Datasets:
Tasks:
Visual Question Answering
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
< 1K
ArXiv:
Tags:
autonomousdriving
License:
| license: cc-by-nc-sa-4.0 | |
| task_categories: | |
| - visual-question-answering | |
| language: | |
| - en | |
| tags: | |
| - autonomousdriving | |
| size_categories: | |
| - n<1K | |
| # SpatialRGPT-Bench-Extended | |
| [](https://arxiv.org/abs/2508.10427) | |
| [](https://turingmotors.github.io/stride-qa/) | |
| [](https://github.com/turingmotors/STRIDE-QA-Dataset) | |
| [](https://huggingface.co/datasets/turing-motors/STRIDE-QA-Dataset) | |
| [](https://huggingface.co/datasets/turing-motors/STRIDE-QA-Bench) | |
| **SpatialRGPT-Bench-Extended** is an extension of [SpatialRGPT-Bench](https://huggingface.co/datasets/a8cheng/SpatialRGPT-Bench) that incorporates driving scene images from Japan. It augments object-centric QA (questions about two objects within an image) with ego-centric QA (questions about the relationship between the ego and a single object in the image). Each QA category contains 466 QA pairs. For further details, please refer to our paper: <https://arxiv.org/abs/2508.10427>. | |
| ## 🔗 Related Links | |
| - Project Page: <https://turingmotors.github.io/stride-qa> | |
| - GitHub: <https://github.com/turingmotors/STRIDE-QA-Dataset> | |
| - STRIDE-QA-Dataset: <https://huggingface.co/datasets/turing-motors/STRIDE-QA-Dataset> | |
| - STRIDE-QA-Bench: <https://huggingface.co/datasets/turing-motors/STRIDE-QA-Bench> | |
| ## 📚 Citation | |
| ```bibxtex | |
| @article{cheng2024spatialrgpt, | |
| title={Spatialrgpt: Grounded spatial reasoning in vision-language models}, | |
| author={Cheng, An-Chieh and Yin, Hongxu and Fu, Yang and Guo, Qiushan and Yang, Ruihan and Kautz, Jan and Wang, Xiaolong and Liu, Sifei}, | |
| journal={Advances in Neural Information Processing Systems}, | |
| volume={37}, | |
| pages={135062--135093}, | |
| year={2024} | |
| } | |
| @misc{strideqa2025, | |
| title={STRIDE-QA: Visual Question Answering Dataset for Spatiotemporal Reasoning in Urban Driving Scenes}, | |
| author={Keishi Ishihara and Kento Sasaki and Tsubasa Takahashi and Daiki Shiono and Yu Yamaguchi}, | |
| year={2025}, | |
| eprint={2508.10427}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV}, | |
| url={https://arxiv.org/abs/2508.10427}, | |
| } | |
| ``` | |
| ## 📄 License | |
| STRIDE-QA-Bench is released under the [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en). | |
| ## 🤝 Acknowledgements | |
| This benchmark is based on results obtained from a project, JPNP20017, subsidized by the New Energy and Industrial Technology Development Organization (NEDO). | |
| We would like to acknowledge the use of the following open-source repositories: | |
| - [SpatialRGPT](https://github.com/AnjieCheng/SpatialRGPT?tab=readme-ov-file) for building dataset generation pipeline | |
| - [SAM 2.1](https://github.com/facebookresearch/sam2) for segmentation mask generation | |
| - [dashcam-anonymizer](https://github.com/varungupta31/dashcam_anonymizer) for anonymization | |
| ## 🔏 Privacy Protection | |
| To ensure privacy protection, human faces and license plates in the images were anonymized using the [Dashcam Anonymizer](https://github.com/varungupta31/dashcam_anonymizer). | |