  Visual Question Answering is a multi-modal task that aims to measure
high-level visual understanding. Contemporary VQA models are restrictive in the
sense that answers are obtained via classification over a limited vocabulary
(in the case of open-ended VQA), or via classification over a set of
multiple-choice-type answers. In this work, we present a completely generative
formulation where a multi-word answer is generated for a visual query. To take
this a step forward, we introduce a new task: ViQAR (Visual Question Answering
and Reasoning), wherein a model must generate the complete answer and a
rationale that seeks to justify the generated answer. We propose an end-to-end
architecture to solve this task and describe how to evaluate it. We show that
our model generates strong answers and rationales through qualitative and
quantitative evaluation, as well as through a human Turing Test.
