  Images can be described in terms of the objects they contain, or in terms of
the types of scene or place that they instantiate. In this paper we address to
what extent pretrained Vision and Language models can learn to align
descriptions of both types with images. We compare 3 state-of-the-art models,
VisualBERT, LXMERT and CLIP. We find that (i) V&L models are susceptible to
stylistic biases acquired during pretraining; (ii) only CLIP performs
consistently well on both object- and scene-level descriptions. A follow-up
ablation study shows that CLIP uses object-level information in the visual
modality to align with scene-level textual descriptions.
