论文标题
在遥感图像中，很少有特征注意的对象检测突出显示模块
Few-shot Object Detection with Feature Attention Highlight Module in Remote Sensing Images
论文作者
论文摘要
近年来，遥感字段中有许多对象检测的应用，这需要大量标记的数据。但是，在许多情况下，数据极为罕见。在本文中，我们提出了一个弹出的对象检测器，该对象检测器旨在仅根据几个示例检测新对象。通过完全利用标记的基类别类别，我们的模型由功能提取器组成，功能注意力突出显示模块以及两个阶段的检测后端可以迅速适应新颖的类。共享参数的预训练特征提取器会产生一般特征。虽然功能关注点重点模块设计为轻巧且简单，以适合几个弹药箱。尽管很简单，但以序列方式提供的信息有助于使一般功能特定于几个弹跳对象。然后将特定于对象的特征传递到两个阶段检测后端以进行检测结果。该实验证明了该方法对几种射击情况的有效性。
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.
