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In this paper, we study leveraging both weakly labeled images and unlabeled images for multi-label image annotation. Motivated by the recent advance in deep learning, we propose an approach called weakly se mi-supervised deep learning for multi-label image annotation (WeSed). In WeSed, a novel weakly weighted pairwise ranking loss is effectively utilized to handle weakly labeled images, while a triplet...
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