  We propose a novel end-to-end curriculum learning approach for sparsely
labelled animal datasets leveraging large volumes of unlabelled data to improve
supervised species detectors. We exemplify the method in detail on the task of
finding great apes in camera trap footage taken in challenging real-world
jungle environments. In contrast to previous semi-supervised methods, our
approach adjusts learning parameters dynamically over time and gradually
improves detection quality by steering training towards virtuous
self-reinforcement. To achieve this, we propose integrating pseudo-labelling
with curriculum learning policies and show how learning collapse can be
avoided. We discuss theoretical arguments, ablations, and significant
performance improvements against various state-of-the-art systems when
evaluating on the Extended PanAfrican Dataset holding approx. 1.8M frames. We
also demonstrate our method can outperform supervised baselines with
significant margins on sparse label versions of other animal datasets such as
Bees and Snapshot Serengeti. We note that performance advantages are strongest
for smaller labelled ratios common in ecological applications. Finally, we show
that our approach achieves competitive benchmarks for generic object detection
in MS-COCO and PASCAL-VOC indicating wider applicability of the dynamic
learning concepts introduced. We publish all relevant source code, network
weights, and data access details for full reproducibility. The code is
available at https://github.com/youshyee/DCL-Detection.
