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---
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}
}
``` |