MetaCLIP

Refines the data curation process for training vision-language models by employing algorithms that leverage CLIP-derived metadata to create a balanced and high-quality dataset from vast sources like CommonCrawl, resulting in improved performance and diversity compared to models trained on CLIP's original dataset.