Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts

TL;DR

This paper introduces Conceptual 12M, a large-scale image-text dataset for vision-and-language pre-training, demonstrating that increased data diversity and scale improve performance on long-tail visual recognition tasks.

Contribution

It presents the creation of CC12M dataset with relaxed data collection constraints, enabling larger and more diverse pre-training data for vision-and-language models.

Findings

CC12M improves downstream task performance

Scaling data enhances long-tail visual recognition

Achieves new state-of-the-art on nocaps and CC benchmarks

Abstract

The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pre-training. However, these datasets are often collected with overrestrictive requirements inherited from their original target tasks (e.g., image caption generation), which limit the resulting dataset scale and diversity. We take a step further in pushing the limits of vision-and-language pre-training data by relaxing the data collection pipeline used in Conceptual Captions 3M (CC3M) [Sharma et al. 2018] and introduce the Conceptual 12M (CC12M), a dataset with 12 million image-text pairs specifically meant to be used for vision-and-language pre-training. We perform an analysis of this dataset and benchmark its effectiveness against CC3M on multiple downstream tasks with an emphasis on long-tail visual recognition. Our results clearly illustrate the benefit of scaling up pre-training data for vision-and-language tasks, as indicated by the new state-of-the-art results on both the nocaps and Conceptual Captions benchmarks.