  We present a neuro-symbolic visual question answering (VQA) pipeline for
CLEVR, which is a well-known dataset that consists of pictures showing scenes
with objects and questions related to them. Our pipeline covers (i) training
neural networks for object classification and bounding-box prediction of the
CLEVR scenes, (ii) statistical analysis on the distribution of prediction
values of the neural networks to determine a threshold for high-confidence
predictions, and (iii) a translation of CLEVR questions and network predictions
that pass confidence thresholds into logic programs so that we can compute the
answers using an ASP solver. By exploiting choice rules, we consider
deterministic and non-deterministic scene encodings. Our experiments show that
the non-deterministic scene encoding achieves good results even if the neural
networks are trained rather poorly in comparison with the deterministic
approach. This is important for building robust VQA systems if network
predictions are less-than perfect. Furthermore, we show that restricting
non-determinism to reasonable choices allows for more efficient implementations
in comparison with related neuro-symbolic approaches without loosing much
accuracy. This work is under consideration for acceptance in TPLP.
