mlsanityData to 7 Oct 2026

SimPropNet: Improved Similarity Propagation for Few-shot Image Segmentation

arXiv 2004.15014 ↗First posted 30 Apr 2020cs.CVcited by 27

Not on the map. Why?

Abstract (arXiv)

Few-shot segmentation (FSS) methods perform image segmentation for a particular object class in a target (query) image, using a small set of (support) image-mask pairs. Recent deep neural network based FSS methods leverage high-dimensional feature similarity between the foreground features of the support images and the query image features. In this work, we demonstrate gaps in the utilization of this similarity information in existing methods, and present a framework - SimPropNet, to bridge those gaps. We propose to jointly predict the support and query masks to force the support features to share characteristics with the query features. We also propose to utilize similarities in the background regions of the query and support images using a novel foreground-background attentive fusion mechanism. Our method achieves state-of-the-art results for one-shot and five-shot segmentation on the PASCAL-5i dataset. The paper includes detailed analysis and ablation studies for the proposed improvements and quantitative comparisons with contemporary methods.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Apr 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 15; 2022: 3; 2023: 6; 2024: 0; 2025: 1; 2026: 2.

27 papers.

Sort:

By area:

  1. 2021Adaptive Prototype Learning and Allocation for Few-Shot Segmentationcited by 207
  2. 2021Hypercorrelation Squeeze for Few-Shot Segmentationcited by 193
  3. 2023Efficient and Explicit Modelling of Image Hierarchies for Image Restorationcited by 145
  4. 2022Self-Support Few-Shot Semantic Segmentationcited by 96
  5. 2021Self-Guided and Cross-Guided Learning for Few-Shot Segmentationcited by 92
  6. 2023Hierarchical Dense Correlation Distillation for Few-Shot Segmentationcited by 84
  7. 2022Feature-Proxy Transformer for Few-Shot Segmentationcited by 58
  8. 2021Learning Meta-class Memory for Few-Shot Semantic Segmentationcited by 56
  9. 2022Integrative Few-Shot Learning for Classification and Segmentationcited by 25
  10. 2021Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentationcited by 23
  11. 2023Learning Expressive Prompting With Residuals for Vision Transformerscited by 21
  12. 2025Not All Data Are Unlearned Equallycited by 12
15 more
  1. 2021APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentationcited by 11
  2. 2021A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentationcited by 10
  3. 2021Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuningcited by 8
  4. 2023Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentationcited by 8
  5. 2021A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentationcited by 6
  6. 2021MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentationcited by 6
  7. 2021Uncertainty-Aware Semi-Supervised Few Shot Segmentationcited by 5
  8. 2021SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypescited by 4
  9. 2021End-to-end One-shot Human Parsingcited by 4
  10. 2021Dual Prototypical Contrastive Learning for Few-shot Semantic Segmentationcited by 3
  11. 2023Few Shot Semantic Segmentation: a review of methodologies, benchmarks, and open challengescited by 3
  12. 2026ATD: Improved Transformer with Adaptive Token Dictionary for Image Restorationcited by 3
  13. 2021Prior-Enhanced Few-Shot Segmentation with Meta-Prototypescited by 1
  14. 2023Hierarchical Dense Correlation Distillation for Few-Shot Segmentation-Extended Abstractcited by 0
  15. 2026Prototype Learning-Based Few-Shot Segmentation for Low-Light Crack on Concrete Structurescited by 0