Toward a World with Intelligent Machines That Can Interpret the Visual World

Abstract

In the previous chapter, we introduced the idea of directly comparing computational models versus human behavior in visual tasks. For example, we assess how models classify an image versus how humans classify the same image. In some tasks, the types of errors made by computational models can be similar to human mistakes. Here we will dig deeper into what current computer vision algorithms can and cannot do. We will highlight the enormous power of current computational models, while at the same time emphasizing some of their limitations and the exciting work ahead of us to build better models.