Learning to Downsample for Segmentation of Ultra-High Resolution Images

TL;DR

This paper introduces a learnable downsampling module optimized jointly with segmentation models to improve accuracy on high-resolution images by focusing sampling on challenging regions, outperforming uniform and existing methods.

Contribution

The work presents a novel end-to-end trainable downsampling approach that adaptively concentrates sampling on difficult image regions, enhancing segmentation performance on ultra-high resolution images.

Findings

Learnable downsampling improves segmentation accuracy.

Adaptive sampling concentrates on object boundaries.

Significant efficiency and accuracy gains over existing methods.

Abstract

Many computer vision systems require low-cost segmentation algorithms based on deep learning, either because of the enormous size of input images or limited computational budget. Common solutions uniformly downsample the input images to meet memory constraints, assuming all pixels are equally informative. In this work, we demonstrate that this assumption can harm the segmentation performance because the segmentation difficulty varies spatially. We combat this problem by introducing a learnable downsampling module, which can be optimised together with the given segmentation model in an end-to-end fashion. We formulate the problem of training such downsampling module as optimisation of sampling density distributions over the input images given their low-resolution views. To defend against degenerate solutions (e.g. over-sampling trivial regions like the backgrounds), we propose a regularisation term that encourages the sampling locations to concentrate around the object boundaries. We find the downsampling module learns to sample more densely at difficult locations, thereby improving the segmentation performance. Our experiments on benchmarks of high-resolution street view, aerial and medical images demonstrate substantial improvements in terms of efficiency-and-accuracy trade-off compared to both uniform downsampling and two recent advanced downsampling techniques.