  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.
