README.md

---
language:
- en
license: mit
size_categories:
- 1K<n<10K
pretty_name: BiVLC
dataset_info:
  features:
  - name: image
    dtype: image
  - name: caption
    dtype: string
  - name: negative_caption
    dtype: string
  - name: negative_image
    dtype: image
  - name: type
    dtype: string
  - name: subtype
    dtype: string
  splits:
  - name: test
    num_bytes: 567921772.034
    num_examples: 2933
  download_size: 536810200
  dataset_size: 567921772.034
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---

# Dataset Card for BiVLC

## Dataset Description
- **Homepage:** https://imirandam.github.io/BiVLC_project_page/
- **Repository:** https://github.com/IMirandaM/BiVLC
- **Paper:** https://arxiv.org/abs/2406.09952
- **Point of Contact:** [Imanol Miranda](mailto:imanol.miranda@ehu.eus)

### Dataset Summary

BiVLC is a benchmark for Bidirectional Vision-Language Compositionality evaluation. Each instance consists of two images and two captions. Using each of the images and captions as a base, a model is asked to select the pair that correctly represents the base versus the hard negative distractor with minor compositional changes. Thus, we can measure image-to-text and text-to-image retrieval with hard negative pairs. To obtain good results on the dataset, it is necessary that the model performs well in both directions for the same instance.

<p align="center">
  <img width="1200" src="https://raw.githubusercontent.com/IMirandaM/BiVLC/main/misc/BiVLC-Examples.svg">
</p>

#### Dataset instances

Each instance of the dataset consists of six fields:
* image: COCO 2017 validation image.
* caption: COCO 2017 validation text describing the COCO image.
* negative_caption: Negative caption generated from the COCO 2017 validation text description by SugarCrepe.
* negative_image: Negative image generated from the negative caption by BiVLC.
* type: Category of the negative instances: Replace, Swap or Add.
* subtype: Subcategory of the negative instances: Object, Attribute or Relation.

#### How to use 

To load data with datasets:
```python
>>> data = load_dataset("imirandam/BiVLC", split = "test")
```

#### Instance example

Each instance has the following structure:
```
{
    'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x332 at 0x7F9BFC0C5430>,
    'caption': 'A man throwing a ball while smiling and on a field.',
    'negative_caption': 'A man throwing a ball while a child is smiling on a field.',
    'negative_image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x512 at 0x7F9BE45571C0>,
    'type': 'add',
    'subtype': 'obj',
}
```

### Dataset statistics
test: 2,933 instances formed by 2 images and 2 captions. 11,732 retrieval instances, 50% text-to-image and 50% image-to-text.

<p align="center">
  <img width="900" src="https://raw.githubusercontent.com/IMirandaM/BiVLC/main/misc/BiVLC-Comb-3.svg">
</p>


### Source Data
- image and caption are from [COCO 2017](https://cocodataset.org/#home) validation split.
- negative_caption is a text description generated from the COCO caption by [SugarCrepe](https://github.com/RAIVNLab/sugar-crepe).

### Dataset curation
<p align="center">
  <img width="900" src="https://raw.githubusercontent.com/IMirandaM/BiVLC/main/misc/BiVLC-Process.svg">
</p>


Step 1 - Uniformly format positive and hard negative captions

Step 2 - Generate hard negative images

Step 3 - Ask to human annotators to choose the best generated image

Step 4 - Filter ambiguous instances

### More examples
<p align="center">
  <img width="1200" src="https://raw.githubusercontent.com/IMirandaM/BiVLC/main/misc/more_examples.svg">
</p>
  
### Training Data
If you need training and validation data, you can use the datasets proposed in the paper in the following links, [TROHN-Text](https://huggingface.co/datasets/imirandam/TROHN-Text) and [TORHN-Img](https://huggingface.co/datasets/imirandam/TROHN-Img).

### Licensing Information

This work is licensed under a MIT License.

## Citation Information
If you find this dataset useful, please consider citing our paper:
```
@misc{miranda2024bivlc,
      title={BiVLC: Extending Vision-Language Compositionality Evaluation with Text-to-Image Retrieval}, 
      author={Imanol Miranda and Ander Salaberria and Eneko Agirre and Gorka Azkune},
      year={2024},
      eprint={2406.09952},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```