Exploring Robust Features for Few-Shot Object Detection in Satellite
  Imagery
Xavier Bou Gabriele Facciolo Rafael Grompone von Gioi Jean-Michel Morel Thibaud Ehret
The goal of this paper is to perform object detection in satellite imagery
with only a few examples, thus enabling users to specify any object class with
minimal annotation. To this end, we explore recent methods and ideas from
open-vocabulary detection for the remote sensing domain. We develop a few-shot
object detector based on a traditional two-stage architecture, where the
classification block is replaced by a prototype-based classifier. A large-scale
pre-trained model is used to build class-reference embeddings or prototypes,
which are compared to region proposal contents for label prediction. In
addition, we propose to fine-tune prototypes on available training images to
boost performance and learn differences between similar classes, such as
aircraft types. We perform extensive evaluations on two remote sensing datasets
containing challenging and rare objects. Moreover, we study the performance of
both visual and image-text features, namely DINOv2 and CLIP, including two CLIP
models specifically tailored for remote sensing applications. Results indicate
that visual features are largely superior to vision-language models, as the
latter lack the necessary domain-specific vocabulary. Lastly, the developed
detector outperforms fully supervised and few-shot methods evaluated on the
SIMD and DIOR datasets, despite minimal training parameters.