Summary of Prompting Large Vision-language Models For Compositional Reasoning, by Timothy Ossowski et al.
Prompting Large Vision-Language Models for Compositional Reasoning
by Timothy Ossowski, Ming Jiang, Junjie Hu
First submitted to arxiv on: 20 Jan 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 The paper proposes a novel generative method that leverages large vision-language models like GPT-4 to depict images and perform compositional reasoning. This approach aims to overcome limitations in current embedding-based methods, which struggle to match images and texts with similar visio-linguistic compositionality, as seen on the Winoground dataset. By prompting these models to generate images and reason step-by-step, the authors demonstrate improved performance on the Winoground dataset, achieving up to 10% accuracy gain when enhanced with optimal descriptions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores new ways for machines to understand pictures and words by using really big language models like GPT-4. These models are normally good at recognizing things in images and understanding text, but they have trouble figuring out how an image is related to some written description. The researchers tried something different: they told the model to draw a picture and then think about what makes that picture similar or different from another one. This helped them do much better on a test called Winoground. |
Keywords
» Artificial intelligence » Embedding » Gpt » Prompting
