mlsanityData to 7 Oct 2026

Hierarchical Dense Correlation Distillation for Few-Shot Segmentation

arXiv 2303.14652 ↗First posted 26 Mar 2023cs.CV cs.AIcited by 84

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Abstract (arXiv)

Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting unseen classes with only a handful of annotations. Previous methods limited to the semantic feature and prototype representation suffer from coarse segmentation granularity and train-set overfitting. In this work, we design Hierarchically Decoupled Matching Network (HDMNet) mining pixel-level support correlation based on the transformer architecture. The self-attention modules are used to assist in establishing hierarchical dense features, as a means to accomplish the cascade matching between query and support features. Moreover, we propose a matching module to reduce train-set overfitting and introduce correlation distillation leveraging semantic correspondence from coarse resolution to boost fine-grained segmentation. Our method performs decently in experiments. We achieve 50.0% mIoU on coco~dataset one-shot setting and 56.0% on five-shot segmentation, respectively.

Key facts. Cited by 84 papers in the map (data through 7 Oct 2026). Builds on 54 of its 64 references that are in the map. First posted on arXiv 26 Mar 2023.

Builds on

54 of 64 references.

References · 54

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  5. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  6. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  7. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  8. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  9. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  10. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  11. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  12. 2017Prototypical Networks for Few-shot Learningcited by 3,001
38 more
  1. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  2. 2016Pyramid Scene Parsing Networkcited by 2,472
  3. 2016Matching Networks for One Shot Learningcited by 2,246
  4. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
  5. 2021LoFTR: Detector-Free Local Feature Matching with Transformerscited by 974
  6. 2019Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillationcited by 491
  7. 2019Generalizing from a Few Examples: A Survey on Few-Shot Learningcited by 462
  8. 2019PANet: Few-Shot Image Semantic Segmentation with Prototype Alignmentcited by 449
  9. 2017One-Shot Learning for Semantic Segmentationcited by 337
  10. 2020Prior Guided Feature Enrichment Network for Few-Shot Segmentationcited by 271
  11. 2018Meta-Learning: A Surveycited by 254
  12. 2022Unsupervised Semantic Segmentation by Distilling Feature Correspondencescited by 241
  13. 2019CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learningcited by 217
  14. 2021Adaptive Prototype Learning and Allocation for Few-Shot Segmentationcited by 207
  15. 2021Hypercorrelation Squeeze for Few-Shot Segmentationcited by 193
  16. 2020Prototype Mixture Models for Few-shot Semantic Segmentationcited by 190
  17. 2020Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformerscited by 190
  18. 2019Feature Weighting and Boosting for Few-Shot Segmentationcited by 189
  19. 2018Neighbourhood Consensus Networkscited by 173
  20. 2018SG-One: Similarity Guidance Network for One-Shot Semantic Segmentationcited by 169
  21. 2020Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?cited by 135
  22. 2021Few-Shot Segmentation via Cycle-Consistent Transformercited by 133
  23. 2022Learning What Not to Segment: A New Perspective on Few-Shot Segmentationcited by 120
  24. 2021Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformercited by 116
  25. 2021Semi-supervised Semantic Segmentation with Directional Context-aware Consistencycited by 112
  26. 2021ResLT: Residual Learning for Long-tailed Recognitioncited by 96
  27. 2022Self-Support Few-Shot Semantic Segmentationcited by 96
  28. 2017Offline bilingual word vectors, orthogonal transformations and the inverted softmaxcited by 94
  29. 2021Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentationcited by 87
  30. 2022Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentationcited by 65
  31. 2020Generalized Few-shot Semantic Segmentationcited by 59
  32. 2020FGN: Fully Guided Network for Few-Shot Instance Segmentationcited by 46
  33. 2022Generalized Parametric Contrastive Learningcited by 45
  34. 2021PFENet++: Boosting Few-shot Semantic Segmentation with the Noise-filtered Context-aware Prior Maskcited by 31
  35. 2022Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentationcited by 28
  36. 2020SimPropNet: Improved Similarity Propagation for Few-shot Image Segmentationcited by 27
  37. 2023Learning Context-aware Classifier for Semantic Segmentationcited by 23
  38. 2022DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentationcited by 22

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 8; 2024: 33; 2025: 28; 2026: 15.

