Summary of Analyzing the Roles Of Language and Vision in Learning From Limited Data, by Allison Chen et al.
Analyzing the Roles of Language and Vision in Learning from Limited Data
by Allison Chen, Ilia Sucholutsky, Olga Russakovsky, Thomas L. Griffiths
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 investigates the role of language in understanding the visual world by analyzing Vision-Language Models (VLMs). By isolating components from these models, researchers identify how language and vision contribute to learning new tasks. The study finds that a language model can recover most of a VLM’s performance without visual input, suggesting that language provides access to prior knowledge and reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores the relationship between language and vision in artificial intelligence. Researchers use special computer models called Vision-Language Models (VLMs) to see how important each is for learning about the world. They take away parts of these models to figure out what makes them work. Surprisingly, a model just using words can do most of what a visual-lingual model does, proving that language helps by giving access to things we already know. |
Keywords
* Artificial intelligence * Language model
