# Few-shot Object Detection with Feature Attention Highlight Module in Remote Sensing Images

arXiv: https://arxiv.org/abs/2009.01616 · HTML: https://mlsanity.com/paper/2009.01616/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2020.

## Built on by (most cited first, 3 of 3)

- [LLM Agent Framework for Intelligent Change Analysis in Urban Environment using Remote Sensing Imagery](https://mlsanity.com/paper/2601.02757/) (2026)
- [MMUEChange: A Generalized LLM Agent Framework for Intelligent Multi-Modal Urban Environment Change Analysis](https://mlsanity.com/paper/2601.05483/) (2026)
- [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 recent years, there are many applications of object detection in remote sensing field, which demands a great number of labeled data. However, in many cases, data is extremely rare. In this paper, we proposed a few-shot object detector which is designed for detecting novel objects based on only a few examples. Through fully leveraging labeled base classes, our model that is composed of a feature-extractor, a feature attention highlight module as well as a two-stage detection backend can quickly adapt to novel classes. The pre-trained feature extractor whose parameters are shared produces general features. While the feature attention highlight module is designed to be light-weighted and simple in order to fit the few-shot cases. Although it is simple, the information provided by it in a serial way is helpful to make the general features to be specific for few-shot objects. Then the object-specific features are delivered to the two-stage detection backend for the detection results. The experiments demonstrate the effectiveness of the proposed method for few-shot cases.