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FGN: Fully Guided Network for Few-Shot Instance Segmentation

arXiv 2003.13954 ↗First posted 31 Mar 2020cs.CVcited by 46

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Abstract (arXiv)

Few-shot instance segmentation (FSIS) conjoins the few-shot learning paradigm with general instance segmentation, which provides a possible way of tackling instance segmentation in the lack of abundant labeled data for training. This paper presents a Fully Guided Network (FGN) for few-shot instance segmentation. FGN perceives FSIS as a guided model where a so-called support set is encoded and utilized to guide the predictions of a base instance segmentation network (i.e., Mask R-CNN), critical to which is the guidance mechanism. In this view, FGN introduces different guidance mechanisms into the various key components in Mask R-CNN, including Attention-Guided RPN, Relation-Guided Detector, and Attention-Guided FCN, in order to make full use of the guidance effect from the support set and adapt better to the inter-class generalization. Experiments on public datasets demonstrate that our proposed FGN can outperform the state-of-the-art methods.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 14; 2022: 7; 2023: 14; 2024: 7; 2025: 3; 2026: 1.

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  1. 2021Hypercorrelation Squeeze for Few-Shot Segmentationcited by 193
  2. 2023Towards Open Vocabulary Learning: A Surveycited by 165
  3. 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 162
  4. 2021DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detectioncited by 106
  5. 2021Generalized Few-Shot Object Detection without Forgettingcited by 88
  6. 2023Hierarchical Dense Correlation Distillation for Few-Shot Segmentationcited by 84
  7. 2021Learning Meta-class Memory for Few-Shot Semantic Segmentationcited by 56
  8. 2021Incremental Few-Shot Instance Segmentationcited by 49
  9. 2021A Survey of Self-Supervised and Few-Shot Object Detectioncited by 35
  10. 2021Dual-Awareness Attention for Few-Shot Object Detectioncited by 29
  11. 2023Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matchingcited by 29
  12. 2021Few-Shot Object Detection: A Comprehensive Surveycited by 28
34 more
  1. 2022Interclass Prototype Relation for Few-Shot Segmentationcited by 25
  2. 2022iFS-RCNN: An Incremental Few-shot Instance Segmentercited by 19
  3. 2024A Simple Image Segmentation Framework via In-Context Examplescited by 19
  4. 2023MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentationcited by 18
  5. 2021FAPIS: A Few-shot Anchor-free Part-based Instance Segmentercited by 15
  6. 2025DC-SAM: In-Context Segment Anything in Images and Videos via Dual Consistencycited by 15
  7. 2021Few-Shot Learning for Road Object Detectioncited by 13
  8. 2022Self-Regularized Prototypical Network for Few-Shot Semantic Segmentationcited by 12
  9. 2023Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentationcited by 12
  10. 2022VizWiz-FewShot: Locating Objects in Images Taken by People With Visual Impairmentscited by 11
  11. 2021Learning to Better Segment Objects from Unseen Classes with Unlabeled Videoscited by 9
  12. 2021Text-based Person Search in Full Images via Semantic-Driven Proposal Generationcited by 9
  13. 2021VideoClick: Video Object Segmentation with a Single Clickcited by 8
  14. 2023UniAP: Towards Universal Animal Perception in Vision via Few-shot Learningcited by 7
  15. 2022Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learningcited by 6
  16. 2023The Art of Camouflage: Few-Shot Learning for Animal Detection and Segmentationcited by 5
  17. 2023Instance-level Few-shot Learning with Class Hierarchy Miningcited by 5
  18. 2024BIV-Priv-Seg: Locating Private Content in Images Taken by People With Visual Impairmentscited by 5
  19. 2024Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Developmentcited by 5
  20. 2024Unsupervised Pre-training with Language-Vision Prompts for Low-Data Instance Segmentationcited by 4
  21. 2022Geodesic-Former: a Geodesic-Guided Few-shot 3D Point Cloud Instance Segmentercited by 3
  22. 2024Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentationcited by 3
  23. 2023ENInst: Enhancing Weakly-supervised Low-shot Instance Segmentationcited by 2
  24. 2023Faster OreFSDet : A Lightweight and Effective Few-shot Object Detector for Ore Imagescited by 2
  25. 2024UniFS: Universal Few-shot Instance Perception with Point Representationscited by 2
  26. 2025Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentationcited by 1
  27. 2021Weak Novel Categories without Tears: A Survey on Weak-Shot Learningcited by 0
  28. 2023Adaptive Base-class Suppression and Prior Guidance Network for One-Shot Object Detectioncited by 0
  29. 2023Hierarchical Dense Correlation Distillation for Few-Shot Segmentation-Extended Abstractcited by 0
  30. 2023ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identificationcited by 0
  31. 2023SKU-Patch: Towards Efficient Instance Segmentation for Unseen Objects in Auto-Storecited by 0
  32. 2024Integrated Image-Text Based on Semi-supervised Learning for Small Sample Instance Segmentationcited by 0
  33. 2025Task-oriented Learnable Diffusion Timesteps for Universal Few-shot Learning of Dense Taskscited by 0
  34. 2026Boundary-by-Mask: Few-Shot Instance Segmentation with Mask-Conditioned Boundary Learning for Texture-Poor Industrial Partscited by 0