Improve dataset card: add metadata, paper/code links, and evaluation instructions

README.md

@@ -1,6 +1,87 @@
 ---
 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 our paper:
-https://arxiv.org/abs/2602.04802
+
+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}
+}
+```