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A Survey of the Application of Deep Learning in Computer Vision

机译：深度学习在计算机视觉中的应用概述

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Deep learning has strong abilities in finding and expressing characteristics of pictures. Recent years, with the arrival of big data era and the development of computers, deep learning has made great breakthroughs and become the focus of the field of computer vision. First the history and classification of deep learning are presented. This thesis also introduces the basic theory of typical deep learning models on computer vision, which include convolutional neural network, recurrent neural network and generative adversarial network. And then summarizing the research situations and progress of deep learning on image classification, image detection, image segmentation as well as video recognition and prediction. Finally, the development and trend of deep learning in the field of computer vision are analyzed. The combination of convolutional neural network and recurrent neural network will be a good choice for video recognition and prediction, which still has a big gap between human beings cognition. And it is the generative adversarial network which has strong ability to generate new samples based on the potential distribution will play an important role in computer vision.
机译：深度学习具有发现和表达图片特征的强大能力。近年来，随着大数据时代的到来和计算机的发展，深度学习取得了巨大的突破，并成为计算机视觉领域的重点。首先介绍了深度学习的历史和分类。本文还介绍了典型的计算机视觉深度学习模型的基础理论，包括卷积神经网络，递归神经网络和生成对抗网络。然后总结了图像分类，图像检测，图像分割以及视频识别和预测的研究现状和深度学习的进展。最后，分析了计算机视觉领域中深度学习的发展和趋势。卷积神经网络和递归神经网络的结合将是视频识别和预测的良好选择，但人类认知之间仍存在较大差距。并且，具有强大的生成能力的对抗网络将基于潜在的分布生成新样本，这将在计算机视觉中发挥重要作用。

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