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.

本文介绍了一种新的任务 ——ViQAR（视觉问题回答和推理），并提出了一种完全生成式的解决方案，它能够为视觉查询生成完整的答案和推理，我们通过定性和定量评估以及人类图灵测试表明，我们的模型能够生成强有力的答案和推理。