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Action detection and temporal segmentation of actions in videos are topics ofincreasing interest. While fully supervised systems have gained much attentionlately, full annotation of each action within the video is costly andimpractical for large amounts of video data. Thus, weakly supervised actiondetection and temporal segmentation methods are of great importance. While mostworks in this area assume an ordered sequence of occurring actions to be given,our approach only uses a set of actions. Such action sets provide much lesssupervision since neither action ordering nor the number of action occurrencesare known. In exchange, they can be easily obtained, for instance, frommeta-tags, while ordered sequences still require human annotation. We introducea system that automatically learns to temporally segment and label actions in avideo, where the only supervision that is used are action sets. An evaluationon three datasets shows that our method still achieves good results althoughthe amount of supervision is significantly smaller than for other relatedmethods.

Action Sets: Weakly Supervised Action Segmentation without Ordering Constraints