Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification

Autor:	Kantor, Charles A., Skreta, Marta, Rauby, Brice, Boussioux, Léonard, Jehanno, Emmanuel, Luccioni, Alexandra, Rolnick, David, Talbot, Hugues
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Machine Learning
Zdroj:	Proc. IJCAI 2021, Workshop on AI for Social Good, Harvard University (2021)
Druh dokumentu:	Working Paper
Popis:	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. Comment: Copyright by the authors. All rights reserved to authors only. Correspondence to: ckantor (at) stanford [dot] edu
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.11285 Zobrazit plný text záznamu View this record from Arxiv