---
license: apache-2.0
task_categories:
- image-text-to-text
language:
- en
tags:
- multimodal
- vlm
- benchmark
- visualized-text
---

# VISTA-Bench

[**Paper**](https://arxiv.org/abs/2602.04802) | [**GitHub**](https://github.com/QingAnLiu/VISTA-Bench)

VISTA-Bench is a systematic benchmark spanning multimodal perception, reasoning, and unimodal understanding. It evaluates **visualized text understanding** by contrasting **pure-text** and **visualized-text (VT)** questions under controlled rendering conditions.

## Dataset Summary

Existing benchmarks predominantly focus on pure-text queries, but in real-world scenarios, language frequently appears as visualized text embedded in images. VISTA-Bench evaluates whether current Vision-Language Models (VLMs) handle such input requests comparably. Extensive evaluation reveals a pronounced modality gap: models that perform well on pure-text queries often degrade substantially when equivalent semantic content is presented as visualized text.

- **Size:** 1,500 instances
- **Composition:** Predominantly multiple-choice questions (MCQ), with a small portion of open-ended queries
- **Task Taxonomy:**
  - **Unimodal Knowledge:** 500 instances
  - **Multimodal Knowledge:** 400 instances
  - **Multimodal Perception:** 300 instances
  - **Multimodal Reasoning:** 300 instances

## Repository Structure

```text
VISTA-Bench/
├─ images/            # original images (for multimodal instances)
├─ questions/         # rendered question/option images (VT setting)
├─ VLMEvalKit/        # evaluation toolkit
├─ VISTA-Bench.tsv    # dataset index
└─ VISTA-Bench-VT.tsv # dataset index (visualized text variant)
```

## Evaluation (VLMEvalKit)

VISTA-Bench is evaluated using `VLMEvalKit`. Before running evaluation, it is recommended to convert the TSV file(s) into a normalized format with absolute image paths.

### 1) Convert TSV to normalized paths

Use the provided helper script to normalize the paths:

```bash
python VISTA-Bench/VLMEvalKit/utils/convert_data_file.py \
  --in   VISTA-Bench/VISTA-Bench.tsv \
  --out  VISTA-Bench/VISTA-Bench_norm.tsv \
  --image-prefix /ABS/PATH/TO/VISTA-Bench
```

### 2) Run evaluation

**Pure-text setting:**
```bash
python /VISTA-Bench/VLMEvalKit/run.py \
  --data VISTA-Bench_norm \
  --model llava_v1.5_7b \
  --verbose
```

**Visualized-text (VT) setting:**
```bash
python /VISTA-Bench/VLMEvalKit/run.py \
  --data VISTA-Bench-VT \
  --model llava_v1.5_7b \
  --verbose
```

## Citation

If you find this dataset useful, please cite the following paper:

```bibtex
@article{liu2026vistabench,
  title={VISTA-Bench: Do Vision-Language Models Really Understand Visualized Text as Well as Pure Text?},
  author={Liu, Qing'an and Feng, Juntong and Wang, Yuhao and Han, Xinzhe and Cheng, Yujie and Zhu, Yue and Diao, Haiwen and Zhuge, Yunzhi and Lu, Huchuan},
  journal={arXiv preprint arXiv:2602.04802},
  year={2026}
}
```