  Multimodal learning has been a field of increasing interest, aiming to
combine various modalities in a single joint representation. Especially in the
area of visiolinguistic (VL) learning multiple models and techniques have been
developed, targeting a variety of tasks that involve images and text. VL models
have reached unprecedented performances by extending the idea of Transformers,
so that both modalities can learn from each other. Massive pre-training
procedures enable VL models to acquire a certain level of real-world
understanding, although many gaps can be identified: the limited comprehension
of commonsense, factual, temporal and other everyday knowledge aspects
questions the extendability of VL tasks. Knowledge graphs and other knowledge
sources can fill those gaps by explicitly providing missing information,
unlocking novel capabilities of VL models. In the same time, knowledge graphs
enhance explainability, fairness and validity of decision making, issues of
outermost importance for such complex implementations. The current survey aims
to unify the fields of VL representation learning and knowledge graphs, and
provides a taxonomy and analysis of knowledge-enhanced VL models.
