  Contrastive vision-language models like CLIP have been found to lack spatial
understanding capabilities. In this paper we discuss the possible causes of
this phenomenon by analysing both datasets and embedding space. By focusing on
simple left-right positional relations, we show that this behaviour is entirely
predictable, even with large-scale datasets, demonstrate that these relations
can be taught using synthetic data and show that this approach can generalise
well to natural images - improving the performance on left-right relations on
Visual Genome Relations.
