Fig. 1.

Two-stream active query suggestion. Active learning methods transform unlabeled data into a feature space to suggest informative queries and improve the base model S. Previous methods optimize their feature extractor (E_s) only on the labeled data. We propose a second one (E_u) trained unsupervisedly on all data to capture diverse image features, which can later be updated by fine-tuning with new annotations.