Logistic regression on frozen DINOv3 features achieves 88.5% macro F1 on the AQUA20 marine species benchmark, matching end-to-end supervised models with only 6% of the labels.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
ACCEPT 1representative citing papers
citing papers explorer
-
Label-efficient underwater species classification with logistic regression on frozen foundation model embeddings
Logistic regression on frozen DINOv3 features achieves 88.5% macro F1 on the AQUA20 marine species benchmark, matching end-to-end supervised models with only 6% of the labels.