Too Large; Data Reduction for Vision-Language Pre-Training

Understand

This paper examines the problems of severe image-text misalignment and high redundancy in the widely-used large-scale Vision-Language Pre-Training (VLP) datasets.

• To address these issues, we propose an efficient and straightforward Vision-Language learning algorithm called TL;DR, which aims to compress the existing large VLP data into a small, high-quality set.
• Our approach consists of two major steps.
• First, a codebook-based encoder-decoder captioner is developed to select representative samples.