In this paper, we tackle the challenge of actively attending to visual scenes
using a foveated sensor. We introduce an end-to-end differentiable foveated
active vision architecture that leverages a graph convolutional network to
process foveated images, and a simple yet effective formulation for foveated
image sampling. Our model learns to iteratively attend to regions of the image
relevant for classification. We conduct detailed experiments on a variety of
image datasets, comparing the performance of our method with previous
approaches to foveated vision while measuring how the impact of different
choices, such as the degree of foveation, and the number of fixations the
network performs, affect object recognition performance. We find that our model
outperforms a state-of-the-art CNN and foveated vision architectures of
comparable parameters and a given pixel or computation budget

本文介绍了一种使用视锥传感器主动关注视觉场景的方法，并提出了一种端到端可区分的视锥主动视觉架构，该架构利用图卷积网络处理视锥图像，并提供一种简单而有效的视锥图像采样形式。我们的模型通过学习迭代地关注与分类相关的图像区域。我们在多个图像数据集上进行了详细实验，比较了我们的方法与先前的视锥视觉方法的性能，同时还测量了不同选择（如视锥程度和网络执行的固定点数）对目标识别性能的影响。我们发现，我们的模型在具有可比参数和给定像素或计算预算的最先进 CNN 和视锥视觉架构上表现更好。