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