  There are two main lines of research on visual question answering (VQA):
compositional model with explicit multi-hop reasoning, and monolithic network
with implicit reasoning in the latent feature space. The former excels in
interpretability and compositionality but fails on real-world images, while the
latter usually achieves better performance due to model flexibility and
parameter efficiency. We aim to combine the two to build an interpretable
framework for real-world compositional VQA. In our framework, images and
questions are disentangled into scene graphs and programs, and a symbolic
program executor runs on them with full transparency to select the attention
regions, which are then iteratively passed to a visual-linguistic pre-trained
encoder to predict answers. Experiments conducted on the GQA benchmark
demonstrate that our framework outperforms the compositional prior arts and
achieves competitive accuracy among monolithic ones. With respect to the
validity, plausibility and distribution metrics, our framework surpasses others
by a considerable margin.
