# Weakly Supervised Few-Shot Segmentation Via Meta-Learning

arXiv: https://arxiv.org/abs/2109.01693 · HTML: https://mlsanity.com/paper/2109.01693/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 49 references that are in the map.
- First posted on arXiv 3 Sep 2021.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment](https://mlsanity.com/paper/1908.06391/) (2019)
- [Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML](https://mlsanity.com/paper/1909.09157/) (2019)
- [ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1604.05144/) (2016)
- [Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation](https://mlsanity.com/paper/1908.10454/) (2019)
- [SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation](https://mlsanity.com/paper/1810.09091/) (2018)
- [Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning](https://mlsanity.com/paper/1710.04043/) (2017)
- [Learning random-walk label propagation for weakly-supervised semantic segmentation](https://mlsanity.com/paper/1802.00470/) (2018)
- [Learning Aerial Image Segmentation from Online Maps](https://mlsanity.com/paper/1707.06879/) (2017)
- [Few-Shot Segmentation Propagation with Guided Networks](https://mlsanity.com/paper/1806.07373/) (2018)
- [XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation](https://mlsanity.com/paper/1904.09229/) (2019)
- [Recurrent neural networks for aortic image sequence segmentation with sparse annotations](https://mlsanity.com/paper/1808.00273/) (2018)
- [Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation](https://mlsanity.com/paper/1907.11835/) (2019)
- [Automatic segmenting teeth in X-ray images: Trends, a novel data set, benchmarking and future perspectives](https://mlsanity.com/paper/1802.03086/) (2018)
- [Accurate Weakly-Supervised Deep Lesion Segmentation using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECIST](https://mlsanity.com/paper/1807.01172/) (2018)
- [Learning to Segment Medical Images from Few-Shot Sparse Labels](https://mlsanity.com/paper/2108.05476/) (2021)
- [A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation](https://mlsanity.com/paper/1906.07367/) (2019)
- [Semantic Change Detection with Hypermaps](https://mlsanity.com/paper/1604.07513/) (2016)
- [Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST](https://mlsanity.com/paper/1801.08614/) (2018)

## Built on by (most cited first, 9 of 9)

- [A Systematic Review of Few-Shot Learning in Medical Imaging](https://mlsanity.com/paper/2309.11433/) (2023)
- [Few-Shot Object Detection: Research Advances and Challenges](https://mlsanity.com/paper/2404.04799/) (2024)
- [Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation](https://mlsanity.com/paper/2311.01989/) (2023)
- [Meta-Learners for Few-Shot Weakly-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2305.06912/) (2023)
- [Weakly Supervised 3D Instance Segmentation without Instance-level Annotations](https://mlsanity.com/paper/2308.01721/) (2023)
- [Few-Shot Deployment of Pretrained MRI Transformers in Brain Imaging Tasks](https://mlsanity.com/paper/2508.05783/) (2025)
- [MetaComp: Learning to Adapt for Online Depth Completion](https://mlsanity.com/paper/2207.10623/) (2022)
- [Learning Robust Correlation with Foundation Model for Weakly-Supervised Few-Shot Segmentation](https://mlsanity.com/paper/2405.19638/) (2024)
- [Meta-learners for few-shot weakly-supervised optic disc and cup segmentation on fundus images](https://mlsanity.com/paper/2512.15061/) (2025)

## Abstract (arXiv)

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.