  High-resolution semantic segmentation requires substantial computational
resources. Traditional approaches in the field typically downscale the input
images before processing and then upscale the low-resolution outputs back to
their original dimensions. While this strategy effectively identifies broad
regions, it often misses finer details. In this study, we demonstrate that a
streamlined model capable of directly producing high-resolution segmentations
can match the performance of more complex systems that generate
lower-resolution results. By simplifying the network architecture, we enable
the processing of images at their native resolution. Our approach leverages a
bottom-up information propagation technique across various scales, which we
have empirically shown to enhance segmentation accuracy. We have rigorously
tested our method using leading-edge semantic segmentation datasets.
Specifically, for the Cityscapes dataset, we further boost accuracy by applying
the Noisy Student Training technique.
