Update README.md

README.md

@@ -1,138 +1,138 @@
----
-license: mit
-task_categories:
-- visual-question-answering
-- image-classification
-language:
-- en
-tags:
-- visual-reasoning
-- transformation
-- benchmark
-- computer-vision
-size_categories:
-- 1K<n<10K
----
-
-# VisualTrans: A Benchmark for Real-World Visual Transformation Reasoning
-
-[![arXiv](https://img.shields.io/badge/arXiv-2508.04043-b31b1b.svg)](http://arxiv.org/abs/2508.04043)
-
-## Dataset Description
-
-VisualTrans is the first comprehensive benchmark specifically designed for Visual Transformation Reasoning (VTR) in real-world human-object interaction scenarios. The benchmark encompasses 12 semantically diverse manipulation tasks and systematically evaluates three essential reasoning dimensions through 6 well-defined subtask types.
-
-## Dataset Statistics
-
-- **Total samples**: 497
-- **Number of manipulation scenarios**: 12
-- **Task types**: 6
-
-### Task Type Distribution
-
-- **count**: 63 samples (12.7%)
-- **procedural_causal**: 86 samples (17.3%)
-- **procedural_interm**: 88 samples (17.7%)
-- **procedural_plan**: 42 samples (8.5%)
-- **spatial_fine_grained**: 168 samples (33.8%)
-- **spatial_global**: 50 samples (10.1%)
-
-
-### Manipulation Scenarios
-
-The benchmark covers 12 diverse manipulation scenarios:
-- Add Remove Lid
-- Assemble Disassemble Legos
-- Build Unstack Lego
-- Insert Remove Bookshelf
-- Insert Remove Cups From Rack
-- Make Sandwich
-- Pick Place Food
-- Play Reset Connect Four
-- Screw Unscrew Fingers Fixture
-- Setup Cleanup Table
-- Sort Beads
-- Stack Unstack Bowls
-
-
-## Dataset Structure
-
-### Files
-
-- `VisualTrans.json`: Main benchmark file containing questions, answers, and image paths
-- `images.zip`: Compressed archive containing all images used in the benchmark
-
-### Data Format
-
-Each sample in the benchmark contains:
-
-```json
-{
-    "task_type": "what",
-    "images": [
-        "scene_name/image1.jpg",
-        "scene_name/image2.jpg"
-    ],
-    "scene": "scene_name",
-    "question": "Question about the transformation",
-    "label": "Ground truth answer"
-}
-```
-
-## Reasoning Dimensions
-
-The framework evaluates three essential reasoning dimensions:
-
-1. **Quantitative Reasoning** - Counting and numerical reasoning tasks
-2. **Procedural Reasoning**
-   - **Intermediate State** - Understanding process states during transformation
-   - **Causal Reasoning** - Analyzing cause-effect relationships
-   - **Transformation Planning** - Multi-step planning and sequence reasoning
-3. **Spatial Reasoning**
-   - **Fine-grained** - Precise spatial relationships and object positioning
-   - **Global** - Overall spatial configuration and scene understanding
-
-## Usage
-
-```python
-import json
-import zipfile
-
-# Load the benchmark data
-with open('VisualTrans.json', 'r') as f:
-    benchmark_data = json.load(f)
-
-# Extract images
-with zipfile.ZipFile('images.zip', 'r') as zip_ref:
-    zip_ref.extractall('images/')
-
-# Access a sample
-sample = benchmark_data[0]
-print(f"Question: {sample['question']}")
-print(f"Answer: {sample['label']}")
-print(f"Images: {sample['images']}")
-```
-
-## Citation
-
-If you use this benchmark, please cite our work:
-
-```bibtex
-@misc{ji2025visualtransbenchmarkrealworldvisual,
-      title={VisualTrans: A Benchmark for Real-World Visual Transformation Reasoning}, 
-      author={Yuheng Ji and Yipu Wang and Yuyang Liu and Xiaoshuai Hao and Yue Liu and Yuting Zhao and Huaihai Lyu and Xiaolong Zheng},
-      year={2025},
-      eprint={2508.04043},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2508.04043}, 
-}
-```
-
-## License
-
-This dataset is released under the MIT License.
-
-## Contact
-
-For questions or issues, please open an issue on our [GitHub repository](https://github.com/WangYipu2002/VisualTrans) or contact the authors.
