Update README.md

README.md

@@ -11,7 +11,7 @@ tags:
 
 # STRIDE-QA-Mini
 
-## Dataset Description
+⚠️ **Note**: The contents of STRIDE-QA-Mini differ from those of the latest dataset described in our [arXiv paper](https://arxiv.org/abs/2508.10427).
 
 **STRIDE-QA-Mini** (**S**patio**T**emporal **R**easoning **I**n **D**riving **E**nvironments for Visual Question Answering) is a compact subset of the STRIDE-QA corpus, built from real urban-driving footage collected by our in-house data-collection vehicles. It is designed for studying spatio-temporal reasoning in autonomous-driving scenes with Vision-Language-Models (VLMs).
 
@@ -29,7 +29,7 @@ Building on the ego-aware layer, we introduce an additional subset of queries th
 
 Together these elements make STRIDE-QA-Mini a concise yet challenging dataset that challenges VLMs to handle not only what they *see* but also what they must predict, skills essential for safe and intelligent autonomous systems.
 
-## Key Features
+## 🔑 Key Features
 
 | Aspect | Details |
 | --- | --- |
@@ -38,18 +38,7 @@ Together these elements make STRIDE-QA-Mini a concise yet challenging dataset th
 | **Driving domain** | Real dash-cam footage collected in Tokyo (urban, suburban, highway, various weather). |
 | **Privacy aware** | Faces and license plates are automatically blurred. |
 
-## Dataset Statistics
-
-| Category | Source file | QA pairs |
-|----------|-------------|----------|
-| Object-centric Spatial QA | `object_centric_spatial_qa.json` | **19,895** |
-| Ego-centric Spatial QA | `ego_centric_spatial_qa.json` | **54,390** |
-| Ego-centric Spatio-temporal QA | `ego_centric_spatiotemporal_qa_short_answer.json` | **28,935** |
-| Images | `images/*.jpg` | **5,539** files |
-
-**Total QA pairs:** 103,220
-
-## Data Fields
+## 🗂️ Data Fields
 
 | Field | Type | Description |
 | --- | --- | --- |
@@ -62,21 +51,47 @@ Together these elements make STRIDE-QA-Mini a concise yet challenging dataset th
 | `region` | `list[list[int]]` | Region tags mentioned in the prompt. |
 | `qa_info` | `list` | Meta data for each message turn in dialogue. |
 
-## Privacy Protection
+## 📊 Dataset Statistics
+
+| Category | Source file | QA pairs |
+|----------|-------------|----------|
+| Object-centric Spatial QA | `object_centric_spatial_qa.json` | **19,895** |
+| Ego-centric Spatial QA | `ego_centric_spatial_qa.json` | **54,390** |
+| Ego-centric Spatio-temporal QA | `ego_centric_spatiotemporal_qa_short_answer.json` | **28,935** |
+| Images | `images/*.jpg` | **5,539** files |
+
+## 🔗 Related Links
+
+- **Paper**: [arXiv:2508.10427](https://arxiv.org/abs/2508.10427)
 
-To ensure privacy protection, human faces and license plates in STRIDE-QA-Mini images were anonymized using the [Dashcam Anonymizer](https://github.com/varungupta31/dashcam_anonymizer). However, this process cannot guarantee perfect anonymization.
+## 📚 Citation
 
+```
+@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
+## 📄 License
 
-STRIDE-QA-Mini is released under the [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en).
+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
+## 🤝 Acknowledgements
 
-This dataset is based on results obtained from a project, JPNP20017, subsidized by the New Energy and Industrial Technology Development Organization (NEDO).
+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
+- [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).