  Fine-grained classification aims at distinguishing between items with similar
global perception and patterns, but that differ by minute details. Our primary
challenges come from both small inter-class variations and large intra-class
variations. In this article, we propose to combine several innovations to
improve fine-grained classification within the use-case of wildlife, which is
of practical interest for experts. We utilize geo-spatiotemporal data to enrich
the picture information and further improve the performance. We also
investigate state-of-the-art methods for handling the imbalanced data issue.
