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Video action segmentation under timestamp supervision has recently receivedmuch attention due to lower annotation costs. Most existing methods generatepseudo-labels for all frames in each video to train the segmentation model.However, these methods suffer from incorrect pseudo-labels, especially for thesemantically unclear frames in the transition region between two consecutiveactions, which we call ambiguous intervals. To address this issue, we propose anovel framework from the perspective of clustering, which includes thefollowing two parts. First, pseudo-label ensembling generates incomplete buthigh-quality pseudo-label sequences, where the frames in ambiguous intervalshave no pseudo-labels. Second, iterative clustering iteratively propagates thepseudo-labels to the ambiguous intervals by clustering, and thus updates thepseudo-label sequences to train the model. We further introduce a clusteringloss, which encourages the features of frames within the same action segmentmore compact. Extensive experiments show the effectiveness of our method.

Timestamp-Supervised Action Segmentation from the Perspective of Clustering