# Weakly Supervised Few-Shot Segmentation Via Meta-Learning Source: [Weakly Supervised Few-Shot Segmentation Via Meta-Learning]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:30:26.199Z Authors: Pedro H. T. Gama, Hugo Oliveira, Jos\\'e Marcato Junior, Jefersson A. dos Santos Published: 2021-09-03T18:20:26.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()