Foveation in the Era of Deep Learning
Published in
British Machine Vision Conference(2023)External Links:
TL
TL;DR
An end-to-end differentiable foveated active vision architecture that leverages a graph convolutional network to process foveation images, and a simple yet effective formulation for foveate image sampling is introduced.
Abstract
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
References41 items
1
ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
2
Consistency driven Sequential Transformers Attention Model for Partially Observable Scenes
3
A ConvNet for the 2020s
4
Biologically Inspired Deep Learning Model for Efficient Foveal-Peripheral Vision
5