README.md

metadata

dataset_info:
  features:
    - name: image
      dtype: image
    - name: region
      dtype: string
    - name: concept
      dtype: string
  splits:
    - name: test
      num_bytes: 134588653
      num_examples: 592
  download_size: 134008473
  dataset_size: 134588653
task_categories:
  - object-detection
tags:
  - cultural
  - visual
  - grounding
size_categories:
  - n<1K
license: cc-by-4.0

GlobalRG - Cultural Visual Grounding Task

Despite recent advancements in vision-language models, their performance remains suboptimal on images from non-western cultures due to underrepresentation in training datasets. Various benchmarks have been proposed to test models' cultural inclusivity, but they have limited coverage of cultures and do not adequately assess cultural diversity across universal as well as culture-specific local concepts. We introduce the GlobalRG-Grounding benchmark, which aims at grounding culture-specific concepts within images from 15 countries.

Note: The answers for the GlobalRG-Grounding benchmark are 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] and fill out this form: https://forms.gle/pSbnGso13co6V4518.

Loading the dataset

To load and use the GlobalRG-Grounding benchmark, use the following commands:

from datasets import load_dataset
globalrg_grounding_dataset = load_dataset('UBCNLP/GlobalRG-Grounding')

Once the dataset is loaded, each instance contains the following fields:

• u_id: A unique identifier for each image-region-concept tuple
• image: The image data in binary format
• region: The cultural region pertaining to the image
• concept: The cultural concept to be grounded in the image.

Usage and License

GlobalRG 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{bhatia-etal-2024-local,
    title = "From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-Language Models",
    author = "Bhatia, Mehar  and
      Ravi, Sahithya  and
      Chinchure, Aditya  and
      Hwang, EunJeong  and
      Shwartz, Vered",
    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.385",
    doi = "10.18653/v1/2024.emnlp-main.385",
    pages = "6763--6782"
}