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 semi-automate 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, and bounds on the amount of annotation effort per image. We
apply this framework to part-based detection, and introduce a novel
algorithm for interactive labeling of deformable part models. The
labeling tool updates and displays in real-time the maximum likelihood
location of all parts as the user clicks and drags the location of one
or more parts. We demonstrate that the system can be used to efficiently
and robustly train part and pose detectors on the CUB Birds-200-a
challenging dataset of birds in unconstrained pose and environment.
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