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.

通过简化网络架构，我们展示了一种直接产生高分辨率分割结果的简化模型，其性能可以与生成低分辨率结果的更复杂系统匹配，并通过底层信息传播技术在不同尺度上提高分割准确性。