The Visual Question Answering (VQA) task combines challenges for processing
data with both Visual and Linguistic processing, to answer basic `common sense'
questions about given images. Given an image and a question in natural
language, the VQA system tries to find the correct answer to it using visual
elements of the image and inference gathered from textual questions. In this
survey, we cover and discuss the recent datasets released in the VQA domain
dealing with various types of question-formats and robustness of the
machine-learning models. Next, we discuss about new deep learning models that
have shown promising results over the VQA datasets. At the end, we present and
discuss some of the results computed by us over the vanilla VQA model, Stacked
Attention Network and the VQA Challenge 2017 winner model. We also provide the
detailed analysis along with the challenges and future research directions.

本篇综述介绍了视觉问答（VQA）任务，包括基于自然语言描述的图像识别以及机器学习模型的研究，主要探讨了近期在该领域中公布的数据集、新的深度学习模型以及基于 VQA 模型的一些应用研究和挑战。