Few-shot Object Detection with Feature Attention Highlight Module in Remote Sensing Images
This work addresses the challenge of data scarcity in remote sensing object detection, offering a solution for applications where labeled examples are rare, though it appears incremental in its approach.
The paper tackles the problem of detecting novel objects in remote sensing images with limited labeled data by proposing a few-shot object detector that leverages a feature attention highlight module to adapt general features to specific few-shot objects, achieving effectiveness in experiments.
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.