PF-MA is a new active learning rule that favors likely-positive uncertain samples to speed up discovery of rare categories in imbalanced visual retrieval.
Op- timizing active learning for low annotation budgets
2 Pith papers cite this work. Polarity classification is still indexing.
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Pre-trained ViT representations combined with active learning and targeted design choices for annotations and selection improve object class retrieval in multi-object scenes.
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Positive-First Most Ambiguous: A Simple Active Learning Criterion for Interactive Retrieval of Rare Categories
PF-MA is a new active learning rule that favors likely-positive uncertain samples to speed up discovery of rare categories in imbalanced visual retrieval.
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Revisiting Human-in-the-Loop Object Retrieval with Pre-Trained Vision Transformers
Pre-trained ViT representations combined with active learning and targeted design choices for annotations and selection improve object class retrieval in multi-object scenes.