Where Do Images Come From? Analyzing Captions to Geographically Profile Datasets

TL;DR

This paper analyzes the geographic origins of image-caption datasets used in text-to-image models, revealing significant biases toward Western countries and correlations with economic factors, impacting diversity and representativeness.

Contribution

It introduces a method to geographically profile datasets using LLMs and provides a comprehensive analysis of dataset biases across multiple languages and regions.

Findings

Western countries dominate dataset representation.

Representation correlates strongly with GDP.

Generated images lack real-world diversity.

Abstract

Recent studies show that text-to-image models often fail to generate geographically representative images, raising concerns about the representativeness of their training data and motivating the question: which parts of the world do these training examples come from? We geographically profile large-scale multimodal datasets by mapping image-caption pairs to countries based on location information extracted from captions using LLMs. Studying English captions from three widely used datasets (Re-LAION, DataComp1B, and Conceptual Captions) across $20$ common entities (e.g., house, flag), we find that the United States, the United Kingdom, and Canada account for $48.0\%$ of samples, while South American and African countries are severely under-represented with only $1.8\%$ and $3.8\%$ of images, respectively. We observe a strong correlation between a country's GDP and its representation in the data ($\rho = 0.82$). Examining non-English subsets for $4$ languages from the Re-LAION dataset, we find that representation skews heavily toward countries where these languages are predominantly spoken. Additionally, we find that higher representation does not necessarily translate to greater visual or semantic diversity. Finally, analyzing country-specific images generated by Stable Diffusion v1.3 trained on Re-LAION, we show that while generations appear realistic, they are severely limited in their coverage compared to real-world images.