  Discriminative clustering has been successfully applied to a number of
weakly-supervised learning tasks. Such applications include person and action
recognition, text-to-video alignment, object co-segmentation and colocalization
in videos and images. One drawback of discriminative clustering, however, is
its limited scalability. We address this issue and propose an online
optimization algorithm based on the Block-Coordinate Frank-Wolfe algorithm. We
apply the proposed method to the problem of weakly supervised learning of
actions and actors from movies together with corresponding movie scripts. The
scaling up of the learning problem to 66 feature length movies enables us to
significantly improve weakly supervised action recognition.
