Summary of What Is the Visual Cognition Gap Between Humans and Multimodal Llms?, by Xu Cao et al.
What is the Visual Cognition Gap between Humans and Multimodal LLMs?
by Xu Cao, Bolin Lai, Wenqian Ye, Yunsheng Ma, Joerg Heintz, Jintai Chen, Jianguo Cao, James M. Rehg
First submitted to arxiv on: 14 Jun 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 In this research paper, Multimodal Large Language Models (MLLMs) are explored for their ability to tackle high-level reasoning tasks in visual cognition. Specifically, the study focuses on abstract visual reasoning (AVR), which involves recognizing patterns and relationships among images and extrapolating to predict subsequent patterns. This cognitive skill is essential during early childhood development. To evaluate MLLMs’ zero-shot AVR capability, a new dataset called MaRs-VQA and a benchmark called VCog-Bench are proposed. The benchmark consists of three datasets that compare the performance of different open-source and closed-source MLLMs with human intelligence. The results show a gap between current MLLMs and human intelligence, highlighting their visual cognitive limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how well Multimodal Large Language Models (MLLMs) can solve problems that require thinking about pictures in a smart way. This is important for learning and development in children. The researchers created a new set of test images called MaRs-VQA to see how well MLLMs can do this kind of thinking without any help or training. They also compared the MLLMs’ performance with human intelligence, which showed that there’s still a lot to be learned from humans. |
Keywords
» Artificial intelligence » Zero shot
