  While interest in models that generalize at test time to new compositions has
risen in recent years, benchmarks in the visually-grounded domain have thus far
been restricted to synthetic images. In this work, we propose COVR, a new
test-bed for visually-grounded compositional generalization with real images.
To create COVR, we use real images annotated with scene graphs, and propose an
almost fully automatic procedure for generating question-answer pairs along
with a set of context images. COVR focuses on questions that require complex
reasoning, including higher-order operations such as quantification and
aggregation. Due to the automatic generation process, COVR facilitates the
creation of compositional splits, where models at test time need to generalize
to new concepts and compositions in a zero- or few-shot setting. We construct
compositional splits using COVR and demonstrate a myriad of cases where
state-of-the-art pre-trained language-and-vision models struggle to
compositionally generalize.
