  Large-scale visual language models are widely used as pre-trained models and
then adapted for various downstream tasks. While humans are known to
efficiently learn new tasks from a few examples, deep learning models struggle
with adaptation from few examples. In this work, we look into task adaptation
in the low-data regime, and provide a thorough study of the existing adaptation
methods for generative Visual Language Models. And we show important benefits
of self-labelling, i.e. using the model's own predictions to self-improve when
having access to a larger number of unlabelled images of the same distribution.
Our study demonstrates significant gains using our proposed task adaptation
pipeline across a wide range of visual language tasks such as visual
classification (ImageNet), visual captioning (COCO), detailed visual captioning
(Localised Narratives) and visual question answering (VQAv2).
