mlsanityData to 14 Oct 2026

Few-Shot Rotation-Invariant Aerial Image Semantic Segmentation

arXiv 2306.11734 ↗First posted 29 May 2023cs.CV eess.IVcited by 7

Not on the map. Why?

Abstract (arXiv)

Few-shot aerial image segmentation is a challenging task that involves precisely parsing objects in query aerial images with limited annotated support. Conventional matching methods without consideration of varying object orientations can fail to activate same-category objects with different orientations. Moreover, conventional algorithms can lead to false recognition of lower-scored rotated semantic objects. In response to these challenges, the authors propose a novel few-shot rotation-invariant aerial semantic segmentation network (FRINet). FRINet matches each query feature rotation-adaptively with orientation-varying yet category-consistent support information. The segmentation predictions from different orientations are supervised by the same label, and the backbones are pre-trained in the base category to boost segmentation performance. Experimental results demonstrate that FRINet achieves state-of-the-art performance in few-shot aerial semantic segmentation benchmark.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 30 of its 73 references that are in the map. First posted on arXiv 29 May 2023.

Builds on

30 of 73 references.

References · 30

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18 more
  1. 2021UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagerycited by 167
  2. 2020Part-aware Prototype Network for Few-shot Semantic Segmentationcited by 162
  3. 2022Learning What Not to Segment: A New Perspective on Few-Shot Segmentationcited by 120
  4. 2022Self-Support Few-Shot Semantic Segmentationcited by 96
  5. 2023Hierarchical Dense Correlation Distillation for Few-Shot Segmentationcited by 84
  6. 2022Learning Non-target Knowledge for Few-shot Semantic Segmentationcited by 57
  7. 2021A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Imagescited by 56
  8. 2022Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentationcited by 37
  9. 2019A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenescited by 33
  10. 2018Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate modelscited by 29
  11. 2021PointFlow: Flowing Semantics Through Points for Aerial Image Segmentationcited by 29
  12. 2022Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentationcited by 28
  13. 2019Boundary Loss for Remote Sensing Imagery Semantic Segmentationcited by 27
  14. 2020Few-shot Object Detection on Remote Sensing Imagescited by 23
  15. 2019SCAttNet: Semantic Segmentation Network with Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Imagescited by 21
  16. 2021Rotation Equivariant Deforestation Segmentation and Driver Classificationcited by 7
  17. 2021Scale-aware Neural Network for Semantic Segmentation of Multi-resolution Remote Sensing Imagescited by 5
  18. 2021Sci-Net: Scale Invariant Model for Buildings Segmentation from Aerial Imagerycited by 2

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 4; 2025: 2.

7 papers.

  1. 2024Bridge the Points: Graph-based Few-shot Segment Anything Semanticallycited by 35
  2. 2023Domain Prompt Learning with Quaternion Networkscited by 17
  3. 2024Open-Vocabulary Remote Sensing Image Semantic Segmentationcited by 11
  4. 2024Boosting Few-Shot Semantic Segmentation Via Segment Anything Modelcited by 4
  5. 2024LERENet: Eliminating Intra-class Differences for Metal Surface Defect Few-shot Semantic Segmentationcited by 1
  6. 2025From Pixels to Images: A Structural Survey of Deep Learning Paradigms in Remote Sensing Image Semantic Segmentationcited by 0
  7. 2025Accelerated Rotation-Invariant Convolution for UAV Image Segmentationcited by 0