Summary of Trustworthy Hate Speech Detection Through Visual Augmentation, by Ziyuan Yang and Ming Yan and Yingyu Chen and Hui Wang and Zexin Lu and Yi Zhang
Trustworthy Hate Speech Detection Through Visual Augmentation
by Ziyuan Yang, Ming Yan, Yingyu Chen, Hui Wang, Zexin Lu, Yi Zhang
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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 The paper proposes a novel hate speech detection method called TrusV-HSD that addresses the uncertainty inherent in hate speech detection by integrating diffused visual images and trustworthy loss. The method learns semantic representations by extracting trustworthy information through multi-modal connections without paired data, outperforming conventional methods on public datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps fight hate speech on social media platforms by developing a new way to detect it. It uses pictures and trustworthy loss to make the detection more accurate and reduce uncertainty. The method is tested on publicly available datasets and shows significant improvements over existing techniques. |
Keywords
» Artificial intelligence » Multi modal