+---
+license: mit
+task_categories:
+- visual-question-answering
+- image-classification
+language:
+- en
+tags:
+- visual reason
+- transformation
+- benchmark
+- computer vision
+size_categories:
+- 1K<n<10K
+---
+
+# VisualTrans: A Benchmark for Real-World Visual Transformation Reasoning
+
+[![arXiv](https://img.shields.io/badge/arXiv-2508.04043-b31b1b.svg)](http://arxiv.org/abs/2508.04043)
+
+## Dataset Description
+
+VisualTrans is the first comprehensive benchmark specifically designed for Visual Transformation Reasoning (VTR) in real-world human-object interaction scenarios. The benchmark encompasses 12 semantically diverse manipulation tasks and systematically evaluates three essential reasoning dimensions through 6 well-defined subtask types.
+
+## Dataset Statistics
+
+- **Total samples**: 497
+- **Number of manipulation scenarios**: 12
+- **Task types**: 6
+
+### Task Type Distribution
+
+- **count**: 63 samples (12.7%)
+- **procedural_causal**: 86 samples (17.3%)
+- **procedural_interm**: 88 samples (17.7%)
+- **procedural_plan**: 42 samples (8.5%)
+- **spatial_fine_grained**: 168 samples (33.8%)
+- **spatial_global**: 50 samples (10.1%)
+
+
+### Manipulation Scenarios
+
+The benchmark covers 12 diverse manipulation scenarios:
+- Add Remove Lid
+- Assemble Disassemble Legos
+- Build Unstack Lego
+- Insert Remove Bookshelf
+- Insert Remove Cups From Rack
+- Make Sandwich
+- Pick Place Food
+- Play Reset Connect Four
+- Screw Unscrew Fingers Fixture
+- Setup Cleanup Table
+- Sort Beads
+- Stack Unstack Bowls
+
+
+## Dataset Structure
+
+### Files
+
+- `VisualTrans.json`: Main benchmark file containing questions, answers, and image paths
+- `images.zip`: Compressed archive containing all images used in the benchmark
+
+### Data Format
+
+Each sample in the benchmark contains:
+
+```json
+{
+    "task_type": "what",
+    "images": [
+        "scene_name/image1.jpg",
+        "scene_name/image2.jpg"
+    ],
+    "scene": "scene_name",
+    "question": "Question about the transformation",
+    "label": "Ground truth answer"
+}
+```
+
+## Reasoning Dimensions
+
+The framework evaluates three essential reasoning dimensions:
+
+1. **Quantitative Reasoning** - Counting and numerical reasoning tasks
+2. **Procedural Reasoning**
+   - **Intermediate State** - Understanding process states during transformation
+   - **Causal Reasoning** - Analyzing cause-effect relationships
+   - **Transformation Planning** - Multi-step planning and sequence reasoning
+3. **Spatial Reasoning**
+   - **Fine-grained** - Precise spatial relationships and object positioning
+   - **Global** - Overall spatial configuration and scene understanding
+
+## Usage
+
+```python
+import json
+import zipfile
+
+# Load the benchmark data
+with open('VisualTrans.json', 'r') as f:
+    benchmark_data = json.load(f)
+
+# Extract images
+with zipfile.ZipFile('images.zip', 'r') as zip_ref:
+    zip_ref.extractall('images/')
+
+# Access a sample
+sample = benchmark_data[0]
+print(f"Question: {sample['question']}")
+print(f"Answer: {sample['label']}")
+print(f"Images: {sample['images']}")
+```
+
+## Citation
+
+If you use this benchmark, please cite our work:
+
+```bibtex
+@misc{ji2025visualtransbenchmarkrealworldvisual,
+      title={VisualTrans: A Benchmark for Real-World Visual Transformation Reasoning}, 
+      author={Yuheng Ji and Yipu Wang and Yuyang Liu and Xiaoshuai Hao and Yue Liu and Yuting Zhao and Huaihai Lyu and Xiaolong Zheng},
+      year={2025},
+      eprint={2508.04043},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2508.04043}, 
+}
+```
+
+## License
+
+This dataset is released under the MIT License.
+
+## Contact
+
+For questions or issues, please open an issue on our [GitHub repository](https://github.com/WangYipu2002/VisualTrans) or contact the authors.