Data to 7 Oct 2026

Prior Guided Feature Enrichment Network for Few-Shot Segmentation

arXiv 2008.01449 ↗First posted 4 Aug 2020cs.CVcited by 271

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Abstract (arXiv)

State-of-the-art semantic segmentation methods require sufficient labeled data to achieve good results and hardly work on unseen classes without fine-tuning. Few-shot segmentation is thus proposed to tackle this problem by learning a model that quickly adapts to new classes with a few labeled support samples. Theses frameworks still face the challenge of generalization ability reduction on unseen classes due to inappropriate use of high-level semantic information of training classes and spatial inconsistency between query and support targets. To alleviate these issues, we propose the Prior Guided Feature Enrichment Network (PFENet). It consists of novel designs of (1) a training-free prior mask generation method that not only retains generalization power but also improves model performance and (2) Feature Enrichment Module (FEM) that overcomes spatial inconsistency by adaptively enriching query features with support features and prior masks. Extensive experiments on PASCAL-5i and COCO prove that the proposed prior generation method and FEM both improve the baseline method significantly. Our PFENet also outperforms state-of-the-art methods by a large margin without efficiency loss. It is surprising that our model even generalizes to cases without labeled support samples. Our code is available at https://github.com/Jia-Research-Lab/PFENet/.

Key facts. Cited by 271 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Aug 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 34; 2022: 48; 2023: 69; 2024: 55; 2025: 39; 2026: 26.

271 papers.

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2022Language-driven Semantic Segmentationcited by 711
2022GroupViT: Semantic Segmentation Emerges from Text Supervisioncited by 529
2021Image Segmentation Using Text and Image Promptscited by 509
2023RoMa: Robust Dense Feature Matchingcited by 296
2022Visual Prompting via Image Inpaintingcited by 248
2021Adaptive Prototype Learning and Allocation for Few-Shot Segmentationcited by 207
2021Hypercorrelation Squeeze for Few-Shot Segmentationcited by 193
2023Towards Open Vocabulary Learning: A Surveycited by 165
2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 162
2021Few-Shot Segmentation via Cycle-Consistent Transformercited by 133
2022Learning What Not to Segment: A New Perspective on Few-Shot Segmentationcited by 120
2021Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformercited by 116

38 more

2022Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentationcited by 114
2023A Comprehensive Survey on Source-free Domain Adaptationcited by 104
2022Self-Support Few-Shot Semantic Segmentationcited by 96
2023Vision-Language Models in Remote Sensing: Current Progress and Future Trendscited by 93
2021Self-Guided and Cross-Guided Learning for Few-Shot Segmentationcited by 92
2023Hierarchical Dense Correlation Distillation for Few-Shot Segmentationcited by 84
2022FS6D: Few-Shot 6D Pose Estimation of Novel Objectscited by 81
2024VRP-SAM: SAM with Visual Reference Promptcited by 75
2021Mining Latent Classes for Few-shot Segmentationcited by 69
2022Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentationcited by 65
2024Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentationcited by 61
2022Feature-Proxy Transformer for Few-Shot Segmentationcited by 58
2022Learning Non-target Knowledge for Few-shot Semantic Segmentationcited by 57
2021Learning Meta-class Memory for Few-Shot Semantic Segmentationcited by 56
2022Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuningcited by 50
2023LLaFS: When Large Language Models Meet Few-Shot Segmentationcited by 48
2022Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentationcited by 43
2023Exploring Effective Factors for Improving Visual In-Context Learningcited by 43
2024OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentationcited by 43
2022Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxelscited by 42
2021One-Shot Object Affordance Detection in the Wildcited by 40
2022Open-vocabulary Semantic Segmentation with Frozen Vision-Language Modelscited by 39
2022Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentationcited by 37
2021Few-shot Segmentation with Optimal Transport Matching and Message Flowcited by 36
2024Bridge the Points: Graph-based Few-shot Segment Anything Semanticallycited by 35
2024Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Modelscited by 34
2023Self-Calibrated Cross Attention Network for Few-Shot Segmentationcited by 32
2021PFENet++: Boosting Few-shot Semantic Segmentation with the Noise-filtered Context-aware Prior Maskcited by 31
2021Novel Class Discovery in Semantic Segmentationcited by 31
2024Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Miningcited by 30
2024Unified Language-driven Zero-shot Domain Adaptationcited by 30
2022Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentationcited by 28
2023SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion Modelscited by 28
2024A Systematic Review on Long-Tailed Learningcited by 27
2023Prototype Adaption and Projection for Few- and Zero-shot 3D Point Cloud Semantic Segmentationcited by 26
2024APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentationcited by 26
2022Integrative Few-Shot Learning for Classification and Segmentationcited by 25
2022Interclass Prototype Relation for Few-Shot Segmentationcited by 25

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