Abstract
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 article, 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 gate recurrent 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.
Highlights
T HANKS to the development recently in computer vision, the past few decades have seen the rapid progress in remote sensing technology which has brought quantities of applications [1]–[4], such as environmental management, forecasts of disasters, and assistance in rescue operations
Development of very high resolution (VHR) remote sensing images provide us with more detailed geospatial objects information including diversities in scale, orientation, and shape
1) We propose a few-shot object detector based on the twostage detector that concentrates on object-level relations in order to fit object-level data available in object detection task
Summary
T HANKS to the development recently in computer vision, the past few decades have seen the rapid progress in remote sensing technology which has brought quantities of applications [1]–[4], such as environmental management, forecasts of disasters, and assistance in rescue operations. Development of very high resolution (VHR) remote sensing images provide us with more detailed geospatial objects information including diversities in scale, orientation, and shape. Among these applications, object detection, which is one of the main tasks in remote sensing, has played an important part with the assistance of the deep convolutional neural networks (CNNs).
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