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Frozen foundation model embeddings enable label-efficient underwater image classification

By PulseAugur Editorial · [1 sources] ·

Researchers have demonstrated a label-efficient method for underwater image classification using frozen foundation model embeddings. By employing DINOv3 ViT-B/16 embeddings and training only a logistic regression classifier on the AQUA20 benchmark, they achieved competitive performance with significantly reduced annotation costs. Even with only 13 labeled images per category, the approach reached an 81.8% mean macro F1 score, approaching the performance of fully supervised methods that use the entire training dataset. AI

IMPACT This approach could significantly reduce the cost and computational resources required for training AI models in specialized domains like underwater image analysis.

RANK_REASON The item is an academic paper detailing a novel methodology for image classification. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Frozen foundation model embeddings enable label-efficient underwater image classification

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The item is an academic paper detailing a novel methodology for image classification. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Thomas Manuel Rost, Martina Figlia, F. Morgado-Dias, Marko Radeta ·

    Label-efficient underwater image classification with logistic regression on frozen foundation model embeddings

    arXiv:2604.00313v3 Announce Type: replace Abstract: 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…

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