Image Captioners Are Scalable Vision Learners Too
Authors: name: Michael Tschannen, name: Manoj Kumar, name: Andreas Steiner, name: Xiaohua Zhai, name: Neil Houlsby, name: Lucas Beyer

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.