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July 11, 2026SensorsOpen Access

LFODet: Lightweight Few-Shot Object Detection with Meta-Learning in Remote Sensing Images

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Authors

HWH B WuUniversity of Science and Technology of ChinaXFXuan FangChangchun University of Science and TechnologyHXHaonan XiongSichuan University

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Implication

Randomized trial shows enhanced object detection accuracy in remote sensing images, suggesting a practical solution for rapid adaptation.

Key Points

  • The aim is to improve detection accuracy while keeping the model lightweight for few-shot learning in remote sensing images.
  • Developed LFODet using meta-learning with two parallel branches for rapid adaptation to novel classes.
  • Integrated Semantic Ghost Channel Attention and Fine-Grained Ghost Spatial Attention for better feature representation.
  • Trained the model through base-class pre-training, meta-learner optimization, and few-shot fine-tuning.
  • Demonstrated stable performance with few-shot learning across diverse scenarios.
  • Achieved improved accuracy on DIOR and NWPU VHR-10 datasets, indicating effective adaptation to new targets.

Cite This Study

Wu et al. (2026) studied this question.

synapsesocial.com/papers/6a51e019c18d7f28ca500ae6https://doi.org/10.3390/s26144371
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Few-Shot Object Detection in Remote Sensing Images via Data Clearing and Stationary Meta-Learning2024 · 13 citations
  2. 2Efficient Meta-Learning Enabled Lightweight Multiscale Few-Shot Object Detection in Remote Sensing Images2024 · 1 citations
  3. 3DG-Net: Few-Shot Remote Sensing Detection with Dynamic Dual-Stream Collaboration and Generative Meta-Learning2026
  4. 4HALD-FSOD: Hierarchical Adaptive Learning with Asymmetric Margin and Loss-Aware Dynamic Weighting for Few-Shot Object Detection in Remote Sensing Imagery2026
  5. 5DAFSDet: Dual-Attention Guided Few-Shot Object Detection in Remote Sensing Images2026 · 1 citations