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

Making the Most of What You Have: Adapting Pre-trained Visual Language Models in the Low-data Regime