Datasets:
File size: 3,819 Bytes
3719b26 8cd0b38 a2a045b 3719b26 8cd0b38 3719b26 8cd0b38 3719b26 8cd0b38 3719b26 8cd0b38 3719b26 8cd0b38 3719b26 8cd0b38 3719b26 8cd0b38 3719b26 8cd0b38 3719b26 8cd0b38 3719b26 8cd0b38 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 |
---
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 |