  We study cultural and socioeconomic diversity in contrastive vision-language
models (VLMs). Using a broad range of benchmark datasets and evaluation
metrics, we bring to attention several important findings. First, the common
filtering of training data to English image-text pairs disadvantages
communities of lower socioeconomic status and negatively impacts cultural
understanding. Notably, this performance gap is not captured by - and even at
odds with - the currently popular evaluation metrics derived from the
Western-centric ImageNet and COCO datasets. Second, pretraining with global,
unfiltered data before fine-tuning on English content can improve cultural
understanding without sacrificing performance on said popular benchmarks.
Third, we introduce the task of geo-localization as a novel evaluation metric
to assess cultural diversity in VLMs. Our work underscores the value of using
diverse data to create more inclusive multimodal systems and lays the
groundwork for developing VLMs that better represent global perspectives.
