---
license: cc-by-sa-4.0
language:
- zh
- en
tags:
- gui
- agents
pretty_name: FunUI
size_categories:
- 1K<n<10K
---
# FunUI Benchmark

## 📖 Introduction
**FunUI** is a bilingual benchmark designed to fill the gap of comprehensive evaluation datasets in the field of **screen understanding**.  
It encompasses **four fundamental tasks** and provides a holistic evaluation platform to assess models’ abilities on mobile UI comprehension.

---

## ✨ Key Features

- **Bilingual**  
  - Includes **2,150 Chinese screens** and **9,347 English screens** from Android devices.  
  - Annotated with about **14k Chinese samples** and **18k English samples**.  
  - The **first benchmark** that enables systematic evaluation of **both Chinese and English screen understanding**.

- **Comprehensive**  
  - Covers multiple dimensions of screen understanding:  
    - **UI Grounding** (element localization)  
    - **UI Referring** (element identification)  
    - **Screen Question Answering**  
    - **Screen Summarization**  
  - Ranges from spatial grounding and entity recognition to integrated analysis of screen content.

- **Diverse**  
  - Provides QA pairs involving **120+ icons and widgets**.  
  - Includes **complex reasoning questions** related to element relations, attributes, arithmetic, and more.  
  - Poses greater challenges compared to commonly used OCR-related tasks.

---

## 📊 Tasks

1. **UI Grounding**  
   - Models are required to localize the target UI element.  

2. **UI Referring**  
   - Models identify the specific UI element described in bbox format.  

3. **Screen Question Answering**  
   - Models answer diverse questions about screen content.  

4. **Screen Summarization**  
   - Models generate summaries of the observed screen.  

---

## 🚀 Applications
- Automated **UI comprehension** and interaction.  
- Development of **intelligent assistants** and mobile automation.  
- **Benchmarking multimodal models** for screen understanding.  


---

## 📜 Citation
If you use **FunUI** benchmark in your research, please cite our paper: 

```bibtex
@article{202408.2137,
    title = {UI-Hawk: Unleashing the Screen Stream Understanding for GUI Agents},
    author = {Jiwen Zhang and Yaqi Yu and Minghui Liao and Wentao Li and Jihao Wu and Zhongyu Wei},
    doi = {10.20944/preprints202408.2137.v1},
    url = {https://doi.org/10.20944/preprints202408.2137.v1},
    year = 2024,
    month = {August},
    publisher = {Preprints},
    journal = {Preprints}
}
```