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What's it going to cost you?: Predicting effort vs. informativeness for multi-label image annotations
2009
2009 IEEE Conference on Computer Vision and Pattern Recognition
Active learning strategies can be useful when manual labeling effort is scarce, as they select the most informative examples to be annotated first. However, for visual category learning, the active selection problem is particularly complex: a single image will typically contain multiple object labels, and an annotator could provide multiple types of annotation (e.g., class labels, bounding boxes, segmentations), any of which would incur a variable amount of manual effort. We present an active
doi:10.1109/cvprw.2009.5206705
fatcat:nlgfala2prce7befigozpvtjku