Rare Wildlife Recognition with Self-Supervised Representation Learning. (arXiv:2211.05636v1 [cs.CV])

Automated animal censuses with aerial imagery are a vital ingredient towards
wildlife conservation. Recent models are generally based on supervised 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 …

arxiv representation representation learning wildlife