From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-Language Models
Abstract: 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. Still, they have limited coverage of cultures and do not adequately assess cultural diversity across universal and culture-specific local concepts. To address these limitations, we introduce the GlobalRG benchmark, comprising two challenging tasks: retrieval across universals and cultural visual grounding. The former task entails retrieving culturally diverse images for universal concepts from 50 countries, while the latter aims at grounding culture-specific concepts within images from 15 countries. Our evaluation across a wide range of models reveals that the performance varies significantly across cultures -- underscoring the necessity for enhancing multicultural understanding in vision-language models.
Paper Type: Long
Research Area: Computational Social Science and Cultural Analytics
Research Area Keywords: Multicultural Understanding, Vision-Language Models, Cultural Diversity, Model Evaluation
Contribution Types: Model analysis & interpretability, Data resources
Languages Studied: english
Submission Number: 4000
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