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

通过研究大型多模态模型（LMMs）在识别文化背景、不同文化的表征准确性以及跨文化边界调整内容的有效性的综合三阶段研究，本文揭示了LMMs文化理解水平的差异，通过Dalle Street数据集验证，进而提出了一种高度可组合的CultureAdapt流水线，从而突出了发展文化感知系统的必要性。

大陆十字路口: 基于大型多模态模型的文化适应自动工艺品提取