---
task_categories:
- visual-question-answering
- image-text-to-text
language:
- en
license: apache-2.0
---
# IC-VCO-Dataset

<p align="left">
  <a href="https://arxiv.org/abs/2605.31312">
    <img src="https://img.shields.io/badge/arXiv-2605.31312-b31b1b.svg" alt="arXiv">
  </a>
   
  <a href="https://github.com/OPPO-Mente-Lab/IC-VCO">
    <img src="https://img.shields.io/badge/GitHub-IC--VCO-181717.svg?logo=github" alt="GitHub">
  </a>
   
  <a href="https://huggingface.co/datasets/OPPOer/IC-VCO-Dataset">
    <img src="https://img.shields.io/badge/🤗%20HuggingFace-IC--VCO--Dataset-ffd21f.svg" alt="Hugging Face Dataset">
  </a>
</p>

This dataset package contains the two IC-VCO training subsets:

- `sft`: supervised fine-tuning examples.
- `preference`: visual contrastive preference examples.

The two subsets intentionally use different schemas, so they are represented as separate Hugging Face dataset configurations instead of separate splits under a single configuration. Each configuration has a `train` split.

## Loading From Hugging Face

```python
from datasets import load_dataset

sft = load_dataset("OPPOer/IC-VCO-Dataset", "sft")
preference = load_dataset("OPPOer/IC-VCO-Dataset", "preference")
```

The planned public dataset repository id is `OPPOer/IC-VCO-Dataset`.

## Local Layout

```text
IC-VCO-Dataset/
  images/
    00000/
    00001/
    ...
  sft/train/metadata.parquet
  preference/train/metadata.parquet
```

Both configurations share the top-level `images/` directory. Paths in `metadata.parquet` use `../../images/<bucket>/...` relative references so that `datasets.load_dataset(..., "sft")` and `datasets.load_dataset(..., "preference")` both materialize an `images` column.

## Acknowledgement

IC-VCO-Dataset is derived from [`iLearn-Lab/NeurIPS25-SymMPO`](https://huggingface.co/datasets/iLearn-Lab/NeurIPS25-SymMPO), which is released under the Apache License 2.0.

## Citation

If you find this dataset helpful, please consider citing our paper:

```
@inproceedings{
deng2026learning,
title={Learning from Fine-Grained Visual Discrepancies: Mitigating Multimodal Hallucinations via In-Context Visual Contrastive Optimization},
author={Haolin Deng and Xin Zou and Zhiwei Jin and Chen Chen and Haonan Lu and Xuming Hu},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=dtHEthIjmu}
}
```