image
imagewidth (px)
168
7.13k
region
stringclasses
52 values
universal
stringclasses
20 values
fiji
drinks
uganda
drinks
japan
drinks
australia
drinks
indonesia
drinks
argentina
drinks
china
drinks
somalia
drinks
jamaica
drinks
sweden
drinks
thailand
drinks
vietnam
drinks
hungary
drinks
spain
drinks
saudi arabia
drinks
uganda
drinks
fiji
drinks
japan
drinks
argentina
drinks
indonesia
drinks
saudi arabia
drinks
china
drinks
somalia
drinks
jamaica
drinks
sweden
drinks
jamaica
drinks
kenya
drinks
thailand
drinks
saudi arabia
drinks
portugal
drinks
ghana
drinks
hungary
drinks
bulgaria
drinks
lebanon
drinks
mexico
drinks
turkey
drinks
ghana
drinks
hungary
drinks
netherlands
drinks
thailand
drinks
tunisia
drinks
canada
drinks
nigeria
drinks
united states of america
drinks
portugal
drinks
morocco
drinks
pakistan
drinks
singapore
drinks
pakistan
drinks
indonesia
drinks
peru
drinks
tunisia
drinks
poland
drinks
canada
drinks
united states of america
drinks
nigeria
drinks
portugal
drinks
singapore
drinks
tanzania
drinks
canada
drinks
pakistan
drinks
peru
drinks
japan
drinks
peru
drinks
mexico
drinks
tanzania
drinks
lebanon
drinks
egypt
drinks
morocco
drinks
france
drinks
chile
drinks
phillippines
drinks
germany
drinks
germany
drinks
mexico
drinks
lebanon
drinks
morocco
drinks
tanzania
drinks
france
drinks
srilanka
drinks
phillippines
drinks
chile
drinks
sweden
drinks
germany
drinks
south africa
drinks
india
drinks
iran
drinks
ghana
drinks
new zealand
drinks
argentina
drinks
vietnam
drinks
fiji
drinks
australia
drinks
india
drinks
india
drinks
new zealand
drinks
united states of america
drinks
vietnam
drinks
singapore
drinks
chile
drinks

GlobalRG - Retrieval Across Universals 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-Retrieval benchmark, which aims at retrieving culturally diverse images for universal concepts from 50 countries.

Note: The answers for the GlobalRG-Retrieval 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_retrieval_dataset = load_dataset('UBCNLP/GlobalRG-Retrieval')

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
  • universal: The universal concept pertaining 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"
}
Downloads last month
85