Summary of Hateful Meme Detection Through Context-sensitive Prompting and Fine-grained Labeling, by Rongxin Ouyang et al.
Hateful Meme Detection through Context-Sensitive Prompting and Fine-Grained Labeling
by Rongxin Ouyang, Kokil Jaidka, Subhayan Mukerjee, Guangyu Cui
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multimedia (cs.MM)
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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 The paper proposes a novel end-to-end conceptual framework for optimizing models in complex multi-modal classification tasks, specifically designed to handle social media content. By incorporating modalities, prompting, labeling, and fine-tuning, the framework achieves state-of-the-art accuracy and AUROC. The study demonstrates that isolated optimizations are effective on their own, showcasing the potential of this approach for enhancing automated moderation strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to solve a problem with social media content being mixed with different types like images and text. This makes it hard to decide if something is good or bad. To help with this, the researchers came up with a new way to make models better at understanding this mix of content. They tested their idea and found that it worked really well, getting the best results so far. They also showed that each part of their approach works by itself. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Multi modal » Prompting
