  Propelling, and propelled by, the "deep learning revolution", recent years
have seen the introduction of ever larger corpora of images annotated with
natural language expressions. We survey some of these corpora, taking a
perspective that reverses the usual directionality, as it were, by viewing the
images as semantic annotation of the natural language expressions. We discuss
datasets that can be derived from the corpora, and tasks of potential interest
for computational semanticists that can be defined on those. In this, we make
use of relations provided by the corpora (namely, the link between expression
and image, and that between two expressions linked to the same image) and
relations that we can add (similarity relations between expressions, or between
images). Specifically, we show that in this way we can create data that can be
used to learn and evaluate lexical and compositional grounded semantics, and we
show that the "linked to same image" relation tracks a semantic implication
relation that is recognisable to annotators even in the absence of the linking
image as evidence. Finally, as an example of possible benefits of this
approach, we show that an exemplar-model-based approach to implication beats a
(simple) distributional space-based one on some derived datasets, while lending
itself to explainability.
