  A core process in human cognition is analogical mapping: the ability to
identify a similar relational structure between different situations. We
introduce a novel task, Visual Analogies of Situation Recognition, adapting the
classical word-analogy task into the visual domain. Given a triplet of images,
the task is to select an image candidate B' that completes the analogy (A to A'
is like B to what?). Unlike previous work on visual analogy that focused on
simple image transformations, we tackle complex analogies requiring
understanding of scenes.
  We leverage situation recognition annotations and the CLIP model to generate
a large set of 500k candidate analogies. Crowdsourced annotations for a sample
of the data indicate that humans agree with the dataset label ~80% of the time
(chance level 25%). Furthermore, we use human annotations to create a
gold-standard dataset of 3,820 validated analogies. Our experiments demonstrate
that state-of-the-art models do well when distractors are chosen randomly
(~86%), but struggle with carefully chosen distractors (~53%, compared to 90%
human accuracy). We hope our dataset will encourage the development of new
analogy-making models. Website: https://vasr-dataset.github.io/
