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This paper focuses on weakly-supervised action alignment, where only theordered sequence of video-level actions is available for training. We propose anovel Duration Network, which captures a short temporal window of the video andlearns to predict the remaining duration of a given action at any point in timewith a level of granularity based on the type of that action. Further, weintroduce a Segment-Level Beam Search to obtain the best alignment, thatmaximizes our posterior probability. Segment-Level Beam Search efficientlyaligns actions by considering only a selected set of frames that have moreconfident predictions. The experimental results show that our alignments forlong videos are more robust than existing models. Moreover, the proposed methodachieves state of the art results in certain cases on the popular Breakfast andHollywood Extended datasets.

Action Duration Prediction for Segment-Level Alignment of Weakly-Labeled Videos