Semantic Segmentation by Semantic Proportions
Halil Ibrahim Aysel Xiaohao Cai Adam Prügel-Bennett
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.