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---
dataset_info:
features:
- name: image
dtype: image
- name: question
dtype: string
splits:
- name: test
num_bytes: 574815661.388
num_examples: 2374
download_size: 580096603
dataset_size: 574815661.388
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
### CulturalVQA
Foundation models and vision-language pre-training have notably advanced Vision Language Models (VLMs), enabling multimodal processing of visual and linguistic data. However, their performance has been typically assessed on general scene understanding - recognizing objects, attributes, and actions - rather than cultural comprehension. We introduce CulturalVQA, a visual question-answering benchmark aimed at assessing VLM's geo-diverse cultural understanding. We curate a diverse collection of 2,378 image-question pairs with 1-5 answers per question representing cultures from 11 countries across 5 continents. The questions probe understanding of various facets of culture such as clothing, food, drinks, rituals, and traditions.
> **Note:** The answers for CulturalVQA benchmark is not publicly available. We are working on creating a competition where participants can upload their predictions and evaluate their models. Stay tuned for more updates!
If you need to urgently need to evaluate please contact [email protected]
### Loading the dataset
To load and use the CulturalVQA benchmark, use the following commands:
```
from datasets import load_dataset
culturalvqa_dataset = load_dataset('mair-lab/CulturalVQA')
```
Once the dataset is loaded each instance contains the following fields:
- `u_id`: A unique identifier for each image-question pair
- `image`: The image data in binary format
- `question`: The question pertaining to the image
### Usage and License
CulturalVQA is a test-only benchmark and can be used to evaluate models. The images are scraped from the internet and are not owned by the authors. All annotations are released under the CC BY-SA 4.0 license.
### Citation Information
If you are using this dataset, please cite
```
@inproceedings{nayak-etal-2024-benchmarking,
title = "Benchmarking Vision Language Models for Cultural Understanding",
author = "Nayak, Shravan and
Jain, Kanishk and
Awal, Rabiul and
Reddy, Siva and
Steenkiste, Sjoerd Van and
Hendricks, Lisa Anne and
Stanczak, Karolina and
Agrawal, Aishwarya",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.329",
pages = "5769--5790"
}
```
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