Summary of Benchmarking Vision Language Models For Cultural Understanding, by Shravan Nayak et al.
Benchmarking Vision Language Models for Cultural Understanding
by Shravan Nayak, Kanishk Jain, Rabiul Awal, Siva Reddy, Sjoerd van Steenkiste, Lisa Anne Hendricks, Karolina Stańczak, Aishwarya Agrawal
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces CulturalVQA, a benchmark for assessing Vision Language Models’ (VLMs) geo-diverse cultural understanding. It focuses on multimodal processing and recognizes objects, attributes, and actions, but lacks cultural comprehension. The study curates 2,378 image-question pairs representing cultures from 11 countries across 5 continents, probing various cultural facets like clothing, food, drinks, rituals, and traditions. VLMs, including GPT-4V and Gemini, are benchmarked on CulturalVQA, revealing disparities in their level of cultural understanding across regions. The results show strong capabilities for North America but significantly lower performance for Africa, highlighting areas where VLMs lack cultural understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to test how well computers can understand different cultures from around the world. It makes a special set of questions and answers that help figure out if these computer models are good at understanding things like what people wear, eat, drink, and do in different cultures. The test shows that some computer models are better than others at understanding certain cultures, but not as good at understanding others. This can help us make computers that are more aware of the differences between cultures. |
Keywords
» Artificial intelligence » Gemini » Gpt
