Since the dawn of Humanity, to communicate both abstract and concrete ideas, visualization through visual imagery has been an effective way. With the advancement of scientific technologies, vision has been imparted to machines like humans do. Computer vision give ability to machines, to receive and analyze visual data on its own, and then make decisions about it, hence computer vision is more than machine learning applied. So, visualization of computer models to learn without being explicitly programmed using machine learning algorithms is called Visual learning. This work aims to review the state-of-the-art in computer vision by highlighting the contributions, challenges and applications. We first provide an overview of important visual learning approaches and their recent developments, and then describes their applications in diverse vision tasks, such as image classification, object detection, object recognition, visual saliency detection, semantic and instance segmentation, human pose estimation and image retrieval. Hardware constraints are also highlighted for better understanding of model selection. Finally, some important challenges, trends and outlooks are also discussed for better design and training of learning modules, along with several directions that may be further explored in the future.

自人类诞生以来，为了传达抽象和具体的思想，通过视觉图像进行可视化一直是一种有效的方法。随着科学技术的进步，视觉已经像人类一样被赋予了机器。计算机视觉使机器具有能力，可以自己接收和分析视觉数据，然后对其做出决策，因此计算机视觉不仅仅是应用机器学习。因此，无需使用机器学习算法进行显式编程即可学习的计算机模型的可视化称为可视学习。这项工作旨在通过突出贡献，挑战和应用程序来回顾计算机视觉的最新技术。我们首先概述重要的视觉学习方法及其最新发展，然后描述它们在各种视觉任务中的应用，例如图像分类，物体检测，物体识别，视觉显着性检测，语义和实例分割，人体姿势估计和图像检索。还突出显示了硬件约束，以更好地了解模型选择。最后，为了更好地设计和培训学习模块，还讨论了一些重要的挑战，趋势和前景，以及今后可能会进一步探索的几个方向。