Summary of Zero Shot Vlms For Hate Meme Detection: Are We There Yet?, by Naquee Rizwan et al.
Zero shot VLMs for hate meme detection: Are we there yet?
by Naquee Rizwan, Paramananda Bhaskar, Mithun Das, Swadhin Satyaprakash Majhi, Punyajoy Saha, Animesh Mukherjee
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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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 research paper investigates the effectiveness of visual language models (VLMs) in detecting hateful memes on social media. The study aims to address the limitation of traditional machine/deep learning models, which require labeled datasets for accurate classification. Instead, VLMs can operate without explicit labels, making them a promising approach for handling zero-shot hate meme detection. The paper employs various prompt settings to focus on zero-shot classification of hateful/harmful memes and analyzes the performance of large VLMs in this task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hate speech on social media is a growing concern, especially when it takes the form of memes. These funny images with captions can spread quickly, but they can also be used to harm people or groups. To stop this kind of hate speech, researchers have been working on ways to detect harmful memes. One problem with traditional methods is that they need lots of labeled data to work well. But what if we could use models that don’t need labels? This study looks at whether big visual language models can help us identify and stop hateful memes. The results show that these models are still not perfect, but they’re a step in the right direction. |
Keywords
* Artificial intelligence * Classification * Deep learning * Prompt * Zero shot
