让深度学习理解生物分类层级，提升水下物种识别精度。
Taxonomy-aware deep learning for hierarchical marine species classification in underwater imagery
- 按分类层级设计损失函数与推理规则，对齐生物学结构
- 在FathomNet数据集上平均分类距离达1.581，接近最优解
- 适合海洋生物监测、生态评估等需要分级识别的场景
从水下影像自动分类海洋物种对于实现大规模海洋生物多样性监测和保护政策制定至关重要。现有方法在不同采集平台间存在严重领域偏移、近缘物种视觉相似度高，以及标注粒度不均（许多样本仅能识别至属或更粗等级）等问题。本文提出一种分类层级感知的深度学习框架，将训练损失与推理规则与生物分类的层级结构对齐，结合分类权重损失、最小风险贝叶斯推理、多尺度特征编码及各层级独立分类头。在包含79个海洋类群、覆盖七个分类等级的FathomNet 2025数据集上，系统取得平均分类距离1.581，较第一名（1.535）仅差3%，其中指标对齐推理与解耦结构带来的增益最大，在分布偏移下优于依赖学习关系的复杂模型。
原文摘要 · Abstract (English)
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.
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