mlsanityData to 7 Oct 2026

Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype Enhancement

arXiv 2312.15731 ↗First posted 25 Dec 2023cs.CVcited by 10

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Abstract (arXiv)

The Few-Shot Segmentation (FSS) aims to accomplish the novel class segmentation task with a few annotated images. Current FSS research based on meta-learning focus on designing a complex interaction mechanism between the query and support feature. However, unlike humans who can rapidly learn new things from limited samples, the existing approach relies solely on fixed feature matching to tackle new tasks, lacking adaptability. In this paper, we propose a novel framework based on the adapter mechanism, namely Adaptive FSS, which can efficiently adapt the existing FSS model to the novel classes. In detail, we design the Prototype Adaptive Module (PAM), which utilizes accurate category information provided by the support set to derive class prototypes, enhancing class-specific information in the multi-stage representation. In addition, our approach is compatible with diverse FSS methods with different backbones by simply inserting PAM between the layers of the encoder. Experiments demonstrate that our method effectively improves the performance of the FSS models (e.g., MSANet, HDMNet, FPTrans, and DCAMA) and achieve new state-of-the-art (SOTA) results (i.e., 72.4% and 79.1% mIoU on PASCAL-5i 1-shot and 5-shot settings, 52.7% and 60.0% mIoU on COCO-20i 1-shot and 5-shot settings). Our code can be available at https://github.com/jingw193/AdaptiveFSS.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 37 of its 51 references that are in the map. First posted on arXiv 25 Dec 2023.

Builds on

37 of 51 references.

References · 37

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25 more
  1. 2022AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognitioncited by 714
  2. 2019PANet: Few-Shot Image Semantic Segmentation with Prototype Alignmentcited by 449
  3. 2017One-Shot Learning for Semantic Segmentationcited by 337
  4. 2019A Baseline for Few-Shot Image Classificationcited by 298
  5. 2019Cross Attention Network for Few-shot Classificationcited by 218
  6. 2019CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learningcited by 217
  7. 2021Hypercorrelation Squeeze for Few-Shot Segmentationcited by 193
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  11. 2021Few-Shot Segmentation via Cycle-Consistent Transformercited by 133
  12. 2021Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformercited by 116
  13. 2020CRNet: Cross-Reference Networks for Few-Shot Segmentationcited by 101
  14. 2021Self-Guided and Cross-Guided Learning for Few-Shot Segmentationcited by 92
  15. 2019TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learningcited by 90
  16. 2018RepMet: Representative-based metric learning for classification and one-shot object detectioncited by 85
  17. 2021Cross-domain Few-shot Learning with Task-specific Adapterscited by 79
  18. 2021Relational Embedding for Few-Shot Classificationcited by 74
  19. 2022Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNetscited by 62
  20. 2022Continual Prompt Tuning for Dialog State Trackingcited by 44
  21. 2021Few-shot Segmentation with Optimal Transport Matching and Message Flowcited by 36
  22. 2019Differentiable Meta-learning Model for Few-shot Semantic Segmentationcited by 31
  23. 2020BriNet: Towards Bridging the Intra-class and Inter-class Gaps in One-Shot Segmentationcited by 28
  24. 2021APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentationcited by 11
  25. 2021LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot Learnerscited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 3; 2026: 3.

10 papers.

  1. 2024Hybrid Mamba for Few-Shot Segmentationcited by 21
  2. 2024Eliminating Feature Ambiguity for Few-Shot Segmentationcited by 19
  3. 2025Unlocking the Power of SAM 2 for Few-Shot Segmentationcited by 11
  4. 2024Beyond Mask: Rethinking Guidance Types in Few-shot Segmentationcited by 3
  5. 2026Few-Shot Semantic Segmentation Meets SAM3cited by 3
  6. 2025Take a Peek: Efficient Encoder Adaptation for Few-Shot Semantic Segmentation via LoRAcited by 1
  7. 2026Unify the Views: View-Consistent Prototype Learning for Few-Shot Segmentationcited by 1
  8. 2024No Re-Train, More Gain: Upgrading Backbones with Diffusion model for Pixel-Wise and Weakly-Supervised Few-Shot Segmentationcited by 0
  9. 2025DFR: A Decompose-Fuse-Reconstruct Framework for Multi-Modal Few-Shot Segmentationcited by 0
  10. 2026Exploiting Target Knowledge from MLLMs for Robust Few-Shot Segmentationcited by 0