  Given an image, a back-ground knowledge, and a set of questions about an
object, human learners answer the questions very consistently regardless of
question forms and semantic tasks. The current advancement in neural-network
based Visual Question Answering (VQA), despite their impressive performance,
cannot ensure such consistency due to identically distribution (i.i.d.)
assumption. We propose a new model-agnostic logic constraint to tackle this
issue by formulating a logically consistent loss in the multi-task learning
framework as well as a data organisation called family-batch and hybrid-batch.
To demonstrate usefulness of this proposal, we train and evaluate MAC-net based
VQA machines with and without the proposed logically consistent loss and the
proposed data organization. The experiments confirm that the proposed loss
formulae and introduction of hybrid-batch leads to more consistency as well as
better performance. Though the proposed approach is tested with MAC-net, it can
be utilised in any other QA methods whenever the logical consistency between
answers exist.
