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Given a large collection of images, very few of which have labels given a priori, how can the authors automatically assign the labels of the remaining majority, and make suggestion for images that may need brand new labels distinct from existing ones? Popular automatic labeling techniques usually scale super linearly with the size of the image set, and/or their performances degrade if limited images bear initial labels. In this paper, they propose QMAS, an efficient solution to the following problems: low-labor labeling (L3) - given a collection of images, very few of which are already labeled with keywords, find the most suitable labels for the remaining ones; and mining and attention routing - with the same input set, output a number of top representative images and top outliers.
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