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%2C enabling multimodal processing of visual and linguistic data. However%2C their performance has been typically assessed on general scene understanding - recognizing objects%2C attributes%2C and actions - rather than cultural comprehension. This study introduces CulturalVQA%2C a visual question-answering benchmark aimed at assessing VLM's geo-diverse cultural understanding. We curate a collection of 2%2C378 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%2C food%2C drinks%2C rituals%2C and traditions. Benchmarking VLMs on CulturalVQA%2C including GPT-4V and Gemini%2C reveals disparity in their level of cultural understanding across regions%2C with strong cultural understanding capabilities for North America while significantly lower performance for Africa. We observe disparity in their performance across cultural facets too%2C with clothing%2C rituals%2C and traditions seeing higher performances than food and drink. These disparities help us identify areas where VLMs lack cultural understanding and demonstrate the potential of CulturalVQA as a comprehensive evaluation set for gauging VLM progress in understanding diverse cultures.?quality=80&w=800)
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