Multilingual Diversity Improves Vision-Language Representations

TL;DR

This paper demonstrates that incorporating multilingual data into vision-language models enhances their performance across diverse tasks and regions, highlighting the value of cultural and linguistic diversity in training datasets.

Contribution

The study systematically shows that multilingual data improves vision-language representations and outperforms English-only datasets on multiple benchmarks.

Findings

Multilingual data improves ImageNet and distribution shift performance.

Including non-English data benefits geographically diverse tasks like GeoDE.

English and non-English data are significantly different in image and text space.

Abstract

Massive web-crawled image-text datasets lay the foundation for recent progress in multimodal learning. These datasets are designed with the goal of training a model to do well on standard computer vision benchmarks, many of which, however, have been shown to be English-centric (e.g., ImageNet). Consequently, existing data curation techniques gravitate towards using predominantly English image-text pairs and discard many potentially useful non-English samples. Our work questions this practice. Multilingual data is inherently enriching not only because it provides a gateway to learn about culturally salient concepts, but also because it depicts common concepts differently from monolingual data. We thus conduct a systematic study to explore the performance benefits of using more samples of non-English origins with respect to English vision tasks. By translating all multilingual image-text pairs from a raw web crawl to English and re-filtering them, we increase the prevalence of (translated) multilingual data in the resulting training set. Pre-training on this dataset outperforms using English-only or English-dominated datasets on ImageNet, ImageNet distribution shifts, image-English-text retrieval and on average across 38 tasks from the DataComp benchmark. On a geographically diverse task like GeoDE, we also observe improvements across all regions, with the biggest gain coming from Africa. In addition, we quantitatively show that English and non-English data are significantly different in both image and (translated) text space. We hope that our findings motivate future work to be more intentional about including multicultural and multilingual data, not just when non-English or geographically diverse tasks are involved, but to enhance model capabilities at large. All translated captions and metadata (language, CLIP score, etc.) are available on HuggingFace.