  In this work, we present a comprehensive three-phase study to examine (1) the
effectiveness of large multimodal models (LMMs) in recognizing cultural
contexts; (2) the accuracy of their representations of diverse cultures; and
(3) their ability to adapt content across cultural boundaries. We first
introduce Dalle Street, a large-scale dataset generated by DALL-E 3 and
validated by humans, containing 9,935 images of 67 countries and 10 concept
classes. We reveal disparities in cultural understanding at the sub-region
level with both open-weight (LLaVA) and closed-source (GPT-4V) models on Dalle
Street and other existing benchmarks. Next, we assess models' deeper culture
understanding by an artifact extraction task and identify over 18,000 artifacts
associated with different countries. Finally, we propose a highly composable
pipeline, CultureAdapt, to adapt images from culture to culture. Our findings
reveal a nuanced picture of the cultural competence of LMMs, highlighting the
need to develop culture-aware systems. Dataset and code are available at
https://github.com/iamshnoo/crossroads
