Timestamp-Supervised Action Segmentation from the Perspective of Clustering

• URL: http://arxiv.org/abs/2212.11694v2
• Date: Sun, 23 Apr 2023 02:45:46 GMT
• Title: Timestamp-Supervised Action Segmentation from the Perspective of Clustering
• Authors: Dazhao Du, Enhan Li, Lingyu Si, Fanjiang Xu, Fuchun Sun
• Abstract summary: Most existing methods generate pseudo-labels for all frames in each video to train the segmentation model. We propose a novel framework from the perspective of clustering, which includes the following two parts. iterative clustering iteratively propagates the pseudo-labels to the ambiguous intervals by clustering, and thus updates the pseudo-label sequences to train the model.
• Score: 12.661218632080207
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video action segmentation under timestamp supervision has recently received much attention due to lower annotation costs. Most existing methods generate pseudo-labels for all frames in each video to train the segmentation model. However, these methods suffer from incorrect pseudo-labels, especially for the semantically unclear frames in the transition region between two consecutive actions, which we call ambiguous intervals. To address this issue, we propose a novel framework from the perspective of clustering, which includes the following two parts. First, pseudo-label ensembling generates incomplete but high-quality pseudo-label sequences, where the frames in ambiguous intervals have no pseudo-labels. Second, iterative clustering iteratively propagates the pseudo-labels to the ambiguous intervals by clustering, and thus updates the pseudo-label sequences to train the model. We further introduce a clustering loss, which encourages the features of frames within the same action segment more compact. Extensive experiments show the effectiveness of our method.

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