# Few-shot Object Detection with Self-adaptive Attention Network for Remote Sensing Images

arXiv: https://arxiv.org/abs/2009.12596 · HTML: https://mlsanity.com/paper/2009.12596/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2020.

## Built on by (most cited first, 14 of 14)

- [DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection](https://mlsanity.com/paper/2108.09017/) (2021)
- [Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances](https://mlsanity.com/paper/2309.06751/) (2023)
- [Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Images](https://mlsanity.com/paper/2303.06817/) (2023)
- [Experience feedback using Representation Learning for Few-Shot Object Detection on Aerial Images](https://mlsanity.com/paper/2109.13027/) (2021)
- [Rethinking Intersection Over Union for Small Object Detection in Few-Shot Regime](https://mlsanity.com/paper/2307.09562/) (2023)
- [LLM Agent Framework for Intelligent Change Analysis in Urban Environment using Remote Sensing Imagery](https://mlsanity.com/paper/2601.02757/) (2026)
- [A Unified Framework for Attention-Based Few-Shot Object Detection](https://mlsanity.com/paper/2201.02052/) (2022)
- [A high-precision underwater object detection based on joint self-supervised deblurring and improved spatial transformer network](https://mlsanity.com/paper/2203.04822/) (2022)
- [A Comparative Attention Framework for Better Few-Shot Object Detection on Aerial Images](https://mlsanity.com/paper/2210.13923/) (2022)
- [Efficient Meta-Learning Enabled Lightweight Multiscale Few-Shot Object Detection in Remote Sensing Images](https://mlsanity.com/paper/2404.18426/) (2024)
- [MMUEChange: A Generalized LLM Agent Framework for Intelligent Multi-Modal Urban Environment Change Analysis](https://mlsanity.com/paper/2601.05483/) (2026)
- [Automating Geometry-Intensive Compliance Checking in BIM: Graph-Based Semantic Reasoning Framework](https://mlsanity.com/paper/2606.12065/) (2026)
- [Few-shot Oriented Object Detection with Memorable Contrastive Learning in Remote Sensing Images](https://mlsanity.com/paper/2403.13375/) (2024)
- [Towards Intelligent Urban Park Development Monitoring: LLM Agents for Multi-Modal Information Fusion and Analysis](https://mlsanity.com/paper/2601.20206/) (2026)

## Abstract (arXiv)

In remote sensing field, there are many applications of object detection in recent years, which demands a great number of labeled data. However, we may be faced with some cases where only limited data are available. In this paper, we proposed a few-shot object detector which is designed for detecting novel objects provided with only a few examples. Particularly, in order to fit the object detection settings, our proposed few-shot detector concentrates on the relations that lie in the level of objects instead of the full image with the assistance of Self-Adaptive Attention Network (SAAN). The SAAN can fully leverage the object-level relations through a relation GRU unit and simultaneously attach attention on object features in a self-adaptive way according to the object-level relations to avoid some situations where the additional attention is useless or even detrimental. Eventually, the detection results are produced from the features that are added with attention and thus are able to be detected simply. The experiments demonstrate the effectiveness of the proposed method in few-shot scenes.