Summary of Predicting Winning Captions For Weekly New Yorker Comics, by Stanley Cao et al.
Predicting Winning Captions for Weekly New Yorker Comics
by Stanley Cao, Sonny Young
First submitted to arxiv on: 12 Jul 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 proposed paper explores the application of image captioning techniques using Vision Transformers (ViTs) to generate captions for New Yorker cartoons, aiming to emulate the wit and humor of winning entries in the New Yorker Cartoon Caption Contest. This task requires sophisticated visual and linguistic processing, along with an understanding of cultural nuances and humor. The authors propose several new baselines for using vision transformer encoder-decoder models to generate captions for this contest. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses computer vision and natural language processing to create funny captions for cartoons from the New Yorker magazine. They want to make a machine that can write humorous captions like the winners of the New Yorker Cartoon Caption Contest do. To do this, they need a model that understands what’s happening in an image, what people find funny, and how to use words correctly. |
Keywords
» Artificial intelligence » Encoder decoder » Image captioning » Natural language processing » Vision transformer
