Duplicate from imirandam/BiVLC

Co-authored-by: Imanol Miranda <imirandam@users.noreply.huggingface.co>

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README.md

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+---
+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}
+}
+```

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