README.md

---
license: apache-2.0
language:
- en
tags:
- video
- segmentation
- object-segmentation
- referring-segmentation
size_categories:
- 10K<n<100K
viewer: false
---

# Long-RVOS: A Comprehensive Benchmark for Long-term Referring Video Object Segmentation

- **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)

## Dataset Description

### Dataset Summary

**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.

### Dataset Statistics

- **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 \
    --output_dir data
```

#### Option 2: Using Hugging Face Hub API

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="iSEE-Laboratory/Long-RVOS",
    repo_type="dataset",
    local_dir="./data"
)
```

#### 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