Browse State-of-the-Art › Unsupervised Action Segmentation
Unsupervised Action Segmentation
9 papers with code · 4 benchmarks · 4 datasets archive 2025-07-28
Unsupervised Action Segmentation is a challenging problem in high-level video understanding, where the goal is to segment a temporally untrimmed sequence into distinct action segments without access to ground truth labels during training. Unlike supervised methods, which rely on annotated datasets, unsupervised approaches aim to discover the underlying structure of actions directly from data. This makes the task particularly valuable for scenarios with limited labeled data or large-scale unlabeled video datasets. The results of Unsupervised Action Segmentation can be further applied to tasks such as action localization and video summarization.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
4 leaderboard tables shown for this task, 4 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| Breakfast (8 rows) | HVQ | Hierarchical Vector Quantization for Unsupervised Action Segmentation | code | — | Compare |
| Youtube INRIA Instructional (8 rows) | LSTM+AL | A Perceptual Prediction Framework for Self Supervised Event Segmentation | code | Syntology ran 0 of 1 samples · 1 unverified | Compare |
| IKEA ASM (5 rows) | HVQ | Hierarchical Vector Quantization for Unsupervised Action Segmentation | code | — | Compare |
| 50 Salads (3 rows) | LSTM+AL | A Perceptual Prediction Framework for Self Supervised Event Segmentation | code | Syntology ran 0 of 1 samples · 1 unverified | Compare |
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
4 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
9 shown of 9 papers with code (16 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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8 Apr 2019 2 repositories listedThe task of temporally detecting and segmenting actions in untrimmed videos has seen an increased attention recently.
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23 Dec 2024 1 repository listedIn this work, we address unsupervised temporal action segmentation, which segments a set of long, untrimmed videos into semantically meaningful segments that are consistent across videos.
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13 Sep 2024 1 repository listedIn this paper, we address a challenging task, synchronous motion captioning, that aim to generate a language description synchronized with human motion sequences.
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1 Apr 2024 1 repository listedWe evaluate our segmentation approach and unsupervised learning pipeline on the Breakfast, 50-Salads, YouTube Instructions and Desktop Assembly datasets, yielding state-of-the-art results for the unsupervised video…
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31 May 2023 1 repository listedThe frame-level prediction module is trained in an unsupervised manner via temporal optimal transport.
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13 Apr 2023 1 repository listedIn this paper, we propose a novel fully unsupervised framework that learns action representations suitable for the action segmentation task from the single input video itself, without requiring any training data.
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27 May 2021 1 repository listed Syntology ran 0 of 2 samples · 2 unverifiedThe temporal optimal transport module enables our approach to learn effective representations for unsupervised activity segmentation.
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20 Mar 2021 1 repository listed Syntology ran 5 of 7 samples · 2 unverified · 1 pointer-only (licence)Action segmentation refers to inferring boundaries of semantically consistent visual concepts in videos and is an important requirement for many video understanding tasks.
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12 Nov 2018 1 repository listed Syntology ran 0 of 1 samples · 1 unverifiedWe also show that the proposed approach is able to learn highly discriminative features that help improve action recognition when used in a representation learning paradigm.
Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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