mlsanityData to 14 Oct 2026

Relevant Intrinsic Feature Enhancement Network for Few-Shot Semantic Segmentation

arXiv 2312.06474 ↗First posted 11 Dec 2023cs.CVcited by 11

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Abstract (arXiv)

For few-shot semantic segmentation, the primary task is to extract class-specific intrinsic information from limited labeled data. However, the semantic ambiguity and inter-class similarity of previous methods limit the accuracy of pixel-level foreground-background classification. To alleviate these issues, we propose the Relevant Intrinsic Feature Enhancement Network (RiFeNet). To improve the semantic consistency of foreground instances, we propose an unlabeled branch as an efficient data utilization method, which teaches the model how to extract intrinsic features robust to intra-class differences. Notably, during testing, the proposed unlabeled branch is excluded without extra unlabeled data and computation. Furthermore, we extend the inter-class variability between foreground and background by proposing a novel multi-level prototype generation and interaction module. The different-grained complementarity between global and local prototypes allows for better distinction between similar categories. The qualitative and quantitative performance of RiFeNet surpasses the state-of-the-art methods on PASCAL-5i and COCO benchmarks.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 34 of its 42 references that are in the map. First posted on arXiv 11 Dec 2023.

Builds on

34 of 42 references.

References · 34

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22 more
  1. 2017One-Shot Learning for Semantic Segmentationcited by 337
  2. 2020Prior Guided Feature Enrichment Network for Few-Shot Segmentationcited by 271
  3. 2019CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learningcited by 217
  4. 2021Adaptive Prototype Learning and Allocation for Few-Shot Segmentationcited by 207
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  14. 2021MPViT: Multi-Path Vision Transformer for Dense Predictioncited by 122
  15. 2020Context Prior for Scene Segmentationcited by 121
  16. 2022Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentationcited by 114
  17. 2023FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentationcited by 95
  18. 2019ACFNet: Attentional Class Feature Network for Semantic Segmentationcited by 75
  19. 2022Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentationcited by 65
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Cited by year

Citing papers per year: 2024: 6; 2025: 5.

11 papers.

  1. 2024Eliminating Feature Ambiguity for Few-Shot Segmentationcited by 19
  2. 2025Unlocking the Power of SAM 2 for Few-Shot Segmentationcited by 11
  3. 2024Prompt-and-Transfer: Dynamic Class-aware Enhancement for Few-shot Segmentationcited by 10
  4. 2024AgMTR: Agent Mining Transformer for Few-shot Segmentation in Remote Sensingcited by 6
  5. 2024MSDNet: Multi-Scale Decoder for Few-Shot Semantic Segmentation via Transformer-Guided Prototypingcited by 5
  6. 2025DynImg: Key Frames with Visual Prompts are Good Representation for Multi-Modal Video Understandingcited by 3
  7. 2024Enhanced Sign Language Translation between American Sign Language (ASL) and Indian Sign Language (ISL) Using LLMscited by 1
  8. 2025Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentationcited by 1
  9. 2025Unbiased Semantic Decoding with Vision Foundation Models for Few-shot Segmentationcited by 1
  10. 2024No Re-Train, More Gain: Upgrading Backbones with Diffusion model for Pixel-Wise and Weakly-Supervised Few-Shot Segmentationcited by 0
  11. 2025DFR: A Decompose-Fuse-Reconstruct Framework for Multi-Modal Few-Shot Segmentationcited by 0