mlsanityData to 14 Oct 2026

Few-shot Semantic Segmentation with Support-induced Graph Convolutional Network

arXiv 2301.03194 ↗First posted 9 Jan 2023cs.CVcited by 3

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Abstract (arXiv)

Few-shot semantic segmentation (FSS) aims to achieve novel objects segmentation with only a few annotated samples and has made great progress recently. Most of the existing FSS models focus on the feature matching between support and query to tackle FSS. However, the appearance variations between objects from the same category could be extremely large, leading to unreliable feature matching and query mask prediction. To this end, we propose a Support-induced Graph Convolutional Network (SiGCN) to explicitly excavate latent context structure in query images. Specifically, we propose a Support-induced Graph Reasoning (SiGR) module to capture salient query object parts at different semantic levels with a Support-induced GCN. Furthermore, an instance association (IA) module is designed to capture high-order instance context from both support and query instances. By integrating the proposed two modules, SiGCN can learn rich query context representation, and thus being more robust to appearance variations. Extensive experiments on PASCAL-5i and COCO-20i demonstrate that our SiGCN achieves state-of-the-art performance.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 40 of its 45 references that are in the map. First posted on arXiv 9 Jan 2023.

Builds on

40 of 45 references.

References · 40

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  5. 2017Mask R-CNNcited by 7,277
  6. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  7. 2015Fast R-CNNcited by 2,933
  8. 2018YOLOv3: An Incremental Improvementcited by 2,659
  9. 2016Pyramid Scene Parsing Networkcited by 2,479
  10. 2016Matching Networks for One Shot Learningcited by 2,255
  11. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,161
  12. 2017Deformable Convolutional Networkscited by 1,488
28 more
  1. 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,133
  2. 2021Semi-Supervised Semantic Segmentation with Cross Pseudo Supervisioncited by 463
  3. 2019PANet: Few-Shot Image Semantic Segmentation with Prototype Alignmentcited by 449
  4. 2017One-Shot Learning for Semantic Segmentationcited by 337
  5. 2020Prior Guided Feature Enrichment Network for Few-Shot Segmentationcited by 271
  6. 2019CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learningcited by 217
  7. 2021K-Net: Towards Unified Image Segmentationcited by 212
  8. 2019Graph Convolutional Networks for Temporal Action Localizationcited by 208
  9. 2021Adaptive Prototype Learning and Allocation for Few-Shot Segmentationcited by 207
  10. 2021Hypercorrelation Squeeze for Few-Shot Segmentationcited by 193
  11. 2020Prototype Mixture Models for Few-shot Semantic Segmentationcited by 190
  12. 2019Feature Weighting and Boosting for Few-Shot Segmentationcited by 189
  13. 2018SG-One: Similarity Guidance Network for One-Shot Semantic Segmentationcited by 169
  14. 2020Part-aware Prototype Network for Few-shot Semantic Segmentationcited by 162
  15. 2020Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?cited by 135
  16. 2021Few-Shot Segmentation via Cycle-Consistent Transformercited by 133
  17. 2018Graph-Based Global Reasoning Networkscited by 120
  18. 2021BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentationcited by 105
  19. 2021Self-Guided and Cross-Guided Learning for Few-Shot Segmentationcited by 92
  20. 2021Mining Latent Classes for Few-shot Segmentationcited by 69
  21. 2019Dual Graph Convolutional Network for Semantic Segmentationcited by 59
  22. 2019Self-Supervised Difference Detection for Weakly-Supervised Semantic Segmentationcited by 58
  23. 2021Learning Meta-class Memory for Few-Shot Semantic Segmentationcited by 56
  24. 2021Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networkscited by 55
  25. 2021Activity Graph Transformer for Temporal Action Localizationcited by 48
  26. 2021PFENet++: Boosting Few-shot Semantic Segmentation with the Noise-filtered Context-aware Prior Maskcited by 31
  27. 2020AdarGCN: Adaptive Aggregation GCN for Few-Shot Learningcited by 4
  28. 2022Budget-aware Few-shot Learning via Graph Convolutional Networkcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.

3 papers.

  1. 2023RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation Networkcited by 17
  2. 2024LERENet: Eliminating Intra-class Differences for Metal Surface Defect Few-shot Semantic Segmentationcited by 1
  3. 2026Unify the Views: View-Consistent Prototype Learning for Few-Shot Segmentationcited by 1