Summary of From Local Concepts to Universals: Evaluating the Multicultural Understanding Of Vision-language Models, by Mehar Bhatia et al.
From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-Language Models
by Mehar Bhatia, Sahithya Ravi, Aditya Chinchure, Eunjeong Hwang, Vered Shwartz
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 proposed GlobalRG benchmark tests the cultural inclusivity of vision-language models by assessing their ability to retrieve culturally diverse images and ground culture-specific concepts. The benchmark consists of two challenging tasks: retrieval across universals, which involves retrieving images from 50 countries for universal concepts, and cultural visual grounding, which aims to ground culture-specific concepts within images from 15 countries. The evaluation reveals that the performance varies significantly across cultures, highlighting the need to enhance multicultural understanding in vision-language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new benchmark called GlobalRG to test how well computer models can understand different cultures around the world. Current models are not very good at this because they were trained using images from mostly Western countries and don’t have enough examples from other cultures. The GlobalRG benchmark has two parts: one where models need to find pictures of universal things like animals or buildings, but from many different countries; and another where models need to understand what specific concepts mean in certain cultures by looking at images. The results show that the models are much worse at understanding some cultures than others, which is a big problem because we want our computer systems to be able to work well with people of all backgrounds. |
Keywords
» Artificial intelligence » Grounding
