  In multi-modal reasoning tasks, such as visual question answering (VQA),
there have been many modeling and training paradigms tested. Previous models
propose different methods for the vision and language tasks, but which ones
perform the best while being sample and computationally efficient? Based on our
experiments, we find that representing the text as probabilistic programs and
images as object-level scene graphs best satisfy these desiderata. We extend
existing models to leverage these soft programs and scene graphs to train on
question answer pairs in an end-to-end manner. Empirical results demonstrate
that this differentiable end-to-end program executor is able to maintain
state-of-the-art accuracy while being sample and computationally efficient.
