Summary of Beyond Visual Understanding: Introducing Parrot-360v For Vision Language Model Benchmarking, by Harsha Vardhan Khurdula et al.
Beyond Visual Understanding: Introducing PARROT-360V for Vision Language Model Benchmarking
by Harsha Vardhan Khurdula, Basem Rizk, Indus Khaitan, Janit Anjaria, Aviral Srivastava, Rajvardhan Khaitan
First submitted to arxiv on: 20 Nov 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 The abstract presents a novel approach to evaluating Vision Language Models (VLMs) by introducing the PARROT-360V Benchmark. This comprehensive benchmark features 2487 visual puzzles that test VLMs on complex visual reasoning tasks, requiring models to integrate multiple data modalities and solve original problems. The authors evaluate leading models GPT-4o, Claude-3.5-Sonnet, and Gemini-1.5-Pro using PARROT-360V and find a notable performance gap between their scores on this benchmark and popular benchmarks. This highlights the limitations of current VLMs in handling complex tasks and underscores the need for more robust evaluation frameworks to advance the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to test machines that can understand pictures and words. They made 2487 puzzles that are hard to solve just by looking at a picture or reading some text. Instead, you have to use both to figure out what’s going on. The authors tested some of the best machines like this using their new test and found that they don’t do very well. This shows that current machines aren’t good enough for real-life problems where you need to think carefully. |
Keywords
» Artificial intelligence » Claude » Gemini » Gpt
