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

2026-06-24

AI Summary

This work addresses the challenges of automatic marine species classification in underwater imagery, particularly cross-platform domain shift, high visual similarity among closely related species, and inconsistent annotation granularity. To tackle these issues, the authors propose a deep learning framework that explicitly incorporates biological taxonomic hierarchy during both training and inference. The approach innovatively integrates taxonomy-aware weighted loss, minimum-risk Bayesian inference, multi-scale feature encoding, and a disentangled hierarchical classification head. This design enhances the model’s generalization under distributional shifts and improves recognition performance across multiple taxonomic granularities. Evaluated on the FathomNet 2025 dataset, the method achieves a mean classification distance of 1.581, approaching the current state-of-the-art result of 1.535, thereby demonstrating its effectiveness and robustness.

Abstract

Automated classification of marine species from underwater imagery is essential for scalable ocean biodiversity monitoring and conservation policy. Existing approaches struggle with severe domain shift across collection platforms, fine-grained visual similarity between closely related species, and uneven annotation granularity, where many specimens can only be identified to genus or a coarser taxonomic rank. We present a taxonomy-aware deep learning framework that aligns both the training loss and the inference rule with the hierarchical structure of biological classification, combining a taxonomy-weighted loss, minimum-risk Bayesian inference, multi-scale feature encoding, and independent per-rank classification heads. Evaluated on the FathomNet 2025 dataset1 (79 marine classes across seven taxonomic ranks), the system achieves a mean taxonomic distance of 1.581, within 3% of the 1st-place solution (1.535), with the largest gains from metric-aligned inference and simple, decoupled components that generalize better than learned dependencies under distribution shift.

Problem

• domain shift
• fine-grained classification
• hierarchical taxonomy
• annotation granularity
• marine species classification

Innovation

• taxonomy-aware learning
• hierarchical classification
• minimum-risk Bayesian inference
• multi-scale feature encoding
• domain shift robustness