README.md

---
license: cc-by-4.0
task_categories:
- image-to-text
- text-to-image
language:
- en
size_categories:
- 1K<n<10K
---
# Dataset Card for ACON Benchmark

## Dataset Summary

Data from: [Are Any-to-Any Models More Consistent Across Modality Transfers Than Specialists?](https://arxiv.org/abs/2505.24211)

```
@inproceedings{chung2025are,
  title={Are Any-to-Any Models More Consistent Across Modality Transfers Than Specialists?},
  author={Chung, Jiwan and Yoon, Janghan and Park, Junhyeong and Lee, Sangeyl and Yang, Joowon and Park, Sooyeon and Yu, Youngjae},
  booktitle={Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year={2025}
}
```

We provide a controlled benchmark to evaluate consistency in modality transfers of any-to-any models.

Please cite our work if you find our data helpful.

### Language

English

## Dataset Structure

Here's an overview of our dataset structure:

```
{
    'image_name': str,  # Unique image identifier.
    'image': PIL.Image,
    'description': str,  # Human-annotated detailed caption aimed for correct replication of the visual details when used as inputs to image generators.
    'Q&A': [  # human-annotated VQAs to be used for VQA-based image similarity evaluation.
      {
        "Question": str,
        "Answer": [
          "T",  # True or False label for the original image
          "T"   # (This label is not directly used for experiments) True or False label for the hidden modified image
        ]
      },
      ...
    ],
    'modification': [  
      {
        "Prompt": str,  # Image editing prompt 
        "Question": str,
        "Answer": [
          "T",  # True or False label for the edited image
        ]
      },
      ...
    ]
}
```

### Data Instances

See above

### Data Fields

See above

### Data Splits

Data splits can be accessed as:

```python
from datasets import load_dataset
data = load_dataset("jiwan-chung/ACON", split='private')
data = load_dataset("jiwan-chung/ACON", split='coco')
```

### Curation Rationale

Full details are in the paper.

### Source Data

We contribute new 500 private images. Also, the COCO subset consist of images selected from COCO 2017 dataset.

### Initial Data Collection

Full details are in the paper.

### Annotations

Full details are in the paper.

#### Annotation Process

Full details are in the paper.

#### Who are the annotators?

Authors of the paper.

### Licencing Information

The annotations and private images we provide are licensed as detailed above. Images from COCO retain their original rights.