Merge branch 'main' of https://huggingface.co/datasets/turing-motors/SpatialRGPT-Bench-Extended

README.md

@@ -1,54 +1,62 @@
----
-license: cc-by-nc-sa-4.0
----
-
-# SpatialRGPT-Bench-Extended
-
-**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: [STRIDE-QA: Visual Question Answering Dataset for Spatiotemporal Reasoning in Urban Driving Scenes](https://arxiv.org/abs/2508.10427).
-
-## 🔗 Related Links
-
-- **Paper**: [arXiv:2508.10427](https://arxiv.org/abs/2508.10427)
-- **GitHub**: [turingmotors/STRIDE-QA-Dataset](https://github.com/turingmotors/STRIDE-QA-Dataset)
-
-
-## 📚 Citation
-
-```
-@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).
+---
+license: cc-by-nc-sa-4.0
+task_categories:
+- visual-question-answering
+language:
+- en
+tags:
+- autonomousdriving
+size_categories:
+- n<1K
+---
+
+# SpatialRGPT-Bench-Extended
+
+**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: [STRIDE-QA: Visual Question Answering Dataset for Spatiotemporal Reasoning in Urban Driving Scenes](https://arxiv.org/abs/2508.10427).
+
+## 🔗 Related Links
+
+- **Paper**: [arXiv:2508.10427](https://arxiv.org/abs/2508.10427)
+- **GitHub**: [turingmotors/STRIDE-QA-Dataset](https://github.com/turingmotors/STRIDE-QA-Dataset)
+
+
+## 📚 Citation
+
+```
+@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).