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Contrastive vision-language models like CLIP have been found to lack spatialunderstanding capabilities. In this paper we discuss the possible causes ofthis phenomenon by analysing both datasets and embedding space. By focusing onsimple left-right positional relations, we show that this behaviour is entirelypredictable, even with large-scale datasets, demonstrate that these relationscan be taught using synthetic data and show that this approach can generalisewell to natural images - improving the performance on left-right relations onVisual Genome Relations.

What's left can't be right -- The remaining positional incompetence of contrastive vision-language models