Summary of Vision Language Models Are Blind, by Pooyan Rahmanzadehgervi et al.
Vision language models are blind
by Pooyan Rahmanzadehgervi, Logan Bolton, Mohammad Reza Taesiri, Anh Totti Nguyen
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 The abstract discusses the performance of large language models with vision capabilities (VLMs) on low-level vision tasks that are easy for humans. Despite scoring high on various benchmarks, VLMs struggle to achieve human-like accuracy on simple tasks such as identifying overlapping circles, intersecting lines, and recognizing circled letters. The best-performing VLM, Claude 3.5 Sonnet, achieves only 74.94% accuracy on average, far from the expected human accuracy of 100%. The paper highlights the limitations of current VLMs in processing precise spatial information and geometric primitives that overlap or are close together. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VLMs, which can understand text and images, are great at many things! However, they’re not so good at some simple tasks that humans find easy. For example, identifying if two shapes overlap or if lines cross is tricky for VLMs. Researchers found that even the best VLMs only get about 75% of these tasks right, which is still far from perfect. This shows that there’s room for improvement in how VLMs process visual information. |
Keywords
» Artificial intelligence » Claude
