Visual Question Answering (VQA) is an emerging area of interest for
researches, being a recent problem in natural language processing and image
prediction. In this area, an algorithm needs to answer questions about certain
images. As of the writing of this survey, 25 recent studies were analyzed.
Besides, 6 datasets were analyzed and provided their link to download. In this
work, several recent pieces of research in this area were investigated and a
deeper analysis and comparison among them were provided, including results, the
state-of-the-art, common errors, and possible points of improvement for future
researchers.

这篇论文调查了目前在视觉问答领域的多项研究，并提供了深入的分析和比较，包括结果、现有技术的状态、常见错误以及未来研究的可能改进点。

视觉问答：近期文献中的技术和普遍趋势调查

Visual Question Answering: A Survey on Techniques and Common Trends in  Recent Literature

This paper investigates the challenge of learning image manifolds,
specifically pose manifolds, of 3D objects using limited training data. It
proposes a DNN approach to manifold learning and for predicting images of
objects for novel, continuous 3D rotations. The approach uses two distinct
concepts: (1) Geometric Style-GAN (Geom-SGAN), which maps images to
low-dimensional latent representations and maintains the (first-order) manifold
geometry. That is, it seeks to preserve the pairwise distances between base
points and their tangent spaces, and (2) uses Euler's elastica to smoothly
interpolate between directed points (points + tangent directions) in the
low-dimensional latent space. When mapped back to the larger image space, the
resulting interpolations resemble videos of rotating objects. Extensive
experiments establish the superiority of this framework in learning paths on
rotation manifolds, both visually and quantitatively, relative to
state-of-the-art GANs and VAEs.

本文提出了一种深度神经网络方法，其中将几何样式生成对抗网络（Geom-SGAN）与欧拉弹性相结合，用于预测连续旋转下物体的图像，并相对于目前的生成对抗网络和变分自编码机在旋转流形上的视觉和数量上进行广泛的实验。

利用几何保持 GAN 和弹性体学习姿态图像流形

Learning Pose Image Manifolds Using Geometry-Preserving GANs and  Elasticae

Image prediction methods often struggle on tasks that require changing the
positions of objects, such as video prediction, producing blurry images that
average over the many positions that objects might occupy. In this paper, we
propose a simple change to existing image similarity metrics that makes them
more robust to positional errors: we match the images using optical flow, then
measure the visual similarity of corresponding pixels. This change leads to
crisper and more perceptually accurate predictions, and does not require
modifications to the image prediction network. We apply our method to a variety
of video prediction tasks, where it obtains strong performance with simple
network architectures, and to the closely related task of video interpolation.
Code and results are available at our webpage:
this https URL

本文提出了一种简单的改变现有图像相似度度量标准的方法，通过使用光流匹配图像并测量对应像素的视觉相似度来使其更加鲁棒性地处理对象位置误差，并应用于各种视频预测任务和视频内插任务中，实现了简单的网络结构，取得了强大的表现。