Datasets:
license: other
license_name: mixed-research-only
task_categories:
- visual-question-answering
- image-to-text
language:
- en
pretty_name: UltraVR
tags:
- ultra-resolution
- ultra-high-resolution
- visual-reasoning
- evidence-grounded-reasoning
- visual-question-answering
- vision-language-models
- multimodal-reasoning
- remote-sensing
- surveillance
- industrial-anomaly-detection
- anomaly-detection
UltraVR
UltraVR is a diagnostic ultra-resolution image VQA benchmark for evidence-grounded reasoning across remote sensing, CCTV surveillance, and industrial anomaly detection domains.
This repository is a data-only release. It provides benchmark QA annotations, selected redistributable AD images, source mapping files for non-redistributed image domains, and license notices.
Keywords: ultra-resolution image understanding; ultra-high-resolution visual reasoning; evidence-grounded reasoning; visual question answering; vision-language models; multimodal reasoning; remote sensing; CCTV surveillance; industrial anomaly detection.
QA-Only Annotation Release
This trial version keeps the final question, options, answer, question type, one image_path, image dimensions, and license notes. No chain-of-thought is included. In JSONL records, AD image_path values point to local files in this repository, while RS and CCTV image_path values point to the original source-dataset image paths.
Domain Summary
| Domain | Source Dataset | Image Files in This Repo | Mapping File | Notes |
|---|---|---|---|---|
| RS | DOTA-v1.5 | No | data/images/rs/mapping.csv |
Raw DOTA images are not redistributed. |
| CCTV | PANDA | No | data/images/cctv/mapping.csv |
PANDA is treated as high-resolution still images/screenshots. |
| AD | MVTec LOCO AD | Yes | Not required | User-provided constructed AD images are included. |
Source Datasets
- RS: DOTA-v1.5, https://captain-whu.github.io/DOTA/dataset.html
- CCTV: PANDA, https://gigavision.cn/data/news?nav=DataSet%20Panda&type=nav&t=1781477597958
- AD: MVTec LOCO AD, https://www.mvtec.com/research-teaching/datasets/mvtec-loco-ad
Repository Structure
UltraVR/
├── README.md
├── LICENSE
├── NOTICE
├── RELEASE_POLICY.md
├── DATA_SCHEMA.md
├── data/
│ ├── annotations/
│ │ ├── ultravr_rs.jsonl
│ │ ├── ultravr_cctv.jsonl
│ │ ├── ultravr_ad.jsonl
│ │ └── ultravr_all.jsonl
│ └── images/
│ ├── rs/
│ │ ├── README.md
│ │ └── mapping.csv
│ ├── cctv/
│ │ ├── README.md
│ │ └── mapping.csv
│ └── ad/
│ ├── README.md
│ └── <AD image files>
└── examples/
├── sample_rs.jsonl
├── sample_cctv.jsonl
└── sample_ad.jsonl
Annotation Files
All annotations are JSONL files. Each line is one QA sample. data/annotations/ultravr_all.jsonl concatenates RS, CCTV, and AD annotations in that order.
data/annotations/ultravr_rs.jsonl: 120 QA samplesdata/annotations/ultravr_cctv.jsonl: 120 QA samplesdata/annotations/ultravr_ad.jsonl: 140 QA samplesdata/annotations/ultravr_all.jsonl: 380 QA samples
License
UltraVR follows a mixed research-only licensing policy. Users must follow the original license and usage terms of each source dataset. See LICENSE, NOTICE, and RELEASE_POLICY.md for details.
Citation
If you use UltraVR in your research, please cite our paper:
UltraVR: A Diagnostic Ultra-Resolution Image-VQA Benchmark for Evidence-Grounded Reasoning
Gexin Huang, Yanting Yang, Myeongkyun Kang, Beidi Zhao, Jun Zhou, Chen Zhou, Gang Wang, Zu-hua Gao, and Xiaoxiao Li.
arXiv:2606.05576, 2026.
@misc{huang2026ultravr,
title = {UltraVR: A Diagnostic Ultra-Resolution Image-VQA Benchmark for Evidence-Grounded Reasoning},
author = {Huang, Gexin and Yang, Yanting and Kang, Myeongkyun and Zhao, Beidi and Zhou, Jun and Zhou, Chen and Wang, Gang and Gao, Zu-hua and Li, Xiaoxiao},
year = {2026},
eprint = {2606.05576},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
doi = {10.48550/arXiv.2606.05576},
url = {https://arxiv.org/abs/2606.05576}
}
Please also cite the original source datasets used by the corresponding UltraVR domains, including DOTA-v1.5, PANDA, and MVTec LOCO AD, when applicable.