论文标题
使用自适应注意网络进行遥感图像的几个射击对象检测
Few-shot Object Detection with Self-adaptive Attention Network for Remote Sensing Images
论文作者
论文摘要
在遥感字段中，近年来有许多对象检测的应用，这需要大量标记的数据。但是，我们可能面临某些情况，只有有限的数据可用。在本文中，我们提出了一个弹出的对象检测器，该对象检测器旨在检测仅提供几个示例的新物体。特别是，为了适应对象检测设置，我们提出的几杆检测器集中于在对象级别上的关系，而不是在自适应注意网络（SAAN）的帮助下而不是完整的图像。 SAAN可以通过关系gru单元充分利用对象级关系，并根据对象级关系以自适应方式同时关注对象特征，以避免在某些情况下，这些情况额外的关注是无用甚至有害的。最终，检测结果是由引起注意的功能产生的，因此可以简单地检测到。实验证明了该方法在几个场景中的有效性。
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.
