mlsanityData to 7 Oct 2026

Iterative Few-shot Semantic Segmentation from Image Label Text

arXiv 2303.05646 ↗First posted 10 Mar 2023cs.CVcited by 10

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Abstract (arXiv)

Few-shot semantic segmentation aims to learn to segment unseen class objects with the guidance of only a few support images. Most previous methods rely on the pixel-level label of support images. In this paper, we focus on a more challenging setting, in which only the image-level labels are available. We propose a general framework to firstly generate coarse masks with the help of the powerful vision-language model CLIP, and then iteratively and mutually refine the mask predictions of support and query images. Extensive experiments on PASCAL-5i and COCO-20i datasets demonstrate that our method not only outperforms the state-of-the-art weakly supervised approaches by a significant margin, but also achieves comparable or better results to recent supervised methods. Moreover, our method owns an excellent generalization ability for the images in the wild and uncommon classes. Code will be available at https://github.com/Whileherham/IMR-HSNet.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 25 of its 29 references that are in the map. First posted on arXiv 10 Mar 2023.

Builds on

25 of 29 references.

References · 25

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  5. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  6. 2021CLIP-Adapter: Better Vision-Language Models with Feature Adapterscited by 1,021
  7. 2022Language-driven Semantic Segmentationcited by 711
  8. 2021DenseCLIP: Language-Guided Dense Prediction with Context-Aware Promptingcited by 530
  9. 2019PANet: Few-Shot Image Semantic Segmentation with Prototype Alignmentcited by 449
  10. 2017One-Shot Learning for Semantic Segmentationcited by 337
  11. 2020Prior Guided Feature Enrichment Network for Few-Shot Segmentationcited by 271
  12. 2019Zero-Shot Semantic Segmentationcited by 245
13 more
  1. 2019CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learningcited by 217
  2. 2021Adaptive Prototype Learning and Allocation for Few-Shot Segmentationcited by 207
  3. 2021Hypercorrelation Squeeze for Few-Shot Segmentationcited by 193
  4. 2020Prototype Mixture Models for Few-shot Semantic Segmentationcited by 190
  5. 2020Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?cited by 135
  6. 2021Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformercited by 116
  7. 2021Self-Guided and Cross-Guided Learning for Few-Shot Segmentationcited by 92
  8. 2021Mining Latent Classes for Few-shot Segmentationcited by 69
  9. 2021Learning Meta-class Memory for Few-Shot Semantic Segmentationcited by 56
  10. 2021Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentationcited by 23
  11. 2021A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentationcited by 10
  12. 2021Boosting Few-shot Semantic Segmentation with Transformerscited by 6
  13. 2021Few-shot Semantic Segmentation with Self-supervision from Pseudo-classescited by 6

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 7; 2025: 1.

10 papers.

  1. 2023Visual and Textual Prior Guided Mask Assemble for Few-Shot Segmentation and Beyondcited by 19
  2. 2024OmniCLIP: Adapting CLIP for Video Recognition with Spatial-Temporal Omni-Scale Feature Learningcited by 14
  3. 2024Training-Free Semantic Segmentation via LLM-Supervisioncited by 8
  4. 2024Beyond Mask: Rethinking Guidance Types in Few-shot Segmentationcited by 3
  5. 2023Language-guided Few-shot Semantic Segmentationcited by 2
  6. 2024AFANet: Adaptive Frequency-Aware Network for Weakly-Supervised Few-Shot Semantic Segmentationcited by 1
  7. 2025Textual and Visual Guided Task Adaptation for Source-Free Cross-Domain Few-Shot Segmentationcited by 1
  8. 2024Learning Robust Correlation with Foundation Model for Weakly-Supervised Few-Shot Segmentationcited by 0
  9. 2024No Re-Train, More Gain: Upgrading Backbones with Diffusion model for Pixel-Wise and Weakly-Supervised Few-Shot Segmentationcited by 0
  10. 2024Symmetrical Joint Learning Support-query Prototypes for Few-shot Segmentationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Unleashing the Potential of the Diffusion Model in Few-shot Semantic Segmentation
  2. 2023Class Enhancement Losses with Pseudo Labels for Zero-shot Semantic Segmentation
  3. 2023A Language-Guided Benchmark for Weakly Supervised Open Vocabulary Semantic Segmentation
  4. 2025Vision and Language Reference Prompt into SAM for Few-shot Segmentation
  5. 2022Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP
  6. 2025DSV-LFS: Unifying LLM-Driven Semantic Cues with Visual Features for Robust Few-Shot Segmentation
  7. 2026Make It Up: Fake Images, Real Gains in Generalized Few-shot Semantic Segmentation
  8. 2022Exploiting Category Names for Few-Shot Classification with Vision-Language Models