  The CLEVR dataset of natural-looking questions about 3D-rendered scenes has
recently received much attention from the research community. A number of
models have been proposed for this task, many of which achieved very high
accuracies of around 97-99%. In this work, we study how systematic the
generalization of such models is, that is to which extent they are capable of
handling novel combinations of known linguistic constructs. To this end, we
test models' understanding of referring expressions based on matching object
properties (such as e.g. "another cube that is the same size as the brown
cube") in novel contexts. Our experiments on the thereby constructed CLOSURE
benchmark show that state-of-the-art models often do not exhibit systematicity
after being trained on CLEVR. Surprisingly, we find that an explicitly
compositional Neural Module Network model also generalizes badly on CLOSURE,
even when it has access to the ground-truth programs at test time. We improve
the NMN's systematic generalization by developing a novel Vector-NMN module
architecture with vector-valued inputs and outputs. Lastly, we investigate how
much few-shot transfer learning can help models that are pretrained on CLEVR to
adapt to CLOSURE. Our few-shot learning experiments contrast the adaptation
behavior of the models with intermediate discrete programs with that of the
end-to-end continuous models.
