  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
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.
