Summary of Locatebench: Evaluating the Locating Ability Of Vision Language Models, by Ting-rui Chiang et al.
LocateBench: Evaluating the Locating Ability of Vision Language Models
by Ting-Rui Chiang, Joshua Robinson, Xinyan Velocity Yu, Dani Yogatama
First submitted to arxiv on: 17 Oct 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 This paper proposes LocateBench, a benchmark to evaluate the ability of artificial intelligence models to locate objects in images based on natural language instructions. The authors experiment with different prompting approaches and measure the accuracy of various large vision language models. Surprisingly, even the strongest model, GPT-4o, falls short by more than 10% compared to human performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to find a specific object in a picture based on what someone tells you. This is called “visual grounding” and it’s really important for things like self-driving cars or helping people with disabilities. The researchers created a special test to see how well computer models do at this task, and they found that even the best models are still not as good as humans. |
Keywords
» Artificial intelligence » Gpt » Grounding » Prompting
