Dual-Margin Embedding for Fine-Grained Long-Tailed Plant Taxonomy

Taxonomic classification of ecological families, genera, andspecies underpins biodiversity monitoring and conservation. Existing com-puter vision methods typically address fine-grained recognition and long-tailed learning in isolation. However, additional challenges such as spa-tiotemporal domain shift, hierarchical taxonomic structure, and previ-ously unseen taxa often co-occur in real-world deployment, leading tobrittle performance under open-world conditions. We propose TaxoNet,an embedding learning framework with a theoretically grounded dual-margin objective that reshapes class decision boundaries under classimbalance to improve fine-grained discrimination while strengtheningrare-class representation geometry. We evaluate TaxoNet in open-worldsettings that capture co-occurring recognition challenges. Leveraging di-verse plant datasets, including Google Auto-Arborist (urban tree im-agery), iNaturalist (Plantae observations across heterogeneous ecosys-tems), and NAFlora-Mini (herbarium collections), we demonstrate thatTaxoNet consistently outperforms strong baselines, including multimodalfoundation models.