Summary of Mvp-bench: Can Large Vision–language Models Conduct Multi-level Visual Perception Like Humans?, by Guanzhen Li et al.
MVP-Bench: Can Large Vision–Language Models Conduct Multi-level Visual Perception Like Humans?
by Guanzhen Li, Yuxi Xie, Min-Yen Kan
First submitted to arxiv on: 6 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 The paper explores the capabilities of Large Visual-Language Models (LVLMs) in performing multi-level visual perception, specifically examining how subtle differences in low-level object recognition affect high-level semantic interpretation. The authors investigate whether LVLMs can capture nuances in human perception by analyzing multimodal tasks that involve recognizing objects and understanding behaviors. For instance, substituting a shopping bag with a gun can drastically change the perceived behavior of a person. By examining the capabilities of LVLMs in these tasks, the paper aims to advance our understanding of how language models can be applied to complex visual perception problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how big computers learn to see and understand pictures like people holding things. Sometimes, if we change what’s being held, it changes what we think is happening. The authors want to know if these computers are good at this kind of thinking. They’re testing the computers on tricky tasks that need both low-level details (like recognizing objects) and high-level understanding (like figuring out behaviors). For example, if someone holds a shopping bag instead of a gun, it changes what we think they might be doing. |
