Dynamic Pretraining of Vision-Language Models

AJ Piergiovanni, Weicheng Kuo, Wei Li, Anelia Angelova

Keywords: pretraining, vision language, sampling, curriculum learning

TL;DR: We propose a dynamic pretraining resampling of tasks that learns faster and better models

Abstract: Vision-Language pretraining aims to learn universal cross-modal representations and to create models with broad capabilities. In this paper, we propose a novel dynamic pretraining resampling for a variety of pretraining tasks. Unlike recent large-scale vision-language approaches, we show that a set of diverse self- and weakly-supervised pretraining tasks dynamically sampled according to task difficulty provides strong performance. Further, the approach is sample-efficient, using much less data and compute to address a range of downstream tasks. We show that a single 330M pretrained model using only smaller and publicly accessible datasets, achieves competitive or SOTA performance on three diverse groups of tasks: visual question answering, text-based image localization by referring expressions, and video question answering. The code will be released.

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