issue of fake news detection by introducing a multimodal approach that leverages Cross-Modal Augmentation (CMA) to enhance few-shot learning. The proposed method transforms n-shot classification into a more robust (n × z)-shot problem, allowing for accurate detection with limited annotated samples. The CMA achieves state-of-the-art results on three benchmark datasets using a surprisingly simple linear probing method. Notably, the approach is significantly more lightweight than prior methods, requiring fewer trainable parameters and epoch times. This paper’s contribution lies in its ability to rapidly acquire proficiency in detecting fake news, making it an important step towards tackling misinformation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: Imagine if you could quickly learn how to spot fake news just by looking at a few examples. That’s what this research is all about! The scientists developed a new way to detect fake news using pictures and words together. They called it Cross-Modal Augmentation, or CMA for short. This method helps us figure out whether something is real or not even when we only have a little information. It worked really well on three different tests and was much faster than other methods. Now, you can use this method to help keep the internet safe from fake news! |
Keywords
» Artificial intelligence » Classification » Few shot » N shot
