  Most contributions on Few-Shot Object Detection (FSOD) evaluate their methods
on natural images only, yet the transferability of the announced performance is
not guaranteed for applications on other kinds of images. We demonstrate this
with an in-depth analysis of existing FSOD methods on aerial images and
observed a large performance gap compared to natural images. Small objects,
more numerous in aerial images, are the cause for the apparent performance gap
between natural and aerial images. As a consequence, we improve FSOD
performance on small objects with a carefully designed attention mechanism. In
addition, we also propose a scale-adaptive box similarity criterion, that
improves the training and evaluation of FSOD methods, particularly for small
objects. We also contribute to generic FSOD with two distinct approaches based
on metric learning and fine-tuning. Impressive results are achieved with the
fine-tuning method, which encourages tackling more complex scenarios such as
Cross-Domain FSOD. We conduct preliminary experiments in this direction and
obtain promising results. Finally, we address the deployment of the detection
models inside COSE's systems. Detection must be done in real-time in extremely
large images (more than 100 megapixels), with limited computation power.
Leveraging existing optimization tools such as TensorRT, we successfully tackle
this engineering challenge.

Computer Vision and Pattern Recognition;Machine Learning