mlsanityData to 7 Oct 2026

Weakly Supervised Few-Shot Segmentation Via Meta-Learning

arXiv 2109.01693 ↗First posted 3 Sep 2021cs.CV cs.LGcited by 9

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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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 26 of its 49 references that are in the map. First posted on arXiv 3 Sep 2021.

Builds on

26 of 49 references.

References · 26

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14 more
  1. 2018SG-One: Similarity Guidance Network for One-Shot Semantic Segmentationcited by 169
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  3. 2018Learning random-walk label propagation for weakly-supervised semantic segmentationcited by 101
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  6. 2019XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generationcited by 28
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  10. 2018Accurate Weakly-Supervised Deep Lesion Segmentation using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECISTcited by 17
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Cited by year

Citing papers per year: 2022: 1; 2023: 4; 2024: 2; 2025: 2.

9 papers.

  1. 2023A Systematic Review of Few-Shot Learning in Medical Imagingcited by 28
  2. 2024Few-Shot Object Detection: Research Advances and Challengescited by 11
  3. 2023Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentationcited by 7
  4. 2023Meta-Learners for Few-Shot Weakly-Supervised Medical Image Segmentationcited by 3
  5. 2023Weakly Supervised 3D Instance Segmentation without Instance-level Annotationscited by 3
  6. 2025Few-Shot Deployment of Pretrained MRI Transformers in Brain Imaging Taskscited by 1
  7. 2022MetaComp: Learning to Adapt for Online Depth Completioncited by 0
  8. 2024Learning Robust Correlation with Foundation Model for Weakly-Supervised Few-Shot Segmentationcited by 0
  9. 2025Meta-learners for few-shot weakly-supervised optic disc and cup segmentation on fundus imagescited by 0