Summary of Xmecap: Meme Caption Generation with Sub-image Adaptability, by Yuyan Chen et al.
XMeCap: Meme Caption Generation with Sub-Image Adaptability
by Yuyan Chen, Songzhou Yan, Zhihong Zhu, Zhixu Li, Yanghua Xiao
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 tackles the challenge of machine-generated humor, particularly in memes, by developing a novel framework called XMeCap. The framework combines supervised fine-tuning and reinforcement learning to generate captions for single-image and multi-image memes. By incorporating both global and local similarities between visuals and text, XMeCap outperforms contemporary models in caption generation, achieving an average evaluation score of 75.85 for single-image memes and 66.32 for multi-image memes. The results demonstrate the potential of machines in understanding and generating humor in a multi-modal setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching computers to understand and create funny images called memes. Memes usually have multiple pictures and captions that work together to make people laugh. The researchers created a new way to train computers to generate these captions, which works better than other methods. They tested their method on different types of memes and found it was more successful than others in creating funny and relevant captions. |
Keywords
» Artificial intelligence » Fine tuning » Multi modal » Reinforcement learning » Supervised
