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---
language:
- zh
license: apache-2.0
size_categories:
- 1K<n<10K
task_categories:
- question-answering
- visual-question-answering
tags:
- life
- art
- society
- environment
- politics
- Chinese traditional culture
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: image
- name: question
dtype: string
- name: option1
dtype: string
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dtype: string
- name: option3
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dtype: string
- name: option5
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- name: option6
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- name: correct_option
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- name: answer
dtype: string
- name: image_type
dtype: string
- name: difficulty
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- name: domain
dtype: string
- name: emotion
dtype: string
- name: rhetoric
dtype: string
- name: explanation
dtype: string
- name: metaphorical_meaning
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splits:
- name: test
num_bytes: 168499787.0
num_examples: 765
- name: dev
num_bytes: 7923760.0
num_examples: 35
download_size: 150440826
dataset_size: 176423547.0
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: dev
path: data/dev-*
---
# CII-Bench
[**🌐 Homepage**](https://cii-bench.github.io/) | [**🤗 Dataset**](https://huggingface.co/datasets/m-a-p/CII-Bench) | [**GitHub**](https://github.com/MING-ZCH/CII-Bench) | [**🤗 Paper**](https://huggingface.co/papers/2410.13854) | [**📖 arXiv**](https://arxiv.org/abs/2410.13854)
<div style="text-align: center;">
<img src="composition.png" width="40%">
</div>
## Introduction
**CII-Bench** comprises 698 **Chinese images**, each accompanied by 1 to 3 multiple-choice questions, totaling 800 questions. CII-Bench encompasses images from six distinct domains: Life, Art, Society, Environment, Politics, and Chinese Traditional Culture. It also features a diverse array of image types, including Illustrations, Memes, Posters, Multi-panel Comics, Single-panel Comics, and Paintings. The detailed statistical information can be found in the image below.
<div style="text-align: center;">
<img src="CII-Bench-type.png" width="80%">
</div>
## Example
Here are some examples of CII-Bench:
<div style="text-align: center;">
<img src="CII-Bench-sample.png" width="80%">
</div>
## Disclaimers
The guidelines for the annotators emphasized strict compliance with copyright and licensing rules from the initial data source, specifically avoiding materials from websites that forbid copying and redistribution.
Should you encounter any data samples potentially breaching the copyright or licensing regulations of any site, we encourage you to [contact](#contact) us. Upon verification, such samples will be promptly removed.
## Contact
- Chenhao Zhang: ch_zhang@hust.edu.cn
- Xi Feng: fengxi@ustc.edu
- Ge Zhang: gezhang@umich.edu
- Shiwen Ni: sw.ni@siat.ac.cn
## Citation
**BibTeX:**
```bibtex
@misc{zhang2024mllmsunderstanddeepimplication,
title={Can MLLMs Understand the Deep Implication Behind Chinese Images?},
author={Chenhao Zhang and Xi Feng and Yuelin Bai and Xinrun Du and Jinchang Hou and Kaixin Deng and Guangzeng Han and Qinrui Li and Bingli Wang and Jiaheng Liu and Xingwei Qu and Yifei Zhang and Qixuan Zhao and Yiming Liang and Ziqiang Liu and Feiteng Fang and Min Yang and Wenhao Huang and Chenghua Lin and Ge Zhang and Shiwen Ni},
year={2024},
eprint={2410.13854},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.13854},
}
``` |