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We are given a set of video clips, each one annotated with an {\em ordered}list of actions, such as "walk" then "sit" then "answer phone" extracted from,for example, the associated text script. We seek to temporally localize theindividual actions in each clip as well as to learn a discriminative classifierfor each action. We formulate the problem as a weakly supervised temporalassignment with ordering constraints. Each video clip is divided into smalltime intervals and each time interval of each video clip is assigned one actionlabel, while respecting the order in which the action labels appear in thegiven annotations. We show that the action label assignment can be determinedtogether with learning a classifier for each action in a discriminative manner.We evaluate the proposed model on a new and challenging dataset of 937 videoclips with a total of 787720 frames containing sequences of 16 differentactions from 69 Hollywood movies.

Weakly Supervised Action Labeling in Videos Under Ordering Constraints