mlsanityData to 14 Oct 2026

Boosting Few-Shot Semantic Segmentation Via Segment Anything Model

arXiv 2401.09826 ↗First posted 18 Jan 2024cs.CVcited by 4

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Abstract (arXiv)

In semantic segmentation, accurate prediction masks are crucial for downstream tasks such as medical image analysis and image editing. Due to the lack of annotated data, few-shot semantic segmentation (FSS) performs poorly in predicting masks with precise contours. Recently, we have noticed that the large foundation model segment anything model (SAM) performs well in processing detailed features. Inspired by SAM, we propose FSS-SAM to boost FSS methods by addressing the issue of inaccurate contour. The FSS-SAM is training-free. It works as a post-processing tool for any FSS methods and can improve the accuracy of predicted masks. Specifically, we use predicted masks from FSS methods to generate prompts and then use SAM to predict new masks. To avoid predicting wrong masks with SAM, we propose a prediction result selection (PRS) algorithm. The algorithm can remarkably decrease wrong predictions. Experiment results on public datasets show that our method is superior to base FSS methods in both quantitative and qualitative aspects.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 42 of its 67 references that are in the map. First posted on arXiv 18 Jan 2024.

Builds on

42 of 67 references.

References · 42

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  5. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  6. 2023Segment Anythingcited by 8,234
  7. 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,864
  8. 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 589
  9. 2023Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentationcited by 490
  10. 2019PANet: Few-Shot Image Semantic Segmentation with Prototype Alignmentcited by 449
  11. 2023AnyDoor: Zero-shot Object-level Image Customizationcited by 412
  12. 2017One-Shot Learning for Semantic Segmentationcited by 337
30 more
  1. 2022Images Speak in Images: A Generalist Painter for In-Context Visual Learningcited by 318
  2. 2020Prior Guided Feature Enrichment Network for Few-Shot Segmentationcited by 271
  3. 2019ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed datacited by 261
  4. 2023Personalize Segment Anything Model with One Shotcited by 250
  5. 2023Track Anything: Segment Anything Meets Videoscited by 234
  6. 2023Inpaint Anything: Segment Anything Meets Image Inpaintingcited by 233
  7. 2019CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learningcited by 217
  8. 2021Hypercorrelation Squeeze for Few-Shot Segmentationcited by 193
  9. 2023Segment and Track Anythingcited by 193
  10. 2019Feature Weighting and Boosting for Few-Shot Segmentationcited by 189
  11. 2018SG-One: Similarity Guidance Network for One-Shot Semantic Segmentationcited by 169
  12. 2020Part-aware Prototype Network for Few-shot Semantic Segmentationcited by 162
  13. 2014Simultaneous Detection and Segmentationcited by 159
  14. 2023Matcher: Segment Anything with One Shot Using All-Purpose Feature Matchingcited by 142
  15. 2021Few-Shot Segmentation via Cycle-Consistent Transformercited by 133
  16. 2022Learning What Not to Segment: A New Perspective on Few-Shot Segmentationcited by 120
  17. 2021Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformercited by 116
  18. 2020CRNet: Cross-Reference Networks for Few-Shot Segmentationcited by 101
  19. 2023Hierarchical Dense Correlation Distillation for Few-Shot Segmentationcited by 84
  20. 2024Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactivelycited by 75
  21. 2021Mining Latent Classes for Few-shot Segmentationcited by 69
  22. 2022Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentationcited by 65
  23. 2022Learning Non-target Knowledge for Few-shot Semantic Segmentationcited by 57
  24. 2023Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Modelcited by 56
  25. 2022Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentationcited by 43
  26. 2023Training-free Object Counting with Promptscited by 34
  27. 2023Self-Calibrated Cross Attention Network for Few-Shot Segmentationcited by 32
  28. 2022Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentationcited by 28
  29. 2022Self-Regularized Prototypical Network for Few-Shot Semantic Segmentationcited by 12
  30. 2023Few-Shot Rotation-Invariant Aerial Image Semantic Segmentationcited by 7

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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024Image Segmentation in Foundation Model Era: A Surveycited by 35
  2. 2025Not All Data Are Unlearned Equallycited by 12
  3. 2024CycleSAM: Few-Shot Surgical Scene Segmentation with Cycle- and Scene-Consistent Feature Matchingcited by 3
  4. 2025Pro2SAM: Mask Prompt to SAM with Grid Points for Weakly Supervised Object Localizationcited by 1