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  1. 2024VRP-SAM: SAM with Visual Reference Promptcited by 75
  2. 2024Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentationcited by 61
  3. 2023LLaFS: When Large Language Models Meet Few-Shot Segmentationcited by 48
  4. 2023Exploring Effective Factors for Improving Visual In-Context Learningcited by 43
  5. 2024OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentationcited by 43
  6. 2024Bridge the Points: Graph-based Few-shot Segment Anything Semanticallycited by 35
  7. 2024Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Modelscited by 34
  8. 2024Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learningcited by 32
  9. 2024Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Miningcited by 30
  10. 2024Unified Language-driven Zero-shot Domain Adaptationcited by 30
  11. 2024Scalable Language Model with Generalized Continual Learningcited by 28
  12. 2024APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentationcited by 26
38 more
  1. 2023DifFSS: Diffusion Model for Few-Shot Semantic Segmentationcited by 22
  2. 2024Adapt Before Comparison: A New Perspective on Cross-Domain Few-Shot Segmentationcited by 22
  3. 2024Hybrid Mamba for Few-Shot Segmentationcited by 21
  4. 2024Rethinking Prior Information Generation with CLIP for Few-Shot Segmentationcited by 20
  5. 2023Boosting Few-shot 3D Point Cloud Segmentation via Query-Guided Enhancementcited by 19
  6. 2024Eliminating Feature Ambiguity for Few-Shot Segmentationcited by 19
  7. 2024Unleashing the Potential of the Diffusion Model in Few-shot Semantic Segmentationcited by 16
  8. 2025DC-SAM: In-Context Segment Anything in Images and Videos via Dual Consistencycited by 15
  9. 2025Less Is More, but Where? Dynamic Token Compression via LLM-Guided Keyframe Priorcited by 15
  10. 2024Domain-Rectifying Adapter for Cross-Domain Few-Shot Segmentationcited by 14
  11. 2026FlashVID: Efficient Video Large Language Models via Training-free Tree-based Spatiotemporal Token Mergingcited by 14
  12. 2023Focus on Query: Adversarial Mining Transformer for Few-Shot Segmentationcited by 13
  13. 2024Prototype Correlation Matching and Class-Relation Reasoning for Few-Shot Medical Image Segmentationcited by 13
  14. 2025Refining CLIP's Spatial Awareness: A Visual-Centric Perspectivecited by 12
  15. 2025Not All Data Are Unlearned Equallycited by 12
  16. 2025Unlocking the Power of SAM 2 for Few-Shot Segmentationcited by 11
  17. 2024Prompt-and-Transfer: Dynamic Class-aware Enhancement for Few-shot Segmentationcited by 10
  18. 2023Partition-A-Medical-Image: Extracting Multiple Representative Sub-regions for Few-shot Medical Image Segmentationcited by 9
  19. 2025No time to train! Training-Free Reference-Based Instance Segmentationcited by 9
  20. 2025DPSeg: Dual-Prompt Cost Volume Learning for Open-Vocabulary Semantic Segmentationcited by 8
  21. 2023Few-Shot Rotation-Invariant Aerial Image Semantic Segmentationcited by 7
  22. 2026HIERAMP: Coarse-to-Fine Autoregressive Amplification for Generative Dataset Distillationcited by 7
  23. 2024AgMTR: Agent Mining Transformer for Few-shot Segmentation in Remote Sensingcited by 6
  24. 2024RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical Image Segmentationcited by 6
  25. 2024Typicalness-Aware Learning for Failure Detectioncited by 6
  26. 2025DSV-LFS: Unifying LLM-Driven Semantic Cues with Visual Features for Robust Few-Shot Segmentationcited by 6
  27. 2024Small Object Few-shot Segmentation for Vision-based Industrial Inspectioncited by 5
  28. 2024Boosting Few-Shot Semantic Segmentation Via Segment Anything Modelcited by 4
  29. 2025Adapting In-Domain Few-Shot Segmentation to New Domains without Source Domain Retrainingcited by 4
  30. 2026Dynamic-dLLM: Dynamic Cache-Budget and Adaptive Parallel Decoding for Training-Free Acceleration of Diffusion LLMcited by 4
  31. 2024Group-On: Boosting One-Shot Segmentation with Supportive Querycited by 3
  32. 2024Beyond Mask: Rethinking Guidance Types in Few-shot Segmentationcited by 3
  33. 2024High-Performance Few-Shot Segmentation with Foundation Models: An Empirical Studycited by 3
  34. 2025ProSAM: Enhancing the Robustness of SAM-based Visual Reference Segmentation with Probabilistic Promptscited by 3
  35. 2026Few-Shot Semantic Segmentation Meets SAM3cited by 3
  36. 2024Show and Grasp: Few-shot Semantic Segmentation for Robot Grasping through Zero-shot Foundation Modelscited by 2
  37. 2025Omni-Referring Image Segmentationcited by 2
  38. 2024LERENet: Eliminating Intra-class Differences for Metal Surface Defect Few-shot Semantic Segmentationcited by 1

50 most cited of 84 All 84 (JSON)