Papers › Action Shuffle Alternating Learning for Unsupervised Action Segmentation
Action Shuffle Alternating Learning for Unsupervised Action Segmentation
Jun Li, Sinisa Todorovic
This paper addresses unsupervised action segmentation. Prior work captures the frame-level temporal structure of videos by a feature embedding that encodes time locations of frames in the video. We advance prior work with a new self-supervised learning (SSL) of a feature embedding that accounts for both frame- and action-level structure of videos. Our SSL trains an RNN to recognize positive and negative action sequences, and the RNN's hidden layer is taken as our new action-level feature embedding. The positive and negative sequences consist of action segments sampled from videos, where in the former the sampled action segments respect their time ordering in the video, and in the latter they are shuffled. As supervision of actions is not available and our SSL requires access to action segments, we specify an HMM that explicitly models action lengths, and infer a MAP action segmentation with the Viterbi algorithm. The resulting action segmentation is used as pseudo-ground truth for estimating our action-level feature embedding and updating the HMM. We alternate the above steps within the Generalized EM framework, which ensures convergence. Our evaluation on the Breakfast, YouTube Instructions, and 50Salads datasets gives superior results to those of the state of the art.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unsupervised Action Segmentation | Breakfast | ASAL | Acc | 52.5 | #4 of 8 | Archive leaderboard | report |
| Unsupervised Action Segmentation | Breakfast | ASAL | F1 | 37.9 | #4 of 8 | Archive leaderboard | report |
| Unsupervised Action Segmentation | Youtube INRIA Instructional | ASAL | Acc | 44.9 | #6 of 8 | Archive leaderboard | report |
| Unsupervised Action Segmentation | Youtube INRIA Instructional | ASAL | F1 | 32.1 | #6 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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