  Automated animal censuses with aerial imagery are a vital ingredient towards
wildlife conservation. Recent models are generally based on deep learning and
thus require vast amounts of training data. Due to their scarcity and minuscule
size, annotating animals in aerial imagery is a highly tedious process. In this
project, we present a methodology to reduce the amount of required training
data by resorting to self-supervised pretraining. In detail, we examine a
combination of recent contrastive learning methodologies like Momentum Contrast
(MoCo) and Cross-Level Instance-Group Discrimination (CLD) to condition our
model on the aerial images without the requirement for labels. We show that a
combination of MoCo, CLD, and geometric augmentations outperforms conventional
models pre-trained on ImageNet by a large margin. Crucially, our method still
yields favorable results even if we reduce the number of training animals to
just 10%, at which point our best model scores double the recall of the
baseline at similar precision. This effectively allows reducing the number of
required annotations to a fraction while still being able to train
high-accuracy models in such highly challenging settings.
