  Image captioning models tend to describe images in an object-centric way,
emphasising visible objects. But image descriptions can also abstract away from
objects and describe the type of scene depicted. In this paper, we explore the
potential of a state-of-the-art Vision and Language model, VinVL, to caption
images at the scene level using (1) a novel dataset which pairs images with
both object-centric and scene descriptions. Through (2) an in-depth analysis of
the effect of the fine-tuning, we show (3) that a small amount of curated data
suffices to generate scene descriptions without losing the capability to
identify object-level concepts in the scene; the model acquires a more holistic
view of the image compared to when object-centric descriptions are generated.
We discuss the parallels between these results and insights from computational
and cognitive science research on scene perception.
