Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2021

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

Abstract

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.
Fichier principal
Vignette du fichier
IJCAI.pdf (265.17 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03150686 , version 1 (30-03-2021)

Licence

Identifiers

Cite

Charles A. Kantor, Marta Skreta, Brice Rauby, Léonard Boussioux, Emmanuel Jehanno, et al.. Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification. IJCAI 2021 - Workshop on AI for Social Good, Jan 2021, Tokyo, Japan. ⟨hal-03150686⟩
957 View
194 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More