关于物体视觉的最新计算模型，您需要了解的内容：模型之旅
What you need to know about the state-of-the-art computational models of object-vision: A tour through the models
摘要：
目标视觉模型一直是计算机视觉和视觉神经科学的研究热点。在过去的几十年里，已经开发了几种模型来从图像中提取视觉特征以执行对象识别任务。其中一些是受到灵长类视觉系统层次结构的启发，另一些是工程模型。这些模型在几个方面有所不同：通过监督训练的模型，没有监督训练的模型，以及完全硬连线且不需要训练的模型（例如特征提取器）。一些模型具有由几个层组成的深层层次结构，而另一些模型则很浅，只有一个或两个处理层。最近，已经开发出了新的模型，这些模型不是手动调整的，而是使用数百万张图像进行训练，通过这些图像，它们学习如何提取与任务相关的信息特征。在这里，我将调查所有这些不同的模型，并为读者提供一个直观的，以及更详细的理解，在每个模型的基础计算。
英文摘要：
Models of object vision have been of great interest in computer vision and visual neuroscience. During the last decades, several models have been developed to extract visual features from images for object recognition tasks. Some of these were inspired by the hierarchical structure of primate visual system, and some others were engineered models. The models are varied in several aspects: models that are trained by supervision, models trained without supervision, and models (e.g. feature extractors) that are fully hard-wired and do not need training. Some of the models come with a deep hierarchical structure consisting of several layers, and some others are shallow and come with only one or two layers of processing. More recently, new models have been developed that are not hand-tuned but trained using millions of images, through which they learn how to extract informative task-related features. Here I will survey all these different models and provide the reader with an intuitive, as well as a more detailed, understanding of the underlying computations in each of the models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
