  The limits of applicability of vision-and-language models are defined by the
coverage of their training data. Tasks like vision question answering (VQA)
often require commonsense and factual information beyond what can be learned
from task-specific datasets. This paper investigates the injection of knowledge
from general-purpose knowledge bases (KBs) into vision-and-language
transformers. We use an auxiliary training objective that encourages the
learned representations to align with graph embeddings of matching entities in
a KB. We empirically study the relevance of various KBs to multiple tasks and
benchmarks. The technique brings clear benefits to knowledge-demanding question
answering tasks (OK-VQA, FVQA) by capturing semantic and relational knowledge
absent from existing models. More surprisingly, the technique also benefits
visual reasoning tasks (NLVR2, SNLI-VE). We perform probing experiments and
show that the injection of additional knowledge regularizes the space of
embeddings, which improves the representation of lexical and semantic
similarities. The technique is model-agnostic and can expand the applicability
of any vision-and-language transformer with minimal computational overhead.
