---
pretty_name: "aRefCOCO"
language:
  - en
license: cc-by-4.0
tags:
  - referring image segmentation
  - referential ambiguity
  - vision-language
  - multimodal
  - dataset
task_categories:
  - image-segmentation
size_categories:
  - 100K<n<1M
dataset_info:
  features:
  - name: entity_id
    dtype: int64
  - name: category_name
    dtype: string
  - name: bbox
    sequence: float64
  - name: descriptions
    sequence: string
  - name: split
    dtype: string
  - name: image
    dtype: image
  - name: mask
    dtype: image
  splits:
  - name: train
    num_examples: 110818
  - name: test
    num_examples: 7050
---

# Dataset Card for aRefCOCO
[![Project Page](https://img.shields.io/badge/Project-Page-green?logo=githubpages)](https://zhenjiemao.github.io/SaFiRe/)
[![Paper](https://img.shields.io/badge/Paper-NeurIPS%202025-red?)](https://arxiv.org/pdf/2510.10160)
[![arXiv](https://img.shields.io/badge/arXiv-paper-red?logo=arxiv)](https://arxiv.org/abs/2510.10160)
[![SaFiRe Model](https://img.shields.io/badge/Model-SaFiRe-black?logo=github)](https://github.com/zhenjiemao/SaFiRe)
[![aRefCOCO Dataset](https://img.shields.io/badge/Dataset-aRefCOCO-blue?logo=github)](https://github.com/zhenjiemao/aRefCOCO)
[![aRefCOCO Dataset](https://img.shields.io/badge/Dataset-aRefCOCO-yellow?logo=huggingface)](https://huggingface.co/datasets/zhenjiemao/aRefCOCO)
[![License: CC BY 4.0](https://img.shields.io/badge/License-CC--BY--4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)

**aRefCOCO** (Ambiguous RefCOCO) is a dataset specifically constructed for Referring Image Segmentation (RIS), focusing on **referential ambiguity** that frequently arises in real-world application.  It introduces **object-distracting expressions**, which involve multiple entities with contextual cues, and **category-implicit expressions**, where the object class is not explicitly stated. Each entity is paired with an image, a target segmentation mask, multiple referring descriptions, and supporting metadata such as bounding boxes and category labels. In addition to the original benchmark used for evaluation, aRefCOCO now provides an extended **train split** to support model training and further research on referential ambiguity in referring segmentation and related tasks.

## Dataset Structure

Each sample contains the following fields:

- `entity_id`: Unique identifier for the entity
- `category_name`: Object category name
- `bbox`: Bounding box coordinates [x, y, width, height]
- `descriptions`: List of referring expressions
- `split`: Dataset split (train/test)
- `image`: PIL Image object
- `mask`: PIL Image object (segmentation mask)

## How to use aRefCOCO Dataset

We provide an example to show how to use this data.

### Basic Usage

```python
from datasets import load_from_disk

# Load the dataset
train_ds = load_from_disk("/path/to/hf_datasets/train")
test_ds = load_from_disk("/path/to/hf_datasets/test")

# Print the total number of samples
print(f"Total number of train samples: {len(train_ds)}")
print(f"Total number of test samples: {len(test_ds)}")

# Get the first sample
sample = train_ds[0]
```

### Example: Basic Data Access

```python
# Retrieve sample data
sample = train_ds[0]

# Access the three core elements
image = sample['image']
mask = sample['mask']
descriptions = sample['descriptions']

# Print sample information
print(f"Entity ID: {sample['entity_id']}")
print(f"Category: {sample['category_name']}")
print(f"BBox: {sample['bbox']}")
print(f"Descriptions: {sample['descriptions']}")
```

### Alternative: PyTorch Dataset & Raw Images

This Hugging Face repository contains the dataset in **Parquet/Arrow format** for easy loading. 

For alternative formats and implementations, please visit the **[GitHub Repository](https://github.com/zhenjiemao/aRefCOCO)** which includes:
- Custom PyTorch Dataset class (`refdataset/refdataset.py`)
- Source images and masks in original quality
- JSONL metadata files
- Additional example scripts


## Citations

If you find our work helpful for your research, please consider citing our work.

```
@article{mao2025safire,
  title={SaFiRe: Saccade-Fixation Reiteration with Mamba for Referring Image Segmentation}, 
  author={Zhenjie Mao and Yuhuan Yang and Chaofan Ma and Dongsheng Jiang and Jiangchao Yao and Ya Zhang and Yanfeng Wang},
  journal={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2025}
}
```

We also recommend other highly related works:
```
@article{yang2024remamber,
  title   = {ReMamber: Referring Image Segmentation with Mamba Twister},
  author  = {Yuhuan Yang and Chaofan Ma and Jiangchao Yao and Zhun Zhong and Ya Zhang and Yanfeng Wang},
  year    = {2024},
  journal = {European Conference on Computer Vision (ECCV)}
}
```