MetaVL: Transferring In-Context Learning Ability From Language Models to
  Vision-Language Models
Masoud Monajatipoor Liunian Harold Li Mozhdeh Rouhsedaghat Lin F. Yang Kai-Wei Chang
Large-scale language models have shown the ability to adapt to a new task via
conditioning on a few demonstrations (i.e., in-context learning). However, in
the vision-language domain, most large-scale pre-trained vision-language (VL)
models do not possess the ability to conduct in-context learning. How can we
enable in-context learning for VL models? In this paper, we study an
interesting hypothesis: can we transfer the in-context learning ability from
the language domain to VL domain? Specifically, we first meta-trains a language
model to perform in-context learning on NLP tasks (as in MetaICL); then we
transfer this model to perform VL tasks by attaching a visual encoder. Our
experiments suggest that indeed in-context learning ability can be transferred
cross modalities: our model considerably improves the in-context learning
capability on VL tasks and can even compensate for the size of the model
significantly. On VQA, OK-VQA, and GQA, our method could outperform the
baseline model while having 20 times fewer parameters.