Label-efficient underwater image classification with logistic regression on frozen foundation model embeddings
Underwater image classification is constrained by the cost of annotation and by the computational and methodological requirements of task-specific model development. We investigate whether frozen general-purpose foundation-model embeddings can reduce these requirements by extracting DINOv3 ViT-B/16 embeddings and training only a logistic regression classifier on the AQUA20 benchmark. We evaluate the approach across a range of annotation budgets, a repeated 80% training-subsample evaluation, and a full-training refit. With only 13 labelled images per category, corresponding to approximately 4% of the benchmark's official training partition, mean macro F1 reaches 81.8%; with 144 images per ...
| Authors | Thomas Manuel Rost, Martina Figlia, F. Morgado-Dias et al. |
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| Primary category | cs.CV |
| Published | 31 March 2026 |
| Last updated at source | 24 August 2026 |
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