Summary of Vialm: a Survey and Benchmark Of Visually Impaired Assistance with Large Models, by Yi Zhao et al.
VIALM: A Survey and Benchmark of Visually Impaired Assistance with Large Models
by Yi Zhao, Yilin Zhang, Rong Xiang, Jing Li, Hillming Li
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 research paper explores the potential of state-of-the-art language models in visually impaired assistance (VIA). The study aims to investigate how large language models (LMs) can be used to help individuals with visual impairments complete daily tasks. The authors present an extensive review of recent LM research and benchmark experiments examining the capabilities of selected LMs in VIA applications. They find that while LMs have potential benefits for VIA, their output is often not well-environment-grounded and lacks fine-grained guidance. For example, GPT-4’s responses had a 25.7% error rate in environment grounding and a 32.1% error rate in providing fine-grained guidance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us understand how big language models can assist people who are blind or have low vision with everyday tasks. The study looks at how well these models can give step-by-step instructions based on pictures of environments and verbal requests from visually impaired users. The results show that while these models have potential, they often struggle to provide accurate information about the environment and detailed guidance. |
Keywords
» Artificial intelligence » Gpt » Grounding
