|
--- |
|
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" |
|
} |
|
``` |
|
|
|
|
