Weakly Supervised Few-Shot Segmentation via Meta-Learning

Semantic segmentation is a classic computer vision task with multiple applications, which includes medical and remote sensing image analysis. Despite recent advances with deep-based approaches, labeling samples (pixels) for training models is laborious and, in some cases, unfeasible. In this paper, we present two novel meta-learning methods, named WeaSeL and ProtoSeg, for the few-shot semantic segmentation task with sparse annotations. We conducted an extensive evaluation of the proposed methods in different applications (12 datasets) in medical imaging and agricultural remote sensing, which are very distinct fields of knowledge and usually subject to data scarcity. The results demonstrated the potential of our method, achieving suitable results for segmenting both coffee/orange crops and anatomical parts of the human body in comparison with full dense annotation.

Automated summary

This paper presents two novel meta-learning methods, WeaSeL and ProtoSeg, for few-shot semantic segmentation with sparse annotations. Extensive evaluation of the proposed methods in different fields (12 datasets) including medical imaging and agricultural remote sensing demonstrates their potential in segmenting coffee/orange crops and anatomical parts of the human body compared to full dense annotation.