Abstract

We introduce a general framework for quickly annotatingan image dataset when previous annotations exist. The newannotations (e.g. part locations) may be quite different fromthe old annotations (e.g. segmentations). Human annotators may be thought of as helping translate the old anno-tations into the new ones. As annotators label images, ouralgorithm incrementally learns a translator from source totarget labels as well as a computer-vision-based structured predictor. These two components are combined to form an improved prediction system which accelerates the annotators’ work through a smart GUI. We show how the method can be applied to translate between a wide variety of annotation types, including bounding boxes, segmentations, 2D and 3D part-based systems, and class and attribute labels. The proposed system will be a useful tool toward exploring new types of representations beyond simple bounding boxes, object segmentations, and class labels, and toward finding new ways to exploit existing large datasets with traditional types of annotations like SUN [36], Image Net [11], and Pascal VOC [12]. Experiments on the CUB-200-2011 and H3D datasets demonstrate 1) our method accelerates collection of part annotations by a factor of 3-20 compared to manual labeling, 2) our system can be used effectively in a scheme where definitions of part, attribute, or action vocabularies are evolved interactively without relabeling the entire dataset, and 3) toward collecting pose annotations, segmentations are more useful than bounding boxes, and part-level annotations are more effective than segmentations.