Semantic Segmentation by Semantic Proportions

TL;DR

This paper introduces a novel semantic segmentation approach that uses rough class proportion information instead of detailed pixel-level annotations, significantly reducing annotation effort and storage needs while maintaining competitive performance.

Contribution

The paper proposes a new method leveraging semantic proportions for segmentation, enabling training with less detailed annotations and serving as a plug-and-play module for existing models.

Findings

Achieves competitive segmentation performance with semantic proportions.

Reduces annotation time, cost, and storage space.

Can enhance existing segmentation models without added complexity.

Abstract

Semantic segmentation is a critical task in computer vision aiming to identify and classify individual pixels in an image, with numerous applications in for example autonomous driving and medical image analysis. However, semantic segmentation can be highly 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; moreover, saving the annotated images could dramatically increase the storage space. In this paper, we propose a novel approach for semantic segmentation, requiring the rough information of individual semantic class proportions, shortened as semantic proportions, rather than the necessity of ground-truth segmentation maps. This greatly simplifies the data annotation process and thus will significantly reduce the annotation time, cost and storage space, opening up new possibilities for semantic segmentation tasks where obtaining the full ground-truth segmentation maps may not be feasible or practical. Our proposed method of utilising semantic proportions can (i) further be utilised as a booster in the presence of ground-truth segmentation maps to gain performance without extra data and model complexity, and (ii) also be seen as a parameter-free plug-and-play module, which can be attached to existing deep neural networks designed for semantic segmentation. Extensive experimental results demonstrate the good performance of our method compared to benchmark methods that rely on ground-truth segmentation maps. Utilising semantic proportions suggested in this work offers a promising direction for future semantic segmentation research.