Few-shot Object Detection in Remote Sensing: Lifting the Curse of
  Incompletely Annotated Novel Objects
Fahong Zhang Yilei Shi Zhitong Xiong Xiao Xiang Zhu
Object detection is an essential and fundamental task in computer vision and
satellite image processing. Existing deep learning methods have achieved
impressive performance thanks to the availability of large-scale annotated
datasets. Yet, in real-world applications the availability of labels is
limited. In this context, few-shot object detection (FSOD) has emerged as a
promising direction, which aims at enabling the model to detect novel objects
with only few of them annotated. However, many existing FSOD algorithms
overlook a critical issue: when an input image contains multiple novel objects
and only a subset of them are annotated, the unlabeled objects will be
considered as background during training. This can cause confusions and
severely impact the model's ability to recall novel objects. To address this
issue, we propose a self-training-based FSOD (ST-FSOD) approach, which
incorporates the self-training mechanism into the few-shot fine-tuning process.
ST-FSOD aims to enable the discovery of novel objects that are not annotated,
and take them into account during training. On the one hand, we devise a
two-branch region proposal networks (RPN) to separate the proposal extraction
of base and novel objects, On another hand, we incorporate the student-teacher
mechanism into RPN and the region of interest (RoI) head to include those
highly confident yet unlabeled targets as pseudo labels. Experimental results
demonstrate that our proposed method outperforms the state-of-the-art in
various FSOD settings by a large margin. The codes will be publicly available
at https://github.com/zhu-xlab/ST-FSOD.