No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Models

TL;DR

This paper investigates how filtering training data to English image-text pairs in contrastive vision-language models can limit cultural and socioeconomic diversity, proposing new evaluation metrics and training strategies to promote inclusivity.

Contribution

It reveals the biases introduced by data filtering, introduces geo-localization as a new diversity metric, and demonstrates that unfiltered global pretraining enhances cultural understanding.

Findings

Filtering biases lower socioeconomic and cultural diversity.

Unfiltered pretraining improves cultural understanding.

Geo-localization effectively measures cultural diversity.

Abstract

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.