Summary of How Vision-language Tasks Benefit From Large Pre-trained Models: a Survey, by Yayun Qi et al.
How Vision-Language Tasks Benefit from Large Pre-trained Models: A Survey
by Yayun Qi, Hongxi Li, Yiqi Song, Xinxiao Wu, Jiebo Luo
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 The proposed paper explores how pre-trained models have revolutionized the field of vision-language tasks, including visual captioning, question answering, and commonsense reasoning. Despite improvements in overall performance, classic challenges persist and hinder further development. The rise of pre-training has led to new paradigms that address these limitations, making pre-trained models a mainstream approach in current research. This paper provides an overview of how vision-language tasks benefit from pre-trained models, discussing main challenges, recent advances, and potential risks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can understand images and words better than before. It looks at what’s been going on in this area of artificial intelligence and how things have improved with new kinds of computer models. Even though things are getting better, there are still some big challenges to overcome. The researchers want to know how these new models can help solve those problems. |
Keywords
» Artificial intelligence » Question answering
