Contrastive pretraining on image-text pairs from the web is one of the most popular large-scale pretraining strategies for vision backbones, especially in the context of large multimodal models. At the same time, image captioning on this type of data is commonly considered an inferior pretraining strategy. In this paper, we perform a fair comparison of these two pretraining strategies, carefully matching training data, compute, and model capacity. Using a standard encoder-decoder transformer, we find that captioning alone is surprisingly effective: on classification tasks, captioning produces vision encoders competitive with contrastively pretrained encoders, while surpassing them on vision & language tasks. We further analyze the effect of the model architecture and scale, as well as the pretraining data on the representation quality, and find that captioning exhibits the same or better scaling behavior along these axes. Overall our results show that plain image captioning is a more powerful pretraining strategy than was previously believed.
@article{arxiv.2306.07915,
title = {Image Captioners Are Scalable Vision Learners Too},
author = {Michael Tschannen and Manoj Kumar and Andreas Steiner and Xiaohua Zhai and Neil Houlsby and Lucas Beyer},
journal= {arXiv preprint arXiv:2306.07915},
year = {2023}
}
Comments
Accepted at NeurIPS 2023. v2 adds SugarCrepe results and more ablations, v3 has minor fixes. v4 adds a code link ( https://github.com/google-research/big_vision ). v5 has minor fixes