Self-Supervised Human Action Recognition

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 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.

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

1 dataset 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

8 shown of 8 papers with code (9 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.

• Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition
  15 Jul 2023 2 repositories listed
  Inspired by SkeletonBYOL, this paper further presents a Cross-Model and Cross-Stream (CMCS) framework.
• Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition
  1 Aug 2020 2 repositories listed Syntology ran 2 of 11 samples · 9 unverified
  In this paper, we for the first time propose a contrastive action learning paradigm named AS-CAL that can leverage different augmentations of unlabeled skeleton data to learn action representations in an unsupervised…
• Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition
  23 Sep 2024 1 repository listed
  Inspired by SkeletonBYOL, this paper further presents a Cross-Model and Cross-Stream (CMCS) framework.
• Modeling the Relative Visual Tempo for Self-supervised Skeleton-based Action Recognition
  1 Jan 2023 1 repository listed
  Specifically, we design a Relative Visual Tempo Learning (RVTL) task to explore the motion information in intra-video clips, and an Appearance-Consistency (AC) task to learn appearance information simultaneously,…
• Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition
  7 Dec 2021 1 repository listed
  In this paper, to make better use of the movement patterns introduced by extreme augmentations, a Contrastive Learning framework utilizing Abundant Information Mining for self-supervised action Representation (AimCLR)…
• Unsupervised Motion Representation Learning with Capsule Autoencoders
  1 Oct 2021 1 repository listed Syntology ran 15 of 20 samples · 5 unverified
  We propose the Motion Capsule Autoencoder (MCAE), which addresses a key challenge in the unsupervised learning of motion representations: transformation invariance.
• Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition
  14 Nov 2020 1 repository listed
  Existing approaches usually learn action representations by sequential prediction but they suffer from the inability to fully learn semantic information.
• PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition
  27 Nov 2019 1 repository listed
  Given inputs of body keypoints sequences obtained during various movements, our system associates the sequences with actions.

Syntology lines on 2 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.