Skeleton Based Action Recognition

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

34 leaderboard tables shown for this task, 34 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. 10 shown of 34 until expanded.

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

30 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

30 shown of 219 papers with code (419 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.

• PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
  2 Dec 2016 110 repositories listed Syntology ran 89 of 164 samples · 75 unverified · 90 pointer-only (licence)
  Point cloud is an important type of geometric data structure.
• Graph Attention Networks
  30 Oct 2017 93 repositories listed Syntology ran 50 of 106 samples · 56 unverified · 43 pointer-only (licence)
  We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph…
• Semi-Supervised Classification with Graph Convolutional Networks
  9 Sep 2016 55 repositories listed Syntology ran 31 of 58 samples · 27 unverified · 22 pointer-only (licence)
  We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.
• Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
  22 May 2017 34 repositories listed Syntology ran 16 of 28 samples · 12 unverified · 7 pointer-only (licence)
  The paucity of videos in current action classification datasets (UCF-101 and HMDB-51) has made it difficult to identify good video architectures, as most methods obtain similar performance on existing small-scale…
• Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
  23 Jan 2018 24 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  Dynamics of human body skeletons convey significant information for human action recognition.
• FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
  6 Dec 2016 12 repositories listed Syntology ran 2 of 21 samples · 19 unverified · 3 pointer-only (licence)
  Particularly on small displacements and real-world data, FlowNet cannot compete with variational methods.
• Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
  13 Mar 2018 11 repositories listed Syntology ran 1 of 5 samples · 4 unverified · 1 pointer-only (licence)
  Experimental results have shown that the proposed IndRNN is able to process very long sequences (over 5000 time steps), can be used to construct very deep networks (21 layers used in the experiment) and still be trained…
• Simplifying Graph Convolutional Networks
  19 Feb 2019 7 repositories listed Syntology ran 3 of 8 samples · 5 unverified
  Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.
• UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
  3 Dec 2012 7 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  To the best of our knowledge, UCF101 is currently the most challenging dataset of actions due to its large number of classes, large number of clips and also unconstrained nature of such clips.
• Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation
  17 Apr 2018 6 repositories listed Syntology ran 4 of 5 samples · 1 unverified · 3 pointer-only (licence)
  Skeleton-based human action recognition has recently drawn increasing attentions with the availability of large-scale skeleton datasets.
• Temporal Convolutional Networks for Action Segmentation and Detection
  16 Nov 2016 5 repositories listed Syntology ran 2 of 21 samples · 19 unverified
  The ability to identify and temporally segment fine-grained human actions throughout a video is crucial for robotics, surveillance, education, and beyond.
• Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
  30 Jun 2016 5 repositories listed Syntology ran 2 of 6 samples · 4 unverified · 2 pointer-only (licence)
  In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as…
• Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition
  29 Jun 2021 4 repositories listed
  One essential problem in skeleton-based action recognition is how to extract discriminative features over all skeleton joints.
• Revisiting Skeleton-based Action Recognition
  28 Apr 2021 4 repositories listed
  In this work, we propose PoseC3D, a new approach to skeleton-based action recognition, which relies on a 3D heatmap stack instead of a graph sequence as the base representation of human skeletons.
• Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
  20 May 2018 4 repositories listed
  In addition, the second-order information (the lengths and directions of bones) of the skeleton data, which is naturally more informative and discriminative for action recognition, is rarely investigated in existing…
• DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition
  12 Oct 2022 3 repositories listed
  Graph convolution networks (GCN) have been widely used in skeleton-based action recognition.
• Generative Action Description Prompts for Skeleton-based Action Recognition
  10 Aug 2022 3 repositories listed Syntology ran 7 of 10 samples · 3 unverified
  More specifically, we employ a pre-trained large-scale language model as the knowledge engine to automatically generate text descriptions for body parts movements of actions, and propose a multi-modal training scheme by…
• Skeleton Aware Multi-modal Sign Language Recognition
  16 Mar 2021 3 repositories listed
  Sign language is commonly used by deaf or speech impaired people to communicate but requires significant effort to master.
• Richly Activated Graph Convolutional Network for Robust Skeleton-based Action Recognition
  9 Aug 2020 3 repositories listed
  More crucially, on the synthetic occlusion and jittering datasets, the performance deterioration due to the occluded and disturbed joints can be significantly alleviated by utilizing the proposed RA-GCN.
• Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
  31 Mar 2020 3 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  Spatial-temporal graphs have been widely used by skeleton-based action recognition algorithms to model human action dynamics.
• Make Skeleton-based Action Recognition Model Smaller, Faster and Better
  23 Jul 2019 3 repositories listed
  Although skeleton-based action recognition has achieved great success in recent years, most of the existing methods may suffer from a large model size and slow execution speed.
• Richly Activated Graph Convolutional Network for Action Recognition with Incomplete Skeletons
  16 May 2019 3 repositories listed
  To enhance the robustness of action recognition models to incomplete skeletons, we propose a multi-stream graph convolutional network (GCN) for exploring sufficient discriminative features distributed over all skeleton…
• Skeleton-OOD: An End-to-End Skeleton-Based Model for Robust Out-of-Distribution Human Action Detection
  31 May 2024 2 repositories listed
  To address these challenges, we propose a novel end-to-end skeleton-based model called Skeleton-OOD, which is committed to improving the effectiveness of OOD tasks while ensuring the accuracy of ID recognition.
• Hulk: A Universal Knowledge Translator for Human-Centric Tasks
  4 Dec 2023 2 repositories listed Syntology ran 12 of 24 samples · 12 unverified
  Human-centric perception tasks, e.
• VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG
  24 Nov 2023 2 repositories listed
  An accurate and efficient epileptic seizure onset detection can significantly benefit patients.
• InfoGCN++: Learning Representation by Predicting the Future for Online Human Skeleton-based Action Recognition
  16 Oct 2023 2 repositories listed
  To overcome this barrier, we introduce InfoGCN++, an innovative extension of InfoGCN, explicitly developed for online skeleton-based action recognition.
• SiT-MLP: A Simple MLP with Point-wise Topology Feature Learning for Skeleton-based Action Recognition
  30 Aug 2023 2 repositories listed
  Graph convolution networks (GCNs) have achieved remarkable performance in skeleton-based action recognition.
• 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.
• Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse Occlusions
  23 Feb 2022 2 repositories listed
  Yet, the research of data-scarce recognition from skeleton sequences, such as one-shot action recognition, does not explicitly consider occlusions despite their everyday pervasiveness.
• Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble
  12 Oct 2021 2 repositories listed
  Current Sign Language Recognition (SLR) methods usually extract features via deep neural networks and suffer overfitting due to limited and noisy data.

Syntology lines on 15 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.