深度学习及其在计算机视觉领域中的应用

首先回顾了计算视觉发展的历史，介绍了神经元、多层感知机和反向传播等人工神经网络的基本知识以及卷积神经网络的发展史及其卷积、池化等基本操作；讨论了AlexNet、VGGNet、GoogLeNet和ResNet等经典卷积神经网络结构，并重点介绍了CapsNet；总结了卷积神经网络在图像分类、语义分割、目标检测以及图像生成等领域的研究进展；最后提出了卷积神经网络研究所面临的挑战以及对CapsNet未来研究的展望。

Review of deep learning and its application in computer vision

Firstly, this paper reviewed the development of computer vision, presented the elementary knowledge of neurons, multi-layer perceptron, back-propagation algorithm, and the development of convolutional neural network(CNN) and its basic operation of convolution and pooled etc.. Secondly, it discussed the structures of typical convolutional neural networks such as AlexNet, VGGNet, GoogLeNet and ResNet etc., and mainly introduced CapsNet. And then it summaried the research progress of the application of CNN in image classification, image semantic segmentation, image target detection and image generation. At last, this paper presented the research challenge of CNN and the future next work for CapsNet.