Summary of Can Mllms Understand the Deep Implication Behind Chinese Images?, by Chenhao Zhang et al.
Can MLLMs Understand the Deep Implication Behind Chinese Images?
by Chenhao Zhang, Xi Feng, Yuelin Bai, Xinrun Du, Jinchang Hou, Kaixin Deng, Guangzeng Han, Qinrui Li, Bingli Wang, Jiaheng Liu, Xingwei Qu, Yifei Zhang, Qixuan Zhao, Yiming Liang, Ziqiang Liu, Feiteng Fang, Min Yang, Wenhao Huang, Chenghua Lin, Ge Zhang, Shiwen Ni
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY)
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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 introduces the Chinese Image Implication understanding Benchmark (CII-Bench), a new evaluation framework for Multimodal Large Language Models (MLLMs) to assess their higher-order perception and understanding capabilities of Chinese images. The CII-Bench stands out by sourcing images from the Chinese Internet, manually reviewing them, and crafting corresponding answers. It also incorporates images representing Chinese traditional culture, such as famous paintings, to reflect the model’s understanding of this context. Through experiments on multiple MLLMs, the paper finds a significant gap between human and machine performance, with humans achieving 78.2% accuracy compared to MLLMs’ 64.4%. The results suggest limitations in MLLMs’ ability to understand high-level semantics and lack a deep knowledge base of Chinese traditional culture. However, incorporating image emotion hints into prompts enhances MLLMs’ accuracy. The CII-Bench aims to advance the journey towards expert artificial general intelligence (AGI) by enabling MLLMs to better understand Chinese semantics and images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn more about how computers can understand pictures from China. It makes a special test, called CII-Bench, to see if machines are good at recognizing what’s in these pictures. The test uses real pictures from the internet and makes sure they’re correct. It also includes pictures of famous Chinese paintings, which is cool! Scientists found that computers aren’t as good as humans at understanding these pictures, but they can get better when given hints about how people feel about certain images. |
Keywords
» Artificial intelligence » Knowledge base » Semantics
