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We propose a framework for large scale learning and annotation of structured models. The system interleaves interactive labeling (where the current model is used to semiautomate the labeling of a new example) and online learning (where a newly labeled example is used to update the current model parameters). This framework is scalable to large datasets and complex image models and is shown to have excellent theoretical and practical properties in terms of train time, optimality guarantees, anddoi:10.1109/iccv.2011.6126450 dblp:conf/iccv/BransonPB11 fatcat:xyku6nqekzgbhh5xp4khti5xei