  Recent vision-language models have shown impressive multi-modal generation
capabilities. However, typically they require training huge models on massive
datasets. As a more scalable alternative, we introduce Prismer, a data- and
parameter-efficient vision-language model that leverages an ensemble of
task-specific experts. Prismer only requires training of a small number of
components, with the majority of network weights inherited from multiple
readily-available, pre-trained experts, and kept frozen during training. By
leveraging experts from a wide range of domains, we show Prismer can
efficiently pool this expert knowledge and adapt it to various vision-language
reasoning tasks. In our experiments, we show that Prismer achieves fine-tuned
and few-shot learning performance which is competitive with current
state-of-the-arts, whilst requiring up to two orders of magnitude less training
data. Code is available at https://github.com/NVlabs/prismer.
