arXiv Machine Learning June 25, 2026 By Dan Zimmerman, Dimitris A. Pados, George Sklivanitis

Taxonomy-aware deep learning for hierarchical marine species classification in underwater imagery

Read the original on arXiv Machine Learning →

arXiv:2606. 25989v1 Announce Type: cross Abstract: Automated classification of marine species from underwater imagery is essential for scalable ocean biodiversity monitoring and conservation policy.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 2

Planktonzilla: Multimodal dataset and models for understanding plankton ecosystems

arXiv:2606. 00080v1 Announce Type: cross Abstract: Marine plankton underpin aquatic food webs and play a key role in global CO2 sequestration, making reliable species identification critical for understanding ocean health and climate feedbacks.

By Alan Gerson Contreras Montanares, Luis Valenzuela, Luis Mart\'i, Nayat Sanchez-Pi

multimodal

arXiv Machine Learning

Jun 19

A High-Resolution Landscape Dataset for Concept-Based XAI With Application to Species Distribution Models

arXiv:2604. 13240v2 Announce Type: replace-cross Abstract: Mapping the spatial distribution of species is essential for conservation policy and invasive species management.

By Augustin de la Brosse, Damien Garreau, Thomas Houet, Thomas Corpetti

llms computer-vision robotics

arXiv AI

Jul 7

Learning Taxonomic Trees with Hierarchical Representation Regularization for Large Multimodal Models

arXiv:2607. 02909v1 Announce Type: cross Abstract: Taxonomies provide key information about the semantic relationships between concepts and the inherent organization of vision and language.

By Hulingxiao He, Zhi Tan, Yuxin Peng

llms rag fine-tuning multimodal

arXiv Machine Learning

Jul 8

Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers

arXiv:2112. 02353v3 Announce Type: replace-cross Abstract: Hierarchical classification aims to sort the object into a hierarchical structure of categories.

By Renzhen Wang, De cai, Kaiwen Xiao, Xixi Jia, Xiao Han, Deyu Meng

benchmarks

arXiv AI

Jul 7

SilvaScenes: Tree Detection and Species Classification from Under-Canopy Images in Natural Forests

arXiv:2510. 09458v2 Announce Type: replace-cross Abstract: Interest in forestry automation is growing alongside rapid advances in deep learning.

By David-Alexandre Duclos, William Guimont-Martin, Gabriel Jeanson, Arthur Larochelle-Tremblay, Martine Lapointe, Th\'eo Defosse, Fr\'ed\'eric Moore, Philippe Nolet, Fran\c{c}ois Pomerleau, Philippe Gigu\`ere

computer-vision benchmarks

arXiv AI

Jul 17

Automated identification of Ichneumonoidea wasps via YOLO-based deep learning: Integrating HiresCam for Explainable AI

arXiv:2603. 16351v2 Announce Type: replace-cross Abstract: Accurate taxonomic identification of parasitoid wasps within the superfamily Ichneumonoidea is essential for biodiversity assessment, ecological monitoring, and biological control programs.

By Joao Manoel Herrera Pinheiro, Gabriela Do Nascimento Herrera, Alvaro Doria Dos Santos, Luciana Bueno Dos Reis Fernandes, Ricardo V. Godoy, Eduardo A. B. Almeida, Helena Carolina Onody, Marcelo Andrade Da Costa Vieira, Angelica Maria Penteado-Dias, Marcelo Becker

computer-vision safety

Related stories

Planktonzilla: Multimodal dataset and models for understanding plankton ecosystems

A High-Resolution Landscape Dataset for Concept-Based XAI With Application to Species Distribution Models

Learning Taxonomic Trees with Hierarchical Representation Regularization for Large Multimodal Models

Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers

SilvaScenes: Tree Detection and Species Classification from Under-Canopy Images in Natural Forests

Automated identification of Ichneumonoidea wasps via YOLO-based deep learning: Integrating HiresCam for Explainable AI