---
title: ViTexQA
canonical_url: "https://www.modelscope.cn/datasets/faker12345/ViTexQA"
md_url: "https://www.modelscope.cn/datasets/faker12345/ViTexQA.md"
repository: faker12345/ViTexQA
last_updated: 2026-07-04
license: "Apache License 2.0"
storage_size: "168 GB"
downloads: 3095
stars: 0
---
# ViTexQA
> ViTexQA - faker12345 在 ModelScope 开源的数据集。数据集文件元信息以及数据文件，请浏览“数据集文件”页面获取。
faker12345/ViTexQA 是 ModelScope 魔搭社区上的数据集，存储大小 168 GB，采用 Apache License 2.0 许可。
- **Repository**: faker12345/ViTexQA
- **License**: Apache License 2.0
- **Storage size**: 168 GB
- **Downloads**: 3095
- **Stars**: 0
- **Last updated**: 2026-07-04
Source: https://www.modelscope.cn/datasets/faker12345/ViTexQA
---
数据集文件元信息以及数据文件，请浏览“数据集文件”页面获取。
当前数据集卡片使用的是默认模版，数据集的贡献者未提供更加详细的数据集介绍，但是您可以通过如下GIT Clone命令，或者ModelScope SDK来下载数据集
# ViTexQA: A Multi-Frame Temporal Perception Dataset for Video Text Question Answering
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[](https://Modelscope.co/datasets/Zhentao1/ViTexQA/tree/main)
---
## 🔥 News
- **2026/06** 🎉 ViTexQA has been accepted to **ECCV 2026**.
- **2026/06** 🤗 ViTexQA dataset is released on Modelscope.
- **Coming Soon** Training code for FrameThinker.
---
## 📖 Introduction
ViTexQA is a large-scale benchmark for **multi-frame video text understanding**.
Unlike existing video text QA datasets, where many questions can still be answered from a single frame, **every question in ViTexQA requires integrating textual information across multiple video frames.**
Our contributions include:
- ✅ 5,147 videos
- ✅ 6,864 QA pairs
- ✅ Multi-frame dependency
- ✅ Temporal Chain-of-Thought annotations
- ✅ Diverse real-world scenarios (sports, news, driving, tutorials, etc.)
- ✅ Synthetic rolling-text video generation pipeline
---
## 📊 Dataset Statistics
ViTexQA contains
| Item | Value |
|------|------:|
| Videos | 5,147 |
| QA pairs | 6,864 |
| Categories | 30 |
| Duration | 363 Hours |
---
---
# 📝 Annotation Pipeline
To ensure that every question requires **genuine multi-frame temporal perception**, we develop a rigorous three-round human annotation pipeline.
The annotation process consists of three stages:
### Round 1 · Initial Annotation
- Two annotators independently create question-answer pairs.
- Questions must require integrating textual information across multiple frames.
- Single-frame answerable questions are rejected.
### Round 2 · Quality Evaluation
Each sample is reviewed by senior evaluators according to:
- Multi-frame dependency
- Answer correctness
- Question clarity
Samples are assigned three quality levels:
- **Score 1:** Reject
- **Score 2:** Revision Required
- **Score 3:** Accepted
### Round 3 · Revision
Samples requiring revision are returned to expert annotators for refinement and re-evaluation until all annotations satisfy the highest quality standard.
This iterative annotation strategy guarantees:
- ✅ Multi-frame dependency
- ✅ High-quality QA pairs
- ✅ Accurate temporal reasoning annotations
- ✅ Diverse and unambiguous questions
# 📥 Dataset Download
The complete dataset is hosted on Modelscope.
Download includes:
```
ViTexQA/
│
├── train.json
├── test.json
│
└── videos/
├── Real_video/
└── Synthetic_video/
```
Each sample contains
- index
- video_name
- question
- answer
- temporal CoT
- duration
- category
---
# 🛠 Synthetic Video Generation
We also release the synthetic rolling-text video generation pipeline used in the paper.
```
synthetic/
├── ILSVRC2012/
├── TextQA/
├── font/
├── textvqa.txt
├── words.txt
├── image2video_rolling.py
└── image2video_textqa.py
```
The pipeline supports
- Random text corpus
- Random fonts
- Random colors
- Shadows
- Transparency
- Scrolling animation
- Typewriter animation
- Multiple transition effects
- Automatic QA annotation generation
Example
```bash
python image2video_rolling.py
python image2video_textqa.py
```
Generated videos can be directly used for training and evaluation.
---
# 📷 Examples
---
# 📄 Paper
If you find our work useful, please consider citing
```bibtex
@article{guo2026vitexqa,
title={ViTexQA: A Multi-Frame Temporal Perception Dataset for Video Text Question Answering},
author={Guo, Zhentao and Duan, Chen and Guan, Tongkun and Wang, Zining and Zhou, Kai and Yan, Pengfei},
journal={arXiv preprint arXiv:2606.24602},
year={2026}
}
```
---
# 🙏 Acknowledgements
We thank all annotators and the open-source community for making this project possible.
---
# ⭐ Star
If ViTexQA is useful for your research, please consider giving this repository a ⭐.
