Semantic segmentation is a critical task in computer vision that aims to
identify and classify individual pixels in an image, with numerous applications
for example autonomous driving and medical image analysis. However, semantic
segmentation can be super challenging particularly due to the need for large
amounts of annotated data. Annotating images is a time-consuming and costly
process, often requiring expert knowledge and significant effort. In this
paper, we propose a novel approach for semantic segmentation by eliminating the
need of ground-truth segmentation maps. Instead, our approach requires only the
rough information of individual semantic class proportions, shortened as
semantic proportions. It greatly simplifies the data annotation process and
thus will significantly reduce the annotation time and cost, making it more
feasible for large-scale applications. Moreover, it opens up new possibilities
for semantic segmentation tasks where obtaining the full ground-truth
segmentation maps may not be feasible or practical. Extensive experimental
results demonstrate that our approach can achieve comparable and sometimes even
better performance against the benchmark method that relies on the ground-truth
segmentation maps. Utilising semantic proportions suggested in this work offers
a promising direction for future research in the field of semantic
segmentation.

提出了一种新的语义分割方法，只需要语义类别比例的大致信息，大大简化了数据标注过程，可行性更强，并取得了可比甚至更好的性能表现，为未来的语义分割研究提供了一个有前途的方向。