| --- |
| 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} |
| } |
| ``` |
|
|
