@conference {1548818,
	title = {Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification},
	booktitle = {IJCAI 2021 Workshop on AI for Social Good},
	year = {2021},
	abstract = {Fine-grained classification aims at distinguishing between items with similar global perception and patterns, but that differ by minute details. Our primary challenges comes 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 fauna, 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.},
	author = {Charles A. Kantor and Marta Skreta and Brice Rauby and L{\'e}onard Boussioux and Emmanuel Jehanno and Alexandra Luccioni and David Rolnick and Hugues Talbot}
}
