Capsule Networks for Computer Vision: A Survey翻译

Capsule Networks for Computer Vision: A Survey

胶囊网络在计算机视觉中的应用：一篇综述

Abstract:

摘要：

Capsule Networks (CapsNets)是一种新颖的深度神经网络架构，旨在克服传统卷积神经网络（CNNs）的一些限制，例如旋转不变性和视角不变性。Capsule Networks使用胶囊来表示图像或对象的各个特征，并且能够学习对象的姿态和空间关系。本文旨在提供对Capsule Networks的综述，重点介绍其在计算机视觉中的应用。我们首先介绍了Capsule Networks的基本原理和结构，并讨论了其与CNNs的区别。然后，我们概述了Capsule Networks在图像分类、目标检测、语义分割和图像生成等任务中的应用。接下来，我们总结了当前在Capsule Networks领域的最新研究进展，并讨论了该领域未来的发展方向。

Capsule Networks (CapsNets) are a novel deep neural network architecture aimed at overcoming some of the limitations of traditional Convolutional Neural Networks (CNNs), such as rotational and viewpoint invariance. Capsule Networks use capsules to represent various features of an image or object and are capable of learning the pose and spatial relationships of objects. This paper aims to provide a survey of Capsule Networks, with a focus on their applications in computer vision. We first introduce the basic principles and structure of Capsule Networks and discuss their differences with CNNs. Then, we outline the applications of Capsule Networks in tasks such as image classification, object detection, semantic segmentation, and image generation. Next, we summarize the latest research developments in the field of Capsule Networks and discuss future directions in this field.