mlsanityData to 14 Oct 2026

Interclass Prototype Relation for Few-Shot Segmentation

arXiv 2211.08681 ↗First posted 16 Nov 2022cs.CVcited by 25

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Abstract (arXiv)

Traditional semantic segmentation requires a large labeled image dataset and can only be predicted within predefined classes. To solve this problem, few-shot segmentation, which requires only a handful of annotations for the new target class, is important. However, with few-shot segmentation, the target class data distribution in the feature space is sparse and has low coverage because of the slight variations in the sample data. Setting the classification boundary that properly separates the target class from other classes is an impossible task. In particular, it is difficult to classify classes that are similar to the target class near the boundary. This study proposes the Interclass Prototype Relation Network (IPRNet), which improves the separation performance by reducing the similarity between other classes. We conducted extensive experiments with Pascal-5i and COCO-20i and showed that IPRNet provides the best segmentation performance compared with previous research.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). Builds on 39 of its 50 references that are in the map. First posted on arXiv 16 Nov 2022.

Builds on

39 of 50 references.

References · 39

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  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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27 more
  1. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,161
  2. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,487
  3. 2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,369
  4. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,143
  5. 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,133
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  10. 2020Prior Guided Feature Enrichment Network for Few-Shot Segmentationcited by 271
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  12. 2019CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learningcited by 217
  13. 2021Adaptive Prototype Learning and Allocation for Few-Shot Segmentationcited by 207
  14. 2021Hypercorrelation Squeeze for Few-Shot Segmentationcited by 193
  15. 2020Prototype Mixture Models for Few-shot Semantic Segmentationcited by 190
  16. 2019Feature Weighting and Boosting for Few-Shot Segmentationcited by 189
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  18. 2014Simultaneous Detection and Segmentationcited by 159
  19. 2020Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?cited by 135
  20. 2021Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformercited by 116
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  23. 2021Mining Latent Classes for Few-shot Segmentationcited by 69
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 7; 2024: 13; 2025: 4; 2026: 1.

25 papers.

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  1. 2023LLaFS: When Large Language Models Meet Few-Shot Segmentationcited by 48
  2. 2024Bridge the Points: Graph-based Few-shot Segment Anything Semanticallycited by 35
  3. 2024Hybrid Mamba for Few-Shot Segmentationcited by 21
  4. 2024Rethinking Prior Information Generation with CLIP for Few-Shot Segmentationcited by 20
  5. 2024Eliminating Feature Ambiguity for Few-Shot Segmentationcited by 19
  6. 2023RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation Networkcited by 17
  7. 2023Detect Everything with Few Examplescited by 14
  8. 2023Focus on Query: Adversarial Mining Transformer for Few-Shot Segmentationcited by 13
  9. 2024A Novel Benchmark for Few-Shot Semantic Segmentation in the Era of Foundation Modelscited by 13
  10. 2024Cross-Domain Few-Shot Semantic Segmentation via Doubly Matching Transformationcited by 13
  11. 2024CrackNex: a Few-shot Low-light Crack Segmentation Model Based on Retinex Theory for UAV Inspectionscited by 12
  12. 2025Prototypes are Balanced Units for Efficient and Effective Partially Relevant Video Retrievalcited by 8
13 more
  1. 2025DSV-LFS: Unifying LLM-Driven Semantic Cues with Visual Features for Robust Few-Shot Segmentationcited by 6
  2. 2023[CLS] Token is All You Need for Zero-Shot Semantic Segmentationcited by 5
  3. 2023Generalized Unbiased Scene Graph Generationcited by 3
  4. 2024Group-On: Boosting One-Shot Segmentation with Supportive Querycited by 3
  5. 2024Beyond Mask: Rethinking Guidance Types in Few-shot Segmentationcited by 3
  6. 2024Show and Grasp: Few-shot Semantic Segmentation for Robot Grasping through Zero-shot Foundation Modelscited by 2
  7. 2024Correlation Weighted Prototype-based Self-Supervised One-Shot Segmentation of Medical Imagescited by 1
  8. 2025ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotationscited by 1
  9. 2023Target-aware Bi-Transformer for Few-shot Segmentationcited by 0
  10. 2024Learning Robust Correlation with Foundation Model for Weakly-Supervised Few-Shot Segmentationcited by 0
  11. 2024Improving 3D Few-Shot Segmentation with Inference-Time Pseudo-Labelingcited by 0
  12. 2025Inter- and Intra-image Refinement for Few Shot Segmentationcited by 0
  13. 2026Exploiting Target Knowledge from MLLMs for Robust Few-Shot Segmentationcited by 0