To understand the real world using various types of data, Artificial Intelligence (AI) is the most used technique nowadays. While finding the pattern within the analyzed data represents the main task. This is performed by extracting representative features step, which is proceeded using the statistical algorithms or using some specific filters. However, the selection of useful features from large-scale data represented a crucial challenge. Now, with the development of convolution neural networks (CNNs), feature extraction operation has become more automatic and easier. CNNs allow to work on large-scale size of data, as well as cover different scenarios for a specific task. For computer vision tasks, convolutional networks are used to extract features and also for the other parts of a deep learning model. The selection of a suitable network for feature extraction or the other parts of a DL model is not random work. So, the implementation of such a model can be related to the target task as well as its computational complexity. Many networks have been proposed and become famous networks used for any DL models in any AI task. These networks are exploited for feature extraction or at the beginning of any DL model which is named backbones. A backbone is a known network trained and demonstrates its effectiveness. In this paper, an overview of the existing backbones, e.g. VGGs, ResNets, DenseNet, etc, is given with a detailed description. Also, a couple of computer vision tasks are discussed by providing a review of each task regarding the backbones used. In addition, a comparison in terms of performance is also provided, based on the backbone used for each task.

中文翻译：

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Backbones-review：计算机视觉中深度学习和深度强化学习方法的特征提取器网络


为了使用各种类型的数据来了解现实世界，人工智能（AI）是当今最常用的技术。主要任务是在分析数据中找到模式。这是通过提取代表性特征步骤来执行的，该步骤使用统计算法或使用一些特定的过滤器来进行。然而，从大规模数据中选择有用的特征是一个严峻的挑战。现在，随着卷积神经网络（CNN）的发展，特征提取操作变得更加自动化和容易。 CNN 可以处理大规模数据，并涵盖特定任务的不同场景。对于计算机视觉任务，卷积网络用于提取特征以及深度学习模型的其他部分。选择合适的网络进行特征提取或深度学习模型的其他部分并不是随机的工作。因此，这种模型的实现可能与目标任务及其计算复杂度有关。许多网络已经被提出并成为用于任何人工智能任务中的任何深度学习模型的著名网络。这些网络用于特征提取或在任何称为骨干网的深度学习模型的开头。主干网络是经过训练并展示其有效性的已知网络。本文概述了现有的主干网络，例如 VGG、ResNet、DenseNet 等，并进行了详细描述。此外，还通过对每个任务所使用的主干进行回顾来讨论几个计算机视觉任务。此外，还根据每个任务使用的主干网提供性能比较。