Summary of Good at Captioning, Bad at Counting: Benchmarking Gpt-4v on Earth Observation Data, by Chenhui Zhang et al.
Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data
by Chenhui Zhang, Sherrie Wang
First submitted to arxiv on: 31 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 paper explores the capabilities of large vision-language models (VLMs) on Earth observation (EO) data, which are less common in VLM training data. The authors propose a comprehensive benchmark to gauge the progress of VLMs toward being useful tools for EO data by assessing their abilities on scene understanding, localization and counting, and change detection tasks. The benchmark includes scenarios like urban monitoring, disaster relief, land use, and conservation. While state-of-the-art VLMs like GPT-4V possess extensive world knowledge that leads to strong performance on open-ended tasks like location understanding and image captioning, their poor spatial reasoning limits usefulness on object localization and counting tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well big computer models can understand pictures taken from space or the air. These models are really good at doing things with words and pictures together, but they haven’t been tested much with Earth observation data. The authors created a test to see how well these models do on different tasks like recognizing what’s in a picture, finding specific things in a picture, and seeing if something has changed over time. They tested the models on real-world scenarios like monitoring cities, helping after natural disasters, and studying how people use land. While the best models are great at understanding words and pictures together, they’re not very good at figuring out what’s in a picture or counting things. |
Keywords
» Artificial intelligence » Gpt » Image captioning » Scene understanding
