Update README.md

README.md

@@ -1,7 +1,5 @@
 ---
 license: apache-2.0
-task_categories:
-- image-segmentation
 language:
 - en
 tags:
@@ -10,38 +8,97 @@ tags:
 - object-segmentation
 - referring-segmentation
 size_categories:
-- n<1K
+- 10K<n<100K
 ---
 
-# Long-RVOS Dataset
+# Long-RVOS: A Comprehensive Benchmark for Long-term Referring Video Object Segmentation
 
-这是 Long-RVOS (Long-term Referring Video Object Segmentation) 数据集的官方 Hugging Face 仓库。
+- **GitHub Repository**: [https://github.com/iSEE-Laboratory/Long_RVOS](https://github.com/iSEE-Laboratory/Long_RVOS)
+- **Project Page**: [https://isee-laboratory.github.io/Long-RVOS/](https://isee-laboratory.github.io/Long-RVOS/)
+- **Paper**: [arXiv:2505.12702](https://arxiv.org/pdf/2505.12702)
 
-## 数据集结构
+## Dataset Description
 
-数据集包含以下分割：
-- `train/`: 训练集
-- `valid/`: 验证集
-- `test/`: 测试集
+### Dataset Summary
 
-文件说明：
-- **所有分割 (train/valid/test) 都包含**: 
-  - `JPEGImages.tar.gz`: 视频帧图像
-  - `meta_expressions.json`: 表达式元数据
-  - `frame_types.json`: 帧类型信息
-  
-- **仅训练和验证集 (train/valid) 包含**:
-  - `Annotations.tar.gz`: 标注掩码（注意：test 集不包含标注）
+**Long-RVOS** is the first large-scale **long-term** referring video object segmentation benchmark, containing 2,000+ videos with an average duration exceeding **60 seconds**. The dataset addresses the challenge of segmenting and tracking objects in long-form videos based on natural language descriptions, advancing the task towards more practical and realistic scenarios.
 
-## 使用方法
+### Dataset Statistics
 
-请使用提供的下载脚本：
+- **Total videos**: 2,193
+- **Average video duration**: 60.3 seconds
+- **Average frames per video**: 361.7
+- **Object categories**: 163
+- **Splits**: Train, Validation, and Test sets
+
+## Dataset Structure
+
+### Data Organization
+
+The dataset is organized as follows:
+
+```
+data/
+└── long_rvos/
+    ├── train/
+    │   ├── JPEGImages/
+    │   │   └── {video_id}/
+    │   │       └── {frame_name}.jpg
+    │   ├── Annotations/
+    │   │   └── {video_id}/
+    │   │       └── {object_id}/
+    │   │           └── {frame_name}.png
+    │   └── meta_expressions.json
+    ├── valid/
+    │   ├── JPEGImages/
+    │   ├── Annotations/
+    │   └── meta_expressions.json
+    └── test/
+        ├── JPEGImages/
+        ├── Annotations/
+        └── meta_expressions.json
+```
+
+### Data Format
+
+- **JPEGImages**: Video frames extracted and stored as JPEG images
+- **Annotations**: Binary mask annotations (PNG format) for each object instance in each visible frame
+- **meta_expressions.json**: JSON file containing referring expressions and metadata
+
+### Annotation Format
+
+The `meta_expressions.json` file contains:
+
+```json
+{
+  "videos": {
+    "{video_id}": {
+      "frames": ["00000", "00001", ...],
+      "expressions": {
+        "{expression_id}": {
+          "exp": "referring expression text",
+          "obj_id": object_id,
+          "exp_type": "static|dynamic|hybrid"
+        }
+      }
+    }
+  }
+}
+```
+
+## Usage (Please refer to the GitHub repository)
+
+### Downloading the Dataset
+
+#### Option 1: Using the Download Script
 
 ```bash
-python scripts/download_dataset.py --repo_id iSEE-Laboratory/Long-RVOS
+python scripts/download_dataset.py \
+    --repo_id iSEE-Laboratory/Long-RVOS \
+    --output_dir data
 ```
 
-或使用 Hugging Face Hub API：
+#### Option 2: Using Hugging Face Hub API
 
 ```python
 from huggingface_hub import snapshot_download
@@ -53,4 +110,33 @@ snapshot_download(
 )
 ```
 
-更多信息请参考项目 README。
+#### Option 3: Manual Download
+
+Download from this repo or [Google Drive](https://drive.google.com/drive/folders/19GXKf8COc_W3ZHsLvhWTzaPrxRedszac?usp=drive_link).
+
+
+## Citation
+
+If you use the Long-RVOS dataset in your research, please cite:
+
+```bibtex
+@article{liang2025longrvos,
+  title={Long-RVOS: A Comprehensive Benchmark for Long-term Referring Video Object Segmentation},
+  author={Liang, Tianming and Jiang, Haichao and Yang, Yuting and Tan, Chaolei and Li, Shuai and Zheng, Wei-Shi and Hu, Jian-Fang},
+  journal={arXiv preprint arXiv:2505.12702},
+  year={2025}
+}
+```
+
+### License
+
+This dataset is licensed under the Apache 2.0 License. Please refer to the LICENSE file for details.
+
+### Contact
+
+For questions, issues, or contributions, please refer to the GitHub repository.
+
+---
+
+**Dataset Version**: 1.0  
+**Last Updated**: 2025