  While progress has been made on the visual question answering leaderboards,
models often utilize spurious correlations and priors in datasets under the
i.i.d. setting. As such, evaluation on out-of-distribution (OOD) test samples
has emerged as a proxy for generalization. In this paper, we present MUTANT, a
training paradigm that exposes the model to perceptually similar, yet
semantically distinct mutations of the input, to improve OOD generalization,
such as the VQA-CP challenge. Under this paradigm, models utilize a
consistency-constrained training objective to understand the effect of semantic
changes in input (question-image pair) on the output (answer). Unlike existing
methods on VQA-CP, MUTANT does not rely on the knowledge about the nature of
train and test answer distributions. MUTANT establishes a new state-of-the-art
accuracy on VQA-CP with a $10.57\%$ improvement. Our work opens up avenues for
the use of semantic input mutations for OOD generalization in question
answering.
