Datasets:
image
image | region
string | concept
string |
---|---|---|
poland | kiszka |
|
poland | ryba po grecku |
|
poland | ser bialy |
|
poland | rogal swietomarcinski |
|
poland | ptasie mleczko |
|
poland | kotlet schabowy |
|
poland | flaki |
|
poland | golonka |
|
poland | placki ziemniaczane |
|
poland | szarlotka |
|
poland | bigos |
|
poland | tatar |
|
poland | pasztecik szczecinski |
|
poland | kartacze |
|
poland | bryndza |
|
poland | basolia |
|
poland | faworki |
|
poland | mizeria |
|
poland | rosol |
|
poland | krowki |
|
poland | pierniki |
|
poland | kasza gryczana |
|
poland | oscypek |
|
poland | kompot |
|
poland | paczek |
|
poland | zurek |
|
poland | kluski slaskie |
|
poland | pierogi |
|
poland | kabanos |
|
poland | smalec |
|
poland | chrzan |
|
poland | barszcz |
|
poland | kogel mogel |
|
poland | zrazy |
|
poland | knysza |
|
poland | sekacz |
|
poland | sledz |
|
poland | makowiec |
|
poland | kaszanka |
|
poland | kielbasa |
|
russia | dymkovo toys |
|
russia | sushki |
|
russia | kvass |
|
russia | podstakannik |
|
russia | pryanik |
|
russia | ushanka |
|
russia | soljanka |
|
russia | faberge eggs |
|
russia | bayan |
|
russia | gzhel |
|
russia | kokoshnik |
|
russia | babushka |
|
russia | garmon |
|
russia | okroshka |
|
russia | gusli |
|
russia | pirozhki |
|
russia | lapti |
|
russia | pelmeni |
|
russia | sarafan |
|
russia | treshchotka |
|
russia | blini |
|
russia | borshch |
|
russia | telnyashka |
|
russia | samovar |
|
russia | shchi |
|
russia | khokhloma |
|
russia | syrniki |
|
russia | valenki |
|
russia | domra |
|
russia | balalaika |
|
russia | budyonovka |
|
russia | kasha |
|
russia | chak-chak |
|
russia | caviar |
|
russia | russian blue |
|
russia | varenyky |
|
russia | matryoshka |
|
brazil | brigadeiro |
|
brazil | farofa |
|
brazil | caipirinha |
|
brazil | cachaca |
|
brazil | bombacha |
|
brazil | espetinho |
|
brazil | samba |
|
brazil | feijoada |
|
brazil | bumba-meu-boi |
|
brazil | tapioca |
|
brazil | alfajor |
|
brazil | empada |
|
brazil | requeijao |
|
brazil | moqueca |
|
brazil | rapadura |
|
brazil | sarongue |
|
brazil | carimbo |
|
brazil | rosca |
|
brazil | romeu e julieta |
|
brazil | pao de queijo |
|
brazil | acai |
|
brazil | vatapa |
|
brazil | churrasco |
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 tupleimage
: The image data in binary formatregion
: The cultural region pertaining to the imageconcept
: 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"
}
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